TW202431026A - Determining substrate profile properties using machine learning - Google Patents

Abstract

Spectral data associated with a first prior substrate and/or a second prior substrate is obtained. A metrology measurement value associated with the first portion of the first prior substrate is determined based on one or more metrology measurement values measured for at least one of a second portion of the first prior substrate or a third portion of a second prior substrate. Training data for training a machine learning model to predict metrology measurement values of a current substrate is generated. Generating the training data includes generating a first training input including the spectral data associated with the first prior substrate and generating a first target output for the first training input, the first target output including the determined metrology measurement value associated with the first portion of the first prior substrate. The training data is provided to train the machine learning model.

Description

Using machine learning to determine substrate profile properties

Embodiments of the present disclosure relate generally to manufacturing systems and, more particularly, to determining profile properties of substrates.

Substrate profile properties are measurements that can be used to evaluate a substrate during or after processing at a manufacturing system. Typically, substrate profile properties are measured using a metrology system that is separate from the manufacturing tool used at the manufacturing system. To measure substrate profile properties, the substrate is removed from the manufacturing tool and measured at the metrology system. After the measurements of the substrate are obtained at the metrology system, the substrate is returned to the manufacturing tool for further processing. Removing a substrate from a manufacturing tool to measure the substrate at the metrology system is a costly operation that results in a reduction in overall process efficiency. Due to the cost of removing the substrate from the manufacturing tool, a small number of substrates processed at the manufacturing system are measured, resulting in a low sampling rate of all substrates processed at the manufacturing system. The metrology values generated on these few substrates are used to make process decisions for other substrates processed at the manufacturing tool that have not been measured. Process decisions based on metrology values generated on a few substrates may result in substrate defects and, in some cases, damage to equipment at the manufacturing system.

Some of the embodiments encompass a method for training a machine learning model to predict metrology measurements of a current substrate being processed at a manufacturing system. The method includes obtaining spectral data associated with a first portion of a first previous substrate at the manufacturing system and at least one of a second portion of the first previous substrate or a third portion of a second previous substrate at the manufacturing system. The method further includes identifying one or more metrology measurements obtained for the at least one of the second portion of the first previous substrate or the third portion of the second previous substrate. The method further includes determining a metrology measurement associated with the first portion of the first previous substrate based on the identified one or more metrology measurements. The method further includes generating training data for training a machine learning model to predict metrology measurements of a current substrate at the manufacturing system. Generating training data includes generating a first training input including spectral data associated with a first portion of a first previous substrate, and generating a first target output for the first training input, the first target output including a determined metrology measurement associated with the first portion of the first previous substrate. The method further includes providing the data to train the machine learning model based on (i) a set of training inputs including the first training input and (ii) a set of target outputs including the first target output.

In some embodiments, an apparatus includes a memory and a processing element coupled to the memory. The processing element is used to perform operations, the operations including obtaining spectral data associated with a first portion of a first previous substrate at a manufacturing system and at least one of a second portion of the first previous substrate or a third portion of a second previous substrate at the manufacturing system. The operations further include identifying one or more metrology measurements obtained for at least one of the second portion of the first previous substrate or the third portion of the second previous substrate. The operations further include determining a metrology measurement associated with the first portion of the first previous substrate based on the identified one or more metrology measurements. The operations further include generating training data for training a machine learning model to predict metrology measurement values for a current substrate at the manufacturing system. Generating training data includes generating a first training input including spectral data associated with a first portion of a first previous substrate, and generating a first target output for the first training input, the first target output including a determined metrology measurement associated with the first portion of the first previous substrate. The operations further include providing the data to train the machine learning model based on (i) a set of training inputs including the first training input and (ii) a set of target outputs including the first target output.

In some embodiments, a non-transitory computer-readable storage medium includes instructions that, when executed by a processing element, cause the processing element to perform operations, the operations comprising obtaining spectral data associated with a first portion of a first previous substrate at a manufacturing system and at least one of a second portion of the first previous substrate or a third portion of a second previous substrate at the manufacturing system. The operations further include identifying one or more metrology measurements obtained for the at least one of the second portion of the first previous substrate or the third portion of the second previous substrate. The operations further include determining a metrology measurement associated with the first portion of the first previous substrate based on the identified one or more metrology measurements. The operations further include generating training data for training a machine learning model to predict metrology measurement values for a current substrate at the manufacturing system. Generating training data includes generating a first training input including spectral data associated with a first portion of a first previous substrate, and generating a first target output for the first training input, the first target output including a determined metrology measurement associated with the first portion of the first previous substrate. The operations further include providing the data to train the machine learning model based on (i) a set of training inputs including the first training input and (ii) a set of target outputs including the first target output.

The properties of a substrate profile (e.g., a surface including three-dimensional (3D) structures, a surface including non-3D structures, etc.) are important to the overall performance of the final, processed substrate and/or the overall production yield of substrates at a manufacturing system. In some cases, the properties of the substrate profile can be monitored by generating metrology measurements of the substrate during or after processing of the substrate at a manufacturing system. Metrology measurements may include etch rate (i.e., the rate at which a material deposited on a substrate surface is etched in a processing chamber), etch rate uniformity (i.e., the variation in etch rate at two or more portions of a substrate surface), critical dimension (i.e., a unit of measurement used to measure the size of a substrate feature (e.g., a line, pillar, opening, space, etc.)), critical dimension uniformity (i.e., the variation in a critical dimension across a substrate surface), edge to edge placement error (EPE) (i.e., the difference between an expected feature included on a substrate surface and the resulting feature), and the like.

Implementations described herein provide methods and systems for training and using a machine learning model to predict metrology measurements for a current substrate being processed at a manufacturing system. The machine learning model may be trained using historical spectral data collected for various portions of previous substrates processed at the manufacturing system. The spectral data may correspond to detected energy wave intensities (i.e., the intensity or amount of energy) for each given wavelength of the detected energy wave. In some embodiments, the spectral data may be generated at a substrate measurement subsystem included in the measurement system. In other or similar embodiments, the spectral data may be generated at another portion of the manufacturing system (e.g., at a processing chamber). The historical spectral data may be provided as training input for the machine learning model. Historical non-spectral data collected for various portions of previous substrates may also be used to train machine learning. For example, eddy current data, capacitance data, etc. may be generated for a substrate and provided as training input to a machine learning model.

In some embodiments, the machine learning model may be further trained using historical spectral data indicating the portion of a previous substrate associated with the historical spectral data. The position data may represent the position and/or orientation of the substrate when the spectral data of the portion of the substrate was measured (i.e., at the substrate metrology subsystem or at the processing chamber). In some embodiments, the historical position data may also be provided as a training input for the machine learning model.

The machine learning model may be further trained using historical metrology measurements collected for previous substrates processed at the manufacturing system. In some embodiments, the historical metrology measurements may be received from a metrology measurement system that is separate from the manufacturing system (referred to as an external metrology measurement system). In other or similar embodiments, the historical metrology measurements may be received from a client device of the manufacturing system. Historical metrology measurements may be generated for each substrate processed at the manufacturing system. The historical metrology measurements may be provided as a target output of the machine learning model.

In some embodiments, training data for training a machine learning model may be generated based on metrology measurements predicted or otherwise determined for a previous substrate at a manufacturing system. In one example, according to embodiments described herein, spectral data may be collected for a first portion of a first previous substrate at a manufacturing system. Spectral data may also be collected for a second portion of the first previous substrate and/or for a third portion of a second previous substrate at the manufacturing system. In some embodiments, metrology measurements may be collected for the second portion of the first previous substrate and/or for the third portion of the second previous substrate (e.g., but not for the first portion of the first previous substrate). An indication of coordinates (e.g., Cartesian coordinates, etc.) for the first portion of the first previous substrate and the second portion of the first previous substrate and/or the third portion of the second previous substrate may be provided as an input to a function. Metrology measurements measured for the second portion of the first previous substrate and/or the third portion of the second previous substrate may also be provided as inputs to the function. In some embodiments, the function may include a linear interpolation function, an extrapolation function, a nearest neighbor interpolation function, or a Euclidean distance function. As an output, the function may provide an indication of one or more metrology measurements of the substrate. The metrology measurement of the first portion of the first previous substrate may be determined based on one or more outputs of the function.

In additional or alternative embodiments, spectral data collected for the first portion may be provided as an input to an additional machine learning model. In some embodiments, the spectral data may be provided with contextual data associated with a first previous substrate. The additional machine learning model may be trained to predict one or more metrology measurements of a previous substrate based on the spectral data of the previous substrate at the manufacturing system and the contextual data. The contextual data may include an indication of a first coordinate associated with a first portion of the first previous substrate, a substrate process performed on the first previous substrate, a time period during which the substrate process was performed on the first previous substrate, a time period during which the spectral data was collected for the first previous substrate, an indication of one or more types of equipment used to perform the substrate process, and the like. One or more outputs of the additional machine learning model may include metrology data (including one or more sets of metrology measurements) and an indication of a confidence level that the respective set of metrology measurements corresponds to a first portion of a first previous substrate. A set of metrology measurements having a confidence level that satisfies a confidence criterion (e.g., exceeds a threshold confidence level, etc.) is identified. The set of metrology measurements may include metrology measurements of the first portion of the first previous substrate. Additional details are provided herein regarding predicting or otherwise determining metrology measurements of a previous substrate at a manufacturing system.

Once trained, the machine learning model can be used to predict metrology measurements for a current substrate being processed at a manufacturing system. Spectral data can be generated for the current substrate (i.e., at a substrate metrology subsystem or at a processing chamber) during or after a substrate process at the manufacturing system. The spectral data can be provided as an input to the trained machine learning model. In some embodiments, position data can also be generated for the current substrate, where the position data is associated with the spectral data. In these embodiments, the position data can be provided as another input to the trained machine learning model with the spectral data. The trained machine learning model may generate one or more outputs including metrology measurements of a previous substrate processed at the manufacturing system and a confidence level associated with the metrology measurements of the previous substrate for a current substrate being processed at the manufacturing system. The metrology measurements of the current substrate being processed at the manufacturing system may be extracted from the one or more outputs. In some embodiments, the metrology measurements of the current substrate may be provided to a user of the manufacturing system via a graphical user interface (GUI) displayed at a client device of the manufacturing system.

Aspects of the present disclosure address the above-described shortcomings of the prior art by providing systems and methods for training and using machine learning models to predict metrology measurements of substrates processed at a manufacturing system. Spectral data and/or non-spectral data may be generated for each substrate at various portions of the manufacturing system (i.e., substrate metrology subsystem, processing chamber, etc.) and provided to the trained machine learning model to determine metrology measurements of the substrate while the substrate remains within the manufacturing system. By determining metrology measurements of the substrate while the substrate remains within the manufacturing system, the substrate is not removed from the manufacturing system during substrate processing, thereby increasing overall system throughput. Additionally, because spectral data and/or spectra may be generated for each substrate processed at a manufacturing system, metrology measurements may be generated for each substrate, resulting in a high sampling rate of all substrates processed at the manufacturing system. Process modifications to a substrate may be made at the manufacturing system based on the metrology measurements of that substrate rather than based on the metrology measurements of another substrate, thereby increasing the likelihood that the process modifications will result in successfully processing the substrate. As a result, the number of defects occurring within the manufacturing system may be reduced, thereby increasing overall system efficiency. Additionally, deviations from expected metrology measurements for a substrate may be detected, and error protocols may be initiated based on the detected deviation (e.g., transmitting an error message to an operator of the manufacturing system, stopping operations at the manufacturing system, etc.), thereby preventing unnecessary damage to the substrate and/or the manufacturing system.

FIG. 1 depicts an illustrative computer system architecture 100 according to aspects of the present disclosure. In some embodiments, the computer system architecture 100 may be included as part of a manufacturing system for processing substrates (e.g., manufacturing system 300 of FIG. 3). The computer system architecture 100 includes a client device 120, manufacturing equipment 124, metrology equipment 128, a prediction server 112 (e.g., generates prediction data, provides model adaptation, uses a knowledge base, etc.), and a data store 140. The prediction server 112 may be part of the prediction system 110. The prediction system 110 may further include server machines 170 and 180. The manufacturing equipment 124 may include a sensor 125, which is configured to capture data of a substrate being processed at the manufacturing system. In some embodiments, the manufacturing equipment 124 and the sensor 126 may be part of a sensor system that includes a sensor server (e.g., a field service server (FSS) at a manufacturing facility) and a sensor identifier reader (e.g., a front opening unified pod (FOUP) radio frequency identification (RFID) reader for the sensor system). In some embodiments, the metrology equipment 128 may be part of a metrology system that includes a metrology server (e.g., a metrology database, a metrology folder, etc.) and a metrology identifier reader (e.g., a FOUP RFID reader for the metrology system).

The manufacturing equipment 124 may follow a recipe or perform operations within a time cycle to produce a product. The manufacturing equipment 124 may include a substrate measurement subsystem, which includes one or more sensors 126, and the one or more sensors 126 are configured to generate spectral data and/or position data of a substrate embedded in the substrate measurement subsystem. The sensor 126 configured to generate spectral data (referred to as a spectral sensing component herein) may include a reflection sensor, an elliptical sensor, a thermal spectral sensor, a capacitive sensor, and the like. In some embodiments, the spectral sensing component may be included in the substrate measurement subsystem or in another part of the manufacturing system. One or more sensors 126 (e.g., eddy current sensors, etc.) may also be configured to generate non-spectral data of the substrate. Additional details regarding the fabrication apparatus 124 and the substrate metrology subsystem are provided with respect to FIGS. 3 and 4 .

In some embodiments, the sensor 126 may provide sensor data associated with the manufacturing equipment 124. The sensor data may include values of one or more of: temperature (e.g., heater temperature), spacing (SP), pressure, high frequency radio frequency (HFRF), voltage of an electrostatic chuck (ESC), current, flow, power, voltage, etc. The sensor data may be associated with or indicative of manufacturing parameters, such as hardware parameters, such as settings or components of the manufacturing equipment 124 (e.g., size, type, etc.), or process parameters of the manufacturing equipment 124. The sensor data may be provided while the manufacturing equipment 124 is executing a manufacturing process (e.g., equipment readings while processing a product). The sensor data 142 may be different for each substrate.

The metrology equipment 128 may provide metrology data associated with substrates (e.g., wafers, etc.) processed by the manufacturing equipment 124. The metrology data may include values of one or more of film property data (e.g., wafer spatial film properties), dimensions (e.g., thickness, height, etc.), dielectric constants, dopant concentrations, densities, defects, etc. In some embodiments, the metrology data may further include values of one or more surface profile property data (e.g., etch rate, etch rate uniformity, critical dimensions of one or more features included on the substrate surface, critical dimension uniformity across the substrate surface, edge placement error, etc.). The metrology data may be of finished products or semi-finished products. The metrology data may be different for each substrate.

The client device 120 may include a computing device, such as a personal computer (PC), a laptop, a mobile phone, a smart phone, a desktop computer, a notebook computer, an Internet-connected television (“smart TV”), an Internet-connected media player (e.g., a Blu-ray player), a set-top box, an over-the-top (OTT) streaming device, a cable box, etc.

In some embodiments, metrology data may be received from a client device 120. The client device 120 may display a graphical user interface (GUI) that enables a user to provide, as input, metrology measurements of substrates processed at a manufacturing system.

Data storage 140 may be a memory (e.g., random access memory), a drive (e.g., a hard drive, a flash drive), a database system, or another type of component or device capable of storing data. Data storage 140 may include multiple storage components (e.g., multiple drives or multiple databases) across multiple computing devices (e.g., multiple server computers). Data storage 140 may store spectral data, non-spectral data, metrology data, and predictive data. The spectral data may include historical spectral data (e.g., spectral data generated for a previous substrate processed at the manufacturing system) and/or a current spectrum (spectral data generated for a current substrate processed at the manufacturing system). The current spectral data may be data for which predictive data is generated. Although embodiments of the present disclosure refer to spectral data used to train machine learning models, it should be noted that embodiments of the present disclosure may also include non-spectral data used to train machine learning models. In some embodiments, the metrology data may include historical metrology data (e.g., metrology measurements of previous substrates processed at the manufacturing system). The data store 140 may also store contextual data associated with substrates processed at the manufacturing system (eg, recipe name, recipe step number, preventive maintenance indicator, operator, etc.).

In some embodiments, the data storage 140 may be configured to store data that is not accessible to users of the manufacturing system. For example, a user of the manufacturing system may not be able to access spectral data, non-spectral data, and/or position data obtained for substrates processed at the manufacturing system. In some embodiments, a user of the manufacturing system (e.g., an operator) may not be able to access all of the data stored at the data storage 140. In other or similar embodiments, a portion of the data stored at the data storage 140 may be inaccessible to the user, while another portion of the data stored at the data storage 140 may be accessible to the user. In some embodiments, one or more portions of the data stored at data storage 140 may be encrypted using an encryption mechanism unknown to the user (e.g., using a private encryption key to encrypt the data). In other or similar embodiments, data storage 140 may include multiple data storages, where data not accessible to the user is stored in one or more first data storages, and data accessible to the user is stored in one or more second data storages.

In some embodiments, the prediction system 110 includes a server machine 170 and a server machine 180. The server machine 170 includes a training set generator 172, which is capable of generating a training data set (e.g., a set of data inputs and a set of target outputs) to train, validate and/or test the machine learning model 190. Some operations of the training set generator 172 are described in detail below with respect to FIG. 2 and FIG. 8 to FIG. 11. In some embodiments, the training set generator 172 may divide the training data into a training set, a validation set, and a test set. In some embodiments, the prediction system 110 generates multiple sets of training data. For example, a first set of training data may correspond to a first type of spectral data (e.g., reflectance spectral data), and a second set of training data may correspond to a second type of spectral data (elliptical spectral data).

The server machine 180 may include a training engine 182, a verification engine 184, a selection engine 185, and/or a testing engine 186. An engine may represent hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, a processing device, etc.), software (e.g., instructions running on a processing device, a general purpose computer system, or a dedicated machine), firmware, microcode, or a combination thereof. The training engine 182 may be capable of training a machine learning model 190. The machine learning model 190 may represent a model artifact created by the training engine 182 using training data that includes training inputs and corresponding target outputs (correct answers for the corresponding training inputs). The training engine 182 can find patterns in the training data that map the training inputs to the target outputs (the answers to be predicted) and provide a machine learning model 190 that captures these patterns. The machine learning model 190 can use one or more of support vector machines (SVM), radial basis functions (RBF), clustering, supervised machine learning, semi-supervised machine learning, unsupervised machine learning, k-nearest neighbor algorithm (k-NN), linear regression, random forests, neural networks (e.g., artificial neural networks), etc.

The validation engine 184 may be capable of validating the trained machine learning models 190 using the corresponding feature set of the validation set from the training set generator 172. The validation engine 184 may determine the accuracy of each of the trained machine learning models 190 based on the corresponding feature set of the validation set. The validation engine 184 may discard the trained machine learning models 190 having an accuracy that does not meet the threshold accuracy. In some embodiments, the selection engine 185 may be capable of selecting the trained machine learning models 190 having an accuracy that meets the threshold accuracy. In some embodiments, the selection engine 185 may be capable of selecting the trained machine learning model 190 having the highest accuracy of the trained machine learning model 190.

The testing engine 186 may be able to test the trained machine learning model 190 using the corresponding feature set of the test set from the training set generator 172. For example, a first trained machine learning model 190 that was trained using the first feature set of the training set may be tested using the first feature set of the test set. The testing engine 186 may determine the trained machine learning model 190 with the highest accuracy among all the trained machine learning models based on the test set.

The prediction server 112 includes a prediction component 114 that can provide spectral data and/or non-spectral data of a portion of a current substrate being processed by the manufacturing system as input to a trained machine learning model 190 and run the trained machine learning model 190 on the input to obtain one or more outputs. As described in detail below with respect to FIG. 4, in some embodiments, the prediction component 114 can also extract data from the output of the trained machine learning model 190 and use confidence data to estimate a metrology measurement value of a portion of the substrate.

The confidence data may include or indicate a confidence level that the metrology value corresponds to the current spectral data and/or the metrology value of one or more properties of the substrate associated with the spectral data. In one example, the confidence level is a real number between 0 and 1 (including 0 and 1), where 0 indicates that there is no confidence that the metrology value corresponds to the one or more properties of the substrate associated with the current spectral data, and 1 indicates that there is absolute confidence that the metrology value corresponds to the one or more properties of the substrate associated with the current spectral data. In some embodiments, the system 100 may use the prediction system 110 to determine the metrology value of the substrate being processed at the manufacturing system, rather than using the metrology device 128 to determine the measured metrology value.

Client devices 120, manufacturing equipment 124, sensors 126, metering devices 128, prediction server 112, data storage 140, server machine 170, and server machine 180 may be coupled to one another via network 130. In some embodiments, network 130 is a public network that provides client devices 120 with access to prediction server 112, data storage 140, and other publicly available computing devices. In some embodiments, network 130 is a private network that provides client devices 120 with access to manufacturing equipment 124, metering devices 128, data storage 140, and other privately available computing devices. The network 130 may include one or more wide area networks (WAN), local area networks (LAN), wired networks (e.g., Ethernet networks), wireless networks (e.g., 802.11 networks or Wi-Fi networks), cellular networks (e.g., Long Term Evolution (LTE) networks), routers, hubs, switches, server computers, cloud computing networks, and/or combinations thereof.

It should be noted that in some other implementations, the functionality of server machines 170 and 180 and prediction server 112 may be provided by fewer machines. For example, in some embodiments, server machines 170 and 180 may be integrated into a single machine, while in some other or similar embodiments, server machines 170 and 180 and prediction server 112 may be integrated into a single machine.

Generally speaking, functions described in one implementation as being performed by server machine 170, server machine 180, and/or prediction server 112 may also be performed on client device 120. Additionally, functions attributed to a particular component may be performed by different or multiple components operating together.

In embodiments, a "user" may be represented as a single individual. However, other embodiments of the present disclosure encompass a "user" that is an entity controlled by multiple users and/or automation sources. For example, a group of individual users joined together as a group of administrators may be considered a "user."

FIG. 2 is a flow chart of a method 200 for training a machine learning model according to aspects of the present disclosure. Method 200 is performed by processing logic, which may include hardware (circuitry, dedicated logic, etc.), software (e.g., running on a general-purpose computer system or a dedicated machine), firmware, or a combination thereof. In one implementation, method 200 may be performed by a computer system, such as the computer system architecture 100 of FIG. 1. In other or similar implementations, one or more operations of method 200 may be performed by one or more other machines not depicted in the figures. In some aspects, one or more operations of method 200 may be performed by a training set generator 172 of a server machine 170.

For simplicity of explanation, the methods are depicted and described as a series of actions. However, the actions according to the present disclosure may occur in various orders and/or simultaneously, and have other actions not presented and described herein. In addition, it may not be necessary to perform all of the depicted actions to implement the methods according to the disclosed subject matter. In addition, those skilled in the art will understand and appreciate that the methods may be represented as a series of interrelated states, either via state diagrams or events. In addition, it should be understood that the methods disclosed in this specification can be stored on an article of manufacture to facilitate the transport and transfer of these methods to a computing device. As used herein, the term article of manufacture is intended to cover a computer program that can be accessed from any computer-readable device or storage medium.

At block 210, the processing logic initializes a training set T to an empty set (e.g., {}). At block 220, the processing logic receives data (e.g., spectral data, non-spectral data, etc.) of a substrate processed at a manufacturing system. In some embodiments, the data may be received from a substrate metrology subsystem integrated with the manufacturing system. In other or similar embodiments, the data may be received from one or more sensors at another portion of the manufacturing system (e.g., a processing chamber, a load gate, a transfer chamber, etc.). It should be noted that in some other embodiments, the data may be received in some other manner and may not be received from a portion of the manufacturing system.

At block 230, the processing logic optionally receives position data of a substrate being processed at the fabrication system. In some embodiments, the position data may be received from a substrate metrology subsystem having the data. In other or similar embodiments, the data may be received from one or more sensors at another portion of the fabrication system. It should be noted that in some other embodiments, the position data may be received in some other manner and may not be received from a portion of the fabrication system.

At block 240, the processing logic receives one or more metrology measurements of the substrate. The metrology measurements of the substrate may be obtained at a metrology measurement system that is separate from the manufacturing system (i.e., an external metrology measurement system). In some embodiments, the external metrology measurement system may be communicatively coupled to the manufacturing system (e.g., via network 130 of FIG. 1 ). In these embodiments, the processing logic may receive one or more metrology measurements of the substrate from the external metrology measurement system via the network. In other embodiments, the metrology measurements may be generated at the external metrology measurement system and provided to the manufacturing system via a client device. For example, a client device connected to the manufacturing system may provide a graphical user interface (GUI) to a user (e.g., an operator) of the manufacturing system. After measuring the substrate at the external metrology subsystem, the user may provide the metrology measurements to the client device via the GUI. In response to receiving the provided metrology measurements, the client device may store the metrology measurements in a data store, such as data store 140 of the manufacturing system.

In some embodiments, the processing logic may determine metrology measurements for a portion of a substrate at a fabrication system based on metrology measurements collected for other portions of the substrate and/or for other substrates. Additional details regarding determining these metrology measurements and using these metrology measurements to generate training data are described below with respect to FIGS. 8-11.

At block 250, the processing logic generates an input/output map. The input/output map represents a training input including or based on data of a substrate, and a target output for the training input, wherein the target output identifies a metrology measurement value of the substrate, and wherein the training input is associated with (or mapped to) the target output. At block 260, the processing logic adds the input/output map to the training set T.

At block 270, the processing logic determines whether the training set T includes a sufficient amount of training data to train the machine learning model. It should be noted that in some implementations, the sufficiency of the training set T may be determined simply based on the number of input/output mappings in the training set, while in some other implementations, the sufficiency of the training set T may be determined based on one or more other criteria (e.g., a measure of the diversity of training examples, etc.) in addition to or in lieu of the number of input/output mappings. In response to determining that the training set T includes a sufficient amount of training data to train the machine learning model, the processing logic provides the training set T to train the machine learning model. In response to determining that the training set does not include a sufficient amount of training data to train the machine learning model, method 200 returns to block 220.

At block 280, the processing logic provides a training set T to train the machine learning model. In one implementation, the training set T is provided to the training engine 182 of the server machine 180 to perform the training. In the case of a neural network, for example, the input values of a given input/output mapping (e.g., spectral data of a previous substrate) are input to the neural network, and the output values of the input/output mapping are stored in the output nodes of the neural network. The connection weights in the neural network are then adjusted according to a learning algorithm (e.g., back propagation, etc.), and the process is repeated for other input/output mappings in the training set T. Following block 280, the machine learning model 190 may be used to estimate metrology values for future substrates processed in a manufacturing system (e.g., according to method 600 of FIG. 6 described below).

FIG. 3 is a schematic top view of an example manufacturing system 300 according to aspects of the present disclosure. The manufacturing system 300 can perform one or more processes on a substrate 302. The substrate 302 can be any suitably rigid, fixed-size planar article suitable for manufacturing electronic components or circuit parts thereon, such as a disk or wafer containing silicon, a patterned wafer, a glass sheet, or the like.

The manufacturing system 300 may include a process tool 304 and a factory interface 306 coupled to the process tool 304. The process tool 304 may include a housing 308 having a transfer chamber 310 therein. The transfer chamber 310 may include one or more processing chambers (also referred to as process chambers) 314, 316, 318 disposed thereabout and coupled thereto. The process chambers 314, 316, 318 may be coupled to the transfer chamber 310 via corresponding ports (e.g., slit valves or the like). The transfer chamber 310 may also include a transfer chamber robot 312 configured to transfer the substrate 302 between the process chambers 314, 316, 318, a load gate 320, and the like. The transfer chamber robot 312 may include one or more arms, wherein each arm includes one or more end effectors at the end of each arm. The end effectors may be configured to handle specific objects, such as wafers.

The processing chambers 314, 316, 318 may be adapted to perform any number of processes on the substrate 302. The same or different substrate processes may occur in each of the processing chambers 314, 316, 318. The substrate processes may include atomic layer deposition (ALD), physical vapor deposition (PVD), chemical vapor deposition (CVD), etching, annealing, curing, pre-cleaning, metal or metal oxide removal, or the like. In some embodiments, the substrate processes may include a combination of two or more of atomic layer deposition (ALD), physical vapor deposition (PVD), chemical vapor deposition (CVD), etching, annealing, curing, pre-cleaning, metal or metal oxide removal, or the like. Other processes may be performed on the substrates therein. The processing chambers 314, 316, 318 may each include one or more sensors configured to capture data of the substrate 302 and/or the environment within the processing chambers 314, 316, 318 before, after, or during substrate processing. In some embodiments, the one or more sensors may be configured to capture spectral data and/or non-spectral data of a portion of the substrate 302.

The load lock 320 may also be coupled to the housing 308 and the transfer chamber 310. The load lock 320 may be configured to interface and couple with the transfer chamber 310 and the factory interface 306 on one side. In some embodiments, the load lock 320 may have an environmentally controlled atmosphere that may be changed from a vacuum environment (where substrates may be transferred to and from the transfer chamber 310) to an atmospheric or near atmospheric inert gas environment (where substrates may be transferred to and from the factory interface 306).

The factory interface 306 can be any suitable housing, such as an Equipment Front End Module (EFEM). The factory interface 306 can be configured to receive substrates 302 from substrate carriers 322 (e.g., Front Opening Unified Pods (FOUPs)) docked at respective loading ports 324 of the factory interface 306. A factory interface robot 326 (shown in phantom) can be configured to transfer substrates 302 between substrate carriers (also referred to as containers) 322 and loading gates 320. In other and/or similar embodiments, the factory interface 306 can be configured to receive replacement parts from replacement parts storage containers 322.

The manufacturing system 300 may also be connected to a client device (not shown) that is configured to provide information to a user (e.g., an operator) regarding the manufacturing system 300. In some embodiments, the client device may provide information to a user of the manufacturing system 300 via one or more graphical user interfaces (GUIs). For example, the client device may provide information regarding one or more modifications to be made to a process recipe for the substrate 302 via the GUI.

Manufacturing system 300 may also include system controller 328. System controller 328 may be and/or include a computing device, such as a personal computer, a server computer, a programmable logic controller (PLC), a microcontroller, or the like. System controller 328 may include one or more processing elements, which may be general-purpose processing elements, such as a microprocessor, a central processing unit, or the like. More specifically, the processing element may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or a processor that implements other instruction sets or a combination of instruction sets. The processing element may also be one or more dedicated processing elements, such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a network processor, or the like. The system controller 328 may include a data storage element (e.g., one or more disk drives and/or solid-state drives), a main memory, a static memory, a network interface, and/or other components. The system controller 328 may execute instructions to perform any one or more of the methods and/or embodiments described herein. In some embodiments, the system controller 328 may execute instructions to perform one or more operations at the manufacturing system 300 according to a process recipe. The instructions may be stored on a computer-readable storage medium, which may include main memory, static memory, secondary storage, and/or a processing element (during execution of the instructions).

The system controller 328 may receive data from sensors included on or within various portions of the fabrication system 300 (e.g., the processing chambers 314, 316, 318, the transfer chamber 310, the load gate 320, etc.). The data received by the system controller 328 may include spectral data and/or non-spectral data of a portion of the substrate 302. For purposes of this description, the system controller 328 is described as receiving data from sensors included within the processing chambers 314, 316, 318. However, the system controller 328 may receive data from any portion of the fabrication system 300 and may use the data received from that portion in accordance with the embodiments described herein. In the illustrative example, the system controller 328 may receive spectral data from one or more sensors used in the processing chambers 314, 316, 318 before, after, or during substrate processing at the processing chambers 314, 316, 318. The data received from the sensors at various portions of the fabrication system 300 may be stored in a data store 350. The data store 350 may be included as a component within the system controller 328, or may be a separate component from the system controller 328. In some embodiments, the data store 350 may be the data store 140 described with respect to FIG.

The manufacturing system 300 may further include a substrate metrology subsystem 340. The substrate metrology subsystem 340 may obtain spectral metrology values of one or more portions of the substrate 300 before or after the manufacturing system 302 processes the substrate 302. In some embodiments, the substrate metrology subsystem 340 may obtain spectral metrology values of one or more portions of the substrate 302 in response to receiving a request for spectral metrology values from the system controller 328. The substrate metrology subsystem 340 may be integrated within a portion of the manufacturing system 300. In some embodiments, the substrate metrology subsystem 340 may be integrated within the factory interface 306. In other or similar embodiments, the substrate metrology subsystem 340 may not be integrated with any portion of the manufacturing system 300, but may instead be a stand-alone component. In such embodiments, the substrate 302 being measured at the system metrology subsystem 340 may be transferred to or from a portion of the fabrication system 300 before or after the fabrication system 300 processes the substrate.

The substrate metrology subsystem 340 may obtain spectral measurements of a portion of the substrate 302 by generating spectral data and/or a spectrum of the portion of the substrate 302. In some embodiments, the substrate metrology subsystem 340 is configured to generate spectral data, non-spectral data, position data, and other substrate property data of the substrate 302 (e.g., thickness of the substrate 302, width of the substrate 302, etc.). After generating the data of the substrate 302, the substrate metrology subsystem 340 may transmit the generated data to the system controller 328. In response to receiving the data from the substrate metrology subsystem 340, the system controller 328 may store the data at the data storage 350.

FIG. 4 is a cross-sectional schematic side view of a substrate metrology subsystem 400 according to aspects of the present disclosure. The substrate metrology subsystem 400 can be configured to obtain measurements of one or more portions of a substrate 302 before or after processing a substrate (e.g., substrate 302 of FIG. 3 ) in a processing chamber. The substrate metrology subsystem 400 can obtain spectral measurements of a portion of the substrate 302 by generating data (e.g., spectral data, non-spectral data, etc.) associated with the portion of the substrate 302. In some embodiments, the substrate metrology subsystem 400 can be configured to generate spectral data, non-spectral data, positional data, and/or other property data associated with the substrate 302. The substrate metrology subsystem 400 may include a controller 430 configured to execute one or more instructions for generating data associated with a portion of the substrate 302 .

The substrate metrology subsystem 400 may detect that the substrate 302 has been transferred to the substrate metrology subsystem 400. In response to detecting that the substrate 302 has been transferred to the substrate metrology subsystem 400, the substrate metrology subsystem 400 may determine a position and/or orientation of the substrate 302. The position and/or orientation of the substrate 302 may be determined based on an identification of a reference position of the substrate 302. The reference position may be a portion of the substrate 302 that includes an identification feature associated with the particular portion of the substrate 302. The controller 328 may determine the identification feature associated with the particular portion of the substrate 302 based on the determined identification information of the substrate 302.

The controller 430 may identify a reference position of the substrate 302 using one or more camera components 450 configured to capture image data of the substrate 302. The camera component 450 may generate image data about one or more portions of the substrate 302 and transmit the image data to the controller 430. The controller 430 may analyze the image data to identify identification features associated with the reference position of the substrate 302. The controller 430 may further determine a position and/or orientation of the substrate 302 as depicted in the image data based on the identified identification features of the substrate 302. The controller 430 may determine a position and/or orientation of the substrate 302 based on the identified identification features of the substrate 302 and the determined position and/or orientation of the substrate 302 as depicted in the image data. In response to determining the position and/or orientation of the substrate 302, the controller 430 may generate position data associated with one or more portions of the substrate 302. In some embodiments, the position data may include one or more coordinates (e.g., Cartesian coordinates, polar coordinates, etc.) each associated with a portion of the substrate 302, wherein each coordinate is determined based on a distance from a reference location of the substrate 302.

The substrate metrology subsystem 400 may include one or more metrology components for measuring the substrate 302. In some embodiments, the substrate metrology subsystem 400 may include one or more spectral sensing components 420 configured to generate spectral data of one or more portions of the substrate 302. As previously discussed, the spectral data may correspond to detected energy wave intensities (i.e., the intensity or amount of energy) for each wavelength of the detected waves. Additional details regarding the collected spectral data are provided with respect to FIG.

The spectrum sensing component 420 may be configured to detect energy waves reflected from a portion of the substrate 302 and generate spectrum data associated with the detected waves. The spectrum sensing component 420 may include a wave generator 422 and a reflected wave receiver 424. In some embodiments, the wave generator 422 may be an optical wave generator configured to generate an optical beam toward a portion of the substrate 302. In these embodiments, the reflected wave receiver 424 may be configured to receive the reflected optical beam from the portion of the substrate 302. The wave generator 422 may be configured to generate an energy stream 426 (e.g., an optical beam) and transmit the energy stream 426 to the portion of the substrate 302. A reflected energy wave 428 may be reflected from the portion of the substrate 302 and received by the reflected wave receiver 424. Although FIG. 3A illustrates a single energy wave reflected off the surface of substrate 302 , multiple energy waves may be reflected off the surface of substrate 302 and received by reflected wave receiver 424 .

In response to the reflected wave receiver 424 receiving the reflected energy wave 428 from the portion of the substrate 302, the spectrum sensing component 420 can measure the wavelength of each wave included in the reflected energy wave 428. The spectrum sensing component 420 can further measure the intensity of each measured wavelength. In response to measuring each wavelength and the intensity of each wavelength, the spectrum sensing component 420 can generate spectrum data for the portion of the substrate 302. The spectrum sensing component 420 can transmit the generated spectrum data to the controller 430. The controller 430 can generate a mapping between the received spectrum data and the position data of the measured portion of the substrate 302 in response to receiving the generated spectrum data.

The substrate metrology subsystem 400 may be configured to generate a particular type of spectral data based on the type of measurement to be obtained at the substrate metrology subsystem 400. In some embodiments, the spectral sensing component 420 may be a first spectral sensing component that is configured to generate one type of spectral data. For example, the spectral sensing component 420 may be configured to generate reflectance spectral data, elliptical spectral data, hyperspectral imaging data, chemical imaging data, thermal spectral data, or transmission spectral data. In these embodiments, the first spectroscopic sensing component can be removed from the substrate metrology subsystem 400 and replaced with a second spectroscopic sensing component that is configured to generate a different type of spectroscopic data (e.g., reflectance spectroscopic data, elliptical spectroscopic data, hyperspectral imaging data, chemical imaging data, eddy spectroscopic data, thermal spectroscopic data, or transmission spectroscopic data).

In some embodiments, one or more metrology components (e.g., the spectroscopic sensing component 420) may be fixed components within the substrate metrology subsystem 400. In such embodiments, the substrate metrology subsystem 400 may include one or more positional components 440 configured to modify the position and/or orientation of the substrate 302 relative to the spectroscopic sensing component 420. In some embodiments, the positional component 440 may be configured to translate the substrate 302 along a first axis and/or a second axis relative to the spectroscopic sensing component 420. In other or similar embodiments, the positional component 440 may be configured to rotate the substrate 302 relative to the spectroscopic sensing component 420 about a third axis.

When the spectrum sensing component 420 generates the spectrum data of one or more portions of the substrate 302, the position component 440 can modify the position and/or orientation of the substrate 302 according to the one or more determined portions to be measured of the substrate 302. For example, before the spectrum sensing component 420 generates the spectrum data of the substrate 302, the position component 440 can position the substrate 302 at Cartesian coordinates (0,0), and the spectrum sensing component 420 can generate the first spectrum data of the substrate 302 at Cartesian coordinates (0,0). In response to the spectrum sensing component 420 generating the first spectrum data of the substrate 302 at the Cartesian coordinate (0,0), the positioning component 440 may translate the substrate 302 along the first axis so that the spectrum sensing component 420 is configured to generate the second spectrum data of the substrate 302 at the Cartesian coordinate (0,1). In response to the spectrum sensing component 420 generating the second spectrum data of the substrate 302 at the Cartesian coordinate (0,1), the controller 430 may rotate the substrate 302 along the second axis so that the spectrum sensing component 420 is configured to generate the third spectrum data of the substrate 302 at the Cartesian coordinate (1,1). This process may occur multiple times until spectrum data is generated for each determined portion of the substrate 302.

In some embodiments, one or more layers 412 of material may be included on the surface of the substrate 302. The one or more layers 412 may include etched material, photoresist material, mask material, deposited material, etc. In some embodiments, the one or more layers 412 may include etched material to be etched according to an etch process performed at a processing chamber. In these embodiments, spectral data may be collected for one or more portions of the etched material of the layer 412 deposited on the substrate 302 that have not been etched according to the previously disclosed embodiments. In other or similar embodiments, the one or more layers 412 may include etched material that has been etched according to an etch process at a processing chamber. In such embodiments, one or more structural features (e.g., lines, posts, openings, etc.) may be etched into one or more layers 412 of substrate 302. In such embodiments, spectral data may be collected for the one or more structural features etched into one or more layers 412 of substrate 302.

In response to receiving at least one of spectral data, positional data, or property data of substrate 302, controller 430 may transmit the received data to system controller 328 for processing and analysis according to embodiments described herein.

FIG. 5 illustrates spectral data 500 collected on a substrate according to aspects of the present disclosure. According to aspects of the present disclosure, spectral data can be generated from reflected energy received by a sensor of the substrate measurement subsystem 400 of FIG. 4 or a processing chamber (e.g., processing chambers 314, 316, 318 of FIG. 3). As shown, the reflected energy waves received by the substrate measurement subsystem 400 can include multiple wavelengths. Each reflected energy wave can be associated with a different portion of the substrate 302. In some embodiments, the intensity of each reflected energy wave received by the substrate measurement subsystem 400 can be measured. As seen in FIG. 5, each intensity of each wavelength in the reflected energy waves received by the substrate measurement subsystem 400 can be measured. The correlation between each intensity and each wavelength can be the basis for forming the spectral data 500. In some embodiments, one or more wavelengths may be associated with intensity values outside of an expected range of intensity values. For example, line 510 may be associated with intensity values outside of an expected range of intensity values (as depicted by line 520). In these embodiments, intensity values outside of the expected range of intensity values may be an indication of the presence of a defect at a portion of substrate 302. According to the previously described embodiments, a process recipe for substrate 302 may be modified based on the indication of a defect at the portion of substrate 302.

FIG. 6 is a flow chart of a method 600 for estimating a metrology value of a substrate profile using a machine learning model according to aspects of the present disclosure. The method 600 is performed by processing logic, which may include hardware (circuitry, dedicated logic, etc.), software (e.g., running on a general purpose computer system or a dedicated machine), firmware, or a combination thereof. In some embodiments, the method 600 may be performed using the prediction server 112 of FIG. 1 and the trained machine learning model 190. In other or similar embodiments, one or more blocks of FIG. 6 may be performed by one or more other machines not depicted in FIG. 1.

At block 610, the processing logic receives spectral data of a substrate being processed at a fabrication system. In some embodiments, the spectral data may be received from a substrate metrology subsystem or another portion of a fabrication system in accordance with the previously described embodiments.

At block 620, processing logic provides spectral data of the substrate as input to the trained machine learning model. At block 630, processing logic obtains output from the machine learning model. At block 640, processing logic extracts confidence data from the output obtained at block 630. In some embodiments, the confidence data includes a confidence level associated with the substrate profile and the metrology value. In one example, the confidence level is a real number between 0 and 1 (including 0 and 1). It should be noted that the confidence level may not be a probability. For example, the sum of the confidence levels of all metrology values may not be equal to 1.

At block 650, the processing logic uses the confidence data to estimate a metrology value for a substrate being processed at a manufacturing system. In some embodiments, if a confidence level of the metrology value satisfies a threshold condition, the substrate is identified as being associated with the metrology value. At block 660, the processing logic optionally provides an indication of the estimated metrology value to a user of the manufacturing system.

In some embodiments, one or more sensors included in a portion of a manufacturing system may be the same type or a similar type of sensor as a sensor included in another portion of the manufacturing system. For example, one or more sensors included in a substrate metrology subsystem that are configured to generate spectral data of a substrate may be the same type or a similar type of sensor as a sensor included in a processing chamber that is also configured to generate spectral data of a substrate. In these embodiments, the spectral data generated by the sensors in the substrate metrology subsystem or the processing chamber may be used to train a machine learning model in accordance with the previously described embodiments. The spectral data collected from the substrate metrology subsystem or the processing chamber may be used as training input to the trained machine learning model. According to the aforementioned embodiments, output from a trained machine learning model can be used to extract metrology measurements associated with a substrate. Thus, in some embodiments, a machine learning model trained using spectral data collected from a substrate metrology subsystem can be used to determine metrology measurements using input spectral data obtained from a processing chamber.

7A-7C illustrate an example GUI 700 for providing an indication of a metrology measurement of a portion of a substrate according to aspects of the present disclosure. In some embodiments, the GUI 700 can be displayed to a user of a manufacturing system via a client device of the manufacturing system.

The GUI 700 may include a first portion 710 that displays one or more interactive components. The first portion 710 may include a chamber selection component that enables a user to select an identifier of a processing chamber of a fabrication system. In response to the processing chamber identifier being selected, data of substrates processed in the selected chamber may be displayed via other portions of the GUI 700. In some embodiments, the chamber selection component may include a drop-down menu that provides a list of one or more processing chambers of a fabrication system that may be selected by the user. In other or similar embodiments, the chamber selection component may include any other type of component that may facilitate user selection of a processing chamber identifier.

The first portion 710 may further include a recipe selection component that enables a user to select an identifier of an operation of a substrate process recipe. In response to the operation identifier being selected, data associated with the selected operation of the process recipe may be displayed via other portions of the GUI 700. In some embodiments, the recipe selection component may include a drop-down menu that provides a list of one or more process recipe operations that may be selected by the user. In other or similar embodiments, the recipe selection component may include any other type of component that may facilitate user selection of an operation identifier.

The first portion 710 may further include a time period selection component that enables a user to select a time period for a process to be performed at the manufacturing system. In response to the time period being selected, data associated with substrates processed at the manufacturing system during the selected time period may be displayed via other portions of the GUI 700. In some embodiments, the time period selection component may include a calendar component that provides a calendar indicating a date and/or time at which a substrate process was performed at the manufacturing system. A user of the manufacturing system may select a first date and/or time and a second date and/or time via the time period selection component of the first portion 710 of the GUI 700. The selected first date and/or time and the selected second date and/or time may define the selected time period. In other or similar embodiments, the time period selection component may include any other type of component that can facilitate user selection of a time period.

The first portion 710 may further include one or more additional components to enable a user to select or provide additional settings associated with a process executed at the manufacturing system. For example, the first portion 710 may include a lower control limit component and/or an upper control limit component that enables a user to provide a lower control limit and/or an upper control limit associated with a process. In another example, the first portion 710 may include a threshold component that enables a user to provide a threshold associated with a process. Any other type of component that may facilitate a user to select or provide additional settings associated with a process may be included in the first portion 710.

The GUI 700 can further include a second portion 712 that provides metrology data associated with substrates processed at the manufacturing system. In some embodiments, the metrology data can be associated with two or more substrates processed at the manufacturing system. In such embodiments, the metrology data can be displayed in a graphical form, such as the graphical form depicted with respect to FIG. 7. In other or similar embodiments, the metrology data can be displayed in any other form suitable for displaying metrology data.

The GUI 700 may further include a second portion 714 that provides a contour plot associated with a substrate being processed at the manufacturing system. The contour plot may provide a user with a visual indication of one or more metrology measurements of a portion of the substrate. For example, the contour plot may provide a user with a visual indication of a film thickness or an etch rate associated with the substrate.

In some embodiments, various types of substrates may be processed at a manufacturing system. For example, blank wafers or patterned wafers may be processed at a manufacturing system. GUI 700 may provide one or more windows to display data associated with each different type of substrate processed at the manufacturing system. A third portion of GUI 700 may include a window selector 716 that facilitates switching between different windows displayed via GUI 700. A user may select an option via window selector 716 to cause different windows associated with a type of substrate to be displayed via GUI 700. For example, as shown in FIG. 7A , in response to a user selecting the “blank” option of window selector 716, data associated with a blank wafer may be displayed via GUI 700. In response to the user selecting the “Patternize” option of the window selector 716 , data associated with the patterned wafer may be displayed via the GUI 700 .

In some embodiments, different types of metrology data may be displayed to the user via the GUI 700, depending on the selected option of the window selector 716. As depicted in FIG. 7B , in response to a user selecting the “Patterning” option of the window selector 716, data associated with one or more patterned wafers processed at the fabrication system is provided. In some embodiments, in response to a user selecting the “Patterning” option of the window selector 716, data associated with a critical dimension (referred to as a CD index) of one or more substrates may be displayed via the second portion 712 of the GUI 700. In other or similar embodiments, data associated with another metrology measurement (e.g., etch rate, etch rate uniformity, critical dimension uniformity, side-to-side placement error, etc.) may be displayed via the second portion 812.

In some embodiments, a user may view data used to generate one or more components of the GUI 700 (e.g., the graphics provided in the second portion 712) by selecting a "raw data" option of the window selector 716. As shown in FIG. 7C, in response to the user selecting the "raw data" option of the window selector 716, raw data 720 associated with one or more substrates processed at the manufacturing system may be provided. The raw data 720 may include a timestamp of when a measurement was generated for the substrate, an identifier of a batch including the substrate, an identifier of the substrate, an operation of a process recipe for the substrate, an identifier of a loop (i.e., two or more repeated operations) of the process recipe, position data associated with the substrate, and a model thickness of the substrate.

In some embodiments, the first portion 710 of the GUI 700 may be displayed independently of the windows provided by the GUI 700. In other or similar embodiments, the first portion 710 may not be displayed for the various windows provided by the GUI 700.

FIG. 8 is a flow chart of a method 800 for generating training data for training a machine learning model according to aspects of the present disclosure. FIG. 10 is a flow chart of a method 1000 for determining metrology measurements of a substrate at a manufacturing system according to aspects of the present disclosure. FIG. 11 is a flow chart of another method 1100 for determining metrology measurements of a substrate at a manufacturing system according to aspects of the present disclosure. Methods 800, 1000, and/or 1100 may be performed by processing logic that may include hardware (circuitry, dedicated logic, etc.), software (e.g., running on a general purpose computer system or a dedicated machine), firmware, or some combination thereof. In some embodiments, methods 800, 1000, and/or 1100 may be performed by the training set generator 172 of FIG. 1. In other or similar embodiments, one or more blocks of FIG. 8, FIG. 10, or FIG. 11 may be performed by one or more other machines not depicted in FIG. 1.

As indicated above, FIG. 8 is a flow chart of a method 800 for generating training data for training a machine learning model. At block 810, processing logic obtains spectral data associated with a first portion of a first previous substrate at a manufacturing system and at least one of a second portion of the first previous substrate or a third portion of a second previous substrate at the manufacturing system. The first previous substrate and/or the second previous substrate may correspond to substrate 302 described herein. In some embodiments, the spectral data may be collected by substrate metrology subsystem 400, as described above. In additional or alternative embodiments, the spectral data may be collected according to other techniques.

At block 820, the processing logic identifies a metrology measurement obtained for at least one of the second portion of the first previous substrate or the third portion of the second previous substrate. In some embodiments, metrology measurements may be obtained using metrology apparatus 128 for the second portion of the first previous substrate and/or the third portion of the second previous substrate, but not for the first portion of the first previous substrate. FIG. 9 illustrates an example substrate 910 and one or more portions of substrate 910 according to aspects of the present disclosure. In the illustrative example, metrology measurements may be generated (e.g., by metrology apparatus 128) at portions 912A, 914A, and/or at other portions of substrate 910 (e.g., at portion 916, at one or more of portions 918, etc.). According to the embodiments and examples provided below, the processing logic may determine metrology measurements for other portions of substrate 910 (e.g., portions 912B, 914B, 920, etc.) and/or portions of another substrate.

Referring back to FIG. 8 , at block 830 , processing logic determines a metrology measurement associated with a first portion of a first previous substrate based on the identified one or more metrology measurements. The metrology measurement of a portion of the substrate located at a particular radial distance from a center point of the substrate may correspond to (e.g., be the same as or similar to) metrology measurements of other portions of the substrate associated with the particular radial distance from the center point. The radial distance represents the distance between the center point of a circular (or substantially circular) shaped object and another point of the circular (or substantially circular) shaped object (e.g., which is not at the center point). In some embodiments, the processing logic may determine a metrology measurement associated with a first portion of a first previous substrate based on metrology measurements generated at portions located at the same or similar radial distance from a center of the first previous substrate.

In an illustrative example, portion 916 of substrate 910 may be at or near a center point of substrate 910. Portion 912A of substrate 910 may be located a first radial distance 922A from portion 916. In some embodiments, spectral data may be generated at portion 912B of substrate 910, as described above. Metrology measurements for portion 912B may not be generated. Processing logic may determine that portion 912B of substrate 910 is also located at or about the first radial distance 922A from portion 916. Therefore, processing logic may determine that the metrology measurements for portion 912B correspond to (e.g., are the same as or approximately the same as) the metrology measurements generated for portion 912A. In another illustrative example, portion 914A of substrate 910 may be located at a second radial distance 922B from portion 916. Processing logic may determine that portion 914B is also located at or about the second radial distance 922B from portion 916. Therefore, processing logic may determine that the metrology measurement of portion 914B corresponds to the metrology measurement of portion 914A.

In additional or alternative embodiments, according to the embodiment of FIG. 10 , the processing logic may determine a metrology measurement associated with a first portion of a first previous substrate based on one or more outputs of the function. As described above, FIG. 10 is a flow chart of a method 1000 for determining a metrology measurement of a substrate at a manufacturing system according to aspects of the present disclosure. At block 1010, the processing logic determines a first coordinate associated with a first portion of a first substrate and a second coordinate associated with a second portion of the first substrate and/or a third portion of a second substrate. In some embodiments, as described above, the processing logic may determine the first coordinate and/or the second coordinate based on position data collected by the substrate metrology subsystem 400. The coordinates may include Cartesian coordinates, polar coordinates, etc.

At block 1020, the processing logic may provide the first coordinate, the second coordinate, and an indication of a metrology measurement obtained for at least one of the second portion of the first previous substrate or the third portion of the second previous substrate as inputs to a function. The function may include a linear interpolation function, an extrapolation function, a nearest neighbor interpolation function, a Euclidean distance function, or the like. At block 1030, the processing logic may obtain one or more outputs of the function. The one or more outputs may include an indication of a metrology measurement associated with the first portion of the first previous substrate. At block 1040, the processing logic determines a metrology measurement associated with the first portion of the first previous substrate based on the obtained one or more outputs.

In an illustrative example, in some embodiments, the metrology apparatus 128 may generate metrology data for a portion 916 and/or one or more portions 918 of the substrate 910. As described above, the substrate metrology subsystem 400 may generate spectral data for the portion 916, one or more portions 918, and/or the portion 920. The processing logic may provide coordinates associated with the portions 916, 918, and/or 920 along with metrology measurements generated for the portions 916 and/or one or more portions 918 as inputs to a linear interpolation function. In some embodiments, the linear interpolation function may interpolate metrology measurements for the portion 920 based on the provided coordinates associated with the portions 916, 918, and/or 920 and the metrology measurements generated for the portions 916 and/or one or more portions 918. Thus, processing logic may determine a metrology measurement associated with portion 920 based on one or more outputs of the linear interpolation function.

In yet additional or alternative embodiments, according to the embodiment of FIG. 11 , the processing logic may determine a metrology measurement associated with a first portion of a first previous substrate based on one or more outputs of a machine learning model. As described above, FIG. 11 is a flow chart of another method 1100 for determining a metrology measurement of a substrate at a manufacturing system according to aspects of the present disclosure. At block 1110, the processing logic may obtain context data associated with the first previous substrate. The context data may include one or more of the following: a first coordinate associated with a first portion of a first previous substrate, a substrate process previously performed on the first previous substrate and/or to be performed on the first previous substrate, a time period during which the substrate process was performed or to be performed, a time period during which spectral data was collected for the first portion of the first previous substrate, an indication of one or more types of equipment associated with the substrate process, and the like.

At block 1120, the processing logic may provide spectral data associated with a first portion of a first previous substrate and the obtained contextual data as inputs to a machine learning model. In some embodiments, the machine learning model may be trained to predict metrology measurements of a previous substrate based on given spectral data and contextual data of a previous substrate at a manufacturing system. In some embodiments, the machine learning model may be trained using a data set including training inputs indicative of spectral data and/or contextual data associated with previous substrates at a manufacturing system. The data set may additionally or alternatively include target outputs for the training inputs, the target outputs indicative of one or more metrology measurements collected for the previous substrate. The machine learning model 190 may use one or more of support vector machine (SVM), radial basis function (RBF), clustering, supervised machine learning, semi-supervised machine learning, unsupervised machine learning, k-nearest neighbor algorithm (k-NN), linear regression, random forest, neural network (e.g., artificial neural network), etc.

In an illustrative example, a training input to a data set may include spectral data and/or contextual data associated with a second portion of a first previous substrate and/or a third portion of a second previous substrate. A target output for the training input may include metrology measurements generated for the second portion of the first previous substrate and/or the third portion of the second previous substrate.

At block 1130, the processing logic may obtain one or more outputs of the machine learning model. The one or more outputs may include metrology data indicating one or more sets of metrology measurement values, and for each set of metrology measurement values, a confidence level that the corresponding set of metrology measurement values corresponds to the first portion of the first previous substrate. At block 1140, the processing logic extracts metrology measurement values associated with the first portion of the first previous substrate from the one or more outputs. The processing logic may extract metrology measurement values from the one or more outputs by identifying a corresponding set of metrology measurement values having a confidence level that satisfies a confidence criterion (e.g., exceeds a confidence threshold, etc.). The identified set of metrology measurement values may include metrology measurement values for the first portion of the first previous substrate.

Referring back to FIG. 8 , at block 840, the processing logic may generate training data for training a machine learning model to predict metrology measurements of a current substrate at a manufacturing system by performing operations at blocks 842 and/or 844. At block 842, the processing logic may generate a first training input comprising spectral data associated with a first portion of a first previous substrate. At block 844, the processing logic may generate a first target output for the first training input. The first target output comprises a determined metrology measurement associated with a first portion of a first previous substrate (e.g., determined according to the embodiments described above). In additional or alternative embodiments, the processing logic may generate a second training input comprising spectral data associated with at least one of the second portion of the first previous substrate and/or the third portion of the second previous substrate. The processing logic may generate a second target output for the second training input, the second target output comprising (several) metrology measurements generated for at least one of the second portion of the first previous substrate and/or the third portion of the second previous substrate. At block 850, the processing logic provides training data to train a machine learning model based on (i) a set of training inputs comprising the first training inputs (and/or the second training inputs) and (ii) a set of target outputs comprising the first target outputs (and/or the second target outputs).

As described above, after training, the machine learning model can be configured to predict metrology measurement values associated with a current substrate at the manufacturing system based on given spectral data. In some embodiments, the machine learning model described with respect to FIG. 8 can be different from the machine learning model described with respect to FIG. 11 because the machine learning model of FIG. 11 is trained to predict metrology measurement values based on spectral data and contextual data associated with previous substrates at the manufacturing system, while the machine learning model described with respect to FIG. 8 is trained to predict metrology measurement values based on spectral data (e.g., and/or contextual data) associated with a current substrate at the manufacturing system.

In some embodiments, a machine learning model may be trained to predict metrology measurements of a current substrate at a manufacturing system according to the embodiments of FIGS. 2 and 8. In some embodiments, the weights of the trained machine learning model may be adjusted based on the source of the metrology measurements used to train the model (e.g., by a developer, engineer, operator of the manufacturing system, etc.). For example, some metrology measurements may be generated by the metrology equipment 128, as described with respect to FIGS. 2 and 8, while other metrology measurements are determined according to the embodiments described with respect to FIGS. 9-11. In some embodiments, the weights of a trained machine learning model may be adjusted so that weights associated with generated metrology measurements are higher than weights associated with determined metrology measurements.

FIG. 12 depicts a block diagram of an illustrative computer system 1200 that operates according to one or more aspects of the present disclosure. In alternative embodiments, the machine can be connected (e.g., networked) to other machines in a local area network (LAN), an intranet, an extranet, or the Internet. The machine can operate as a server or client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet computer, a set-top box (STB), a personal digital assistant (PDA), a cellular phone, a web appliance, a server, a network router, a switch or a bridge, or any machine capable of executing a set of instructions (sequentially or otherwise) that specify actions to be taken by that machine. In addition, although only a single machine is shown, the term "machine" should also be construed to include any collection of machines (e.g., computers) that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methods discussed herein. In an embodiment, the computing device 1200 may correspond to the system controller 328 of FIG. 3 or the controller 430 of FIG. 4.

The example computing device 1200 includes a processing element 1202, a main memory 1204 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) (e.g., synchronous DRAM (SDRAM)), etc.), a static memory 1206 (e.g., flash memory, static random access memory (SRAM)), etc.), and a secondary memory (e.g., a data storage element 1228), which communicate with each other via a bus 1208.

Processing element 1202 may represent one or more general purpose processors, such as a microprocessor, a central processing unit, or the like. More specifically, processing element 1202 may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor implementing other instruction sets, or a processor implementing a combination of instruction sets. Processing element 1202 may also be one or more special purpose processing elements, such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a network processor, or the like. The processing element 1202 may also be or include a system on a chip (SoC), a programmable logic controller (PLC), or other types of processing elements. The processing element 1202 is configured to execute processing logic for performing the operations and steps discussed herein.

The computing device 1200 may further include a network interface device 1222 for communicating with a network 1264. The computing device 1200 may also include a video display unit 1210 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device 1212 (e.g., a keyboard), a cursor control device 1214 (e.g., a mouse), and a signal generating device 1220 (e.g., a speaker).

The data storage element 1228 may include a machine-readable storage medium (or more specifically, a non-transitory computer-readable storage medium) 1224, on which one or more sets of instructions 1226 are stored to implement any one or more of the methods or functions described herein. The non-transitory storage medium represents a storage medium other than a carrier. During the execution of the instructions 1226 by the computer device 1200, the instructions 1226 may also be stored in the main memory 1204 and/or the processing element 1202 in whole or in part, and the main memory 1204 and the processing element 1202 also constitute a computer-readable storage medium.

The computer-readable storage medium 1224 may also be used to store the model 190 and data used to train the model 190. The computer-readable storage medium 1224 may also store a software library containing methods for calling the model 190. Although the computer-readable storage medium 1224 is shown as a single medium in the example embodiments, the term "computer-readable storage medium" should be construed to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated cache memory and servers) storing one or more sets of instructions. The term "computer-readable storage medium" should also be deemed to include any medium that can store or encode a set of instructions for execution by a machine and cause the machine to perform any one or more of the methods of the present disclosure. The term "computer-readable storage medium" should accordingly be deemed to include but not be limited to solid-state memories, and optical and magnetic media.

The previous description sets forth many specific details, such as examples of specific systems, components, methods, etc., in order to provide a good understanding of several embodiments of the present disclosure. However, it will be apparent to one skilled in the art that at least some embodiments of the present disclosure may be practiced without these specific details. In other cases, well-known components or methods are not described in detail or are presented in the form of simple block diagrams in order to avoid unnecessarily obscuring the present disclosure. Therefore, the specific details set forth are merely illustrative. Specific implementations may differ from these illustrative details and are still contemplated to be within the scope of the present disclosure.

References throughout this specification to "one embodiment" or "an embodiment" mean that the particular features, structures, characteristics described in conjunction with the embodiment are included in at least one embodiment. Therefore, the phrases "in one embodiment" or "in an embodiment" appearing in various places throughout this specification do not necessarily all refer to the same embodiment. In addition, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or". When the term "about" or "approximately" is used herein, it is intended to mean that the nominal value presented is accurate to within ±10%.

Although the operations of the methods are shown and described herein in a particular order, the order of operations of each method may be changed so that certain operations may be performed in a reverse order, so that certain operations may be performed at least partially concurrently with other operations. In another embodiment, instructions or sub-operations of different operations may be performed in an intermittent and/or alternating manner.

It should be understood that the above description is intended to be illustrative and not limiting. Many other embodiments will become apparent to those skilled in the art upon reading and understanding the above description. Therefore, the scope of the present disclosure should be determined by reference to the appended claims, along with the entire scope of equivalents to which the claims are entitled.

100: Computer system architecture 110: Prediction system 112: Prediction server 114: Prediction component 120: Client device 124: Manufacturing equipment 126: Sensor 128: Measuring equipment 130: Network 140: Data storage 170: Server machine 172: Training set generator 180: Server machine 182: Training engine 184: Verification engine 186: Selection engine 188: Testing engine 190: Machine learning model 200: Method 210: Block 220: Block 230: Block 240: Block 250: Block 260: Block 270: Block 280: Block 300: Manufacturing System 302: Substrate 304: Process Tool 306: Factory Interface 308: Housing 310: Transfer Chamber 312: Transfer Chamber Robot 314: Process Chamber 316: Process Chamber 318: Process Chamber 320: Load Gate 322: Substrate Carrier 324: Load Port 326: Factory Interface Robot 328: System Controller 340: Substrate Measurement Subsystem 350: Data Storage 400: Substrate Measurement Subsystem 412: Layer 420: Spectral Sensing Components 422: Wave generator 424: Reflected wave receiver 426: Energy flow 428: Reflected energy wave 430: Controller 440: Position component 450: Camera component 500: Spectral data 510: Line 520: Line 600: Method 610: Block 620: Block 630: Block 640: Block 650: Block 660: Block 700: GUI 710: Part I 712: Part II 714: Contour map 716: Window selector 720: Raw data 800: Method 810: Block 820: Block 830: Block 840: block 842: block 844: block 850: block 910: example substrate 912A: portion 912B: portion 914A: portion 914B: portion 916: portion 918: portion 920: portion 922A: first radial distance 922B: second radial distance 1000: method 1010: block 1020: block 1030: block 1040: block 1100: method 1110: block 1120: block 1130: block 1140: block 1200: Computing device 1202: Processing element 1204: Main memory 1206: Static memory 1208: Bus 1210: Video display unit 1212: Alphanumeric input device 1214: Cursor control device 1220: Signal generating device 1222: Network interface device 1224: Machine readable storage medium 1226: Command 1228: Data storage element 1264: Network

The present disclosure is illustrated by way of example and not by way of limitation in the figures of the accompanying drawings in which like reference numerals indicate similar elements. It should be noted that different references to "one" or "an" embodiment in the present disclosure do not necessarily represent the same embodiment, and such references mean at least one.

FIG. 1 depicts an illustrative computer system architecture according to aspects of the present disclosure.

FIG. 2 is a flow chart of a method for training a machine learning model according to an aspect of the present disclosure.

FIG. 3 is a top view schematic diagram of an example manufacturing system according to an aspect of the present disclosure.

FIG. 4 is a schematic cross-sectional side view of a substrate metrology subsystem according to an aspect of the present disclosure.

FIG. 5 shows spectral data collected on a substrate according to aspects of the present disclosure.

FIG. 6 is a flow chart of a method for estimating metrology values of a substrate profile using a machine learning model according to aspects of the present disclosure.

7A-7C illustrate an example GUI for providing an indication of estimated metrology values for a substrate profile according to aspects of the present disclosure.

FIG. 8 is a flow chart of a method for generating training data for training a machine learning model according to an aspect of the present disclosure.

FIG. 9 illustrates an example of determining metrology measurements for a substrate according to aspects of the present disclosure.

FIG. 10 is a flow chart of a method for determining a metrology measurement of a substrate at a manufacturing system according to aspects of the present disclosure.

FIG. 11 is a flow chart of another method for determining metrology measurements of a substrate at a manufacturing system according to aspects of the present disclosure.

FIG. 12 depicts a block diagram of an illustrative computer system that operates according to one or more aspects of the present disclosure.

Domestic storage information (please note in the order of storage institution, date, and number) None Foreign storage information (please note in the order of storage country, institution, date, and number) None

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810: Block

820: Block

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842: Block

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850:Block

Claims (20)

A method comprising the steps of: Obtaining spectral data associated with a first portion of a first previous substrate at a manufacturing system and at least one of a second portion of the first previous substrate or a third portion of a second previous substrate at the manufacturing system; Identifying one or more metrology measurements obtained for at least one of the second portion of the first previous substrate or the third portion of the second previous substrate; Determining a metrology measurement associated with the first portion of the first previous substrate based on the one or more identified metrology measurements; Generating training data for training a machine learning model to predict metrology measurements of a current substrate at the manufacturing system, wherein the step of generating the training data comprises the steps of: Generating a first training input comprising the spectral data associated with the first portion of the first previous substrate; and Generating a first target output for the first training input, the first target output comprising the determined metrology measurement associated with the first portion of the first previous substrate; and Providing the training data to train the machine learning model based on (i) a set of training inputs comprising the first training input and (ii) a set of target outputs comprising the first target output. The method of claim 1, wherein the step of determining the metrology measurement associated with the first portion of the first previous substrate comprises the steps of: providing one or more first coordinates associated with the first portion of the first previous substrate, one or more second coordinates associated with at least one of the second portion of the first previous substrate or the third portion of the second previous substrate, and an indication of the one or more metrology measurement values measured for at least one of the second portion of the first previous substrate or the third portion of the second previous substrate as inputs to a function, wherein the metrology measurement associated with the first portion of the substrate is determined based on one or more outputs of the function. A method as described in claim 2, wherein the function comprises at least one of a linear interpolation function, an extrapolation function, a nearest neighbor interpolation function or a Euclidean distance function. The method of claim 1, wherein the step of determining the metrology measurement associated with the first portion of the substrate comprises the following steps: providing the acquired spectral data associated with the first portion of the first previous substrate and contextual data associated with the first previous substrate as input to an additional machine learning model, wherein the additional machine learning model is trained to predict metrology measurements of previous substrates at the manufacturing system based on given spectral data and contextual data of the previous substrates, and wherein the additional machine learning model is trained using a data set that includes the spectral data associated with at least one of the second portion of the first previous substrate or the third portion of the second previous substrate, and the one or more metrology measurements measured for at least one of the second portion of the first previous substrate or the third portion of the second previous substrate; and The metrology measurement is extracted from one or more outputs of the additional machine learning model. The method of claim 4, wherein the one or more outputs of the additional machine learning model include metrology data indicating one or more sets of metrology measurement values, and for each set of metrology measurement values, a corresponding set of metrology measurement values corresponds to a confidence level of the first portion of the first previous substrate, and wherein the step of extracting the metrology measurement values from the one or more outputs includes the steps of: Identifying the corresponding set of metrology measurement values having a confidence level that satisfies a confidence criterion, wherein the identified corresponding set of metrology measurement values includes the metrology measurement values. A method as described in claim 4, wherein the context data associated with the first previous substrate includes at least one of the following: one or more first coordinates associated with the first portion of the first previous substrate, a substrate process performed on the first previous substrate, a time period during which the substrate process is performed on the first previous substrate, a time period during which the spectral data is collected for the first portion of the first previous substrate, or an indication of one or more types of equipment used to perform the substrate process. The method of claim 1, wherein the step of determining the metrology measurement associated with the first portion of the first previous substrate comprises the steps of: determining a first radial distance between a center point of the first previous substrate and the first portion of the first previous substrate; and determining a second radial distance between the center portion of the first previous substrate and at least one of the second portion of the first previous substrate or between a center portion of the second previous substrate and the third portion of the second previous substrate, in response to determining that the first radial distance corresponds to the second radial distance, determining that the metrology measurement associated with the first portion of the first previous substrate corresponds to at least one of the one or more identified metrology measurements obtained for at least one of the second portion of the first previous substrate or the third portion of the second previous substrate. The method of claim 1, wherein the step of generating the training data further comprises the steps of: generating a second training input comprising the spectral data associated with at least one of the second portion of the first previous substrate or the third portion of the second previous substrate; and generating a second target output for the second training input, the second target output comprising the one or more metrology measurements measured for at least one of the second portion of the first previous substrate or the third portion of the second previous substrate, wherein the set of training inputs further comprises the second training input, and wherein the set of target outputs further comprises the second target output. A system comprising: a memory; and a processing element coupled to the memory, the processing element for performing operations comprising the following steps: obtaining spectral data associated with a first portion of a first previous substrate at a manufacturing system and at least one of a second portion of the first previous substrate or a third portion of a second previous substrate at the manufacturing system; identifying one or more metrology measurements obtained for at least one of the second portion of the first previous substrate or the third portion of the second previous substrate; determining a metrology measurement associated with the first portion of the first previous substrate based on the one or more identified metrology measurements; Generating training data for training a machine learning model to predict metrology measurements of a current substrate at the manufacturing system, wherein the step of generating the training data comprises the steps of: Generating a first training input comprising the spectral data associated with the first portion of the first previous substrate; and Generating a first target output for the first training input, the first target output comprising the determined metrology measurement associated with the first portion of the first previous substrate; and Providing the training data to train the machine learning model based on (i) a set of training inputs comprising the first training input and (ii) a set of target outputs comprising the first target output. The system of claim 9, wherein the step of determining the metrology measurement associated with the first portion of the first previous substrate comprises the steps of: providing one or more first coordinates associated with the first portion of the first previous substrate, one or more second coordinates associated with at least one of the second portion of the first previous substrate or the third portion of the second previous substrate, and an indication of the one or more metrology measurement values measured for at least one of the second portion of the first previous substrate or the third portion of the second previous substrate as inputs to a function, wherein the metrology measurement associated with the first portion of the substrate is determined based on one or more outputs of the function. The system of claim 10, wherein the function comprises at least one of a linear interpolation function, an extrapolation function, a nearest neighbor interpolation function, or a Euclidean distance function. A system as described in claim 9, wherein the step of determining the metrology measurement associated with the first portion of the substrate comprises the following steps: providing the acquired spectral data associated with the first portion of the first previous substrate and contextual data associated with the first previous substrate as input to an additional machine learning model, wherein the additional machine learning model is trained to predict metrology measurements of previous substrates at the manufacturing system based on given spectral data and contextual data of the previous substrates, and wherein the additional machine learning model is trained using a data set that includes the spectral data associated with at least one of the second portion of the first previous substrate or the third portion of the second previous substrate, and the one or more metrology measurements measured for at least one of the second portion of the first previous substrate or the third portion of the second previous substrate; and The metrology measurement is extracted from one or more outputs of the additional machine learning model. The system of claim 12, wherein the one or more outputs of the additional machine learning model include metrology data indicating one or more sets of metrology measurement values, and for each set of metrology measurement values, a corresponding set of metrology measurement values corresponds to a confidence level of the first portion of the first previous substrate, and wherein the step of extracting the metrology measurement values from the one or more outputs includes the steps of: Identifying the corresponding set of metrology measurement values having a confidence level that satisfies a confidence criterion, wherein the identified corresponding set of metrology measurement values includes the metrology measurement values. A system as described in claim 12, wherein the context data associated with the first previous substrate includes at least one of: one or more first coordinates associated with the first portion of the first previous substrate, a substrate process performed on the first previous substrate, a time period during which the substrate process is performed on the first previous substrate, a time period during which spectral data is collected for the first portion of the first previous substrate, or an indication of one or more types of equipment used to perform the substrate process. The system of claim 9, wherein the step of determining the metrology measurement associated with the first portion of the first previous substrate comprises the steps of: determining a first radial distance between a center point of the first previous substrate and the first portion of the first previous substrate; and determining a second radial distance between the center portion of the first previous substrate and at least one of the second portion of the first previous substrate or between a center portion of the second previous substrate and the third portion of the second previous substrate, in response to determining that the first radial distance corresponds to the second radial distance, determining that the metrology measurement associated with the first portion of the first previous substrate corresponds to at least one of the one or more identified metrology measurements obtained for at least one of the second portion of the first previous substrate or the third portion of the second previous substrate. A non-transitory computer-readable storage medium comprising instructions which, when executed by a processing element, cause the processing element to perform operations comprising the steps of: obtaining spectral data associated with a first portion of a first previous substrate at a manufacturing system and at least one of a second portion of the first previous substrate or a third portion of a second previous substrate at the manufacturing system; identifying one or more metrological measurements obtained for at least one of the second portion of the first previous substrate or the third portion of the second previous substrate; determining a metrological measurement associated with the first portion of the first previous substrate based on the one or more identified metrological measurements; Generating training data for training a machine learning model to predict metrology measurements of a current substrate at the manufacturing system, wherein the step of generating the training data comprises the steps of: Generating a first training input comprising the spectral data associated with the first portion of the first previous substrate; and Generating a first target output for the first training input, the first target output comprising the determined metrology measurement associated with the first portion of the first previous substrate; and Providing the training data to train the machine learning model based on (i) a set of training inputs comprising the first training input and (ii) a set of target outputs comprising the first target output. A non-transitory computer-readable storage medium as described in claim 16, wherein the step of determining the metrology measurement associated with the first portion of the first previous substrate comprises the following steps: Providing one or more first coordinates associated with the first portion of the first previous substrate, one or more second coordinates associated with at least one of the second portion of the first previous substrate or the third portion of the second previous substrate, and an indication of the one or more metrology measurement values measured for at least one of the second portion of the first previous substrate or the third portion of the second previous substrate as inputs to a function, wherein the metrology measurement associated with the first portion of the substrate is determined based on one or more outputs of the function. A non-transitory computer-readable storage medium as described in claim 17, wherein the function includes at least one of a linear interpolation function, an extrapolation function, a nearest neighbor interpolation function, or a Euclidean distance function. A non-transitory computer-readable storage medium as described in claim 16, wherein the step of determining the metrological measurement value associated with the first portion of the substrate comprises the following steps: providing the acquired spectral data associated with the first portion of the first previous substrate and contextual data associated with the first previous substrate as input to an additional machine learning model, wherein the additional machine learning model is trained to predict metrology measurements of previous substrates at the manufacturing system based on given spectral data and contextual data of the previous substrates, and wherein the additional machine learning model is trained using a data set that includes the spectral data associated with at least one of the second portion of the first previous substrate or the third portion of the second previous substrate, and the one or more metrology measurements measured for at least one of the second portion of the first previous substrate or the third portion of the second previous substrate; and The metrology measurement is extracted from one or more outputs of the additional machine learning model. A non-transitory computer-readable storage medium as claimed in claim 19, wherein the one or more outputs of the additional machine learning model include metrology data indicating one or more sets of metrology measurement values, and for each set of metrology measurement values, a corresponding set of metrology measurement values corresponds to a confidence level of the first portion of the first previous substrate, and wherein the step of extracting the metrology measurement values from the one or more outputs includes the steps of: Identifying the corresponding set of metrology measurement values having a confidence level that satisfies a confidence criterion, wherein the identified corresponding set of metrology measurement values includes the metrology measurement values.