CN118116817A - Real-time wafer processing quality estimation method and electronic device - Google Patents

Abstract

The embodiment of the invention provides a method for estimating the processing quality of a wafer in real time and an electronic device. The method comprises the following steps: acquiring a processing state signal generated by wafer processing equipment when processing the wafer; and deriving a real-time machining quality of the machining operation by inputting the machining state signal into a machine learning model, wherein the machine learning model estimates the real-time machining quality of the machining operation in response to the machining state signal.

Description

Method and electronic device for real-time wafer processing quality estimation

Technical Field

The present invention relates to a technology for managing wafer processing quality, and more particularly to a method and an electronic device for real-time wafer processing quality estimation.

Background technique

After the silicon wafer is ground, the inspectors will check the various specifications of the silicon wafer (such as the cleanliness, roughness, total thickness variation (TTV), bow and warp of the silicon wafer surface). When checking the roughness of the silicon wafer surface, the inspectors generally use a roughness meter for measurement. For specifications such as TTV, warp, and bow, the inspectors use non-contact wafer measuring instruments to ensure that the accuracy or quality of the silicon wafer surface meets the requirements.

Since wafer grinding is a mass production process, but the current inspection manpower cannot afford comprehensive inspection, most of the wafer accuracy or quality inspections are carried out by sampling. However, this approach will not be able to respond to abnormal phenomena in the processing process and make real-time adjustments, so it is difficult to ensure that every chip meets the requirements.

Summary of the invention

In view of this, the present invention provides a method and an electronic device for real-time wafer processing quality estimation, which can be used to solve the above technical problems.

An embodiment of the present invention provides a method for real-time wafer processing quality estimation, which is suitable for an electronic device, comprising: obtaining at least one processing status signal generated by a wafer processing device when performing a processing operation on a wafer; and obtaining at least one real-time processing quality of the processing operation by inputting at least one processing status signal into at least one machine learning model, wherein the at least one machine learning model estimates at least one real-time processing quality of the processing operation in response to the at least one processing status signal.

An embodiment of the present invention provides an electronic device, comprising a storage circuit and a processor. The storage circuit stores a program code. The processor is coupled to the storage circuit and accesses the program code to execute: obtaining at least one processing status signal generated by a wafer processing device when performing a processing operation on a wafer; and obtaining at least one real-time processing quality of the processing operation by inputting the at least one processing status signal into at least one machine learning model, wherein the at least one machine learning model estimates at least one real-time processing quality of the processing operation in response to the at least one processing status signal.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are included to provide a further understanding of the present invention, and are incorporated in and constitute a part of this specification. The accompanying drawings illustrate embodiments of the present invention and together with the description serve to explain the principles of the present invention.

FIG1 is a schematic diagram of an electronic device according to an embodiment of the present invention;

FIG2 is a flow chart of a method for real-time wafer processing quality estimation according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of a wafer processing device according to an embodiment of the present invention.

Detailed ways

Reference will now be made in detail to exemplary embodiments of the present invention, examples of which are illustrated in the accompanying drawings. Whenever possible, the same reference numerals are used in the drawings and the description to refer to the same or like parts.

Please refer to FIG. 1, which is a schematic diagram of an electronic device according to an embodiment of the present invention. In different embodiments, the electronic device 100 is, for example, various smart devices and/or computer devices. In some embodiments, the electronic device 100 can also be integrated into a wafer processing device to be used as a processing device/human-machine interface in the wafer processing device, but is not limited thereto.

1 , the electronic device 100 includes a storage circuit 102 and a processor 104. The storage circuit 102 is, for example, any type of fixed or removable random access memory (RAM), read-only memory (ROM), flash memory, hard disk or other similar devices or a combination of these devices, and can be used to record multiple program codes or modules.

The processor 104 is coupled to the memory circuit 102 and may be a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors combined with a digital signal processor core, a controller, a microcontroller, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), any other type of integrated circuit, a state machine, an Advanced RISC Machine (ARM) based processor, and the like.

In an embodiment of the present invention, the processor 104 can access the modules and program codes recorded in the storage circuit 102 to implement the method for real-time wafer processing quality estimation proposed by the present invention, the details of which are described as follows.

Please refer to FIG2 , which is a flow chart of a method for real-time wafer processing quality estimation according to an embodiment of the present invention. The method of this embodiment can be executed by the electronic device 100 of FIG1 . The following describes the details of each step of FIG2 with the components shown in FIG1 .

First, in step S210, the processor 104 obtains a processing status signal generated by the wafer processing equipment when performing a processing operation on the wafer. In some embodiments, the processing operation performed by the wafer processing equipment on the wafer includes, for example, grinding the wafer by the wafer processing equipment.

Please refer to FIG. 3, which is a schematic diagram of a wafer processing device according to an embodiment of the present invention. In FIG. 3, the wafer processing device 300 may include a turntable 310 and a grinding wheel 320. In one embodiment, the turntable 310 may be used to carry a wafer 330 to be ground (which has a radius _Rw , for example), and may rotate at a speed _Nw , thereby driving the wafer 330 to rotate at a speed _Nw . The grinding wheel 320 (which has a radius _Rg , for example) may have a spindle 321 and a grinding portion 322, wherein the spindle 321 may rotate at a speed _Ng , and the grinding portion 322 may be provided with abrasive particles for grinding the wafer 330.

In some embodiments, the grinding wheel 320 may have reference points A, B, and C, and the operator may adjust the positions of the reference points A, B, and C to set the tilt angle of the grinding wheel 320 when grinding the wafer 330. In addition, the rotation speed _Nw , the rotation speed _Ng , the grinding wheel feed rate of the grinding wheel 320, and the eccentric distance (offset) between the turntable 310 and the spindle 321 may also be set by the operator according to the needs. Thus, the wafer processing equipment 300 can perform corresponding grinding processing on the wafer 330 according to the above settings of the operator.

However, in some embodiments, if the operator fails to properly set the above values, after the wafer processing equipment 300 completes the grinding process on the wafer 330 , the specifications of the wafer 330 may not meet the requirements.

To avoid the above situation, the processor 104 can obtain in real time in step S210 a processing status signal generated when the wafer processing equipment 300 performs a grinding process on the wafer 330 .

In different embodiments, the processing status signal may include various signals according to the needs of the designer. In the first embodiment, a first accelerometer may be installed on the spindle 321. In this case, during the process of grinding the wafer 330 by the grinding wheel 320, the spindle 321 will vibrate accordingly, and the first accelerometer will detect the corresponding first vibration signal (hereinafter referred to as V1). Based on this, during the process of grinding the wafer 330 by the grinding wheel 320, the processor 104 may obtain the first vibration signal V1 from the first accelerometer as the processing status signal.

In the second embodiment, a second accelerometer may be installed on the main shaft of the turntable 310. In this case, when the grinding wheel 320 grinds the wafer 330, the main shaft of the turntable 310 will vibrate accordingly, and the second accelerometer will detect a corresponding second vibration signal (hereinafter referred to as V2). Therefore, when the grinding wheel 320 grinds the wafer 330, the processor 104 can obtain the second vibration signal V2 from the second accelerometer as a processing status signal.

In the third embodiment, a microphone device may be disposed near the wafer processing equipment 300, and the microphone device may be used to collect the sound generated by the wafer processing equipment 300 during the processing operation of the wafer 330. Therefore, during the process of the grinding wheel 320 grinding the wafer 330, the processor 104 may obtain a sound signal (hereinafter referred to as AU1) from the microphone device as a processing status signal.

In the fourth embodiment, the processor 104 may also take any combination of the first vibration signal V1, the second vibration signal V2 and the sound signal AU1 as the considered processing status signal, but is not limited thereto.

After obtaining the considered machining state signal, in step S220, the processor 104 obtains the real-time machining quality of the above-mentioned machining operation by inputting the machining state signal into the machine learning model.

In an embodiment of the present invention, the real-time processing quality includes, for example, at least one of the real-time surface roughness, total thickness variation, warpage, and curvature of the wafer 330. In some embodiments, the real-time processing quality can be understood as being estimated by a machine learning model based on a processing status signal.

In one embodiment, different real-time processing qualities may correspond to different machine learning models. For example, real-time surface roughness, total thickness variation, warpage, and curvature may correspond to the first machine learning model, the second machine learning model, the third machine learning model, and the fourth machine learning model, respectively. In this case, the processor 104 may input the considered processing state signal into the first machine learning model, the second machine learning model, the third machine learning model, and the fourth machine learning model, respectively, so that the first machine learning model, the second machine learning model, the third machine learning model, and the fourth machine learning model respectively respond to the processing state signal to provide estimated real-time surface roughness, total thickness variation, warpage, and curvature.

In another embodiment, different real-time processing qualities may also be estimated by the same machine learning model at the same time. For example, real-time surface roughness, total thickness variation, warpage, and curvature (or any combination thereof) may correspond to a specific machine learning model at the same time. In this case, the processor 104 may input the considered processing state signal into the specific machine learning model, so that the specific machine learning model can simultaneously provide estimated real-time surface roughness, total thickness variation, warpage, and curvature in response to the processing state signal, but is not limited thereto.

In one embodiment, in order to enable each machine learning model to have the above-mentioned capabilities, during the training process of each machine learning model, the designer can feed specially designed training data into each machine learning model so that each machine learning model can perform corresponding learning. For example, after obtaining a processing status signal that has been marked as corresponding to a certain surface roughness (represented by R) (for example, a measurement from the process of grinding a certain wafer), the processor 104 can generate a corresponding feature vector based on it and feed it into the first machine learning model corresponding to the surface roughness. As a result, the first machine learning model can learn the relevant features of the processing status signal related to the above-mentioned surface roughness (i.e., R) from this feature vector. In this case, when the first machine learning model receives the feature vector corresponding to the above-mentioned processing status signal in the future, the first machine learning model can accordingly estimate that the surface roughness of the wafer currently being ground is R, but it is not limited to this.

For another example, after obtaining a processing state signal (e.g., measured during a grinding process of a wafer) that has been labeled as corresponding to a certain total thickness variation (represented by T), the processor 104 can generate a corresponding feature vector and feed it into a second machine learning model corresponding to the total thickness variation. As a result, the second machine learning model can learn the relevant features of the processing state signal related to a certain total thickness variation (i.e., T) from this feature vector. In this case, when the second machine learning model receives the feature vector corresponding to the above-mentioned processing state signal in the future, the second machine learning model can accordingly estimate that the total thickness variation of the wafer currently being ground is T, but it is not limited to this.

For another example, after obtaining a processing status signal (e.g., measured during a grinding process of a wafer) that has been labeled as corresponding to a certain surface roughness (e.g., R), total thickness variation (e.g., T), warp (represented by BW), and bow (represented by W), the processor 104 can generate a corresponding feature vector and feed it into the corresponding specific machine learning model. As a result, the specific machine learning model can learn the relevant features of the processing status signal related to the above-mentioned surface roughness (i.e., R), total thickness variation (i.e., T), warp (i.e., BW), and bow (i.e., W) from this feature vector. In this case, when the specific machine learning model receives the feature vector corresponding to the above-mentioned processing status signal in the future, the specific machine learning model can accordingly estimate that the surface roughness of the wafer being ground is R, the total thickness variation is T, the warp is BW, and the bow is W, but it is not limited to this.

In one embodiment, the pattern/component of the processing status signal obtained by the processor 104 in step S210 must be the same as the training data used to train the machine learning model.

For example, if the training data used when training a machine learning model is composed of a third vibration signal corresponding to the first vibration signal V1 (for example, measured by the first accelerometer when the wafer processing equipment 300 performs a processing operation on a wafer), then the processing status signal obtained by the processor 104 in step S210 must be composed of the first vibration signal V1.

For another example, if the training data used in training a machine learning model is composed of the third vibration signal mentioned above and a fourth vibration signal corresponding to the second vibration signal V2 (for example, measured by the second accelerometer when the wafer processing equipment 300 performs a processing operation on a wafer), then the processing status signal obtained by the processor 104 in step S210 must be composed of the first vibration signal V1 and the second vibration signal V2.

For another example, if the training data used in training a machine learning model is composed of the third vibration signal, the fourth vibration signal and another sound signal corresponding to the sound signal AU1 (for example, measured by a microphone device when the wafer processing equipment 300 performs a processing operation on a wafer), then the processing status signal obtained by the processor 104 in step S210 must be composed of the first vibration signal V1, the second vibration signal V2 and the sound signal AU1.

In different embodiments, the above machine learning models can be implemented based on various existing machine learning algorithms according to the needs of the designer. For ease of explanation, it is assumed that the first, second, third, and fourth machine learning models are all implemented based on the random forest algorithm, but it is not limited to this.

In an embodiment of the present invention, the processor 104 may first select one suitable for training the first machine learning model (corresponding to surface roughness) from a plurality of first hyperparameter combinations based on random search and 5-fold cross-validation. In one embodiment, the top 10 of the first hyperparameter combinations may be as shown in Table 1 below, and the meaning of each parameter in Table 1 may be as shown in Table 2.

serial number Parameter 1 Parameter 2 Parameter 3 Parameter 4 Parameter 5 Parameter 6 0 twenty four 18 77 True 0.091369 1 1 41 12 55 True 0.084644 2 2 121 20 33 True 0.082841 3 3 70 12 33 True 0.080626 4 4 63 14 66 True 0.080136 5 5 124 14 twenty two True 0.077544 6 6 144 8 55 True 0.077255 7 7 137 14 33 True 0.076922 8 8 180 16 44 True 0.073233 9 9 79 4 66 True 0.072590 10

Table 1

Table 2

In the scenario of Table 1, the first hyperparameter combination numbered 0 can be used as the optimal hyperparameter combination for training the first machine learning model, but is not limited to this.

In one embodiment, the processor 104 may first select one suitable for training the second machine learning model (corresponding to TTV) from a plurality of second hyperparameter combinations based on random search and 5-fold cross validation. In one embodiment, the top 10 of the second hyperparameter combinations may be shown in Table 3 below, and the meaning of each parameter in Table 3 may refer to the content of Table 2.

serial number Parameter 1 Parameter 2 Parameter 3 Parameter 4 Parameter 5 Parameter 6 0 20 6 twenty two True 0.046149 1 1 121 2 twenty two True 0.042938 2 2 126 8 88 True 0.036442 3 3 6 10 33 True 0.033491 4 4 27 8 99 True 0.032685 5 5 148 14 twenty two True 0.032246 6 6 61 18 99 True 0.031406 7 7 113 2 66 True 0.031187 8 8 150 16 twenty two True 0.028614 9 9 179 4 33 True 0.027713 10

table 3

In the scenario of Table 3, the second hyperparameter combination numbered 0 can be used, for example, as the optimal hyperparameter combination for training the second machine learning model, but is not limited to this.

In one embodiment, the processor 104 may first select one suitable for training the third machine learning model (corresponding to the warping) from a plurality of third hyperparameter combinations based on random search and 5-fold cross validation. In one embodiment, the top 10 of the third hyperparameter combinations may be shown in Table 4 below, and the meaning of each parameter in Table 4 may refer to the content of Table 2.

serial number Parameter 1 Parameter 2 Parameter 3 Parameter 4 Parameter 5 Parameter 6 0 25 14 88 True 0.025933 1 1 128 4 1 True -0.009232 2 2 101 12 1 True -0.010955 3 3 156 18 1 True -0.014757 4 4 113 18 twenty two True -0.020440 5 5 101 2 1 True -0.020785 6 6 68 14 88 True -0.022272 7 7 176 12 1 True -0.022826 8 8 142 16 66 True -0.022875 9 9 127 18 1 True -0.022938 10

Table 4

In the scenario of Table 4, the third hyperparameter combination numbered 0 can be used, for example, as the optimal hyperparameter combination for training the third machine learning model, but is not limited to this.

In one embodiment, the processor 104 may first select one suitable for training the fourth machine learning model (corresponding to the curvature) from a plurality of fourth hyperparameter combinations based on random search and 5-fold cross validation. In one embodiment, the top 10 of the fourth hyperparameter combinations may be shown in Table 5 below, and the meaning of each parameter in Table 5 may refer to the content of Table 2.

serial number Parameter 1 Parameter 2 Parameter 3 Parameter 4 Parameter 5 Parameter 6 0 6 12 55 True -0.304832 1 1 15 20 11 True -0.317027 2 2 11 20 twenty two True -0.417495 3 3 44 2 1 True -0.434407 4 4 28 6 110 True -0.438435 5 5 33 6 33 True -0.486140 6 6 8 18 1 True -0.501083 7 7 133 4 33 True -0.541256 8 8 67 2 44 True -0.552761 9 9 75 6 1 True -0.555465 10

table 5

In the scenario of Table 5, the fourth hyperparameter combination numbered 0 can be used, for example, as the optimal hyperparameter combination for training a fourth machine learning model, but is not limited to this.

2 again, after obtaining the real-time processing quality of the processing operation, the processor 104 may further determine whether the obtained real-time processing quality is abnormal. In one embodiment, in response to determining that the real-time processing quality is abnormal, the processor 104 may provide a warning message (step S230).

For example, if the obtained real-time processing quality is an estimated surface roughness, the processor 104 may compare the estimated surface roughness with the surface roughness threshold. If the estimated surface roughness is higher than the surface roughness threshold, the processor 104 may, for example, determine that the real-time processing quality is abnormal, and then provide a corresponding warning message for reference by relevant personnel and take corresponding improvement measures.

For another example, if the real-time processing quality obtained is the estimated TTV and warpage, the processor 104 may compare the estimated TTV and warpage with the TTV threshold and the warpage threshold, respectively. If the estimated TTV is higher than the TTV threshold, or the estimated warpage is higher than the warpage threshold, the processor 104 may, for example, determine that the real-time processing quality is abnormal, and then provide a corresponding warning message for reference by relevant personnel and take corresponding improvement measures.

In summary, the embodiments of the present invention can determine the corresponding real-time processing quality based on the processing status signal generated by the wafer processing equipment when the wafer is processed. Therefore, all wafers in processing can be detected in real time. In addition, the embodiments of the present invention can also provide a warning message when it is determined that the real-time processing quality of the wafer is abnormal, so that relevant personnel can make corresponding adjustments accordingly, so that the wafer can meet the specification requirements.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims (11)

1. A method for real-time wafer processing quality estimation, suitable for electronic devices, characterized by comprising: Obtaining at least one processing status signal generated by the wafer processing equipment when performing a processing operation on the wafer; and At least one real-time processing quality of the processing operation is obtained by inputting the at least one processing status signal into at least one machine learning model, wherein the at least one machine learning model estimates the at least one real-time processing quality of the processing operation in response to the at least one processing status signal. 2 . The method according to claim 1 , wherein the wafer processing equipment comprises a turntable and a grinding wheel, and the processing operation comprises grinding the wafer located on the turntable by the grinding wheel of the wafer processing equipment. 3. The method according to claim 1, wherein the at least one processing status signal comprises a first vibration signal, and the step of obtaining the at least one processing status signal generated by the wafer processing equipment when performing the processing operation on the wafer comprises: The first vibration signal is obtained from a first accelerometer, wherein the first accelerometer is installed on a grinding wheel spindle of the wafer processing equipment. 4. The method according to claim 2, wherein the at least one processing status signal comprises a second vibration signal, and the step of obtaining the at least one processing status signal generated by the wafer processing equipment when performing the processing operation on the wafer comprises: The second vibration signal is obtained from a second accelerometer, wherein the second accelerometer is installed on a turntable spindle of a turntable, and the turntable carries the wafer. 5. The method according to claim 1, wherein the at least one processing status signal comprises a sound signal, and the step of obtaining the at least one processing status signal generated by the wafer processing equipment when performing the processing operation on the wafer comprises: The sound signal is obtained from a microphone device, wherein the microphone device is used to collect the sound generated by the wafer processing equipment during the process of performing the processing operation on the wafer. 6 . The method of claim 1 , wherein the at least one real-time processing quality comprises at least one of real-time surface roughness, total thickness variation, warpage, and bow of the wafer. 7. The method according to claim 1, wherein the at least one machine learning model corresponds to the at least one real-time processing quality, respectively, and the step of obtaining the at least one real-time processing quality of the processing operation by inputting the at least one processing state signal into the at least one machine learning model comprises: The at least one processing status signal is input into each of the machine learning models, wherein each of the machine learning models outputs the corresponding real-time processing quality in response to the at least one processing status signal. 8. The method of claim 1, wherein the at least one machine learning model comprises a specific machine learning model, wherein the step of obtaining the at least one real-time processing quality of the processing operation by inputting the at least one processing status signal into the at least one machine learning model comprises: The at least one processing status signal is input into the specific machine learning model, wherein the specific machine learning model outputs the at least one real-time processing quality in response to the at least one processing status signal. 9. The method according to claim 1, further comprising: In response to determining that the at least one real-time processing quality is abnormal, providing a warning message. 10. The method according to claim 1, wherein each of the machine learning models is trained based on a hyperparameter combination selected from a plurality of first hyperparameter combinations by random search and 5-fold cross validation. 11. An electronic device, comprising: a storage circuit storing program code; and A processor coupled to the storage circuit and accessing the program code to execute: Obtaining at least one processing status signal generated by the wafer processing equipment when performing a processing operation on the wafer; and At least one real-time processing quality of the processing operation is obtained by inputting the at least one processing status signal into at least one machine learning model, wherein the at least one machine learning model estimates the at least one real-time processing quality of the processing operation in response to the at least one processing status signal.