TW202407638A - Mask inspection for semiconductor specimen fabrication - Google Patents

Abstract

There is provided a system and method of inspecting a mask usable for fabricating a semiconductor specimen. The method comprises obtaining an original defect image comprising defective pixels representative of a defect candidate and a location of the defect candidate, and a bank of defect images of the defect candidate and a bank of reference images acquired at a plurality of focus levels; determining an optimal focus among the plurality of focus levels, and generating a composite defect image at the optimal focus; aligning the original defect image with the composite defect image to identify an area of target pixels in the composite defect image corresponding to the defective pixels; and for each focus level, providing a measurement indicative of displacement between the set of defect images and at least one reference image, giving rise to a plurality of measurements corresponding to the plurality of focus levels.

Description

Mask inspection for semiconductor sample manufacturing

The subject matter currently disclosed relates generally to the field of mask inspection, and more specifically to the detection and measurement of defects in photomasks.

Current demands for high density and high performance associated with very large-scale integrated fabrication of microelectronic components require sub-micron features, increased transistor speed and circuit speed, and improved reliability. As semiconductor manufacturing processes advance, pattern dimensions (such as pad width) and other types of critical dimensions continue to shrink. These demands require the formation of component features with high accuracy and uniformity, which in turn requires careful monitoring of the manufacturing process, including automated inspection of components while they are still in semiconductor wafer form.

Semiconductor components are typically manufactured using photolithography masks (also called photomasks, masks, or reticles) in photolithography processes. The photolithography process is one of the main processes in semiconductor device manufacturing and involves patterning the wafer surface according to the circuit design of the semiconductor device to be produced. This type of circuit design begins by forming a pattern on a mask. Therefore, in order to obtain working semiconductor components, the mask must be defect-free. Masks are manufactured through complex manufacturing processes and may be affected by various defects and variations.

Additionally, masking often creates many dies on the wafer in a repetitive manner. Therefore, any defects on the mask will be repeated multiple times on the wafer and will result in defects in multiple components. Establishing a process worthy of production requires tight control of the entire photolithography process, especially given the large-scale circuit integration and continued reduction in semiconductor device size.

Various mask inspection methods have been developed and used. According to certain conventional mask design and evaluation techniques, a mask is created and used to expose the wafer through the mask, and then a check is performed to determine whether the features/pattern of the mask have been transferred to the wafer according to the design. Any changes in the final printed features from the intended design may require design modifications, mask repair, creation of new masks, and/or exposure of new wafers.

Alternatively, the mask can be inspected directly using various mask inspection tools. The inspection process may include a plurality of inspection steps. The inspection step may be performed multiple times during the fabrication process of the mask, for example after the fabrication or processing of certain layers, etc. Additionally or alternatively, each inspection step may be repeated multiple times, for example multiple times for different mask locations, or multiple times for the same mask location with different inspection settings.

Mask inspection generally involves producing some inspection output (eg, image, signal, etc.) for the mask by directing light or electrons to the mask and detecting the light or electrons from the mask. Once the output is generated, defect detection is typically performed by applying defect detection methods and/or algorithms to the output. Typically, the goal of inspection is to provide high sensitivity and accuracy for defect detection and/or associated measurements on the mask.

According to certain aspects of the presently disclosed subject matter, a computerized system for inspecting masks useful for fabricating semiconductor samples is provided, the system including: an inspection tool configured to: provide a computerized system including: The original defect image of one or more defect primitives, and the position of the candidate defect on the mask; and based on the position, obtain a defect image library of the candidate defect at a plurality of focus levels in the entire focus process window and a reference image library, the defect image library includes a group of defect images acquired at each focus level, and the reference image library includes a group of reference images acquired at each focus level; and processing and memory circuitry (PMC) operably connected to the inspection tool and configured to: determine the best focus among a plurality of focus levels and generate a composite defect image based on the set of defect images at the best focus; Aligning the original defect image with the synthesized defect image to identify areas of one or more target primitives in the composite defect image corresponding to the one or more defect primitives; and for each focus level, based on the The region is provided to provide measurements indicative of displacement between a set of defect images at a focus level and at least one reference image derived from the set of reference images, thereby producing a plurality of measurements corresponding to a plurality of focus levels.

In addition to the features described above, a system in accordance with this aspect of the presently disclosed subject matter may include one or more of the features (i) through (xvi) listed below in any desired combination or permutation technically possible : (i) The candidate defects are derived from a list of candidate defects selected from a defect map indicating the distribution of candidate defects on the mask or portion thereof. (ii) The inspection tool is further configured to calibrate the printing threshold (PT). Providing measurements includes applying PT to the set of defect images and the set of reference images at a focus level, thereby producing a set of binary defect images and a set of binary reference images, and based on the set of binary defect images and the set of binary reference images group to perform measurements. (iii) The defect image library and the reference image library are obtained by placing the candidate defects at the optimal position in the field of view (FOV) of the inspection tool, where the optimal position is selected to at least reduce the error caused by FOV distortion. Noise. (iv) A plurality of focus levels are predefined based on the focus step size according to accuracy and throughput requirements. (v) The plurality of focus levels further includes one or more focus levels that extend the focus process window. (vi) Optimal focus is determined by applying focus measurements at each focus level to at least one defect image in the set of defect images. (vii) The aligning further includes verifying the registerability of the patterns included in the composite defect image, and determining a region in the composite defect image based on the verification. (viii) Verification of registerability includes offsetting the pattern in a set of directions with corresponding offsets to obtain a set of offset images, performing image registration between the synthetic defect image and the set of offset images , and determine the registrability based on the results of image registration. (ix) The PMC is further configured to determine the best focus among the plurality of focus levels with respect to the reference image library, and in response to an offset between the best focus of the reference image and the best focus of the defect image, based on The offset is used to correlate the corresponding focus levels of the reference image and the defect image. (x) The at least one reference image is a composite reference image generated by combining sets of reference images. (xi) The defect image group consists of one defect image, and the composite defect image is a defect image. (xii) said providing measurements includes measuring a displacement of a difference image between each defect image of the set of defect images and at least one reference image in said region, thereby producing a corresponding to the set of defect images Displacement group, and generate measurement results based on the displacement group. (xiii) The mask is a multi-die mask, the defect image library is captured for candidate defects located in the inspection die, and the reference image library is captured from the corresponding location in the reference die. (xiv) The mask is a single-die mask, and the defect image library and the reference image library are obtained from different areas in the same die that share similar design patterns. (xv) Repeat the provision of the original defect image, acquisition, determination, alignment, and Provide steps for measurement. (xvi) The inspection tool is an actinic inspection tool configured to simulate the optical configuration of a photolithography tool that may be used to fabricate semiconductor samples.

According to other aspects of the presently disclosed subject matter, a method of inspecting a mask that may be used to fabricate a semiconductor sample is provided, the method being performed by a processing and memory circuitry (PMC), and the method includes: from an inspection tool Acquire: an original defect image including one or more defect primitives representing the candidate defect, and the position of the candidate defect on the mask; and based on the position, obtain at a plurality of focus levels in the entire focus process window A defect image library and a reference image library of candidate defects, the defect image library includes a group of defect images acquired at each focus level, and the reference image library includes a group of reference images acquired at each focus level. ; and determine the best focus among multiple focus levels, and generate a composite defect image based on the defect image group at the best focus; align the original defect image and the composite defect image to identify the composite defect image a region of one or more target primitives in the image corresponding to one or more defective primitives; and for each focus level, providing an indication based on the region that the set of defective images at the focus level differs from the reference image Measurements of the displacement between the at least one reference image are summed together to produce a plurality of measurement results corresponding to a plurality of focus levels.

This aspect of the disclosed subject matter may include, mutatis mutandis, one or more of the features (i) to (xvi) listed above with respect to the system, in any desired combination or permutation technically possible. .

According to other aspects of the presently disclosed subject matter, a non-transitory computer-readable medium is provided that includes instructions that, when executed by a computer, cause the computer to perform a method of inspecting a mask that may be used to fabricate a semiconductor sample. The method includes: obtaining from the inspection tool: an original defect image including one or more defect primitives representing the candidate defect, and a position of the candidate defect on the mask; and based on the position, in the entire focused process window A defect image library and a reference image library of candidate defects acquired at a plurality of focus levels, the defect image library includes a group of defect images acquired at each focus level, and the reference image library includes a group of defect images acquired at each focus level. The reference image group acquired at the same level; and determining the best focus among multiple focus levels, and generating a composite defect image based on the defect image group at the best focus; combining the original defect image and the composite defect image Align to identify a region of one or more target primitives in the composite defect image corresponding to one or more defect primitives; and for each focus level, provide an indication at the focus level based on the region A measurement of the displacement between the set of defective images and at least one reference image derived from the set of reference images, thereby producing a plurality of measurement results corresponding to a plurality of focus levels.

This aspect of the disclosed subject matter may include, mutatis mutandis, one or more of the features (i) to (xvi) listed above with respect to the system, in any desired combination or permutation technically possible. .

In the detailed description that follows, many specific details are set forth to provide a thorough understanding of the content of this case. However, those skilled in the art will understand that the presently disclosed subject matter may be practiced without these specific details. In other instances, well-known methods, procedures, components and circuits have not been described in detail so as not to obscure the subject matter of the present disclosure.

As will be apparent from the following discussion, it should be understood that terms such as "inspect," "provide," "obtain," "determine," "align," and "calibrate" are used throughout the discussion of this specification unless otherwise specifically stated. , "apply", "execute", "place", "verify", "offset", "associate", "measure", "repeat", "obtain", "simulate" and other terms refer to the manipulation of computers Data and/or operation(s) and/or process(es) that transform data into other data, said data represented as physical (such as electronic) quantities and/or said data represented as physical objects. The term "computer" shall be broadly construed to encompass any type of hardware-based electronic device having data processing capabilities, including, by way of non-limiting examples, mask inspection systems, inspection tools, EPD estimation systems and the like disclosed in this case. corresponding part.

The term "mask" used in this specification is also known as "photolithography mask" or "photomask" or "photomask". Such terms should be interpreted equivalently and broadly to cover template-retaining circuit designs to be patterned on a semiconductor wafer in a photolithography process (e.g., a layout that defines specific layers of an integrated circuit). For example, the mask can be implemented as a fused silica plate covered with a pattern of opaque, transparent and phase-shifted areas that are projected onto the wafer during the photolithography process. For example, the mask may be an extreme ultraviolet (EUV) mask or an argon fluoride (ArF) mask. As another example, the mask may be a memory mask (which can be used to fabricate memory components) or a logic mask (which can be used to fabricate logic components).

The terms "inspection" or "mask inspection" as used in this specification should be construed broadly to encompass the use of evaluating the accuracy and completeness of photomasks fabricated relative to a circuit design and the accuracy of producing the circuit design on the wafer. Any operation that represents the capability. Inspection may include any kind of operations related to various types of defect detection, defect review and/or defect classification, and/or during and/or after the mask fabrication process, and/or when the mask is used for semiconductor sample fabrication measurement operations during the period. Non-destructive inspection tools can be used to provide inspection after mask fabrication. As non-limiting examples, the inspection process may include one or more of the following operations: scanning (in a single or multiple scans) using an inspection tool, imaging, sampling, detecting, measuring, classifying, and/or Regarding the mask or other operations provided by its sections. Likewise, mask checking may also be interpreted to include, for example, the generation of check recipe(s) and/or other setup operations prior to the actual checking of the mask. It should be noted that, unless otherwise specifically stated, the term "inspection" or its synonyms used in this specification is not limited to the resolution or size of the inspection area. Various non-destructive inspection tools include, by way of non-limiting example, optical inspection tools, scanning electron microscopes, atomic force microscopes, and the like.

The term "metrometric operation" as used in this specification should be interpreted broadly to encompass any metrological operation procedure used to extract metrological information related to one or more structural elements on a semiconductor sample (such as a mask). In some embodiments, metrology operations may include measurement operations such as, for example, critical dimension (CD) measurements performed on certain structural elements on a sample, including but not limited to the following: dimensions (e.g., line width, line spacing) , contact diameter, component size, edge roughness, grayscale statistics, etc.), component shape, distance within or between components, related angles, coverage information associated with components corresponding to different design levels, etc. For example, by using image processing technology to analyze measurement results such as measurement images. It should be noted that, unless otherwise specifically stated, the term "measurement" or its synonyms used in this specification is not limited to measurement technology, measurement resolution, or the size of the inspection area.

The term "sample" as used in this specification should be interpreted broadly to encompass any type of wafers, related structures, assemblies, and/or components thereof used in the fabrication of semiconductor integrated circuits, magnetic heads, flat panel displays, and other semiconductor fabricated articles.

The term "defect" as used in this specification should be interpreted broadly to cover any type of abnormal or inappropriate features/functions formed on a sample. In some cases, the defect may be a Defect of Interest (DOI), which is a real defect that, when printed on the wafer, has some impact on the functionality of the fabricated component and therefore is detected in compliance with Customer Benefits. For example, any "fatal" defect that may lead to yield loss can be represented as a DOI. In some other cases, a defect may be nuisance (also known as a "false positive" defect) and can be ignored because the defect has no effect on the completed component functionality.

The term "candidate defect" as used in this specification should be interpreted broadly to encompass suspected defect locations on the mask that are detected as having a relatively high probability of being a Defect of Interest (DOI). Therefore, at the time of review, the candidate defect may actually be a DOI, or in some other case, the candidate defect may be a nuisance caused by different changes during inspection (e.g., process changes, color changes, mechanical and electrical changes, etc.) Or random noise.

As used herein, the terms "non-transitory memory" and "non-transitory storage media" should be construed broadly to encompass any volatile or non-volatile computer memory suitable for the subject matter presently disclosed. These terms shall be deemed to include a single medium or multiple media (e.g., centralized or distributed databases, and/or associated caches and servers) that store one or more sets of instructions. These terms shall also be deemed to include any medium capable of storing or encoding a set of instructions for execution by a computer and causing the computer to perform any one or more of the methods described herein. Accordingly, these terms shall be deemed to include, but are not limited to, read-only memory ("ROM"), random access memory ("RAM"), magnetic disk storage media, optical storage media, flash memory devices, etc.

It is to be understood that certain features of the presently disclosed subject matter that are described in the context of separate embodiments may also be provided in combination in a single embodiment, unless specifically stated otherwise. Conversely, various features of the presently disclosed subject matter that are described in the context of a single embodiment may also be provided separately or in any suitable subgroup combination. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the methods and apparatus.

With this in mind, attention turns to FIG. 1 , which illustrates a functional block diagram of a mask inspection system in accordance with certain embodiments of the presently disclosed subject matter.

The inspection system 100 shown in FIG. 1 may be used to inspect masks during or after a mask manufacturing process, and/or during a semiconductor sample manufacturing process using the mask. As mentioned above, inspection as referred to herein may be construed to cover any inspection/detection of defects, classification of various types of defects and/or metrology operations (such as, for example, critical dimension (CD) measurements) on the mask or parts thereof. type of operation. In accordance with certain embodiments of the presently disclosed subject matter, the illustrated inspection system 100 includes a computer-based system 101 capable of automatically inspecting and detecting defects on masks.

As mentioned above, the defects to be detected herein may refer to any type of anomalies or improper features/functions formed on the mask relative to the original design. Defects to be detected on the mask may include various defects such as bridges, protrusions, broken wires, critical dimension (CD) related defects, contact anomalies (such as missing contacts, contact merging, contact shrinkage, etc.) , or any other type of defect. For example, in some cases, the defect to be detected may be related to edge positioning displacement (EPD), which indicates the difference between the actual position of one or more edges/contours of a printed feature on the mask and its planned/intended position. deviation (also called EPD defect in this article). In some embodiments, system 101 may be configured to perform CD measurements for detected EPD defects and provide EPD estimates thereof. In this case, the system 101 is also called an EPD estimation system, which is a subsystem of the inspection system 100 .

System 101 may be operably connected to a mask inspection tool 120 configured to scan the mask and capture one or more images of the mask for inspection of the mask. The term "mask inspection tool" as used in this specification should be interpreted broadly to encompass any type of inspection tool that can be used in processes related to mask inspection, including, by way of non-limiting example, scanning of a mask or portion thereof ( in a single or multiple scans), imaging, sampling, detection, measurement, classification and/or other processes.

Without limiting the scope of this disclosure in any way, it should also be noted that mask inspection tool 120 may be implemented as various types of inspection machines, such as optical inspection tools, electron beam tools, and the like. In some cases, mask inspection tool 120 may be a relatively low-resolution inspection tool (eg, an optical inspection tool, a low-resolution scanning electron microscope (SEM), etc.). In some cases, mask inspection tool 120 may be a relatively high-resolution inspection tool (eg, high-resolution SEM, atomic force microscope (AFM), transmission electron microscope (TEM), etc.). In some cases, the inspection tool can provide both low-resolution image data and high-resolution image data. In some embodiments, mask inspection tool 120 has metrology capabilities and may be configured to perform metrology operations on captured images. The generated image data (low-resolution image data and/or high-resolution image data) may be transmitted to system 101 directly or via one or more intermediate systems. This document is not limited to any particular type of mask inspection tool and/or the resolution of the image data produced by the inspection tool.

According to certain embodiments, the mask inspection tool may be implemented as an actinic inspection tool configured to simulate/emulate the optical configuration of a photolithography tool (eg, a scanner or stepper) that may be used to fabricate semiconductor samples, e.g. By projecting the pattern formed in the mask onto the wafer, as described in further detail below with respect to FIG. 5 .

Turning now to FIG. 5 , illustrated is a schematic diagram of an actinic inspection tool and a photolithography tool in accordance with certain embodiments of the presently disclosed subject matter.

Similar to photolithography tool 520, actinic inspection tool 500 may include an illumination source 502 configured to generate light at an exposure wavelength (e.g., laser). Illumination optics 504 and projection optics 508 may include one or more optical elements (such as, for example, lenses, apertures, spatial filters, etc.).

In photolithography tool 520 , a mask is positioned at mask holder 506 and optically aligned to project an image of the circuit pattern to be replicated onto a wafer placed on wafer holder 512 (e.g., by Use various stepping, scanning and/or imaging techniques to create or replicate patterns on wafers). Unlike photolithography tool 520 , photochemical inspection tool 500 does not place wafer holder 512 , but instead places detector 510 (such as, for example, a charge coupled device (CCD)) in place of the wafer holder, where detector 510 Configured to detect light projected through the mask and generate an image of the mask.

As can be seen, actinic inspection tool 500 is configured to simulate the optical configuration of photolithography tool 520, including but not limited to, for example, lighting/exposure conditions such as, for example, wavelength, partial coherence of the exposure light, pupil shape, illumination aperture, Numerical aperture (NA), etc., these conditions are used to expose photoresist during the actual photolithography process during semiconductor component manufacturing. Therefore, the mask image 514 acquired by the detector 510 is expected to be similar to the image 516 of a wafer fabricated using a mask via a photolithography tool. The mask image obtained using this actinic inspection tool is also called an aerial image. The bird's-eye view image is provided to the system 101 for further processing, as described below.

According to certain embodiments, in some cases, mask inspection tool 120 may be implemented as a non-actinic inspection tool, such as, for example, a general optical inspection tool, an electron beam tool (eg, SEM), or the like. In this case, the inspection tool's detector interfaces with the specific type of microscope used and digitizes the image information from the microscope to obtain an image of the mask.

Simulations can be performed on the acquired images to simulate the optical configuration of the photolithography tool, thereby producing an overhead image. In some cases, the image simulation may be performed by the system 101 (e.g., simulation functionality may be integrated into the PMC 102 of the system 101 by including an image simulation model in the PMC 102), while in other cases, the image simulation may be performed by the system 101. Image simulations may be performed by a processing module of the mask inspection tool 120 , or by a separate simulation engine/unit operatively connected to the mask inspection tool 120 and the system 101 .

For purposes of illustration only, certain embodiments described below are provided for images acquired by an actinic mask inspection tool. Those skilled in the art will readily appreciate that the teachings of the presently disclosed subject matter are equally applicable to images acquired by any other suitable techniques and inspection tools and further converted into overhead images using appropriate simulation models. The term "overhead image" should be interpreted broadly to encompass images acquired by actinic mask inspection tools as well as overhead images simulated from images acquired by non-actinic inspection tool(s).

System 101 includes a processor and memory circuitry (PMC) 102 operatively connected to a hardware-based I/O interface 126 . PMC 102 is configured to provide processing required by the operating system, as described in further detail with reference to Figures 2, 3, and 4, and includes a processor (not separately shown) and memory (not separately shown). The processor of PMC 102 may be configured to execute a number of functional modules in accordance with computer readable instructions implemented on non-transitory computer readable memory included in the PMC. Such functional modules are hereafter referred to as included in the PMC.

A processor as referred to herein may refer to one or more general-purpose processing devices, such as a microprocessor, central processing unit, or the like. More specifically, the processor 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 or implementation that implements other instruction sets Instruction set combination of processors. The processor may also be one or more specialized processing devices, such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a network processor, etc. The processor is configured to execute instructions for performing the operations and steps discussed herein.

Memory as referred to herein may include main memory (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) (such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM), etc. ) and static memory (such as flash memory, static random access memory (SRAM), etc.).

As mentioned previously, in some embodiments, system 101 may be configured to detect defects on the mask, such as EPD defects. EPD defects can be caused by a variety of factors, such as physical effect(s) during the mask manufacturing process, and/or other factors such as oxidation (which may occur gradually during mask use), particles, scratches, crystal growth, static electricity Discharge (ESD), etc. If such defects are not detected before wafer mass production, they will be repeated many times on the production wafer, which may lead to defects in multiple semiconductor components (for example, affecting the function of the component and failing to achieve the expected performance), And adversely affects yield, especially given the reduction in semiconductor component size in advanced processes for large-scale circuit integration and photomasking.

In order to perform CD measurements on EPD defects (or candidate defects) detected by a mask inspection tool, it is often necessary to move the mask to a separate metrology tool that captures the mask at the location of the candidate defect. of additional images and perform measurements on the additional images. The use of two tools (inspection tools and metrology tools) and switching between them is time-consuming (thus affecting inspection throughput) and cost-effective. Additionally, since the two tools often have different coordinate systems, it is difficult to ensure that additional images captured by the metrology tool come from the exact location reported by the inspection tool, not to mention that these tools may also be inconsistent with navigation relative to the given coordinates. Errors are associated. Therefore, in some cases it may happen that the metrology tool unknowingly obtains additional images at the wrong location and the measurements obtained at such locations are invalid/meaningless and in some cases even Provide misleading information to users.

In addition, photolithography tools such as those described above are designed to print masked circuit patterns onto semiconductor samples within a process window (which can be defined with respect to various photolithography parameters) in order to fabricate integrated circuits with high yields. For example, one of the major impacts on yield is related to the focusing process window (also known as the defocusing process window), which refers to the focal range of the photolithography tool within which the semiconductor sample should be printed to meet expectations output. It is desirable to examine how the circuit patterns on the mask respond to changes in various photolithography parameters within the process window (such as, for example, focusing on different focus levels within the process window), e.g., by estimating printing defects/errors (such as those within the process window). different parameters associated with the EPD). This estimate can provide the user with an indication of how different parameters may affect wafer yield throughout the process window.

In accordance with certain embodiments of the presently disclosed subject matter, improved mask inspection systems and methods are proposed that are configured to perform CD measurements for detected EPD defects with greater accuracy and improvements The yield provides a range of EPD estimates across the entire process window.

According to certain embodiments, mask inspection system 100 includes a mask inspection tool 120 and a subsystem 101 operatively connected to the inspection tool and configured for EPD estimation as previously described. The mask inspection tool 120 may be configured to provide a raw defect image including one or more defect primitives representing candidate defects, and the location of the candidate defects on the mask. The mask inspection tool 120 may be further configured to acquire a library of defect images and a library of reference images for candidate defects at a plurality of focus levels throughout the focus process window based on the location. The defect image library includes a set of defect images acquired at each focus level, and the reference image library includes a set of reference images acquired at each focus level.

Functional modules included in the PMC 102 of the system 101 may include an image processing module 104, an alignment module 106, and a measurement module 108. The image processing module 104 may be configured to determine the best focus among a plurality of focus levels and generate a composite defect image based on the set of defect images at the best focus. Alignment 106 may be configured to align the original defect image with the composite defect image to identify regions of one or more target primitives in the composite defect image that correspond to one or more defect primitives. The measurement module 108 may be configured to provide, for each focus level, a measurement indicative of a displacement between the set of defect images and at least one reference image derived from the set of reference images for the focus level based on the region ( For example, EPD measurement), thereby producing a plurality of measurement results corresponding to a plurality of focus levels.

The operation of system 100, system 101, PMC 102 and the functional modules therein will be described in further detail with reference to Figures 2, 3 and 4.

According to certain embodiments, system 100 may include storage unit 122. The storage unit 122 may be configured to store any data required to operate the system 100 and the system 101 , such as data related to inputs and outputs of the system 100 and the system 101 , as well as intermediate processing results generated by the system 101 . For example, the storage unit 122 may be configured to store original defect images, a defect image library and a reference image library generated by the mask inspection tool 120 and/or derivatives thereof (eg, preprocessed images). Accordingly, images can be retrieved from storage unit 122 and provided to PMC 102 for further processing.

In some embodiments, system 100 may optionally include a computer-based graphical user interface (GUI) 124 configured to enable user-specific input related to system 101 . For example, a visual representation of the mask may be presented to the user (eg, via a display forming part of the GUI 124), such as an image of the mask or a portion thereof. The user may be provided via the GUI with the option to define certain command arguments such as, for example, process window parameters, such as, for example, the range of the process window, focus step size, etc., defects to be acquired at a given focus level. Number of images and reference images, printing thresholds, etc. In some cases, the user can also view the operation results on the GUI, such as the acquired image library, a plurality of measurement results corresponding to different focus levels, and/or further inspection results.

As mentioned above, the system 101 is configured to receive a library of defect images and a library of reference images of candidate defects at a plurality of focus levels throughout the focus process window via the I/O interface 126 . These images may include images captured by the mask inspection tool 120 (and/or derivatives thereof) and/or pre-processed images exported from captured images obtained through various pre-processing stages, etc. Note that in some cases, images may include associated numerical data (e.g., metadata, handcrafted attributes, etc.). It should also be noted that in some embodiments the image material relates to a target layer of semiconductor components to be printed on the wafer.

The system 101 is further configured to process the received image and send the operation results (e.g., a plurality of measurement results corresponding to different focus levels, etc.) to the storage unit 122 via the I/O interface 126 and/or for rendering. GUI 124, and/or mask inspection tool 120.

According to some embodiments, in addition to the system 101 , the mask inspection system 100 may also include one or more inspection modules, such as, for example, additional defect detection module(s) and/or an automated defect review module (ADR). ) and/or an automatic defect classification module (ADC) and/or a metrology related module and/or other inspection modules that can be used to perform additional inspection of the mask. One or more inspection modules may be implemented as stand-alone computers, or their functionality (or at least some of their functionality) may be integrated with the mask inspection tool 120 . In some embodiments, the output obtained from the system 101 may be used by the mask inspection tool 120 and/or one or more inspection modules (or portions thereof) to further inspect the mask.

Those skilled in the art will readily appreciate that the teachings of the presently disclosed subject matter are not limited to the system shown in Figure 1; equivalent and/or modified functionality may be combined or divided in another manner, and may be implemented in software and firmware. Any appropriate combination of software and/or hardware.

Note that in some cases the mask checking system shown in Figure 1 can be implemented in a distributed computing environment. For example, the mask inspection tool 120 and the subsystem 101 may be distributed on different devices (local and/or remote devices) and may be connected by a communication network. For example, mask inspection tool 120 may be located within a manufacturing facility, and subsystem 101 may be a process server remotely connected to the inspection tool. Additionally or alternatively, the above-mentioned functional modules included in the PMC 102 may also be distributed on several local and/or remote devices and connected through a communication network. It should also be noted that in other embodiments, one or more of the mask inspection tool 120 , the storage unit 122 and/or the GUI 124 may be external to the system 100 and communicate with the system 101 via the I/O interface 126 . System 101 may be implemented as a stand-alone computer(s) for use with a mask inspection tool. Alternatively, various functions of system 101 may be at least partially integrated with mask inspection tool 120 to facilitate and enhance the functionality of mask inspection tool 120 in inspection-related processes.

Although not necessarily so, the operations of system 101 and system 100 may correspond to some or all stages of the method described with respect to FIGS. 2-4. Likewise, the methods described with respect to FIGS. 2 to 4 and possible implementations thereof may be implemented by system 101 and system 100 . Therefore, it should be noted that the embodiments discussed with respect to the methods described in Figures 2 to 4 may also be implemented, mutatis mutandis, as various embodiments of system 101 and system 100, and vice versa.

Referring now to FIG. 2 , illustrated therein is a generalized flow diagram for mask inspection of masks that may be used to fabricate semiconductor samples, in accordance with certain embodiments of the presently disclosed subject matter.

A raw defect image including one or more defect primitives representing candidate defects may be provided (202) (eg, by mask inspection tool 120). The position of the candidate defect on the mask is also obtained (e.g., in mask coordinates). According to some embodiments, the candidate defects are from a list of candidate defects selected from a defect map indicating a distribution of candidate defects over the mask (or at least a portion of the mask).

According to some embodiments, the defect map is a result from a preliminary inspection of the mask (eg, by mask inspection tool 120). Figure 3 illustrates preliminary processing prior to the present mask inspection and EPD estimation process in accordance with certain embodiments of the presently disclosed subject matter.

A candidate defect list may be selected (302) from the defect map generated by the preliminary inspection of the mask (eg, by a mask inspection tool or a separate defect detection module), as exemplified in more detail below.

For example, a plurality of inspection images of the mask may be obtained sequentially during scanning of the mask, each inspection image representing a corresponding portion of the mask. A plurality of defect maps corresponding to a plurality of inspection images may be generated (eg, by a defect detection module of a mask inspection tool). Each defect map may be generated using at least one reference image and may indicate a candidate defect distribution on the corresponding inspection image. For example, at least one differential image may be generated based on differences between primitive values of the inspection image and primitive values of the at least one reference image. The defect map may be generated by using a detection threshold to determine the location of a suspected defect (ie, a candidate defect) based on at least one differential image. In some cases, multiple defect maps may be combined to obtain a masked defect map.

The term defect map used in this article may be interpreted to mean a defect map corresponding to a portion of the mask, or a defect map of the entire mask. In some embodiments, the generated defect map may further indicate one or more defect characteristics of the candidate defect, such as, for example, the location, intensity (of the defect signal), size, etc. of the candidate defect. Based on the positions of candidate defects revealed by the defect map, these candidate defects can be located in the corresponding inspection images.

In some embodiments, a list of candidate defects may be selected from the defect map based on the intensity of the candidate defects. In some other cases, preliminary EPD measurements of individual candidate defects may be estimated, and a list of candidate defects may be selected based on the ranking of the EPD measurements of the candidate defects in the graph and a predefined EPD threshold. For example, candidate defects in the defect map can be arranged in ascending order according to their preliminary EPD measurement results, and N (N can be a predetermined number) candidate defects with EPD measurement results greater than a predefined EPD threshold can be selected to constitute candidate defects. list.

For each given candidate defect in the selected list, an image patch may be extracted from the inspection image at the location of the given candidate defect. The image patch includes one or more defect primitives representing a given candidate defect, and is hereafter referred to as the original defect image (the term original is used with respect to the later acquired defect image as referenced below at block 204 ).

The original defect image and the location of a given candidate defect on the mask (eg in mask coordinates) may be stored (eg in storage unit 122) for further processing. As part of the preliminary processing, the printing threshold can be calibrated (304) before being applied to later acquired images, as will be described in further detail below.

Some embodiments of the presently disclosed subject matter propose using the same mask inspection tool to directly capture a new image based on the location of the candidate defect, rather than sending the location of the candidate defect to a separate metrology tool to capture a new image and Perform EPD measurements on new images. Specifically, the mask inspection tool is used to obtain (204) a defect image library and a reference image library of the candidate defect at a plurality of focus levels in the entire focus process window based on the location of the candidate defect. The defect image library includes a group of defect images acquired at each focus level, and the reference image library includes a group of reference images acquired at each focus level. As mentioned above, the defect image and the reference image are bird's-eye view images.

Using one tool instead of two separate tools to capture both the original defect image and the new image library avoids differences in coordinate systems in different tools and minimizes navigation errors associated with the tools, thereby improving the quality of images obtained later. Accuracy of EPD measurement results. In addition, this significantly reduces inspection costs and increases throughput.

For each defect image, one or more reference images may be acquired and used as a reference for comparison, such as in a D2D inspection, for example. For example, in the case where the mask to be inspected is a multi-die mask (where the mask domain includes multiple dies with the same/similar design pattern) and the candidate defect is located in the detection die on the mask, it can be obtained from One or more reference images are acquired for one or more reference dies (eg, neighboring dies of the inspection die) on the mask (at locations corresponding to the candidate defects).

As another example, where the mask is a single die mask (where the mask domain includes only one die), defect images and one or more Reference images where different areas share the same/similar design pattern. For example, any suitable algorithm may be used to identify regions sharing similar design patterns based on the masked design information, as will be described in further detail below. The acquisition of the defect image library and the reference image library is described below with reference to FIGS. 6 and 7 .

Referring to FIG. 6 , which is a schematic diagram of exemplary defect images and reference images for a given candidate defect on a mask, in accordance with certain embodiments of the presently disclosed subject matter.

As shown, multi-die mask 600 has a mask domain that includes nine dies that share the same design pattern. Many candidate defects detected during preliminary processing are instantiated on the mask (indicated by asterisks). For a given candidate defect 602 in the inspected die of mask 600, a defect image 604 surrounding the candidate defect 602 may be obtained by the mask inspection tool. Similarly, the reference image 606 of the defect image 604 may be acquired at a corresponding location of a reference die (eg, an adjacent die) of the inspection die.

The mask inspection tool only images a portion of the mask (also known as the tool's field of view (FOV)) at a time. The size and dimensions of the FOV may vary depending on factors such as different tool configurations. In one example, an inspection image corresponding to a rectangular FOV may be approximately 1000 primitives long and 1000 primitives wide. In another example, an inspection image corresponding to a rectangular FOV may be approximately 800 primitives by 1600 primitives in size. Inspection image 608 is illustrated in FIG. 6 . In some cases, when an inspection image containing a candidate defect is captured, the candidate defect may be placed in the center of the FOV. The defect image can then be extracted from the center of the inspection image.

In some embodiments, when an inspection image containing a candidate defect is captured, the candidate defect may be placed at the optimal location of the FOV. As illustrated in inspection image 608, candidate defect 602 is placed in an optimal location near the center of the FOV. In some cases, an optimal location may be selected to reduce various noises, including but not limited to noise caused by FOV distortion, for example. The term FOV distortion as used herein refers to image intensity variations and non-uniformities at different locations within the FOV of an image. This may be caused by certain optical system aberrations, including but not limited to, for example, astigmatism, uneven illumination in the field, distortion due to lens shape, and speckle. For example, the optimal location can be calculated by overlaying one or more images that provide information about potentially problematic locations in the FOV (problematic locations may represent aberrant primitives in the FOV). These plots may come from a previously performed calibration process for a specific toolset. The calibration process is based on statistical and theoretical knowledge of the hardware behavior of these tools. The entire process minimizes interaction with potentially problematic sensor areas and navigates the tool's FOV to optimally center it so that the area of interest utilizes the optimal sensor area.

Defect images can be extracted from inspection images in predetermined sizes (eg, 32*32 primitives, 64*64 primitives, 100*100 primitives, etc.). The defect image includes one or more defect primitives representing candidate defects. One or more defective primitives are also referred to as defect spots in this article, and their size may be, for example, 4*4 primitives or 2*2 primitives.

Therefore, in some embodiments, as discussed above, the defect image library may each be obtained by placing candidate defects at optimal locations within the FOV of the inspection tool (e.g., the FOV of the tool's image sensor), And the reference image library is acquired at the corresponding position in the reference grain.

Reference is now made to FIG. 7 , which illustrates a library of defect images and a library of reference images acquired for a given candidate defect on a mask in accordance with certain embodiments of the presently disclosed subject matter.

Continuing with the example of FIG. 6 , for a candidate defect 602 , a defect image library 702 and a reference image library 704 are acquired at a plurality of focus levels throughout the focus process window 700 . Defect image library 702 includes a set of defect images acquired at each focus level (eg, the four defect images illustrated in Figure 7). Similarly, the reference image library 704 includes a set of reference images acquired at each focus level (eg, four reference images corresponding to four defect images).

As mentioned above, the focusing process window refers to the focusing range of the photolithography tool, and semiconductor samples should be printed within this range to meet the desired yield. In some cases, the range of the process window may be predefined by the manufacturer of the semiconductor sample. As shown in FIG. 7 , the focus process window 700 includes a plurality of focus levels, which may be predefined based on the focus step size 706 . The focus step size may be determined, for example, based on the accuracy and throughput requirements of the manufacturer's process. For example, the focus range of the process window can be [-500 nm, +500 nm], and the focus step size can be 100 nm. In this case, there will be a total of 11 focus levels (including the upper boundary of the focus range (denoted as +PW in the figure) and the lower boundary (denoted as -PW)).

In some embodiments, there may be a fill range that extends the original range of the focus process window (illustrated as fill range 708 in Figure 7). The fill range can be defined based on the accuracy requirements of the manufacturer's process. In such cases, the plurality of focus levels may also include one or more focus levels within the fill range. For example, the total number of focus levels (or steps) can be calculated as: (process window + fill range * 2) / focus step size.

At each focus level, a set of defect images (e.g., the four defects exemplified in Figure 7 image) without changing any tool configuration. The focus level of the inspection tool can then be adjusted to the next of the plurality of focus levels in the process window, and another set of defect images can be acquired in a similar manner. Once a plurality of focus levels are traversed, multiple sets of defect images are obtained, thereby forming a defect image library 702 .

Similarly, a reference image library 704 may be obtained in a similar manner, including a plurality of sets of reference images acquired at a plurality of focus levels for corresponding locations of the reference die. It should be noted that in this example, although one reference location is used for each candidate defect and one reference image library 704 is obtained at the reference location, in some other cases multiple references may be identified (e.g., from masks multiple reference locations in multiple reference dies), and multiple reference image libraries can be acquired at multiple reference locations and used as references for the defect image library instead of just one reference image library.

It should also be noted that although the set of images (defect image or reference image) acquired at a given focus level includes multiple images (e.g., four images), as illustrated in Figure 7, this is only are for illustrative and illustrative purposes and are not intended to limit the content of this case in any way. In some embodiments, a set of images acquired at a given focus level may include a single image.

The option to group multiple defect/reference images into a group can effectively reduce false alarms caused by random noise appearing in defect/reference images, thereby increasing detection sensitivity and measurement accuracy. For example, in some cases, multiple reference images in a set can be filtered and combined to create an optimal reference image that can be used to suppress random noise and reveal defective images and Real differences between reference images.

In some embodiments, the mask inspection tool (such as, for example, mask inspection tool 120) referred to herein for capturing original defect images as well as a library of defect images and reference images is an actinic inspection tool, such as, for example, , the Aera mask inspection tool from Applied Materials Inc. The actinic inspection tool is configured to simulate the optical configuration of a photolithography tool (eg, a scanner or stepper) that may be used to fabricate semiconductor wafers from the mask, as described above with reference to FIG. 5 .

The image acquired by such an actinic inspection tool (i.e., a bird's-eye view image) is expected to be similar to an image of a wafer fabricated by a photolithography tool using a mask. In other words, the actinic mask inspection tool is configured to capture a mask image that mimics how the design pattern in the mask will actually appear in the physical wafer after the manufacturing process.

In some cases, actinic inspection tools may not be used to inspect masks. In this case, a non-actinic inspection tool (such as, for example, a general optical inspection tool, an electron beam tool, etc.) may be used to obtain a non-overhead image of the mask. Simulations can be performed on the acquired non-overhead image to simulate the optical configuration of the photolithography tool, thereby producing a masked overhead image. Therefore, in some embodiments, the mask inspection method described with reference to FIG. 2 may also include the following preliminary steps: obtaining an image library acquired by a non-actinic inspection tool, and (for example, by the image processing module of PMC 102 104, or by a processing module of the mask inspection tool 120, etc.) to perform simulation on the image to simulate the optical configuration of the photolithography tool, thereby generating a library of defect images (i.e., bird's-eye view images).

In some embodiments, the obtained library of defect images (and/or reference images) may be pre-processed before further processing. Preprocessing may include one or more of the following operations: interpolation (eg, where the image has a relatively low resolution), noise filtering, focus correction, aberration compensation, image format transformation, etc.

It should be noted that the content of this case is not limited to the specific modality of the mask inspection tool and/or the type of images obtained thereby and/or the pre-processing operations required to process the images.

Continuing with the description of FIG. 2 , once the mask inspection tool obtains a defect image library and a reference image library of candidate defects at a plurality of focus levels, the images may be transmitted to a system operatively connected to the mask inspection tool. 101 and proceed with further processing. In some embodiments, the best focus among a plurality of focus levels may be determined and may be generated based on a set of defect images at the best focus (e.g., by the image processing module 104 in the PMC 102) ( 208) Synthesize defect images.

According to some embodiments, optimal focus may be determined by applying focus measurements to at least one defect image in the set of defect images at each focus level to identify the resulting image pattern (i.e., the pattern of the sample being imaged). ) of the highest contrast image. For example, a focus score may be calculated based on focus measurements of at least one image for each focus level, and the image with the largest score identified.

According to certain embodiments, the focus score of an image may be calculated using different focus measurements that evaluate the degree of focus (eg, sharpness/contrast) of the image, and this disclosure is not limited to a specific focus score calculation. For example, a gradient-based focus measurement based on the gradient or approximation of the first derivative of the image can be used to calculate the focus score. This focus measurement follows the assumption that a focused image presents sharper edges than a blurred image. Therefore, the energy of the gradient can be used to estimate the degree of focus. Similarly, Laplacian-based focus measurements based on the second derivative of the image can also be used. As another example, statistics-based focus measurements based on textual descriptors of the image may be used. This focus measurement follows the assumption that a defocused image can be interpreted as a texture whose smoothness increases with increasing defocus level.

It should be noted that the focus measurements as described above are shown for illustrative purposes only and should not be considered to limit the content of this case in any way. Other suitable focus measurements, such as, for example, wavelet-based focus measurements, or image contrast-based focus measurements, may be used in addition to or instead of the above-described measurements.

Once the best focus among the plurality of focus levels is determined, a composite defect image can be generated based on the set of defect images at the best focus. Referring to FIG. 8 , there is illustrated a set of defect images at optimal focus in accordance with certain embodiments of the presently disclosed subject matter.

A defect image library including a plurality of sets of defect images obtained at a plurality of focus levels is illustrated in FIG. 8 . In this example, each set of defect images includes five images. In other words, the defect image library includes five image packages, each package including a plurality of images corresponding to a plurality of focus levels. Based on the focus measurements illustrated above, a focus score can be calculated for at least one image in each set of defect images. For example, in some cases, one image from each set of images can be selected, and a focus score can be calculated for the selected image. For example, the selected image may be a corresponding image in an image package. In some other cases, focus scores can be calculated for all images in each group, and a normalized focus score for each group can be generated by combining the scores for all images. A plurality of focus scores (or normalized focus scores) can be sorted, and the best focus score can be selected. Select the focus level from which the best focus score is exported as the best focus.

As shown in FIG. 8 , assume that the best focus 800 is selected, and the image group 802 is the image acquired at the best focus 800 . A composite defect image may be generated based on the set of defect images 802, for example, by combining/averaging the set of defect images. Since the images in a set are acquired consecutively without changing any tool configuration, it should be recognized that there may rarely be any offset between images, or in some cases only a minor offset (e.g. , sub-pixel offset). In some embodiments, the image groups may be registered before combining/averaging. For example, as shown in the figure, the four images on the side can be registered with the image in the middle, for example using the Lucas-Kanade registration algorithm. The registered images can be summed and then averaged to produce a composite defect image.

Image registration as referred to in this case may involve measuring the offset between two images and moving one image relative to the other in order to correct the offset. Offsets may be caused by various factors such as, for example, navigation errors caused by tool drift (eg, scanner and/or stage drift). Registration can be achieved according to any suitable registration algorithm known in the art. For example, registration may be performed using one or more of the following algorithms: region-based algorithms, feature-based registration, or phase-correlated registration. An example of a region-based approach is registration using optical flow, such as the Lucas-Canard (LK) algorithm described above. Feature-based methods are based on finding different information points ("features") in two images and calculating the required transformation between each pair based on the correspondence between the features. This allows elastic registration (i.e. non-rigid registration) where different regions are moved separately. Phase-correlated registration is accomplished using frequency domain analysis (where phase differences in the Fourier domain are converted to registration in the image domain).

We believe that the composite defect image generated as described above suppresses random noise and therefore has higher accuracy than the individual defect images of the image group. In the case where the image group consists of a single defect image, there is no need to generate a composite defect image, or in other words, the single image can be regarded as a composite defect image.

Once the composite defect image is generated, the original defect image can be aligned (210) with the composite defect image (e.g., via alignment module 106 in PMC 102) to identify differences between the composite defect image and the original defect image. The area of one or more target primitives corresponding to one or more defective primitives in the defect image. In some cases, in order to align the images, it is necessary to first check whether the patterns contained in the synthetic defect image/original defect image are registerable.

Referring to FIG. 4 , illustrated therein is a generalized flow diagram for alignment between original and synthesized defect images in accordance with certain embodiments of the presently disclosed subject matter.

In some embodiments, aligning may include verifying (400) the registerability of a pattern contained in the synthesized defect image, and determining (408) a region of one or more target primitives in the synthesized defect image based on the verification. (Also referred to as the target area in this article). In some cases, the pattern's registerability may be determined based on the periodicity of the pattern relative to the image size.

In some embodiments, verification of registerability may be performed by offsetting the pattern toward a set of directions with corresponding offsets (402) to obtain a set of offset images. Image registration is performed (404) between the shifted image groups, and registrability is determined (406) based on the results of the image registration.

9 is a schematic diagram of verification of the registerability of an exemplary pattern in accordance with certain embodiments of the presently disclosed subject matter.

Illustrated is a defect image 902 with a specific line pattern. To determine the registerability of the pattern, the pattern is offset to a set of eight different directions, resulting in eight offset images as shown. Image registration algorithms, such as any of the algorithms illustrated above, may be used to perform registration between the unoffset image 902 and each of the eight offset images. The pattern is registerable if all (or most) of the shifted images can be correctly registered with the unshifted image. Otherwise, the pattern is considered unregisterable.

The region of one or more target primitives in the composite defect image may be determined based on verified registerability. For example, where the pattern is considered registerable, e.g. by performing image registration between the two images and finding the corresponding target primitive based on the offset of the registration, it is relatively easy to identify defects related to the original The target primitive corresponding to the defective primitive in the image. In this case, the target area may be determined to include the identified target primitive. In some cases, the target area may additionally include a relatively small primitive dilation that extends the target primitives to tolerate smaller registration errors.

In treating the pattern as unregisterable, it is generally difficult to identify the target primitives corresponding to the defective primitives in the original defective image because the unregisterable patterns are often repeated in the image and thus impossible Separate one repeating feature from another repeating feature. In order not to miss the target primitive, the target area can be a relatively large area. For example, the size of the target area may be determined based on the size of defect spots of defect primitives in the original defect image with larger primitive expansion.

The target area so determined may be used as a location indication in the defect image library for performing EPD measurements, as described below with reference to block 212.

Specifically, for each focus level, measurements may be provided (212) based on a region of one or more target primitives (eg, by measurement module 108 in PMC 102), which measurements indicate the intensity at the focus level. Displacement between the set of defective images and at least one reference image derived from the set of reference images. Once measurements are obtained for each of the plurality of focus levels, a plurality of measurements corresponding to the plurality of focus levels can be provided. For example, the measurement results are EPD measurement results as described above.

Once the target area is identified in the synthetic defect image, as mentioned above, the corresponding area in each image of the defect image library can be identified based on the position of the target area in the synthetic defect image (where it is assumed that within the image library There may be little or no offset between the images).

10 illustrates an example of a raw defect image, a defect image from a defect image library, and a target area identified in the defect image, in accordance with certain embodiments of the presently disclosed subject matter.

As shown, image 1002 is the original defect image as described above with reference to block 202, and image 1004 is the defect image obtained from the defect image library as described above with reference to block 204. As illustrated, the two images include a non-registerable pattern with repeating lines and spaces. As mentioned before, in this case, it is difficult to identify the exact target primitive 1006 in the defect image 1004 that corresponds to the defect primitive 1001 in the original defect image 1002. As mentioned above, the target area 1008 can be determined to be large enough so that it is considered to cover the target primitive.

As shown in the figure, the position of the target primitive 1006 in the defect image 1004 and the position of the defect primitive 1001 in the original defect image 1002 are offset, even though the two images were captured using the same inspection tool. of. If an EPD measurement is performed directly based on the location of the defect primitive 1001 in the original defect image 1002 without performing an alignment process and deciding on the target area 1008 , an invalid measurement will occur because the measurement is from a different location than the offset actual defect location. performed in the wrong location. Using the determined target area, EPD estimation can be performed within the target area so that EPD defects at the actual location are not missed, as described in detail below.

According to some embodiments, in order to perform measurements at the location of the identified target area, a printing threshold (PT) may be applied to the set of defect images and the set of reference images for a given focus level, thereby producing a binary set of defect images and a binary reference image set, and the measurement can be performed based on the binary defect image set and the binary reference image set. The binary image provides information on the structural elements/features of the corresponding portion of the mask that can be printed on a semiconductor sample (e.g., a wafer).

During the photolithography process, the wafer is covered with a chemical photoresist that responds to the amount of energy absorbed. If the mask is illuminated above (or in some cases below) a certain intensity (thus causing chemical changes in the resist), a pattern is printed on the wafer. This intensity level is referred to below as the printing threshold. (PT).

Reference is now made to FIG. 12, which is a schematic diagram of a general photolithography and pattern transfer process based on printing thresholds in accordance with certain embodiments of the presently disclosed subject matter.

As shown, diagram 1200 illustrates an exemplary mask that includes a transparent area 1202 (eg, made of quartz) that transmits light when illuminated and an opaque area 1204 (eg, made of chrome) that blocks light. become). The plurality of images (overlook images) obtained as mentioned above refer to images captured by a detector that collects transmitted light through a mask.

In fact, the actual wafer manufacturing process for manufacturing tools (eg, scanners or steppers) includes a photolithography process followed by a resist process and an etching process. The wafer is coated with photoresist, which is a light-sensitive material. Depending on the process, exposure to light hardens or softens portions of the resist. After exposure, the wafer is developed, causing photoresist to dissolve in certain areas based on the amount of transmitted light (i.e., light intensity) the areas received during exposure.

For example, a waveform 1205 is illustrated that represents the intensity of transmitted light. If a given area of photoresist is exposed to less than a certain intensity of transmitted light, a pattern will be printed on the wafer. These photoresist areas and photoresist-free areas reproduce the design pattern on the mask. Therefore, the specific intensity is called printing threshold 1205, as illustrated in Figure 12. The developed wafer is then exposed to a solvent, which etches away the silicon in the portions of the wafer that are no longer protected by the photoresist coating, creating printed wafer 1208 (for a given layer).

Thus, in an actinic inspection tool that mimics the optical configuration of a wafer fabrication tool, waveform 1205 represents the transmitted light that will be captured by the detector of the actinic inspection tool to form the first image. Since in actinic inspection tools the detector replaces the wafer and there is no actual resist and etching process, in order to obtain an image similar to the printed wafer, a printing threshold of 1205 needs to be applied to the overhead image to Mimics the effects of the resist and etching process, thereby producing a binary image similar to the printed pattern on wafer 1208. Specifically, the binary image provides information about the plurality of structural elements of the mask that can be printed on the wafer.

It is important to note that although in this example, patterns below the printing threshold are shown as printable on the wafer (i.e., positive resist), this is not necessarily the case. In some other cases, the opposite may be true, i.e., patterns above the printing threshold can be printed on the wafer (i.e., negative resist). The content of this case is not limited to specific resist processes or printing thresholds used to render printable features for specific applications.

According to some embodiments, the printing threshold may be preliminarily calibrated (304) before being applied to the image library, as shown in Figure 3.

In some embodiments, PT may be calculated based on a representative pattern with known dimensions (eg, a pattern selected from a "design intent" CAD clip). In some cases, the representative pattern should be long enough to enable PT to be calculated and averaged over multiple locations therein, thereby reducing tool noise and providing high accuracy PT calculations.

For example, after obtaining an overhead image of a representative pattern, a mask inspection tool can simulate the wafer resist to convert the overhead image into a pattern with a width and length corresponding to the "design intent" in the corresponding CAD data. Binary image. Gray scale (GL) thresholds can be calculated for all primitives along the length of the representative pattern, and PT can be calculated as the average (eg, minimum, maximum, average, median, or other statistically based) GL threshold. Optionally, the accuracy of PT can be improved by further calibration based on exposure conditions and/or calibration based on position in the frame.

Reference is now made to FIG. 11 , which illustrates examples of a binary defect image, a binary reference image, and their difference images in accordance with certain embodiments of the presently disclosed subject matter.

Image 1004 described with reference to FIG. 10 is a defect image obtained from a defect image library with reference to block 204 . Image 1103 is a reference image for defect image 1004. For example, as illustrated in Figure 7, image 1004 and image 1103 may be a pair of images 710 from a defect image library 702 and a reference image library 704 at the same focus level.

Image 1102 is a binary defect image obtained by applying a printing threshold on defect image 1004 . Image 1104 is a binary reference image obtained by applying a printing threshold on reference image 1103 . The two binary images may be compared at the identified target area 1008 (eg, by subtracting one from the other), thereby producing a binary difference image 1106 representing the differences within the target area. As shown, the difference/deviation in the binary difference image 1106 indicates the edge position displacement (EPD) between the edges/contours of line structures in the two images.

In some cases, the differences obtained from binary images can be quite subtle and may be mixed with random edge roughness. It should be noted that EPD defects differ from edge roughness (which may be caused by different variations in the manufacturing process) in at least the following ways: i) EPD is localized (existing at local locations on the contour), while edge roughness is always present present along the edge; and ii) the magnitude of the EPD is relatively more pronounced (i.e., stronger/larger) compared to the magnitude of the fine roughness along the edge.

To verify the validity of the identified differences, a GL difference image 1108 can be exported by comparing the defect image 1004 with the reference image 1103 . The GL difference image 1108 can be used to verify that the differences indicated in the binary difference image are indeed associated with the EPD defects shown in the GL difference image.

It should be noted that in some embodiments, the defect image and the corresponding reference image should be registered before comparison (eg, using any of the image registration algorithms as previously described). Alternatively, in some embodiments, registration may be skipped. For example, registration may be omitted where it can be estimated that there may be no substantial offset between the defect image and the reference image.

Figure 13 illustrates an example of EPD measurements on a binary difference image in accordance with certain embodiments of the presently disclosed subject matter. As shown, the maximum distance 1302 between two edges/contours in the binary difference image is measured as a measure of edge displacement.

The above-described measurement process described with reference to FIGS. 11 and 13 may be repeated for each defect image in the defect image library. In particular, for each focus level of the plurality of focus levels, a difference image derived in a target area between each defect image of the set of defect images and at least one reference image may be measured. displacement, thereby generating a displacement group corresponding to the defect image group. EPD measurements can be generated based on sets of displacements (eg, by averaging the sets of displacements).

In some embodiments, at a given focus level, each defect image may be compared to a corresponding reference image, as illustrated in image pair 710 of FIG. 7 . In some cases, a composite reference image can be generated by combining sets of reference images at a given focus level, thereby reducing various random noises. The synthesized reference image can be used as a reference image for each defect image in the set of defect images at a given focus level, resulting in a differential image with improved SNR.

According to some embodiments, optimal focus may be determined for a library of reference images among a plurality of focus levels in a manner similar to that described with reference to block 208 . In response to a shift between the best focus of the reference image and the best focus of the defect image, corresponding focus levels of the reference image and the defect image may be associated based on the shift. Figure 14 illustrates an exemplary scenario in which the best focus 1102 of the defect image library is offset from the best focus 1104 of the reference image library, in accordance with certain embodiments of the presently disclosed subject matter. In this case, starting from the optimal focus level, the corresponding focus levels from the defect image library and the reference image library can be correlated. For a defect image at a given focus level, the reference image used for comparison is taken from the associated focus level.

Once an EPD measurement is obtained for each focus level, a plurality of EPD measurements can be provided, corresponding to a plurality of focus levels in the focus process window. 15 illustrates an exemplary graphical representation of a plurality of EPD measurements corresponding to a plurality of focus levels, in accordance with certain embodiments of the presently disclosed subject matter. As mentioned previously, it may be desirable to examine how the circuit patterns on the mask respond to changes in focus levels within the process window. EPD measurement estimation can provide users with information on how different focus levels within the process window may affect wafer yield. In some cases, such drawings may be rendered on the GUI 124. Optionally, in the case where there is a relatively large difference (relative to the change threshold) between the EPD measurement results at different focus levels in the process window, the EPD defect can be marked as a defect for further review by the user. Optionally, in the case where the EPD measurement results at different focus levels exceed the corresponding predefined EPD threshold, the EPD defect can be marked as a defect for further review by the user.

EPD measurements may be used by the mask inspection tool 120 and/or one or more inspection modules included in the mask inspection system 100 for further inspection of the mask, such as, for example, additional defect detection, defect review, defect classification. , metrology-related operations (e.g., CD measurement results) and/or any other inspection operations.

As described above with reference to block 302, the process with reference to FIG. 2 may be repeated for one or more additional candidate defects from the candidate defect list selected from the defect map indicating the distribution of candidate defects on the mask. In some embodiments, a region of interest (ROI) to be inspected on the mask may be predefined, and the process described above may be used to inspect one or more candidate defects in the ROI. In some cases, the ROI can be defined as the entire mask, while in other cases, the ROI can be defined as a portion of the mask.

It should be noted that while the mask inspection process described with reference to Figure 2 is illustrated using the example of a multi-die mask as shown in Figure 6, this is in no way intended to limit the content of this case in any way. It should be understood that the proposed method and system can be similarly applied to the aforementioned single-die masking.

For example, for a candidate defect located in an inspection area within a single die, one or more reference areas from the same die that share the same design pattern as the inspection area may be used as a reference for comparison. Reference areas in single-die masks can be identified in a variety of ways. Design information for a die (or portion(s) thereof) may include various design patterns with specific geometries and arrangements.

In some embodiments, design data for a single die mask may be received, and a plurality of design groups may be retrieved, each design group corresponding to one or more die regions having the same design pattern. Therefore, regions in the die corresponding to the same design pattern can be identified. It should be noted that design patterns can be considered "the same" when they are the same, or highly related, or similar to each other. Various similarity measures and algorithms may be applied to match and cluster similar design patterns, and the content of this document should not be construed as being limited to any specific measures used to derive design groups. The clustering of design groups (ie, the division of CAD data into design groups) can be performed in advance or by the PMC 102 as a preliminary step in the current inspection process.

Optionally, in some embodiments, in response to the EPD measurement results, a further decision may be made on how to respond to the EPD defect, for example, whether to accept the mask, repair the mask, or reject the mask. This can be accomplished, for example, by evaluating whether EPD defects, after being printed, affect the functionality of semiconductor samples fabricated using masks. In some cases, possible processing operations in response to the presence of EPD defects may include one or more of the following: repairing the mask, defining the mask as defective, defining the mask as functional , generate mask repair instructions, etc. For example, if these EPD defects are unacceptable, the mask can be sent to a masking shop for repair or rejection.

Optionally, in some embodiments, at least one of the following outputs/indications, or any combination thereof, may be provided: (i) providing qualification criteria for masks to be shipped from the mask shop; (ii) providing provide input to the production process; (iii) provide input to the semiconductor sample fabrication process; (iv) provide input to the simulation model used in the photolithography process; (v) provide calibration maps for the photolithography tool; and (vi) identify masking Mask areas characterized by larger than expected CD changes.

It should be noted that the mask applicable to the currently disclosed inspection process may be any kind of mask, including but not limited to memory mask and/or logic mask, and/or ArF mask and/or EUV mask. wait. The content of this case is not limited to the specific type or function of the mask to be examined.

According to some embodiments, the mask checking process described above with reference to FIGS. 2, 3, and 4 may be included as part of an inspection recipe that may be used by system 101 and/or inspection tool 120 for runtime online mask inspection. Accordingly, the presently disclosed subject matter also includes systems and methods for generating inspection recipes during the recipe setup phase, wherein the recipes include the steps described with reference to Figures 2, 3, and 4 (and various embodiments thereof). It should be noted that the term "inspection recipe" should be interpreted broadly to cover any recipe that can be used by an inspection tool to perform operations related to any type of mask inspection, including embodiments such as those described above.

It should be noted that the examples shown in this document, such as, for example, mask inspection tool architecture and configuration, mask types and/or layouts, exemplary image libraries, process windows and focus levels, and the above-mentioned image patterns, etc. , are shown for illustrative purposes and should not be regarded as limiting the content of this case in any way. Other suitable examples/implementations may be used in addition to or in place of the above.

An advantage of certain embodiments of the mask inspection process described herein is that, for a given candidate defect, the images used for EPD measurements (e.g., defect image library) are acquired by the same inspection tool that acquired the original defect image. , thus avoiding differences in coordinate systems in different tools and minimizing the navigation errors associated with the tools, thereby improving the accuracy of the measurement results. In addition, using one inspection tool instead of two tools (for example, one inspection tool to capture the original defect image, and a metrology tool to re-capture the new image for measurement) can significantly reduce inspection costs and increase throughput.

Furthermore, during the EPD estimation process, the original defect images are available and used to align with the newly captured defect image library in order to identify accurate target areas corresponding to the original defect primitives, which further ensures Accuracy of position measured by EPD.

Additional advantages of certain embodiments of the mask inspection process include acquiring image libraries across different focus levels throughout the process window, enabling an estimate of how circuit patterns on the mask may respond to changes in different focus levels (e.g., By estimating EPD measurements that indicate printing errors associated with different focus levels) and providing an indication of how different parameters can impact wafer yield across the process window.

Additionally, the option to acquire groups of images (especially multiple images in a defect image group or a reference image group) at each given focus level can effectively suppress random noise in the acquired images And it reduces false alarms in the generated differential images, thereby improving the detection sensitivity and accuracy of EPD measurement.

Additionally, determining the best focus among multiple focus levels can identify the actual best focus for each image library (which can vary with respect to various factors, such as different image patterns) to recalibrate the extent of the process window. The image from the optimal focus level is used to align with the original defect image, which further ensures the accuracy of the registration and identification of the target area for performing EPD measurements.

It is to be understood that the application of the present disclosure is not limited to the details set forth in the description or shown in the drawings contained herein.

It should also be understood that a system in accordance with the teachings herein may be implemented, at least in part, on a suitably programmed computer. Likewise, this case contemplates a computer program that can be read by a computer for use in executing the method of this case. The content of this case also contemplates non-transitory computer-readable memory that tangibly embodies a program of instructions that can be executed by a computer to perform the method of the content of this case.

The teachings are capable of other embodiments and of being practiced and carried out in various ways. Therefore, it is to be understood that the expressions and terminology used herein are for the purpose of description and should not be regarded as limiting. Accordingly, those skilled in the art will appreciate that the concepts upon which this disclosure is based may readily be utilized as a basis for the design of other structures, methods, and systems for carrying out the several purposes of the presently disclosed subject matter.

Those skilled in the art will readily understand that various modifications and changes can be applied to the embodiments of the present disclosure as described above without departing from the scope of the present disclosure as defined by the appended claims.

100: Check system 101: Computer-based systems 102:PMC 104:Image processing module 106:Alignment 108:Measurement module 120: Mask inspection tool 122:Storage unit 124: Graphical user interface (GUI) 126:I/O interface 202:Block 204:Block 208: Square 210:block 212:square 302: Square 304:Block 400:block 402:Block 404:Block 406:Block 408:Block 500: Photochemical inspection tools 502: Illumination source 504: Illuminating optical devices 506:Mask holder 508:Projection optical device 510:Detector 512:Wafer holder 514: Mask image 516:Image 520:Light lithography tools 600:Multi-grain mask 602: Given candidate defects 604:Defective image 606:Reference image 608: Check image 700: Focus on process window 702: Defect Image Library 704:Reference image library 706: Focus step size 708: Fill range 710: pair of images 800: Best focus 802:Image group 902: Defect image 1001: Defect primitive 1002: Original defect image 1004:Image 1006: Accurate target primitive 1008: Target area 1102:Image 1103:Reference image 1104:Best Focus 1106: Binary difference image 1108:GL difference image 1200:Icon 1202:Transparent area 1204: Opaque area 1208: Printed wafer 1302:Maximum distance

In order to understand the content of the present application and to see how it can be carried out in practice, reference will now be made to the accompanying drawing, in which embodiments will be described by way of non-limiting example only:

1 illustrates a functional block diagram of a mask inspection system in accordance with certain embodiments of the presently disclosed subject matter.

2 illustrates a general flow diagram for mask inspection of masks that may be used to fabricate semiconductor samples, in accordance with certain embodiments of the presently disclosed subject matter.

Figure 3 illustrates preliminary processing prior to the present mask inspection and EPD estimation process in accordance with certain embodiments of the presently disclosed subject matter.

4 illustrates a general flow diagram for alignment between original defect images and synthesized defect images in accordance with certain embodiments of the presently disclosed subject matter.

Figure 5 illustrates a schematic diagram of an actinic inspection tool and a photolithography tool in accordance with certain embodiments of the presently disclosed subject matter.

Figure 6 is a schematic diagram of exemplary defect images and reference images for a given candidate defect on a mask, in accordance with certain embodiments of the presently disclosed subject matter.

Figure 7 illustrates a library of defect images and a library of reference images obtained for a given candidate defect on a mask, in accordance with certain embodiments of the presently disclosed subject matter.

Figure 8 illustrates a set of defect images at best focus in accordance with certain embodiments of the presently disclosed subject matter.

9 is a schematic diagram of registrationability verification of an exemplary pattern in accordance with certain embodiments of the presently disclosed subject matter.

Figure 10 illustrates an example of an original defect image, a defect image in a defect image library, and a target area identified in the defect image, in accordance with certain embodiments of the presently disclosed subject matter.

Figure 11 illustrates an example of a binary defect image, a binary reference image, and their difference image, in accordance with certain embodiments of the presently disclosed subject matter.

12 is a schematic diagram of a general photolithography and pattern transfer process based on printing thresholds, in accordance with certain embodiments of the presently disclosed subject matter.

Figure 13 illustrates an example of EPD measurements on a binary difference image in accordance with certain embodiments of the presently disclosed subject matter.

Figure 14 illustrates an exemplary scenario in which the best focus 1102 of the defect image library is offset from the best focus 1104 of the reference image library, in accordance with certain embodiments of the presently disclosed subject matter.

15 illustrates an exemplary graphical representation of EPD measurements corresponding to focus levels, in accordance with certain embodiments of the presently disclosed subject matter.

Domestic storage information (please note in order of storage institution, date and number) without Overseas storage information (please note in order of storage country, institution, date, and number) without

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Claims (22)

A computerized system for inspecting masks that may be used to fabricate semiconductor samples, the system comprising: An inspection tool configured to: providing a raw defect image resulting from the preliminary inspection of the mask including one or more defect primitives representing candidate defects, and the location of the candidate defect on the mask; and Based on the position, a defect image library and a reference image library of the candidate defect are acquired at a plurality of focus levels in the entire focusing process window, and the defect image library includes defects acquired at each focus level. a set of images, and the reference image library includes a set of reference images acquired at each focus level; and Processing and memory circuitry (PMC) operatively connected to the inspection tool and configured to: determining a best focus among the plurality of focus levels and generating a composite defect image based on the set of defect images at the best focus; Aligning the original defect image with the composite defect image to identify regions of one or more target primitives in the composite defect image corresponding to the one or more defect primitives; and For each focus level, providing a measurement indicative of a displacement between the set of defect images at the focus level and at least one reference image derived from the set of reference images, based on the identified area. , thereby generating a plurality of measurement results corresponding to the plurality of focus levels. A computerized system according to claim 1, wherein said candidate defects are from a candidate defect list selected from a defect map indicating a distribution of candidate defects on said mask or portion thereof. The computerized system of claim 1, wherein said inspection tool is further configured to calibrate a printing threshold (PT), and said providing measurements includes applying said PT at said focus level to said set of defective images and said The reference image set is generated, thereby generating a binary defect image set and a binary reference image set, and the measurement is performed based on the binary defect image set and the binary reference image set. The computerized system according to claim 1, wherein the defect image library and the reference image library are obtained by placing the candidate defects at an optimal position in the field of view (FOV) of the inspection tool , where the optimal position is chosen to at least reduce noise caused by FOV distortion. The computerized system of claim 1, wherein the plurality of focus levels further includes one or more focus levels that extend the focus process window. The computerized system of claim 1, wherein the aligning further includes verifying the registerability of patterns included in the composite defect image, and determining the pattern in the composite defect image based on the verification. said area. The computerized system according to claim 6, wherein the registrationability verification includes shifting the pattern in a set of directions by a corresponding shift amount to obtain a set of shifted images, wherein the composite defect image and Image registration is performed between the offset image groups, and the registrability is determined based on the results of the image registration. The computerized system of claim 1, wherein the PMC is further configured to determine the best focus among the plurality of focus levels with respect to the reference image library, and in response to the best focus of the reference image An offset from an optimal focus of the defect image based on which the corresponding focus levels of the reference image and the defect image are associated. The computerized system of claim 1, wherein said at least one reference image is a composite reference image generated by combining said sets of reference images. The computerized system according to claim 1, wherein said defect image group consists of one defect image, and said composite defect image is said defect image. A computerized system according to claim 1, wherein said providing measurements includes measuring a difference image between each defect image of said set of defect images and said at least one reference image in said region. displacement, thereby generating a displacement group corresponding to the defect image group, and generating the measurement result based on the displacement group. The computerized system of any one of the preceding claims, wherein the mask is a multi-die mask and the defect image library is captured for the candidate defects located in the inspected die, And the reference image library is captured from the corresponding position in the reference die. The computerized system of any one of claims 1 to 11, wherein the mask is a single die mask and the defect image library and the reference image library are derived from a shared similar design pattern. Obtained from different regions in the same grain. A computerized system according to any one of claims 1 to 11, wherein for one or more additional candidates from a candidate defect list selected from a defect map indicating the distribution of candidate defects on the mask or part thereof Defect, repeat the steps of providing the original defect image, acquiring, determining, aligning, and providing measurements. The computerized system of any one of claims 1 to 11, wherein the inspection tool is an actinic inspection tool configured to simulate the optical configuration of a photolithography tool that may be used to fabricate the semiconductor sample. A computerized method of inspecting masks useful for fabricating semiconductor samples, said method being performed by a processing and memory circuitry (PMC) and said method comprising: Get from inspection tool: A raw defect image generated from the preliminary inspection of the mask including one or more defect primitives representing candidate defects, and the location of the candidate defect on the mask; and Based on the position, a defect image library and a reference image library of the candidate defect acquired at a plurality of focus levels in the entire focusing process window, where the defect image library includes a defect image library acquired at each focus level. a set of defect images, and the reference image library includes a set of reference images acquired at each focus level; and determining a best focus among the plurality of focus levels and generating a composite defect image based on the set of defect images at the best focus; Aligning the original defect image with the composite defect image to identify regions of one or more target primitives in the composite defect image corresponding to the one or more defect primitives; and For each focus level, providing a measurement indicative of a displacement between the set of defect images at the focus level and at least one reference image derived from the set of reference images based on the identified area. , thereby generating a plurality of measurement results corresponding to the plurality of focus levels. The computerized method of claim 16, further comprising obtaining a print threshold (PT) from said inspection tool, and wherein said providing a measurement includes applying said PT at said focus level to said set of defect images and said The reference image set is generated, thereby generating a binary defect image set and a binary reference image set, and the measurement is performed based on the binary defect image set and the binary reference image set. The computerized method of claim 16, wherein said aligning further includes verifying registerability of patterns included in said composite defect image, and determining, based on said verification, whether said area. The computerized method of claim 18, wherein the registrationability verification includes shifting the pattern in a set of directions by a corresponding shift amount to obtain a set of shifted images, wherein the composite defect image and Image registration is performed between the offset image groups, and the registrability is determined based on the results of the image registration. The computerized method of any one of claims 16 to 19, wherein the mask is a multi-die mask and the defect image library is captured for the candidate defects located in the inspected die. , and the reference image library is captured from the corresponding position in the reference die. The computerized method of any one of claims 16 to 19, wherein the mask is a single-die mask and the defect image library and the reference image library are derived from a shared similar design pattern. Obtained from different regions in the same grain. A non-transitory computer-readable storage medium that tangibly embodies instruction programs that, when executed by a computer, cause the computer to perform the method described in any one of claims 16 to 21.