ACTIVE LEARNING-BASED DEFECT LOCATION IDENTIFICATION

A method and apparatus for identifying locations to be inspected on a substrate is disclosed. A defect location prediction model is trained using a training dataset associated with other substrates to generate a prediction of defect or non-defect and a confidence score associated with the prediction for each of the locations based on process-related data associated with the substrates. Those of the locations determined by the defect location prediction model as having confidences scores satisfying a confidence threshold are added to a set of locations to be inspected by an inspection system. After the set of locations are inspected, the inspection results data is obtained, and the defect location prediction model is incrementally trained by using the inspection results data and process-related data for the set of locations as training data.

TECHNICAL FIELD

The embodiments provided herein relate to semiconductor manufacturing, and more particularly to inspecting a semiconductor substrate.

BACKGROUND

In manufacturing processes of integrated circuits (ICs), unfinished or finished circuit components are inspected to ensure that they are manufactured according to design and are free of defects. Inspection systems utilizing optical microscopes or charged particle (e.g., electron) beam microscopes, such as a scanning electron microscope (SEM) can be employed. As the physical sizes of IC components continue to shrink, accuracy and yield in defect detection become more important.

However, imaging resolution and throughput of inspection tools struggles to keep pace with the ever-decreasing feature size of IC components. The accuracy, resolution, and throughput of such inspection tools may be limited by lack of accuracy in detecting a wafer displacement.

SUMMARY

The embodiments provided herein disclose a particle beam inspection apparatus, and more particularly, an inspection apparatus using a plurality of charged particle beams.

In some embodiments, there is provided a non-transitory computer-readable medium having instructions that, when executed by a computer, cause the computer to execute a method for identifying locations to inspect on a substrate. The method includes: selecting a plurality of locations on the substrate to inspect based on a first sub-model of a defect location prediction model that is trained using an initial training dataset associated with other substrates to generate a prediction of defect or non-defect for each of the locations; using a second sub-model of the defect location prediction model that is trained using the initial training dataset, generating a confidence score for each of the locations based on process-related data associated with the substrate, wherein the confidence score is indicative of a confidence in the prediction for the corresponding location; adding each of the locations for which the confidence score satisfies one of a plurality of confidence thresholds to a set of locations to be inspected by an inspection system; obtaining inspection results data; and incrementally training the defect location prediction model by providing the inspection results data and process-related data for the set of locations as training data to the defect location prediction model.

In some embodiments, there is provided a non-transitory computer-readable medium having instructions that, when executed by a computer, cause the computer to execute a method for identifying locations to inspect on a first substrate using a machine learning model and for training the machine learning model to identify locations to inspect on a second substrate based on inspection results of the locations on the first substrate. The method includes: inputting process-related data associated with the substrate to a defect location prediction model; generating, using the defect location prediction model, a prediction of defect or non-defect for each of a plurality of locations on the substrate, wherein each prediction is associated with a confidence score that is indicative of a confidence in the prediction for the corresponding location; adding each of the locations for which the confidence score satisfies one of a plurality of confidence thresholds to a set of locations to be inspected by an inspection system; obtaining inspection results data for the set of locations from the inspection system; and inputting the inspection results data and process-related data for the set of locations to the defect location prediction model for training the defect location prediction model.

In some embodiments, there is provided a method for identifying locations to inspect on a first substrate using a machine learning model and for training the machine learning model to identify locations to inspect on a second substrate based on inspection results of the locations on the first substrate. The method includes: inputting process-related data associated with the substrate to a defect location prediction model; generating, using the defect location prediction model, a prediction of defect or non-defect for each of a plurality of locations on the substrate, wherein each prediction is associated with a confidence score that is indicative of a confidence in the prediction for the corresponding location; adding each of the locations for which the confidence score satisfies a confidence threshold to a set of locations to be inspected by an inspection system; obtaining inspection results data for the set of locations from the inspection system; and inputting the inspection results data and process-related data for the set of locations to the defect location prediction model for training the defect location prediction model.

In some embodiments, there is provided an apparatus for identifying locations to inspect on a first substrate using a machine learning model and for training the machine learning model to identify locations to inspect on a second substrate based on inspection results of the locations on the first substrate. The apparatus includes: a memory storing a set of instructions; and at least one processor configured to execute the set of instructions to cause the apparatus to perform a method, which includes: inputting process-related data associated with the substrate to a defect location prediction model; generating, using the defect location prediction model, a prediction of defect or non-defect for each of a plurality of locations on the substrate, wherein each prediction is associated with a confidence score that is indicative of a confidence in the prediction for the corresponding location; adding each of the locations for which the confidence score satisfies a confidence threshold to a set of locations to be inspected by an inspection system; obtaining inspection results data for the set of locations from the inspection system; and inputting the inspection results data and process-related data for the set of locations to the defect location prediction model for training the defect location prediction model.

In some embodiments, a non-transitory computer readable medium that stores a set of instructions that is executable by at least one processor of a computing device to cause the computing device to perform a method discussed above.

DETAILED DESCRIPTION

Electronic devices are constructed of circuits formed on a piece of silicon called a substrate. Many circuits may be formed together on the same piece of silicon and are called integrated circuits or ICs. The size of these circuits has decreased dramatically so that many more of them can fit on the substrate. For example, an IC chip in a smart phone can be as small as a thumbnail and yet may include over 2 billion transistors, the size of each transistor being less than 1/1000th the size of a human hair. Making these extremely small ICs is a complex, time-consuming, and expensive process, often involving hundreds of individual steps. Errors in even one step have the potential to result in defects in the finished IC rendering it useless. Thus, one goal of the manufacturing process is to avoid such defects to maximize the number of functional ICs made in the process, that is, to improve the overall yield of the process.

One component of improving yield is monitoring the chip making process to ensure that it is producing a sufficient number of functional integrated circuits. One way to monitor the process is to inspect the chip circuit structures at various stages of their formation. Inspection can be carried out using a scanning electron microscope (SEM). An SEM can be used to image these extremely small structures, in effect, taking a “picture” of the structures. The image can be used to determine if the structure was formed properly and also if it was formed in the proper location. If the structure is defective, then the process can be adjusted so the defect is less likely to recur.

Inspecting a substrate is a resource intensive process and inspecting all locations on the substrate may not only consume significant computing resources, but also time. For example, it may a number of days to inspect an entire substrate. One of the ways to make the inspection process more efficient (e.g., minimize the resources consumed) is to identify locations on the substrate that are likely to have a defect and inspect only those identified locations instead of all locations. For example, prior methods used a machine learning (ML) model to predict locations that are likely to have a defect. The prior methods determine whether a location on the substrate is having a defect or not. However, the prior methods have drawbacks. For example, some of these methods are inaccurate, e.g., they either miss defective locations or identify a non-defective location as having a defect. Because of the inaccuracy in predictions, the inspection systems may miss inspecting such defective locations, thus resulting in a defective finished IC. In another example, such prior methods are not self-repairing. That is, if a method predicts a specified location as having a defect for a particular substrate, then it continues to predict such similar locations on any subsequently inspected substrate as having a defect regardless of whether those locations have a defect or not, rendering the inspection process useless or less effective.

Embodiments of the present disclosure discuss an inspection method that assigns a confidence score, which is indicative of a confidence of a defect prediction for each location of a substrate, and selects all those locations having a confidence score satisfying a confidence threshold for inspection. For example, a first prediction model may predict that a specified location has no defect and a second prediction model may determine a confidence score for the specified location indicating that the confidence of the prediction is low (e.g., confidence score below a specified confidence threshold). By selecting those locations with a low confidence score, the embodiments may not miss any (or miss fewer than prior methods) defective locations for inspection. The inspection method of the disclosed embodiments is also self-repairing. After the locations with a low confidence score are inspected by an inspection system (e.g., SEM), the inspection results data (e.g., SEM image of the inspected locations, information such as whether a location is defective or not based on the actual inspection) obtained from the inspection system are fed back to the prediction models to adjust their predictions regarding those locations. By inputting the actual inspection results of those locations with low confidence score to the prediction models, the prediction models are further trained to predict the likelihood of defect at such locations for any subsequently inspected substrate with a greater accuracy. By incrementally training the prediction models with the inspection results from every substrate that is inspected subsequently, the prediction models may start generating predictions for such locations with a greater confidence score, minimize the number of locations to be inspected, thereby improving the yield.

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. The following description refers to the accompanying drawings in which the same numbers in different drawings represent the same or similar elements unless otherwise represented. The implementations set forth in the following description of exemplary embodiments do not represent all implementations. Instead, they are merely examples of apparatuses and methods consistent with aspects related to the disclosed embodiments as recited in the appended claims. For example, although some embodiments are described in the context of utilizing electron beams, the disclosure is not so limited. Other types of charged particle beams may be similarly applied. Furthermore, other imaging systems may be used, such as optical imaging, photo detection, x-ray detection, etc.

Reference is now made toFIG.1, which illustrates an example electron beam inspection (EBI) system100consistent with embodiments of the present disclosure. As shown inFIG.1, charged particle beam inspection system100includes a main chamber10, a load-lock chamber20, an electron beam tool40, and an equipment front end module (EFEM)30. Electron beam tool40is located within main chamber10. While the description and drawings are directed to an electron beam, it is appreciated that the embodiments are not used to limit the present disclosure to specific charged particles.

EFEM30includes a first loading port30aand a second loading port30b. EFEM30may include additional loading port(s). First loading port30aand second loading port30breceive wafer front opening unified pods (FOUPs) that contain wafers (e.g., semiconductor wafers or wafers made of other material(s)) or samples to be inspected (wafers and samples are collectively referred to as “wafers” hereafter). One or more robot arms (not shown) in EFEM30transport the wafers to load-lock chamber20.

Load-lock chamber20is connected to a load/lock vacuum pump system (not shown), which removes gas molecules in load-lock chamber20to reach a first pressure below the atmospheric pressure. After reaching the first pressure, one or more robot arms (not shown) transport the wafer from load-lock chamber20to main chamber10. Main chamber10is connected to a main chamber vacuum pump system (not shown), which removes gas molecules in main chamber10to reach a second pressure below the first pressure. After reaching the second pressure, the wafer is subject to inspection by electron beam tool40. In some embodiments, electron beam tool40may comprise a single-beam inspection tool. In other embodiments, electron beam tool40may comprise a multi-beam inspection tool.

Controller50may be electronically connected to electron beam tool40and may be electronically connected to other components as well. Controller50may be a computer configured to execute various controls of charged particle beam inspection system100. Controller50may also include processing circuitry configured to execute various signal and image processing functions. While controller50is shown inFIG.1as being outside of the structure that includes main chamber10, load-lock chamber20, and EFEM30, it is appreciated that controller50can be part of the structure.

While the present disclosure provides examples of main chamber10housing an electron beam inspection system, it should be noted that aspects of the disclosure in their broadest sense, are not limited to a chamber housing an electron beam inspection system. Rather, it is appreciated that the foregoing principles may be applied to other chambers as well.

Reference is now made toFIG.2, which illustrates a schematic diagram illustrating an example electron beam tool40that can be a part of the example charged particle beam inspection system100ofFIG.1, consistent with embodiments of the present disclosure. An electron beam tool40(also referred to herein as apparatus40) comprises an electron source101, a gun aperture plate171with a gun aperture103, a pre-beamlet forming mechanism172, a condenser lens110, a source conversion unit120, a primary projection optical system130, a sample stage (not shown inFIG.2), a secondary imaging system150, and an electron detection device140. Primary projection optical system130can comprise an objective lens131. Electron detection device140can comprise a plurality of detection elements1401,1402, and140_3. Beam separator160and deflection scanning unit132can be placed inside primary projection optical system130. It may be appreciated that other commonly known components of apparatus40may be added/omitted as appropriate.

Electron source101can comprise a cathode, an extractor or an anode, wherein primary electrons can be emitted from the cathode and extracted or accelerated to form a primary electron beam102that forms a crossover (virtual or real)101s. Primary electron beam102can be visualized as being emitted from crossover101s.

Source conversion unit120may comprise an image-forming element array (not shown inFIG.2), an aberration compensator array (not shown), a beam-limit aperture array (not shown), and a pre-bending micro-deflector array (not shown). The image-forming element array can comprise a plurality of micro-deflectors or micro-lenses to form a plurality of parallel images (virtual or real) of crossover101swith a plurality of beamlets of primary electron beam102.FIG.2shows three beamlets102_1,102_2, and102_3as an example, and it is appreciated that the source conversion unit120can handle any number of beamlets.

In some embodiments, source conversion unit120may be provided with beam-limit aperture array and image-forming element array (both are not shown). The beam-limit aperture array may comprise beam-limit apertures. It is appreciated that any number of apertures may be used, as appropriate. Beam-limit apertures may be configured to limit sizes of beamlets102_1,102_2, and102_3of primary electron beam102. The image-forming element array may comprise image-forming deflectors (not shown) configured to deflect beamlets102_1,102_2, and102_3by varying angles towards primary optical axis100_1. In some embodiments, deflectors further away from primary optical axis100_1may deflect beamlets to a greater extent. Furthermore, image-forming element array may comprise multiple layers (not illustrated), and deflectors may be provided in separate layers. Deflectors may be configured to be individually controlled independent from one another. In some embodiments, a deflector may be controlled to adjust a pitch of probe spots (e.g.,102_1S,102_2S, and102_3S) formed on a surface of sample1. As referred to herein, pitch of the probe spots may be defined as the distance between two immediately adjacent probe spots on the surface of sample1.

A centrally located deflector of image-forming element array may be aligned with primary optical axis100_1of electron beam tool40. Thus, in some embodiments, a central deflector may be configured to maintain the trajectory of beamlet102_1to be straight. In some embodiments, the central deflector may be omitted. However, in some embodiments, primary electron source101may not necessarily be aligned with the center of source conversion unit120. Furthermore, it is appreciated that whileFIG.2shows a side view of apparatus40where beamlet102_1is on primary optical axis100_1, beamlet102_1may be off primary optical axis100_1when viewed from a different side. That is, in some embodiments, all of beamlets102_1,102_2, and102_3may be off-axis. An off-axis component may be offset relative to primary optical axis100_1.

The deflection angles of the deflected beamlets may be set based on one or more criteria. In some embodiments, deflectors may deflect off-axis beamlets radially outward or away (not illustrated) from primary optical axis100_1. In some embodiments, deflectors may be configured to deflect off-axis beamlets radially inward or towards primary optical axis100_1. Deflection angles of the beamlets may be set so that beamlets102_1,102_2, and102_3land perpendicularly on sample1. Off-axis aberrations of images due to lenses, such as objective lens131, may be reduced by adjusting paths of the beamlets passing through the lenses. Therefore, deflection angles of off-axis beamlets102_2and102_3may be set so that probe spots102_2S and102_3S have small aberrations. Beamlets may be deflected so as to pass through or close to the front focal point of objective lens131to decrease aberrations of off-axis probe spots102_2S and102_3S. In some embodiments, deflectors may be set to make beamlets102_1,102_2, and102_3land perpendicularly on sample1while probe spots102_1S,102_2S, and102_3S have small aberrations.

Condenser lens110is configured to focus primary electron beam102. The electric currents of beamlets102_1,102_2, and102_3downstream of source conversion unit120can be varied by adjusting the focusing power of condenser lens110or by changing the radial sizes of the corresponding beam-limit apertures within the beam-limit aperture array. The electric currents may be changed by both, altering the radial sizes of beam-limit apertures and the focusing power of condenser lens110. Condenser lens110may be an adjustable condenser lens that may be configured so that the position of its first principle plane is movable. The adjustable condenser lens may be configured to be magnetic, which may result in off-axis beamlets102_2and102_3illuminating source conversion unit120with rotation angles. The rotation angles may change with the focusing power or the position of the first principal plane of the adjustable condenser lens. Accordingly, condenser lens110may be an anti-rotation condenser lens that may be configured to keep the rotation angles unchanged while the focusing power of condenser lens110is changed. In some embodiments, condenser lens110may be an adjustable anti-rotation condenser lens, in which the rotation angles do not change when the focusing power and the position of the first principal plane of condenser lens110are varied.

Electron beam tool40may comprise pre-beamlet forming mechanism172. In some embodiments, electron source101may be configured to emit primary electrons and form a primary electron beam102. In some embodiments, gun aperture plate171may be configured to block off peripheral electrons of primary electron beam102to reduce the Coulomb effect. In some embodiments, pre-beamlet-forming mechanism172further cuts the peripheral electrons of primary electron beam102to further reduce the Coulomb effect. Primary electron beam102may be trimmed into three primary electron beamlets102_1,102_2, and102_3(or any other number of beamlets) after passing through pre-beamlet forming mechanism172. Electron source101, gun aperture plate171, pre-beamlet forming mechanism172, and condenser lens110may be aligned with a primary optical axis100_1of electron beam tool40.

Pre-beamlet forming mechanism172may comprise a Coulomb aperture array. A center aperture, also referred to herein as the on-axis aperture, of pre-beamlet-forming mechanism172and a central deflector of source conversion unit120may be aligned with primary optical axis100_1of electron beam tool40. Pre-beamlet-forming mechanism172may be provided with a plurality of pre-trimming apertures (e.g., a Coulomb aperture array). InFIG.2, the three beamlets102_1,102_2and102_3are generated when primary electron beam102passes through the three pre-trimming apertures, and much of the remaining part of primary electron beam102is cut off. That is, pre-beamlet-forming mechanism172may trim much or most of the electrons from primary electron beam102that do not form the three beamlets102_1,102_2and102_3. Pre-beamlet-forming mechanism172may cut off electrons that will ultimately not be used to form probe spots102_1S,102_2S and102_3S before primary electron beam102enters source conversion unit120. In some embodiments, a gun aperture plate171may be provided close to electron source101to cut off electrons at an early stage, while pre-beamlet forming mechanism172may also be provided to further cut off electrons around a plurality of beamlets. AlthoughFIG.2demonstrates three apertures of pre-beamlet forming mechanism172, it is appreciated that there may be any number of apertures, as appropriate.

In some embodiments, pre-beamlet forming mechanism172may be placed below condenser lens110. Placing pre-beamlet forming mechanism172closer to electron source101may more effectively reduce the Coulomb effect. In some embodiments, gun aperture plate171may be omitted when pre-beamlet forming mechanism172is able to be located sufficiently close to source101while still being manufacturable.

Objective lens131may be configured to focus beamlets102_1,102_2, and102_3onto a sample1for inspection and can form three probe spots102_1s,102_2s, and102_3son surface of sample1. Gun aperture plate171can block off peripheral electrons of primary electron beam102not in use to reduce Coulomb interaction effects. Coulomb interaction effects can enlarge the size of each of probe spots102_1s,102_2s, and102_3s, and therefore deteriorate inspection resolution.

Beam separator160may be a beam separator of Wien filter type comprising an electrostatic deflector generating an electrostatic dipole field E1and a magnetic dipole field B1(both of which are not shown inFIG.2). If they are applied, the force exerted by electrostatic dipole field E1on an electron of beamlets102_1,102_2, and102_3is equal in magnitude and opposite in direction to the force exerted on the electron by magnetic dipole field B1. Beamlets102_1,102_2, and102_3can therefore pass straight through beam separator160with zero deflection angles.

Deflection scanning unit132can deflect beamlets102_1,102_2, and102_3to scan probe spots102_1s,102_2s, and102_3sover three small scanned areas in a section of the surface of sample1. In response to incidence of beamlets102_1,102_2, and102_3at probe spots102_1s,102_2s, and102_3s, three secondary electron beams102_1se,102_2se, and102_3semay be emitted from sample1. Each of secondary electron beams102_1se,102_2se, and102_3secan comprise electrons with a distribution of energies including secondary electrons (energies ≤50 eV) and backscattered electrons (energies between 50 eV and landing energies of beamlets102_1,102_2, and102_3). Beam separator160can direct secondary electron beams102_1se,102_2se, and102_3setowards secondary imaging system150. Secondary imaging system150can focus secondary electron beams102_1se,102_2se, and102_3seonto detection elements140_1,1402, and140_3of electron detection device140. Detection elements1401,1402, and140_3can detect corresponding secondary electron beams102_1se,102_2se, and102_3seand generate corresponding signals used to construct images of the corresponding scanned areas of sample1.

InFIG.2, three secondary electron beams102_1se,102_2se, and102_3serespectively generated by three probe spots102_1S,102_2S, and102_3S, travel upward towards electron source101along primary optical axis100_1, pass through objective lens131and deflection scanning unit132in succession. The three secondary electron beams102_1se,102_2seand102_3seare diverted by beam separator160(such as a Wien Filter) to enter secondary imaging system150along secondary optical axis150_1thereof. Secondary imaging system150focuses the three secondary electron beams102_1se-102_3seonto electron detection device140which comprises three detection elements140_1,140_2, and140_3. Therefore, electron detection device140can simultaneously generate the images of the three scanned regions scanned by the three probe spots102_1S,102_2S and102_3S, respectively. In some embodiments, electron detection device140and secondary imaging system150form one detection unit (not shown). In some embodiments, the electron optics elements on the paths of secondary electron beams such as, but not limited to, objective lens131, deflection scanning unit132, beam separator160, secondary imaging system150and electron detection device140, may form one detection system.

In some embodiments, controller50may comprise an image processing system that includes an image acquirer (not shown) and a storage (not shown). The image acquirer may comprise one or more processors. For example, the image acquirer may comprise a computer, server, mainframe host, terminals, personal computer, any kind of mobile computing devices, and the like, or a combination thereof. The image acquirer may be communicatively coupled to electron detection device140of apparatus40through a medium such as an electrical conductor, optical fiber cable, portable storage media, IR, Bluetooth, internet, wireless network, wireless radio, among others, or a combination thereof. In some embodiments, the image acquirer may receive a signal from electron detection device140and may construct an image. The image acquirer may thus acquire images of sample1. The image acquirer may also perform various post-processing functions, such as generating contours, superimposing indicators on an acquired image, and the like. The image acquirer may be configured to perform adjustments of brightness and contrast, etc. of acquired images. In some embodiments, the storage may be a storage medium such as a hard disk, flash drive, cloud storage, random access memory (RAM), other types of computer readable memory, and the like. The storage may be coupled with the image acquirer and may be used for saving scanned raw image data as original images, and post-processed images.

In some embodiments, the image acquirer may acquire one or more images of a sample based on one or more imaging signals received from electron detection device140. An imaging signal may correspond to a scanning operation for conducting charged particle imaging. An acquired image may be a single image comprising a plurality of imaging areas or may involve multiple images. The single image may be stored in the storage. The single image may be an original image that may be divided into a plurality of regions. Each of the regions may comprise one imaging area containing a feature of sample1. The acquired images may comprise multiple images of a single imaging area of sample1sampled multiple times over a time sequence or may comprise multiple images of different imaging areas of sample1. The multiple images may be stored in the storage. In some embodiments, controller50may be configured to perform image processing steps with the multiple images of the same location of sample1.

In some embodiments, controller50may include measurement circuitries (e.g., analog-to-digital converters) to obtain a distribution of the detected secondary electrons. The electron distribution data collected during a detection time window, in combination with corresponding scan path data of each of primary beamlets102_1,102_2, and102_3incident on the wafer surface, can be used to reconstruct images of the wafer structures under inspection. The reconstructed images can be used to reveal various features of the internal or external structures of sample1, and thereby can be used to reveal any defects that may exist in the wafer.

In some embodiments, controller50may control a motorized stage (not shown) to move sample1during inspection. In some embodiments, controller50may enable the motorized stage to move sample1in a direction continuously at a constant speed. In other embodiments, controller50may enable the motorized stage to change the speed of the movement of sample1over time depending on the steps of scanning process. In some embodiments, controller50may adjust a configuration of primary projection optical system130or secondary imaging system150based on images of secondary electron beams102_1se,102_2se, and102_3se.

AlthoughFIG.2shows that electron beam tool40uses three primary electron beams, it is appreciated that electron beam tool40may use two or more number of primary electron beams. The present disclosure does not limit the number of primary electron beams used in apparatus40.

Reference is now made toFIG.3, which is a schematic diagram illustrating a semiconductor processing system.FIG.3illustrates a conventional semiconductor processing system300having a scanner305, a development tool320, an etching tool325, an ash tool330, a monitoring tool335, a point determination tool345, and a verification unit350. The scanner305may include a control unit310. The semiconductor processing system300may aid in a computer guided inspection of a substrate, as described below.

The scanner305may expose a substrate coated with photoresist to a circuit pattern to be transferred to the substrate. The control unit310may control an exposure recipe used to expose the substrate. The control unit310may adjust various exposure recipe parameters, for example, exposure time, source intensity, and exposure dose. A high density focus map (HDFM)315may be recorded corresponding to the exposure.

The development tool320may develop the pattern on the exposed substrate by removing the photoresist from unwanted regions. For a positive photoresist, the portion of the photoresist that is exposed to light in scanner305becomes soluble to the photoresist developer and the unexposed portion of the photoresist remains insoluble to the photoresist developer. For a negative photoresist, the portion of the photoresist that is exposed to light in scanner305becomes insoluble to the photoresist developer and the unexposed portion of the photoresist remains soluble to the photoresist developer.

The etching tool325may transfer the pattern to one or more films under the photoresist by etching the films from portions of the substrate where the photoresist has been removed. Etching tool325can be a dry etch or wet etch tool.

The ash tool330can remove the remaining photoresist from the etched substrate and the pattern transfer process to the film on the substrate can be completed.

The monitoring tool335may inspect the processed substrate at one or more locations on the substrate to generate monitor results. The monitor results may be based on spatial pattern determination, size measurement of different pattern features or a positional shift in different pattern features. The inspection locations can be determined by the point determination tool345. In some embodiments, the monitoring tool is part of the EBI system100ofFIG.1or may be the electron beam tool40.

The point determination tool345may include one or more prediction models to determine the inspection locations on the substrate based on the HDFM315and weak point information340. In some embodiments, the point determination tool345may generate a prediction for each of the locations on the substrate that predicts a likelihood of the location being a defective (or non-defective) location. For example, the point determination tool345may assign a probability value to each of the locations that indicates a probability that the location is a defective (or non-defective) location.

The weak point information340may include information regarding locations with a high probability of problems related to the patterning process. The weak point information340may be based on the transferred pattern, various process parameters and properties of the wafer, scanner305, or etching tool325.

The verification unit350may compare the monitor results from monitoring tool335with corresponding design parameters to generate verified results. The verification unit350may provide the verified results to the control unit310of scanner305. The control unit310may adjust the exposure recipe for subsequent substrates based on the verified results. For example, the control unit310may decrease exposure dose of scanner305for some locations on subsequent substrates based on the verified results.

While the foregoing description describes the semiconductor processing system300as having the scanner305, the development tool320, the etching tool325, the ash tool330, the semiconductor processing system300is not restricted to the foregoing tools and may have additional tools that aid in printing a pattern on the substrate. In some embodiments, two or more tools may be combined to form a composite tool that provides functionalities of multiple tools. Additional details with respect to the semiconductor processing system300may be found in U.S. Patent Publication No. 2019/0187670, which is incorporated by reference in its entirety.

The following paragraphs describe an improved defect location prediction model405that predicts defective locations on a substrate with greater accuracy than prior tools (e.g., point determination tool345). In some embodiments, the defect location prediction model405is trained using an active learning technique to generate predictions with greater accuracy. In the active learning technique, a trained defect location prediction model405(e.g., that is trained using an initial dataset) is not only used to generate predictions regarding defective locations on a substrate to be inspected, but is also further trained using actual inspection results of the predicted locations (e.g., obtained from an inspection system) to update the defect location prediction model405based on the actual inspection results of the predicted locations. Such a training process may be performed incrementally, e.g., with actual inspection results for every substrate that is subsequently analyzed by the defect location prediction model405, which may result in an improvement of a prediction accuracy of the defect location prediction model405. The active learning-based defect location identification method is described at least with reference toFIGS.4and7below.

FIG.4is a block diagram of a system400for predicting defective locations on a substrate410, consistent with various embodiments of the present disclosure. The system400includes a defect location prediction model405, an inspection tool465and a feedback tool470. The defect location prediction model405includes a location prediction model450, a confidence model455, and a location selection component460. In some embodiments, prior to generating the predictions for a substrate (e.g., substrate410), the defect location prediction model405is trained using an initial training dataset, which is described at least with reference toFIG.6.

In some embodiments, the location prediction model450is a machine learning (ML) model and is similar to the point determination tool345ofFIG.3. The location prediction model450generates predictions415a-nfor a number of locations, n, on a substrate410indicating whether a location is likely to be a defective location or a non-defective location. A prediction415aassociated with a “location a” on the substrate410may include a likelihood of whether the “location a” is a defective location or a non-defective location. For example, the prediction may include a probability of “0.8,” which indicates that there is a “80%” likelihood that the “location a” has a defect and “20%” likelihood that the “location a” does not have a defect. Accordingly, the location prediction model450may classify the “location a” as a defective location. Other types of classification techniques, which do not use probability values, may be used to classify the locations into defective locations and non-defective locations. In some embodiments, the location prediction model450generates the prediction415abased on process-related data435associated with the substrate410. In some embodiments, the process-related data435may be similar to the weak point information340. The process-related data435may include data associated with various tools and processes of the semiconductor processing system300such as the development tool320, the etching tool325, the ash tool330, or other processes. For example, the process-related data435may include metrology data such as critical dimension (CD) measurements, aberrations, edge placement errors (EPE), thickness of film on the substrate410, or other such data that may contribute to a defect.

In some embodiments, the confidence model455is an ML model. The confidence model455analyzes the process related data435and generates confidence scores420a-nthat indicate a level of confidence in the predictions415a-ngenerated for each of the locations by the location prediction model450. For example, a confidence score420aindicates a level of confidence in the prediction415athat the “location a” is defective. The confidence model455may use any of a number of scales in generating a confidence score. For example, the confidence score420acan be a value in a range of “0” to “1” in which the higher the value the higher is the confidence of the prediction. In some embodiments, the confidence model455may assign a higher confidence score if the process-related data435is similar to any of the previously analyzed process-related data or assign a lower confidence score if the process-related data435is not similar to any of the previously analyzed process-related data. A confidence score may be determined using any of a number of active learning methods. For example, the confidence score may be determining using a random forest model, as described below with reference toFIG.5A, or using a querying by committee (QBC) active learning method, as described below with reference toFIG.5B.

FIG.5Ais a block diagram for determining a confidence score using a random forest model, consistent with embodiments of the present disclosure. In the random forest model, the location prediction model450generates a number of predictions, e.g., prediction501-prediction509, for each location, and the confidence model455determines the confidence score for that location as a function of the predictions501-509, e.g., based on a variance511of all the predictions. Additional details with respect to random forest model may be found in the article G. A. Susto, “A dynamic sampling strategy based on confidence level of virtual metrology predictions”,Proc.28th Annu. SEMI Adv. Semiconductor Manuf Conf. (ASMC), May 2017, which is hereby incorporated by reference in its entirety.

FIG.5Bis a block diagram for determining a confidence score using a QBC method, consistent with embodiments of the present disclosure. In the QBC method, a number of location prediction models450a-n(e.g., a diverse committee of location predication models450a-n) may be used to generate predictions, e.g., prediction521to prediction529, for each location on the substrate410. The confidence model455may determine a confidence score as a function of the predictions521-529, e.g., based on a variance531of predictions521-529. For example, the confidence model455obtains a prediction for a “location a” from each location prediction model450a-nof the committee and then calculate the confidence score531as a variance of the predictions521-529obtained from the committee. Additional details with respect to the QBC active learning method and other active learning methods may found in the articles titled “Committee-based sampling for training probabilistic classifiers,” Dagan, I., & Engelson, S. P. (1995),Proc. of12th Intl. Conf. on Machine Learning(ICML-95); “Employing EM and pool-based active learning for text classification,” McCallum, A., & Nigam, K. (1998),Proc. of15th Intl. Conf. on Machine Learning(ICML-98); “Query learning strategies using boosting and bagging,” Abe, N., & Mamitsuka, H. (1998),Proc. of15th Intl. Conf. on Machine Learning(ICML-98); and an electronic book titled “An introduction to active learning,” Jennifer Prendki, (2018), all of which are hereby incorporated by reference in their entirety.

Referring back toFIG.4, the location selection component460selects all those locations on the substrate410associated with a prediction having a confidence score satisfying location selection criteria. For example, the location selection component460may select all those locations that are predicted to be defective and are associated with a confidence score exceeding a first confidence threshold. In another example, the location selection component460may select all those locations associated with a confidence score below a second confidence threshold regardless of whether those locations are predicted to be defective or non-defective. The location selection component460may add the selected locations to a sampling plan425, which may be input to an inspection tool465for inspecting the selected locations. The sampling plan425may include information regarding the locations on the substrate410(e.g., (x, y) coordinates) that are to be inspected by the inspection tool465. The inspection tool465may inspect the locations of the substrate410based on the sampling plan425and output the actual inspection results430(e.g., not predicted) for the inspected locations. In some embodiments, the inspection results430may include an image of an inspected location (e.g., SEM image), location information of the inspected location (e.g., (x, y) coordinates) and whether that location is found to be defective or non-defective. In some embodiments, the inspection tool465may include the monitoring tool335ofFIG.3or the electron beam tool40ofFIG.1for performing the inspection, and may include the verification unit350that compares the inspection results430with design parameters of a pattern to be printed on the substrate410to generate the inspection results430.

The feedback tool470may input the inspection results430along with the process-related data of those locations back to the defect location prediction model405to further train the defect location prediction model405with the actual inspection results430of the selected locations. By training the defect location prediction model405with the actual inspection results from the inspection tool465, a cost function of the defect location prediction model405may reduce and a prediction accuracy of the defect location prediction model405may improve (e.g., increase). In some embodiments, the cost function may be indicative of a deviation between the predictions and the actual inspection results430, and the prediction accuracy may be indicative of a number of correct predictions compared to a total number of predictions. By incrementally training the defect location prediction model405(e.g., training the defect location prediction model405with the actual inspection results from the inspection tool465every time a prediction is made for a new or a different substrate), the cost function is minimized and thus, the prediction accuracy is maximized. As the prediction accuracy improves, the defect location prediction model405may predict locations that are likely to be defective with a greater confidence.

In some embodiments, the location selection component460may be configured to control a selection of the locations for inspection (e.g., by adjusting one or more confidence thresholds). For example, when the prediction accuracy of the defect location prediction model405is below an accuracy threshold, the location selection component460may have a greater first confidence threshold so that locations that are predicted to be defective with high confidence scores (e.g., s>x, where s is the score and x is a first confidence threshold) are selected for inspection while those with lower confidence scores (e.g., s<x) are ignored. As the prediction accuracy improves, the location selection component460may decrease the first confidence threshold so that locations that are predicted to be defective with even lower confidence scores (e.g., s>y and y<x, where y is the adjusted first confidence threshold) are selected for inspection. In another example, when the prediction accuracy of the defect location prediction model405is below an accuracy threshold, the location selection component460may have a greater second confidence threshold so that locations which are associated with lower confidence scores (e.g., s<a and a<x, where a is a second confidence threshold) are selected for inspection regardless of whether they are predicted to be defective or non-defective. As the prediction accuracy improves, the location selection component460may decrease the second confidence threshold so that locations that are predicted to be defective with very low confidence scores (e.g., s<b and b<a, where b is a second confidence threshold) are selected for inspection. In some embodiments, the location selection component460may also be configured to control the selection of the locations for inspection based on the available resources (e.g., time and computing resources of the inspection tool465) for inspection. The location selection component460may adjust the confidence thresholds according to the available resources. For example, the lower the available resources, the lesser is the number of locations selected for inspection. In some embodiments, the confidence thresholds, the accuracy threshold, the available resources, or the number of locations to be inspected may be user configurable.

FIG.6is a block diagram illustrating training of the defect location prediction model405using an initial training dataset, consistent with various embodiments of the present disclosure. The defect location prediction model405may have to be trained using an initial training dataset605before it can be used to generate predictions for a substrate, such as the substrate410ofFIG.4. The initial training dataset605may be a labeled dataset, which includes process-related data610a-nand inspection results615a-nof “n” number of substrates. For example, for a substrate “A,” the initial training dataset605may include process-related data610aand inspection results615aassociated with the substrate “A.” In some embodiments, the process-related data610amay be similar to the process-related data435and may include metrology data such as CD measurements, aberrations, EPE, thickness of film on the substrate “A”, or other such data that may contribute to a defect. In some embodiments, the inspection results615amay be similar to the inspection results430and may include an image of an inspected location (e.g., SEM image), location information of the inspected location (e.g., (x, y) coordinates) and whether that location is found to be defective or non-defective. The labeled dataset may be obtained from various sources, including tools of the semiconductor processing system300ofFIG.3.

The location prediction model450and the confidence model455, as mentioned above at least with reference toFIG.4, may be ML models. The training of the defect location prediction model405may be an iterative process in which each iteration may involve analyzing process-related data610associated with a substrate, determining the cost functions and updating a configuration of the defect location prediction model405based on the cost function, all of which are described below in greater detail. In some embodiments, the defect location prediction model405may be trained in a “batch” fashion instead of as an iterative process. For example, the training dataset605having process-related data610a-nand inspection results615a-nof “n” number of substrates may be input collectively. Upon inputting the process-related data610aand inspection results615a, the location prediction model450generates predictions625a1-625axfor “x” number of locations on the substrate “A” and the confidence model assigns confidence scores630a1-630axfor the predictions625a1-625ax, respectively. The defect location prediction model405then compares the predicted results with the inspection results615ato determine a cost function650of the defect location prediction model405, which may be indicative of a deviation between the predicted results625a1-625axand the actual inspection results615a. The defect location prediction model405may update its configurations (e.g., weights, biases, or other parameters of location prediction model450or the confidence model455) based on the cost function650or other reference feedback information (e.g., user indication of accuracy, reference labels, or other information) to minimize the cost function650. The above process is repeated iteratively with process-related data and inspection results associated with a different substrate in each iteration until a termination condition is satisfied. The termination condition may include a predefined number of iterations, cost function satisfies a specified threshold, or other such conditions. After the termination condition is satisfied, the defect location prediction model405may be considered to be “trained” and may be used for identifying or predicting defective locations in a new substrate (e.g., a substrate that has not been analyzed using the defect location prediction model405yet).

In some embodiments, although the trained defect location prediction model405may be used to predict defective locations in a new substrate, such as a substrate410, the trained defect location prediction model405may be further trained using active learning ML method to further improve the prediction accuracy. In the active learning ML method, the trained defect location prediction model405is trained with selectively labeled data, e.g., actual inspection results of the locations for which predictions are generated using the trained defect location prediction model405, to further improve the prediction accuracy, e.g., especially in cases where the defect location prediction model405is analyzing process-related data that is not similar to any of the previously analyzed (either during the training of the defect location prediction model405or during the actual prediction of defect location) process-related data. Such active learning methods may overcome a “concept drift” problem, a scenario in which if the ML model may become stale and the accuracy may degrade if it is not updated on a regular basis with new training data. In the semiconductor processing fields, the fabrication processes may change continuously and therefore, the process-related data associated with the substrate may also change. In some embodiments, even if the process-related data is not drifting, the relationship between the process-related data and defect/non-defect label may drift as a function of time (e.g., caused by some hidden process variable that may not be available to the ML model). If the trained defect location prediction model405is input with process-related data that is not similar to, or is significantly different from, the previously analyzed process-related data the predictions generated from the defect location prediction model405may not be accurate. By incrementally training the trained defect location prediction model405(e.g., as described at least with reference toFIG.4) with the actual inspection results of the locations for which predictions are generated using the trained defect location prediction model405, the “concept drift” problem may be overcome and the prediction accuracy may be improved.

FIG.7is a flow diagram of a process700for predicting defective locations on a substrate, consistent with embodiments of the present disclosure. In some embodiments, the process700may be implemented in the system400ofFIG.4. At operation P701, process-related data associated with a substrate is input to the defect location prediction model405. For example, the process-related data435associated with the substrate410that includes metrology data such as CD measurements, aberrations, EPE, thickness of film on the substrate410, or other such data that may con0tribute to a defect may be input to the defect location prediction model405.

At operation P703, locations705on the substrate410to be inspected may be selected based on the predictions generated by the location prediction model450. For example, the location prediction model450generates predictions415a-nfor a number of locations, n, on the substrate410indicating whether a location is likely to be a defective location or a non-defective location. In some embodiments, the location prediction model450is initially trained using an initial training dataset to predict defective locations, as described at least with reference toFIG.6.

At operation P705, confidence scores420a-nare generated for each of the predictions associated with locations705. A confidence score may indicate a level of confidence in the corresponding prediction. For example, a confidence score420aindicates a level of confidence in the prediction415athat a “location a” is defective. In some embodiments, the higher the confidence score the higher is the confidence in the associated prediction. In some embodiments, the confidence model455may assign a higher confidence score if the process-related data435is similar to any of the previously analyzed process-related data or assign a lower confidence score otherwise. A confidence score may be determined using any of a number of active learning methods. For example, the confidence score may be determining using a random forest model, as described at least with reference toFIG.5A, or using a QBC active learning method, as described at least with reference toFIG.5B.

At operation P707, those of the locations705associated with a prediction having a confidence score satisfying location selection criteria are added to a set of locations707to be inspected by the inspection tool465. For example, the location selection component460may add all those locations705that are predicted to be defective and are associated with a confidence score exceeding a first confidence threshold to the set of locations707. In another example, the location selection component460may add all those locations associated with a confidence score below a second confidence threshold to the set of locations707regardless of whether the prediction for those locations is defective or non-defective.

At operation P709, inspection results430are obtained for the set of locations707from the inspection tool465. The location selection component460may add information (e.g., (x, y) coordinates) regarding the set of locations707to a sampling plan425and input the sampling plan425to the inspection tool465. The inspection tool465may inspect the set of locations707on the substrate410and output the actual inspection results430. In some embodiments, the inspection results430may include an image of an inspected location (e.g., SEM image), location information of the inspected location (e.g., (x, y) coordinates) and whether that location is found to be defective or non-defective.

At operation P711, the inspection results430of the set of locations707and the process-related data of those locations are fed back to the defect location prediction model405to further train the defect location prediction model405with the actual inspection results430of the set of locations. In some embodiments, the defect location prediction tool is incrementally trained by performing operations P701to P711every time a prediction is made for a new or a different substrate. That is, the defect location prediction model405is trained with the actual inspection results from the inspection tool465every time a prediction is made for a new or a different substrate. By incrementally training the defect location prediction model405, the cost function associated with the defect location prediction model405is minimized and thus, the prediction accuracy of the defect location prediction model405is maximized. As the prediction accuracy improves, the defect location prediction model405may predict locations that are likely to be defective with a greater confidence.

FIG.8is a block diagram that illustrates a computer system800which can assist in implementing the methods, flows, modules, components, or the apparatus disclosed herein. Computer system800includes a bus802or other communication mechanism for communicating information, and a processor804(or multiple processors804and805) coupled with bus802for processing information. Computer system800also includes a main memory806, such as a random-access memory (RAM) or other dynamic storage device, coupled to bus802for storing information and instructions to be executed by processor804. Main memory806also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor804. Computer system800further includes a read only memory (ROM)808or other static storage device coupled to bus802for storing static information and instructions for processor804. A storage device810, such as a magnetic disk or optical disk, is provided and coupled to bus802for storing information and instructions.

Computer system800may be coupled via bus802to a display812, such as a cathode ray tube (CRT) or flat panel or touch panel display for displaying information to a computer user. An input device814, including alphanumeric and other keys, is coupled to bus802for communicating information and command selections to processor804. Another type of user input device is cursor control816, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor804and for controlling cursor movement on display812. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane. A touch panel (screen) display may also be used as an input device.

According to one embodiment, portions of one or more methods described herein may be performed by computer system800in response to processor804executing one or more sequences of one or more instructions contained in main memory806. Such instructions may be read into main memory806from another computer-readable medium, such as storage device810. Execution of the sequences of instructions contained in main memory806causes processor804to perform the process steps described herein. One or more processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in main memory806. In an alternative embodiment, hard-wired circuitry may be used in place of or in combination with software instructions. Thus, the description herein is not limited to any specific combination of hardware circuitry and software.

Various forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to processor804for execution. For example, the instructions may initially be borne on a magnetic disk of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system800can receive the data on the telephone line and use an infrared transmitter to convert the data to an infrared signal. An infrared detector coupled to bus802can receive the data carried in the infrared signal and place the data on bus802. Bus802carries the data to main memory806, from which processor804retrieves and executes the instructions. The instructions received by main memory806may optionally be stored on storage device810either before or after execution by processor804.

Computer system800can send messages and receive data, including program code, through the network(s), network link820, and communication interface818. In the Internet example, a server830might transmit a requested code for an application program through Internet828, ISP826, local network822and communication interface818. One such downloaded application may provide all or part of a method described herein, for example. The received code may be executed by processor804as it is received, and/or stored in storage device810, or other non-volatile storage for later execution. In this manner, computer system800may obtain application code in the form of a carrier wave.

1. A non-transitory computer-readable medium having instructions that, when executed by a computer, cause the computer to execute a method for identifying locations to inspect on a substrate, the method comprising:selecting a plurality of locations on the substrate to inspect based on a first sub-model of a defect location prediction model that is trained using an initial training dataset associated with other substrates to generate a prediction of defect or non-defect for each of the locations;using a second sub-model of the defect location prediction model that is trained using the initial training dataset, generating a confidence score for each of the locations based on process-related data associated with the substrate, wherein the confidence score is indicative of a confidence in the prediction for the corresponding location;adding each of the locations for which the confidence score satisfies one of a plurality of confidence thresholds to a set of locations to be inspected by an inspection system;obtaining inspection results data; andincrementally training the defect location prediction model by providing the inspection results data and process-related data for the set of locations as training data to the defect location prediction model.
2. The computer-readable medium of clause 1, wherein incrementally training the second sub-model is an iterative process in which each iteration includes:training the first sub-model using inspection results data and process-related data of a different substrate that has not been inspected in any of prior iterations.
3. The computer-readable medium of clause 1, wherein adding each of the locations includes:adding each of the locations to the set of locations when the confidence score of the prediction of defect for the corresponding location exceeds a first confidence threshold of the confidence thresholds.
4. The computer-readable medium of clause 1, wherein adding each of the locations includes:adding each of the locations to the set of locations when the confidence score of the prediction of defect or non-defect for the corresponding location is below a second confidence threshold of the confidence thresholds.
5. The computer-readable medium of clause 1 further comprising:determining a prediction accuracy of the defect location prediction model based on a number of correct predictions and a total number of predictions.
6. The computer-readable medium of clause 5, wherein incrementally training the defect location prediction model increases the prediction accuracy.
7. The computer-readable medium of clause 5 further comprising:adjusting the confidence thresholds based on a change in the prediction accuracy
8. The computer-readable medium of clause 7, wherein adjusting the confidence thresholds includes decreasing a first confidence threshold of the confidence thresholds as the prediction accuracy improves, wherein the first confidence threshold is used to select those of the locations for which the prediction of defect is associated with the confidence score exceeding the first confidence threshold.
9. The computer-readable medium of clause 7, wherein adjusting the confidence thresholds includes decreasing a second confidence threshold of the confidence thresholds as the prediction accuracy improves, wherein the second confidence threshold is used to select those of the locations for which the prediction of defect or non-defect is associated with the confidence score below the second confidence threshold.
10. The computer-readable medium of clause 7, wherein adjusting the confidence thresholds includes increasing a first confidence threshold of the confidence thresholds as the prediction accuracy degrades, wherein the first confidence threshold is used to select those of the locations for which the prediction of defect is associated with the confidence score exceeding the first confidence threshold.
11. The computer-readable medium of clause 7, wherein adjusting the confidence thresholds includes increasing a second confidence threshold of the confidence thresholds as the prediction accuracy degrades, wherein the second confidence threshold is used to select those of the locations for which the prediction of defect or non-defect is associated with the confidence score below the second confidence threshold.
12. The computer-readable medium of clause 1, wherein the first sub-model is configured to generate a probability value for each of the predictions, the probability value indicative of a probability that the corresponding location is a defect location or a non-defect location.
13. The computer-readable medium of clause 1, wherein generating the confidence score includes:generating the confidence score for a specified location of the locations based on a comparison of process-related data associated with the specified location and process-related data in the initial training dataset or the training data used to train the defect location prediction model.
14. The computer-readable medium of clause 1, wherein the defect location prediction model includes a plurality of first sub-models, and wherein generating the confidence score includes:obtaining, from each of the first sub-models, a probability value associated with the prediction for a specified location of the locations, andgenerating the confidence score for the specified location as a function of the probability values obtained from the first sub-models.
15. The computer-readable medium of clause 1, wherein obtaining the inspection results data includes obtaining the inspection results data from the inspection system.
16. The computer-readable medium of clause 1, wherein the inspection results data includes, for each location of the set of locations, information regarding whether that location has a defect or not.
17. The computer-readable medium of clause 16, wherein the inspection results data indicates that a specified location of the set of locations has a defect based on a number of defects detected in the specified location satisfying a defect threshold.
18. The computer-readable medium of clause 1, wherein the process-related data includes, for each of the locations, data associated with multiple processes involved in forming a pattern on the substrate.
19. The computer-readable medium of clause 18, wherein the data includes metrology data associated with the multiple processes.
20. The computer-readable medium of clause 1, wherein the initial training dataset includes process-related data associated with a plurality of substrates.
21. A non-transitory computer-readable medium having instructions that, when executed by a computer, cause the computer to execute a method for identifying locations to inspect on a first substrate using a machine learning model and for training the machine learning model to identify locations to inspect on a second substrate based on inspection results of the locations on the first substrate, the method comprising:inputting process-related data associated with a substrate to a defect location prediction model;generating, using the defect location prediction model, a prediction of defect or non-defect for each of a plurality of locations on the substrate, wherein each prediction is associated with a confidence score that is indicative of a confidence in the prediction for the corresponding location;adding each of the locations for which the confidence score satisfies one of a plurality of confidence thresholds to a set of locations to be inspected by an inspection system;obtaining inspection results data for the set of locations from the inspection system; andinputting the inspection results data and process-related data for the set of locations to the defect location prediction model for training the defect location prediction model.
22. The computer-readable medium of clause 21 further comprising:incrementally training the defect location prediction model, wherein the incremental training is an iterative process in which each iteration includes:training the defect location prediction model using inspection results data and process-related data of a different substrate that has not been inspected in any of prior iterations.
23. The computer-readable medium of clause 21, wherein adding each of the locations includes:adding each of the locations to the set of locations when the confidence score of the prediction of defect for the corresponding location exceeds a first confidence threshold of the confidence thresholds.
24. The computer-readable medium of clause 21, wherein adding each of the locations includes:adding each of the locations to the set of locations when the confidence score of the prediction of defect or non-defect for the corresponding location is below a second confidence threshold of the confidence thresholds.
25. The computer-readable medium of clause 21 further comprising:determining a prediction accuracy of the defect location prediction model based on a number of correct predictions and a total number of predictions.
26. The computer-readable medium of clause 25 further comprising:adjusting the confidence thresholds based on a change in the prediction accuracy.
27. The computer-readable medium of clause 21, wherein generating the prediction includes: prior to inputting the process-related data of the substrate, training the defect location prediction model using an initial training dataset associated with other substrates to generate the prediction of defect or non-defect for each of the locations for the corresponding substrate, wherein the initial training dataset includes process-related data of the other substrates.
28. The computer-readable medium of clause 21, wherein generating the prediction includes:generating the confidence score for a specified location of the locations based on a comparison of process-related data associated with the specified location and process-related data associated with other substrates used to train the defect location prediction model.
29. The computer-readable medium of clause 21, wherein generating the prediction includes:obtaining, from each of a plurality of prediction models, a probability value associated with the prediction of a defect or non-defect for a specified location of the locations, andgenerating the confidence score for the specified location as a function of the probability values obtained from the prediction models.
30. A method for identifying locations to inspect on a first substrate using a machine learning model and for training the machine learning model to identify locations to inspect on a second substrate based on inspection results of the locations on the first substrate, the method comprising:inputting process-related data associated with a substrate to a defect location prediction model;generating, using the defect location prediction model, a prediction of defect or non-defect for each of a plurality of locations on the substrate, wherein each prediction is associated with a confidence score that is indicative of a confidence in the prediction for the corresponding location;adding each of the locations for which the confidence score satisfies a confidence threshold to a set of locations to be inspected by an inspection system;obtaining inspection results data for the set of locations from the inspection system; andinputting the inspection results data and process-related data for the set of locations to the defect location prediction model for training the defect location prediction model.
31. The method of clause 30 further comprising:incrementally training the defect location prediction model, wherein the incremental training is an iterative process in which each iteration includes:training the defect location prediction model using inspection results data and process-related data of a different substrate that has not been inspected in any of prior iterations.
32. The method of clause 30, wherein adding each of the locations includes:adding each of the locations to the set of locations when the confidence score of the prediction of defect for the corresponding location exceeds a first confidence threshold of the confidence thresholds.
33. The method of clause 30, wherein adding each of the locations includes:adding each of the locations to the set of locations when the confidence score of the prediction of defect or non-defect for the corresponding location is below a second confidence threshold of the confidence thresholds.
34. The method of clause 30 further comprising:determining a prediction accuracy of the defect location prediction model based on a number of correct predictions and a total number of predictions.
35. The method of clause 34 further comprising:adjusting the confidence thresholds based on a change in the prediction accuracy.
36. The method of clause 30, wherein generating the prediction includes:prior to inputting the process-related data of the substrate, training the defect location prediction model using an initial training dataset associated with other substrates to generate the prediction of defect or non-defect for each of the locations for the corresponding substrate.
37. The method of clause 30, wherein generating the prediction includes:generating the confidence score for a specified location of the locations based on a comparison of process-related data associated with the specified location and process-related data associated with other substrates used to train the defect location prediction model.
38. The method of clause 30, wherein generating the prediction includes:obtaining, from each of a plurality of prediction models, a probability value associated with a prediction of a specified location of the locations being a defect or non-defect, andgenerating the confidence score for the specified location as a function of the probability values obtained from the prediction models.
39. An apparatus for identifying locations to inspect on a first substrate using a machine learning model and for training the machine learning model to identify locations to inspect on a second substrate based on inspection results of the locations on the first substrate, the apparatus comprising:a memory storing a set of instructions; andat least one processor configured to execute the set of instructions to cause the apparatus to perform a method of:inputting process-related data associated with a substrate to a defect location prediction model;generating, using the defect location prediction model, a prediction of defect or non-defect for each of a plurality of locations on the substrate, wherein each prediction is associated with a confidence score that is indicative of a confidence in the prediction for the corresponding location;adding each of the locations for which the confidence score satisfies a confidence threshold to a set of locations to be inspected by an inspection system;obtaining inspection results data for the set of locations from the inspection system; andinputting the inspection results data and process-related data for the set of locations to the defect location prediction model for training the defect location prediction model.
40. The apparatus of clause 39, wherein the method further comprises:incrementally training the defect location prediction model, wherein the incremental training is an iterative process in which each iteration includes:training the defect location prediction model using inspection results data and process-related data of a different substrate that has not been inspected in any of prior iterations.
41. The apparatus of clause 39, wherein adding each of the locations includes:adding each of the locations to the set of locations when the confidence score of the prediction of defect for the corresponding location exceeds a first confidence threshold of the confidence thresholds.
42. The apparatus of clause 39, wherein adding each of the locations includes:adding each of the locations to the set of locations when the confidence score of the prediction of defect or non-defect for the corresponding location is below a second confidence threshold of the confidence thresholds.
43. The apparatus of clause 39 further comprising:determining a prediction accuracy of the defect location prediction model based on a number of correct predictions and a total number of predictions.
44. The apparatus of clause 43 further comprising:adjusting the confidence thresholds based on a change in the prediction accuracy.
45. The apparatus of clause 39, wherein generating the prediction includes:prior to inputting the process-related data of the substrate, training the defect location prediction model using an initial training dataset associated with other substrates to generate the prediction of defect or non-defect for each of the locations for the corresponding substrate.
46. The apparatus of clause 39, wherein generating the prediction includes:generating the confidence score for a specified location of the locations based on a comparison of process-related data associated with the specified location and process-related data associated with other substrates used to train the defect location prediction model.
47. The apparatus of clause 39, wherein generating the prediction includes:obtaining, from each of a plurality of prediction models, a probability value associated with the prediction of defect or non-defect for a specified location of the locations, andgenerating the confidence score for the specified location as a function of the probability values obtained from the prediction models.
48. A non-transitory computer-readable medium having instructions recorded thereon, the instructions when executed by a computer implementing the method of any of the above clauses.

A non-transitory computer readable medium may be provided that stores instructions for a processor of a controller (e.g., controller50ofFIG.1) to carry out, among other things, image inspection, image acquisition, stage positioning, beam focusing, electric field adjustment, beam bending, condenser lens adjusting, activating charged-particle source, beam deflecting, and at least a portion of processes600and700. Common forms of non-transitory media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic data storage medium, a Compact Disc Read Only Memory (CD-ROM), any other optical data storage medium, any physical medium with patterns of holes, a Random Access Memory (RAM), a Programmable Read Only Memory (PROM), and Erasable Programmable Read Only Memory (EPROM), a FLASH-EPROM or any other flash memory, Non-Volatile Random Access Memory (NVRAM), a cache, a register, any other memory chip or cartridge, and networked versions of the same.

Relative dimensions of components in drawings may be exaggerated for clarity. Within the description of drawings, the same or like reference numbers refer to the same or like components or entities, and only the differences with respect to the individual embodiments are described. As used herein, unless specifically stated otherwise, the term “or” encompasses all possible combinations, except where infeasible. For example, if it is stated that a component may include A or B, then, unless specifically stated otherwise or infeasible, the component may include A, or B, or A and B. As a second example, if it is stated that a component may include A, B, or C, then, unless specifically stated otherwise or infeasible, the component may include A, or B, or C, or A and B, or A and C, or B and C, or A and B and C.

It will be appreciated that the embodiments of the present disclosure are not limited to the exact construction that has been described above and illustrated in the accompanying drawings, and that various modifications and changes may be made without departing from the scope thereof. The present disclosure has been described in connection with various embodiments, other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.