Methods, systems, and media for generating metrology measurements

Abstract

Disclosed herein are techniques for configuring metrology tasks. In some embodiments, a method comprises obtaining an initial semiconductor image; performing segmentation on the initial semiconductor image; determining one or more labels based on the segmentation; determining one or more reference lines based on the one or more labels and the segmentation; and configuring one or more metrology measurement tasks based on the one or more labels and the one or more reference lines, wherein the one or more metrology tasks are configured to be utilized to process a batch of semiconductor images automatically based on segmentation of images of the batch of semiconductor images.

Description

PCT / US25 / 50078 08 October 2025 (08.10.2025)12139-1WO / LAM1P079WOMETHODS, SYSTEMS, AND MEDIA FOR GENERATING METROLOGY MEASUREMENTSINCORPORATION BY REFERENCE

[0001] A PCT Request Form is filed concurrently with this specification as part of the present application. Each application that the present application claims benefit of or priority to as identified in the concurrently filed PCT Request Form is incorporated by reference herein in its entirety and for all purposes.BACKGROUND

[0002] Metrology measurements made on semiconductor images may be useful, e.g., for process engineers, to determine if a given fabrication process produces wafers / substrates that meet desired target specifications. However, it can be difficult to perform the metrology measurements on semiconductor images.

[0003] The background description provided herein is for the purposes of generally presenting the context of the disclosure. Work of the presently named inventors, to the extent it is described in this background section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure.SUMMARY

[0004] Disclosed herein are techniques for generating metrology measurements. According to some embodiments, a method for configuring metrology tasks may involve obtaining an initial semiconductor image. The method may further involve performing segmentation on the initial semiconductor image. The method may further involve determining one or more labels based on the segmentation. The method may further involve determining one or more reference lines based on the one or more labels and the segmentation. The method may further involve configuring one or more metrology measurement tasks based on the one or more labels and the one or more reference lines, wherein the one or more metrology tasks are configured to be utilized to process a batch of semiconductor images automatically based on segmentation of images of the batch of semiconductor images.PCT / US25 / 50078 08 October 2025 (08.10.2025)12139-1WO / LAM1P079WO

[0005] According to some embodiments, a method for utilizing configured metrology tasks to process batches of semiconductor images may involve obtaining one or more semiconductor images to be processed and one or more configured metrology measurement tasks, the one or more configured metrology measurement tasks configured based on a segmentation of an initial semiconductor image. The method may further involve processing the one or more semiconductor images by: performing segmentation on the one or more semiconductor images; and based on the one or more configured metrology measurement tasks and the segmentation, determining one or more metrology measurements for the semiconductor image. The method may further involve generating a report based on the one or more metrology measurements for the one or more semiconductor images.BRIEF DESCRIPTION OF THE DRAWINGS

[0006] FIG. 1 is a flowchart of an example process for configuring one or more metrology tasks in accordance with some embodiments.

[0007] FIG. 2 is a flowchart of an example process for performing metrology measurements on a batch of semiconductor images based on one or more configured metrology tasks in accordance with some embodiments.

[0008] FIGS. 3 A, 3B, and 3C are example user interfaces for configuring a metrology task in accordance with some embodiments.

[0009] FIG. 4 illustrates an example user interface for performing metrology measurements on a batch of semiconductor images in accordance with some embodiments.

[0010] FIG. 5 presents an example computer system that may be employed to implement certain embodiments described herein.DETAILED DESCRIPTION

[0011] In the following description, numerous specific details are set forth to provide a thorough understanding of the presented embodiments. The disclosed embodiments may be practiced without some or all of these specific details. In other instances, well-known process operations have not been described in detail to not unnecessarily obscure the disclosed embodiments. While the disclosed embodiments will be described inPCT / US25 / 50078 08 October 2025 (08.10.2025)12139-1WO / LAM1P079WO conjunction with the specific embodiments, it will be understood that it is not intended to limit the disclosed embodiments.

[0012] Metrology measurements made on semiconductor images may be useful, e.g., for process engineers, to determine if a given fabrication process produces wafers / substrates that meet desired target specifications. However, it can be difficult to perform the metrology measurements on semiconductor images. For example, conventionally, each semiconductor image may have to be individually processed, which may involve specifying, for each individual image, points at which metrology measurements are to be made. Moreover, using conventional techniques, points at which metrology measurements are to be made may need to be specified manually (e.g., by a human), which is resource and time intensive.

[0013] Disclosed herein are techniques for configuring metrology measurement tasks such that a configured metrology measurement task may be applied automatically to a batch of semiconductor images. In particular, a metrology measurement task may be configured based on segmentation of a semiconductor image. The segmentation may allow labels on the image, e.g., each specifying particular objects in the image, to be identified. Based on the labels and the segmentation, reference lines may be automatically (e.g., algorithmically) identified. For example, reference lines may be identified which correspond to interfaces between two objects identified based on the segmentation. A metrology measurement may then be specified based on the reference lines (e.g., as a distance between two reference lines). The labels to be used, the reference lines to be used, and the metrology measurement to be made based on the reference line may all be associated with a particular metrology measurement task, which is referred to herein as a “configured metrology measurement task,” or a “configured task.” The configured metrology measurement task may then be saved and applied to batches of semiconductor images. Because both the configured metrology measurement task and the images to be processed utilize segmentation, the task may be applied to any semiconductor image without requiring manual specification of particular objects, reference lines, etc. Using the techniques disclosed herein, more precise, accurate, and robust measurements (e.g., critical dimension (CD) metrology measurements) can be made. Moreover, such precise, accurate, and robust measurements may be made in an automated manner, thereby reducing resources required to make such metrology measurements while concurrently improving precision and accuracy. Note that, as usedPCT / US25 / 50078 08 October 2025 (08.10.2025)12139-1WO / LAM1P079WO herein, “semiconductor image” refers to any images taken using microscopy techniques, such as optical microscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), scanning transmission electron microscopy (STEM), critical dimension SEM (CD-SEM), or the like.

[0014] Figure 1 is a flowchart of an example process 100 for configuring one or more metrology tasks in accordance with some embodiments. Blocks of process 100 may be executed by one or more processors of a computing device (e.g., a server device, a desktop computer, a laptop computer, etc.). An example of such a computing device is shown in and described below in connection with Figure 5. In some embodiments, blocks of process 100 may be executed in an order other than what is shown in Figure 1. In some implementations, two or more blocks of process 100 may be executed substantially in parallel. In some implementations, one or more blocks of process 100 may be omitted.

[0015] Process 100 can begin at 102 by obtaining an initial semiconductor image. The semiconductor image may have been captured using any suitable type of imaging technology, such as Scanning Electron Microscopy (SEM), Critical Dimension Scanning Electron Microscopy (CD-SEM), or the like. In some embodiments, the initial semiconductor image may be received via a user interface.

[0016] By way of example, Figure 3A illustrates an example user interface 300 that may be used to select an initial semiconductor image. For example, input 302 may be used to select an initial semiconductor image 304, which is displayed in user interface 300.

[0017] Referring back to Figure 1, at 104, process 100 can perform segmentation on the initial semiconductor image. For example, process 100 can provide the initial semiconductor image to a trained machine learning model configured to perform segmentation. In general, segmentation of the initial semiconductor image may result in a segmented image, where blocks of the segmented image are coded as being associated with a particular object. For example, regions of the initial semiconductor image identified as belonging to a first object (e.g., a fin) may be coded in a first color or with a first code, and regions of the initial semiconductor image identified as belonging to a second object may be coded in a second color or with a second code. Note that segmentation may be performed using a trained machine learning model, which may have any suitable architecture (e.g., a convolutional neural network, or the like). The model may be particular to segmenting semiconductor images. For example, the model may bePCT / US25 / 50078 08 October 2025 (08.10.2025)12139-1WO / LAM1P079WO trained on semiconductor images.

[0018] At 106, process 100 can determine one or more labels based on the segmentation. For example, in some embodiments, process 100 can identify likely labels for different objects identified based on the segmentation. In some embodiments, process 100 can determine the one or more labels by first identifying a set of likely labels for different objects based on the segmentation, and subsequently receiving a selection of one or more labels from the identified set of likely labels via a user input.

[0019] By way of example, Figure 3B illustrates an example user interface 320 for presenting a set of candidate labels and allowing a user to select one or more labels. For example, after segmentation of the initial semiconductor image and after one or more labels have been identified based on the segmentation, selection of button 322 in user interface 320 may cause a label visualizer panel 324 to be presented. Within label visualizer panel 324, labels that have been identified based on the segmentation are presented for user selection. As illustrated, label visualizer panel 324 illustrates different possible labels that may be selected, such as “foreground,” “mold,” “fin top,” etc. As illustrated, each candidate label illustrates a corresponding segmentation. For example, “fin top” label 326 illustrates the portions of the segmented image that have been identified as belonging to the “fin top” object class, which is shown in a dark region. Note that the labels that are presented within label visualizer panel 324 may be automatically (e.g., algorithmically) identified based on the segmentation of the initial semiconductor image. A user may select one or more of the labels presented within label visualizer panel 324, which are to be associated with the metrology measurement task.

[0020] Referring back to Figure 1, at 108, process 100 can determine one or more reference lines based on the one or more labels and the segmentation. For example, for a given selected label (e.g., from a user interface, as shown in Figure 3B), the one or more reference lines may correspond to particular portions of the object associated with the label and / or interfaces between the object associated with the label and other objects identified in the segmentation. By way of example, in an instance in which a “fin top” label is selected (e.g., as shown in and described above in connection with Figure 3B), the one or more reference lines may correspond to a line across the top of the fins, a line at the bottom of the fins, a line in the center (e.g., as measured by height) of the fins, etc. In general, reference lines may correspond to a middle portion of an object (e.g., as measured by height of the object and / or width of the object) and / or a line correspondingPCT / US25 / 50078 08 October 2025 (08.10.2025)12139-1WO / LAM1P079WO to the interface between the object and other object and / or a background region. Note that the one or more reference lines may be automatically (e.g., algorithmically) identified based on the one or more labels and / or the segmentation, and may be presented (e.g., via a user interface) for selection and / or modification by a user.

[0021] Turning to Figure 3C, an example user interface 350 for viewing, selecting, and / or modifying reference lines is shown in accordance with some embodiments. A reference line reference line which corresponds to an object interface boundary may be overlaid. The reference line may be selected for association with a metrology measurement task, and / or modified in any suitable manner via panel 354.

[0022] Referring back to Figure 1, at 110, process 100 can configure one or more metrology measurement tasks based on the one or more labels and the one or more reference lines. For example, in some embodiments, process 100 can configure one or more metrology measurement tasks (e.g., “fin top height,” “fin top width,” etc.) as a measurement to be performed using the one or more reference lines which are associated with the one or more labels. By way of example, in an instance in which two reference lines are determined and / or selected (e.g., as shown in and described above in connection with Figure 3C), the metrology measurement task may be configured as being a distance between the two reference lines. Note that because the one or more metrology measurement tasks are based on the one or more labels and the one or more reference lines which are inherently based on the segmentation of the initial semiconductor image, the configured one or more metrology measurement tasks may then be applied to any semiconductor images which are then segmented in the same manner. By way of example, in an instance in which a label of a fin top is selected (e.g., as described above in connection with block 106 and Figure 3B) and two reference lines are determined and / or selected (e.g., as described above in connection with block 108 and Figure 3C), the metrology measurement task may correspond to a distance between the two reference lines which are applied to the top and bottom of objects labeled as “fin top.” Accordingly, other semiconductor images may be segmented (e.g., by providing the images to a trained machine learning model), and the labels and reference lines as specified in the configured metrology measurement task may be applied automatically (e.g., without explicit user input), and the metrology measurements may be made.

[0023] Note that the configured metrology measurement task may be configured based on an initial semiconductor image, and saved for future use (e.g., to process a batch ofPCT / US25 / 50078 08 October 2025 (08.10.2025)12139-1WO / LAM1P079WO images, as shown in and described below in connection with Figure 2). Additionally, it should be noted that a measurement task may be configured based on a group of images.

[0024] Figure 2 is a flowchart of an example process 200 for processing semiconductor images using a configured metrology measurement task in accordance with some embodiments. Blocks of process 200 may be executed by one or more processors of a computing device (e.g., a server device, a desktop computer, a laptop computer, etc.). An example of such a computing device is shown in and described below in connection with Figure 5. In some embodiments, blocks of process 200 may be executed in an order other than what is shown in Figure 2. In some implementations, two or more blocks of process 200 may be executed substantially in parallel. In some implementations, one or more blocks of process 200 may be omitted.

[0025] Process 200 can begin at 202 by obtaining one or more semiconductor images to be processed and one or more configured metrology measurement tasks. An indication of the one or more semiconductor images and the one or more configured metrology measurement tasks may be received via a user interface.

[0026] By way of example, Figure 4 illustrates an example user interface 400. Panel 402 may be used to select one or more configured metrology measurement tasks (e.g., that were configured using the techniques shown in and described above in connection with Figure 1). Note that image 404 may be a first image of the one or more semiconductor images that is to first undergo processing. The one or more semiconductor images may be indicated in the user interface, e.g., by selecting a particular directory in which the images are stored.

[0027] Referring back to Figure 2, at 204, process 200 can, for one of the one or more semiconductor images, perform segmentation. As described above in connection with block 104 of Figure 1, segmentation may be trained by providing the semiconductor image to a trained machine learning model configured to perform segmentation. Note that the model used to perform segmentation to process a batch of semiconductor images as part of process 200 may be the same as the model used to perform segmentation to configure a metrology measurement task as discussed above in connection with process 100 of Figure 1.

[0028] At 206, process 200 can, based on the configured metrology measurement task(s) and the segmentation, determine one or more metrology measurements for thePCT / US25 / 50078 08 October 2025 (08.10.2025)12139-1WO / LAM1P079WO semiconductor image. For example, the configured metrology measurement task(s) may indicate particular labels to be identified based on the segmentation, as well as one or more reference lines to be placed based on the labels and the segmentation. By way of example, the task may indicate that “fin top” objects are to be identified in the image based on the segmentation, and reference lines corresponding to particular regions of the fin top objects are to be placed (e.g., corresponding to the top of the fin top object, the bottom of the fin top object, the middle with respect to height, etc.). The task may then indicate one or more measurements that are to be made with respect to the reference lines, such as the distance between two reference lines. It should be noted that, in some implementations, a measurement may be made based on a label alone, using a label in conjunction with a reference line, or between reference lines.

[0029] At 208, process 200 can determine whether all of the semiconductor images that were obtained at block 202 have been processed. Responsive to determining that not all images have been processed (“no” at 208), process 200 can loop back to 204 and can segment the next image.

[0030] Conversely, responsive to determining that all images have been processed (“yes” at 208), process 200 can proceed to block 210 and can generate a report based on the one or more metrology measurements for the one or more semiconductor images. In some embodiments, the report may include each measurement made for each individual image. In some embodiments, the report may additionally and / or alternatively include aggregate data. Aggregate data may include measurements that are aggregated (e.g., averaged) across objects within a given image (e.g., across all fin top objects in a single image) and / or that are aggregated across multiple images (e.g., across all fin top objects in all images obtained at block 202).CONTEXT FOR DISCLOSED COMPUTATIONAL EMBODIMENTS

[0031] Systems (e.g., one or more computing devices, such as laptop computers, desktop computers, server devices, etc.) as described herein may include logic for configuring metrology measurement tasks and / or processing semiconductor images using configured metrology measurement tasks.

[0032] The analysis logic may be designed and implemented in any of various ways. For example, the logic can be implemented in hardware and / or software. Examples are presented in the controller section herein. Hardware-implemented control logic may bePCT / US25 / 50078 08 October 2025 (08.10.2025)12139-1WO / LAM1P079WO provided in any of a variety of forms, including hard coded logic in digital signal processors, application-specific integrated circuits, and other devices that have algorithms implemented as hardware. Analysis logic may also be implemented as software or firmware instructions configured to be executed on a general-purpose processor. System control software may be provided by “programming” in a computer readable programming language.

[0033] The computer program code for controlling processes in a process sequence can be written in any conventional computer readable programming language: for example, assembly language, C, C++, Pascal, Fortran, Python, or others. Compiled object code or script is executed by the processor to perform the tasks identified in the program. Also as indicated, the program code may be hard coded.

[0034] Integrated circuits used in logic may include chips in the form of firmware that store program instructions, digital signal processors (DSPs), chips defined as application specific integrated circuits (ASICs), and / or one or more microprocessors, or microcontrollers that execute program instructions (e.g., software). Program instructions may be instructions communicated in the form of various individual settings (or program files), defining operational parameters for carrying out a particular analysis application.

[0035] Figure 5 is a block diagram of an example of the computing device 500 suitable for use in implementing some embodiments of the present disclosure. For example, device 500 may be suitable for implementing some or all functions as shown in and described above in connection with Figures 1 and / or 2.

[0036] Computing device 500 may include a bus 502 that directly or indirectly couples the following devices: memory 504, one or more central processing units (CPUs) 506, one or more graphics processing units (GPUs) 508, a communication interface 510, input / output (VO) ports 512, input / output components 514, a power supply 516, and one or more presentation components 518 (e.g., display(s)). In addition to CPU 506 and GPU 508, computing device 500 may include additional logic devices that are not shown in Figure 5, such as but not limited to an image signal processor (ISP), a digital signal processor (DSP), an ASIC, an FPGA, or the like.

[0037] Although the various blocks of Figure 5 are shown as connected via the bus 502 with lines, this is not intended to be limiting and is for clarity only. For example, in some embodiments, a presentation component 518, such as a display device, may be consideredPCT / US25 / 50078 08 October 2025 (08.10.2025)12139-1WO / LAM1P079WO an I / O component 514 (e.g., if the display is a touch screen). As another example, CPUs 506 and / or GPUs 508 may include memory (e.g., the memory 504 may be representative of a storage device in addition to the memory of the GPUs 508, the CPUs 506, and / or other components). In other words, the computing device of Figure 5 is merely illustrative. Distinction is not made between such categories as “workstation,” “server,” “laptop,” “desktop,” “tablet,” “client device,” “mobile device,” “hand-held device,” “electronic control unit (ECU),” “virtual reality system,” and / or other device or system types, as all are contemplated within the scope of the computing device of Figure 5.

[0038] Bus 502 may represent one or more busses, such as an address bus, a data bus, a control bus, or a combination thereof. The bus 502 may include one or more bus types, such as an industry standard architecture (ISA) bus, an extended industry standard architecture (EISA) bus, a video electronics standards association (VESA) bus, a peripheral component interconnect (PCI) bus, a peripheral component interconnect express (PCIe) bus, and / or another type of bus.

[0039] Memory 504 may include any of a variety of computer-readable media. The computer-readable media may be any available media that can be accessed by the computing device 500. The computer-readable media may include both volatile and nonvolatile media, and removable and non-removable media. By way of example, and not limitation, the computer-readable media may comprise computer-storage media and / or communication media.

[0040] The computer-storage media may include both volatile and nonvolatile media and / or removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, and / or other data types. For example, memory 504 may store computer-readable instructions (e.g., that represent a program(s) and / or a program element(s), such as an operating system. Computer-storage media may include, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD- ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computing device 500. As used herein, computer storage media does not comprise signals per se.PCT / US25 / 50078 08 October 2025 (08.10.2025)12139-1WO / LAM1P079WO

[0041] The communication media may embody computer-readable instructions, data structures, program modules, and / or other data types in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” may refer to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, the communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer-readable media.

[0042] CPU(s) 506 may be configured to execute the computer-readable instructions to control one or more components of the computing device 500 to perform one or more of the methods and / or processes described herein. CPU(s) 506 may each include one or more cores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.) that are capable of handling a multitude of software threads simultaneously. CPU(s) 506 may include any type of processor and may include different types of processors depending on the type of computing device 500 implemented (e.g., processors with fewer cores for mobile devices and processors with more cores for servers). For example, depending on the type of computing device 500, the processor may be an ARM processor implemented using Reduced Instruction Set Computing (RISC) or an x86 processor implemented using Complex Instruction Set Computing (CISC). Computing device 500 may include one or more CPUs 506 in addition to one or more microprocessors or supplementary coprocessors, such as math co-processors.

[0043] GPU(s) 508 may be used by computing device 500 to render graphics (e.g., 3D graphics). GPU(s) 508 may include many (e.g., tens, hundreds, or thousands) of cores that are capable of handling many software threads simultaneously. GPU(s) 508 may generate pixel data for output images in response to rendering commands (e.g., rendering commands from CPU(s) 506 received via a host interface). GPU(s) 508 may include graphics memory, such as display memory, for storing pixel data. The display memory may be included as part of memory 504. GPU(s) 508 may include two or more GPUs operating in parallel (e.g., via a link). When combined, each GPU 508 can generate pixel data for different portions of an output image or for different output images (e.g., a first GPU for a first image and a second GPU for a second image). Each GPU can include its own memory or can share memory with other GPUs.PCT / US25 / 50078 08 October 2025 (08.10.2025)12139-1WO / LAM1P079WO

[0044] In examples where the computing device 500 does not include the GPU(s) 508, the CPU(s) 506 may be used to render graphics.

[0045] Communication interface 510 may include one or more receivers, transmitters, and / or transceivers that enable computing device 500 to communicate with other computing devices via an electronic communication network, included wired and / or wireless communications. Communication interface 510 may include components and functionality to enable communication over any of a number of different networks, such as wireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE, ZigBee, etc.), wired networks (e.g., communicating over Ethernet), low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.), and / or the internet.

[0046] RO ports 512 may enable the computing device 500 to be logically coupled to other devices including RO components 514, presentation component(s) 518, and / or other components, some of which may be built in to (e.g., integrated in) computing device 500. Illustrative RO components 514 include a microphone, mouse, keyboard, joystick, track pad, satellite dish, scanner, printer, wireless device, etc. RO components 514 may provide a natural user interface (NUI) that processes air gestures, voice, or other physiological inputs generated by a user. In some instances, inputs may be transmitted to an appropriate network element for further processing. An NUI may implement any combination of speech recognition, stylus recognition, facial recognition, biometric recognition, gesture recognition both on screen and adjacent to the screen, air gestures, head and eye tracking, and touch recognition (as described in more detail below) associated with a display of computing device 500. Computing device 500 may be include depth cameras, such as stereoscopic camera systems, infrared camera systems, RGB camera systems, touchscreen technology, and combinations of these, for gesture detection and recognition. Additionally, computing device 500 may include accelerometers or gyroscopes (e.g., as part of an inertia measurement unit (IMU)) that enable detection of motion. In some examples, the output of the accelerometers or gyroscopes may be used by computing device 500 to render immersive augmented reality or virtual reality.

[0047] Power supply 516 may include a hard-wired power supply, a battery power supply, or a combination thereof. Power supply 516 may provide power to computing device 500 to enable the components of computing device 500 to operate.

[0048] Presentation component(s) 518 may include a display (e.g., a monitor, a touchPCT / US25 / 50078 08 October 2025 (08.10.2025)12139-1WO / LAM1P079WO screen, a television screen, a heads-up-display (HUD), other display types, or a combination thereof), speakers, and / or other presentation components. Presentation component(s) 518 may receive data from other components (e.g., GPU(s) 508, CPU(s) 506, etc.), and output the data (e.g., as an image, video, sound, etc.).

[0049] The disclosure may be described in the general context of computer code or machine-useable instructions, including computer-executable instructions such as program modules, being executed by a computer or other machine, such as a personal data assistant or other handheld device. Generally, program modules including routines, programs, objects, components, data structures, etc., refer to code that perform particular tasks or implement particular abstract data types. The disclosure may be practiced in a variety of system configurations, including hand-held devices, consumer electronics, general-purpose computers, more specialty computing devices, etc. The disclosure may also be practiced in distributed computing environments where tasks are performed by remote-processing devices that are linked through a communications network.Additional Considerations

[0050] As used in this specification and appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the content and context dictates otherwise. For example, reference to “a cell” includes a combination of two or more such cells. Unless indicated otherwise, an “or” conjunction is used in its correct sense as a Boolean logical operator, encompassing both the selection of features in the alternative (A or B, where the selection of A is mutually exclusive from B) and the selection of features in conjunction (A or B, where both A and B are selected).

[0051] It is to be understood that the phrases “for each <item> of the one or more <items>,” “each <item> of the one or more <items>,” or the like, if used herein, are inclusive of both a single-item group and multiple-item groups, i.e., the phrase “for ... each” is used in the sense that it is used in programming languages to refer to each item of whatever population of items is referenced. For example, if the population of items referenced is a single item, then “each” would refer to only that single item (despite the fact that dictionary definitions of “each” frequently define the term to refer to “every one of two or more things”) and would not imply that there must be at least two of those items. Similarly, the term “set” or “subset” should not be viewed, in itself, as necessarily encompassing a plurality of items — it will be understood that a set or a subset can encompass only one member or multiple members (unless the context indicatesPCT / US25 / 50078 08 October 2025 (08.10.2025)12139-1WO / LAM1P079WO otherwise).

[0052] The use, if any, of ordinal indicators, e.g., (a), (b), (c)... or the like, in this disclosure and claims is to be understood as not conveying any particular order or sequence, except to the extent that such an order or sequence is explicitly indicated. For example, if there are three steps labeled (i), (ii), and (iii), it is to be understood that these steps may be performed in any order (or even concurrently, if not otherwise contraindicated) unless indicated otherwise. For example, if step (ii) involves the handling of an element that is created in step (i), then step (ii) may be viewed as happening at some point after step (i). Similarly, if step (i) involves the handling of an element that is created in step (ii), the reverse is to be understood. It is also to be understood that use of the ordinal indicator “first” herein, e.g., “a first item,” should not be read as suggesting, implicitly or inherently, that there is necessarily a “second” instance, e.g., “a second item.”

[0053] Various computational elements including processors, memory, instructions, routines, models, or other components may be described or claimed as “configured to” perform a task or tasks. In such contexts, the phrase “configured to” is used to connote structure by indicating that the component includes structure (e.g., stored instructions, circuitry, etc.) that performs the task or tasks during operation. As such, the unit / circuit / component can be said to be configured to perform the task even when the specified component is not necessarily currently operational (e.g., is not on).

[0054] The components used with the “configured to” language may refer to hardware — for example, circuits, memory storing program instructions executable to implement the operation, etc. Additionally, “configured to” can refer to generic structure (e.g., generic circuitry) that is manipulated by software and / or firmware (e.g., an FPGA or a general- purpose processor executing software) to operate in manner that is capable of performing the recited task(s). Additionally, “configured to” can refer to one or more memories or memory elements storing computer executable instructions for performing the recited task(s). Such memory elements may include memory on a computer chip having processing logic. In some contexts, “configured to” may also include adapting a manufacturing process (e.g., a semiconductor fabrication facility) to fabricate devices (e.g., integrated circuits) that are adapted to implement or perform one or more tasks.

[0055] Although the foregoing embodiments have been described in some detail forPCT / US25 / 50078 08 October 2025 (08.10.2025)12139-1WO / LAM1P079WO purposes of clarity of understanding, it will be apparent that certain changes and modifications may be practiced within the scope of the appended claims. It should be noted that there are many alternative ways of implementing the processes, systems, and apparatus of the present embodiments. Accordingly, the present embodiments are to be 5 considered as illustrative and not restrictive, and the embodiments are not to be limited to the details given herein.

Claims

CLAIMSWhat is claimed is:

1. A method for configuring metrology tasks, the method comprising: obtaining an initial semiconductor image; performing segmentation on the initial semiconductor image; determining one or more labels based on the segmentation; determining one or more reference lines based on the one or more labels and the segmentation; and configuring one or more metrology measurement tasks based on the one or more labels and the one or more reference lines, wherein the one or more metrology tasks are configured to be utilized to process a batch of semiconductor images automatically based on segmentation of images of the batch of semiconductor images.

2. The method of claim 1, wherein determining the one or more labels comprises: identifying one or more objects in the initial semiconductor image based on the segmentation; and presenting the identified one or more objects as candidate labels for selection within a user interface, the determine one or more labels corresponding to the selected candidate labels.

3. The method of claim 1, wherein determining the one or more reference lines comprises placing one or more lines at one or more of: a top of objects in the segmentation corresponding to the determined one or more labels; a bottom of objects in the segmentation corresponding to the determined one or more labels; a particular region of objects in the segmentation corresponding to the determined one or more labels; or an interface between objects in the segmentation corresponding to the determined one or more labels and another object in the segmentation.

4. The method of claim 1, wherein determining the one or more reference lines comprises: placing one or more candidate reference lines based on the one or more labels and the segmentation; andpresenting the one or more candidate reference lines for selection within a user interface, the determined one or more reference lines corresponding to the selected one or more candidate reference lines.

5. The method of claim 1, wherein the one or more metrology measurement tasks specify one or more measurements to be made with respect to the determine one or more reference lines.

6. The method of claim 5, wherein the one or more measurements correspond to a distance between two or more reference lines.

7. A method for utilizing configured metrology tasks to process batches of semiconductor images, the method comprising: obtaining one or more semiconductor images to be processed and one or more configured metrology measurement tasks, the one or more configured metrology measurement tasks configured based on a segmentation of an initial semiconductor image; processing the one or more semiconductor images by: performing segmentation on the one or more semiconductor images; and based on the one or more configured metrology measurement tasks and the segmentation, determining one or more metrology measurements for the semiconductor image; generating a report based on the one or more metrology measurements for the one or more semiconductor images.

8. The method of claim 7, wherein determining the one or more metrology measurements comprises determining one or more reference lines based on the segmentation, wherein the one or more reference lines are specified in the one or more configured metrology measurement tasks, and wherein the one or more metrology measurements are with respect to the one or more reference lines.

9. The method of claim 8, further comprising prior to determining the one or more reference lines, identifying one or more labels in the one or more semiconductor images based on the segmentation of the corresponding one or more semiconductor images,wherein the one or more labels are specified in the one or more configured metrology measurement tasks.

10. The method of claim 7, wherein the generated report comprises raw data for each of the one or more metrology measurements and for each of the one or more semiconductor images.

11. The method of claim 7, wherein the generated report comprises aggregated data aggregated across the one or more semiconductor images.

12. A non-transitory computer-readable media storing instructions which, when executed by one or more processors, cause performance of: obtaining an initial semiconductor image; performing segmentation on the initial semiconductor image; determining one or more labels based on the segmentation; determining one or more reference lines based on the one or more labels and the segmentation; and configuring one or more metrology measurement tasks based on the one or more labels and the one or more reference lines, wherein the one or more metrology tasks are configured to be utilized to process a batch of semiconductor images automatically based on segmentation of images of the batch of semiconductor images.

13. The non-transitory computer-readable media of claim 12, wherein determining the one or more labels comprises: identifying one or more objects in the initial semiconductor image based on the segmentation; and presenting the identified one or more objects as candidate labels for selection within a user interface, the determine one or more labels corresponding to the selected candidate labels.

14. The non-transitory computer-readable media of claim 12, wherein determining the one or more reference lines comprises placing one or more lines at one or more of: a top of objects in the segmentation corresponding to the determined one or more labels; a bottom of objects in the segmentation corresponding to the determined one or more labels; a particular region of objects in the segmentation corresponding to the determinedone or more labels; or an interface between objects in the segmentation corresponding to the determined one or more labels and another object in the segmentation.

15. The non-transitory computer-readable media of claim 12, wherein determining the one or more reference lines comprises: placing one or more candidate reference lines based on the one or more labels and the segmentation; and presenting the one or more candidate reference lines for selection within a user interface, the determined one or more reference lines corresponding to the selected one or more candidate reference lines.

16. The non-transitory computer-readable media of claim 12, wherein the one or more metrology measurement tasks specify one or more measurements to be made with respect to the determine one or more reference lines.

17. The non-transitory computer-readable media of claim 16, wherein the one or more measurements correspond to a distance between two or more reference lines.

18. A non-transitory computer-readable media storing instructions which, when executed by one or more processors, cause performance of: obtaining one or more semiconductor images to be processed and one or more configured metrology measurement tasks, the one or more configured metrology measurement tasks configured based on a segmentation of an initial semiconductor image; processing the one or more semiconductor images by: performing segmentation on the one or more semiconductor images; and based on the one or more configured metrology measurement tasks and the segmentation, determining one or more metrology measurements for the semiconductor image; generating a report based on the one or more metrology measurements for the one or more semiconductor images.

19. The non-transitory computer-readable media of claim 18, wherein determining the one or more metrology measurements comprises determining one or more referencelines based on the segmentation, wherein the one or more reference lines are specified in the one or more configured metrology measurement tasks, and wherein the one or more metrology measurements are with respect to the one or more reference lines.

20. The non-transitory computer-readable media of claim 19, wherein the instructions further cause performance of: prior to determining the one or more reference lines, identifying one or more labels in the one or more semiconductor images based on the segmentation of the corresponding one or more semiconductor images, wherein the one or more labels are specified in the one or more configured metrology measurement tasks.

21. The non-transitory computer-readable media of claim 18, wherein the generated report comprises raw data for each of the one or more metrology measurements and for each of the one or more semiconductor images.

22. The non-transitory computer-readable media of claim 18, wherein the generated report comprises aggregated data aggregated across the one or more semiconductor images.

23. A system comprising: one or more processors; and one or more processor-readable media storing instructions which, when executed by the one or more processors, cause performance of: obtaining an initial semiconductor image; performing segmentation on the initial semiconductor image; determining one or more labels based on the segmentation; determining one or more reference lines based on the one or more labels and the segmentation; and configuring one or more metrology measurement tasks based on the one or more labels and the one or more reference lines, wherein the one or more metrology tasks are configured to be utilized to process a batch of semiconductor images automatically based on segmentation of images of the batch of semiconductor images.

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