Tool matching using digital twins

Abstract

To provide a method and apparatus that reduce mismatches between measurement instruments, without being exclusive.SOLUTION: An apparatus control system 200 for tool matching includes a master controller and a plurality of electronic controllers 220. Each of the electronic controllers supports a digital twin 100 and controls configuration parameters of a corresponding measurement instrument 210. Based on instrument drift data, the master controller estimates configuration parameter changes of the measurement instrument, and receives a plurality of reports evaluating estimated configuration parameter changes. Each report is generated using the respective digital twin based on a respective subset of the estimated configuration parameter changes. The master controller further instructs the electronic controllers to implement the estimated configuration parameter changes when the reports indicate the effectiveness of the estimated configuration parameter changes for tool matching of the measurement instruments at a future time.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] Various examples relate generally to metrology and more particularly, but not exclusively, to methods and apparatus for reducing inconsistencies between measurement devices. Summary of the Invention

[0002] Applied metrology, technical metrology, or industrial metrology is concerned with the application of measurements to manufacturing or other industrial processes to ensure the suitability of measuring equipment, the calibration of such equipment, and quality control for its intended purpose. For example, obtaining good measurements is important in many industries because measurements tend to significantly affect the value and quality of the final product, as well as production costs. In some cases, traceability and correctability of equipment performance between calibrations are important capabilities to have in order to provide high confidence in measurement results and good consistency between multiple measurement equipment.

[0003] Disclosed herein are various examples, aspects, features, and embodiments of a tool matching system having a master controller that communicates with electronic controllers of individual measurement equipment in a fleet of such equipment to determine and implement configuration parameter changes directed to, among other things, maintaining the fleet in an acceptable tool matching state during fleet-wide calibration. In some examples, the master controller estimates fleet-wide configuration parameter changes based on equipment drift data received from the electronic controllers, validates the effectiveness of the estimated fleet-wide configuration parameter changes for tool matching via digital twin simulation, and pushes an appropriate subset of the validated fleet-wide configuration parameter changes to individual measurement equipment in the fleet. In at least some use cases, the implemented fleet-wide configuration parameter changes beneficially reduce the frequency of fleet-wide calibrations and associated production line interruptions.

[0004] One example provides an automated tool matching method for a plurality of measurement instruments, the method including: estimating, using a first controller, configuration parameter changes for the plurality of measurement instruments based on equipment drift data received from a plurality of second controllers, the estimated parameter changes being directed to tool matching the plurality of measurement instruments at a future time, each of the second controllers being configured to support a respective digital twin of a corresponding one of the measurement instruments and further configured to control configuration parameters of the corresponding one of the measurement instruments; receiving, using the first controller, a plurality of reports from the plurality of second controllers evaluating the estimated configuration parameter changes, each report generated using a respective digital twin based on a respective subset of the estimated configuration parameter changes of the plurality of measurement instruments; and if the plurality of reports indicate effectiveness of the estimated configuration parameter changes for tool matching the plurality of measurement instruments at a future time, instructing, using the first controller, the plurality of second controllers to implement the estimated configuration parameter changes.

[0005] Another example provides a tool matching system comprising: a first controller; and a plurality of second controllers, each second controller configured to support a respective digital twin of a corresponding one of a plurality of measurement devices and further configured to control configuration parameters of the corresponding one of the measurement devices, wherein the first controller is configured to: estimate configuration parameter changes for the plurality of measurement devices based on equipment drift data received from the plurality of second controllers, the estimated parameter changes being directed to tool matching the plurality of measurement devices at a future time; receive from the plurality of second controllers a plurality of reports evaluating the estimated configuration parameter changes, each report generated using a respective digital twin based on a respective subset of the estimated configuration parameter changes of the plurality of measurement devices; and if the plurality of reports indicate effectiveness of the estimated configuration parameter changes for tool matching of the plurality of measurement devices at a future time, instruct the plurality of second controllers to implement the estimated configuration parameter changes. [Brief explanation of the drawings]

[0006] The foregoing aspects and many of the attendant advantages of the present disclosure will become more readily appreciated as the same become better understood by reference to the following detailed description, when taken in conjunction with the accompanying drawings, in which: [Figure 1] FIG. 1 is a block diagram illustrating a digital twin processing pipeline, according to some examples. [Figure 2] FIG. 1 is a block diagram illustrating an equipment control system that may be used for tool matching, according to some examples. [Figure 3] 3 is a flow diagram illustrating a tool matching method implemented in the master controller of the equipment control system of FIG. 2, according to some examples. [Figure 4]3 is a flow diagram illustrating a tool matching method implemented in the equipment control controller of the equipment control system of FIG. 2, according to some examples. [Figure 5] 1 is a block diagram illustrating a computing device, according to some examples. DETAILED DESCRIPTION OF THE INVENTION

[0007] As semiconductor device features shrink in semiconductor manufacturing processes, controlling the critical dimensions of such features is becoming a priority for many semiconductor manufacturing plants (often referred to as fabs). For example, the critical dimension (CD) of a transistor gate is typically on the order of a few nanometers. Each nanometer deviation from the target gate length can affect the device's operating speed. Additionally, if the post-etch gate CD is too small, threshold voltage shifts and leakage currents can render the corresponding semiconductor device inoperable. In an automated foundry environment, the target gate CD can be achieved in several different ways. For example, in-line process monitoring can be used to adjust lithography and etching tools to improve the fab's CD performance and reduce final wafer-to-wafer CD variation.

[0008] A key component of in-line process monitoring involves the use of dimensional measurement equipment. Technical requirements for such equipment include high measurement accuracy and good consistency of performance. Exemplary challenges associated with measurements performed with such equipment include, but are not limited to, increasing the measurement accuracy of individual measurement equipment, reducing discrepancies in dimensional measurements between different individual measurement equipment in a production line, and reducing the variability in dimensional measurements taken by individual measurement equipment over time.

[0009] For example, a scanning electron microscope SEM and / or transmission electron microscopes Electron microscopes, such as transmission electron microscopes (TEM), can be used in semiconductor fabrication factories to measure various dimensions, including the CD, of semiconductor devices. A typical electron microscope is a sophisticated and technologically complex instrument characterized by a relatively large number of configuration parameters. During operation, the electron microscope can be calibrated to establish the relationship between the configuration parameter values and certain physical measures. The process of bringing the microscope into its proper operating configuration can involve several different calibration types.

[0010] One example of a calibration procedure is magnification calibration. More specifically, the projector system lens of an electron microscope has a specific current that results in a specific magnification of the image of the inspected object in the plane of the camera sensor. Magnification calibration relates the lens current to the pixel size in the captured image. For example, a lens current of 200 mA may correspond to an image scale of 100 nm per pixel. Additional non-limiting examples of calibration procedures include image shift calibration, stage shift calibration, and focus calibration. Image shift calibration relates the deflector coil current to changes in beam position on the specimen plane. Stage shift calibration relates the magnitude of the stimulus applied to the microscope stage motor drive to changes in specimen position. Focus calibration relates the objective lens current to the position of the focal point along the longitudinal axis of the electron beam (often referred to as defocus).

[0011] After a microscope is calibrated, it typically begins to exhibit performance drift, which causes deviations from expected performance. As used herein, the term "deviation" refers to the difference between the calibration setpoint and the actual exhibited state or behavior of the instrument. As an example, during focus calibration, the exhibited behavior may be 1 nm of electron beam defocus at an objective lens current of 10 mA. However, over time, the electron beam defocuses by 10 nm at the same objective lens current. A difference of 9 nm (=10-1) is referred to as a deviation from the corresponding focus calibration setpoint. A gradual change in the setpoint over time is referred to as "drift." Some additional examples of microscope drift include, but are not limited to, magnification drift, image shift drift, stage shift drift, eucentric height drift, and gun tilt drift. As used herein, the term "drift data" refers to a digital representation of processed or unprocessed sensor and / or detector signals, based on which deviations from expected performance can be identified and / or quantified. In some examples, such sensor and / or detector signals are generated using one or more of a direct electron detection camera, a CCD camera, a high-speed digital camera, a video camera optically coupled to a fluorescent screen (such as the commercially available FluCam device), and a scanning transmission electron microscope (STEM) detector.

[0012] In some examples, an electron microscope is provided with a digital twin. As used herein, the term "digital twin" refers to a virtual model of the corresponding equipment. Such a model typically uses data received from various sensors and / or detectors associated with the equipment over the equipment's lifecycle to simulate the equipment's behavior, monitor operation, and / or recommend configuration adjustments. The digital twin can be used, for example, to view the equipment's status on demand. As sensors collect data from the equipment, sensor data can be used to update the digital twin in real time. In various examples, the digital twin can provide an up-to-date, accurate representation of the equipment's characteristics and status, including the drift described above. As used herein, the term "real-time" refers to a computer-based process that receives data, processes the received data, and controls the corresponding environment by generating a response quickly enough to affect the environment without significant delay. A real-time response is often understood to be on the order of milliseconds, or sometimes microseconds. In the context of digital twins, "real-time" updating means that the digital twin accurately represents the corresponding actual equipment at any given time.

[0013] For purposes of illustration, and without any implied limitation, exemplary embodiments are described below with reference to an electron microscope. However, various embodiments are not so limited. As used herein, the term "measurement instrument" should be interpreted to encompass at least the following types of instruments: electron microscopes, focused ion beam (FIB) instruments, and dual beam (e.g., FIB / SEM) instruments. Based on the description provided, one of ordinary skill in the art will be able to make and use various embodiments corresponding to various types of measurement instruments without any undue experimentation.

[0014] FIG. 1 is a block diagram illustrating a processing pipeline 101 of a digital twin 100, according to some examples. In the illustrated example, digital twin 100 represents a TEM instrument (not explicitly shown in FIG. 1 ) with which digital twin 100 communicates via interface 102. Processing pipeline 101 includes multiple model modules 110-126 corresponding to different physical components of the TEM instrument. Illustratively, the following model modules are shown: electron gun and accelerator model module 110, condenser lens model module 112, probe corrector model module 114, objective lens model module 116, specimen model module 118, image corrector model module 120, projector lens system model module 122, observation and recording device model module 124, and image filter and camera model module 126. In other examples, a different set of model modules (than that shown) may be used to implement processing pipeline 101.

[0015] Different model modules 110-126 may communicate with each other, for example, as shown in FIG. 1 . For example, the output of one model module in processing pipeline 101 may serve as an input to another model module in processing pipeline 101. In some examples, the concatenation of model modules 110-126 is configured to provide a simulation of how various settings of the electron microscope column relate to signals detected at associated sensors or detectors. In some examples, the input to a model module includes a wave function or ray diagram, and the corresponding output of that model module includes a modified wave function or ray diagram, where the modification is obtained using a computational model of how parameter settings of corresponding components of the electron microscope column module (e.g., lens currents, etc.) affect the associated electron beam characteristics. Wave-optics model module 130 and geometric-optics model module 140 are configured to appropriately communicate with individual model modules 110-126 to support electron beam propagation simulations encompassing the optical path between the electron source (represented by electron gun and accelerator model module 110) and the electron sink (represented by image filter and camera model module 126) of the TEM instrument. Using both modules 130, 140 for such simulations enables digital twin 100 to appropriately account for the effects of the wave-particle duality exhibited by electrons in at least some components and / or configurations of the TEM instrument.

[0016] During operation, the model modules 110-126 undergo periodic or continuous parameter updates performed using the calibration and validation module 150. In various examples, such parameter updates are based on a comparison of actual measurement results received by the calibration and validation module 150 from the TEM instrument with corresponding model simulations performed using the model modules 110-126, 130, 140. For example, if the comparison reveals a sufficiently large discrepancy, one or more parameters used in the model modules 110-126 are updated to properly account for various drifts occurring in the TEM instrument in the digital twin 100.

[0017] Different TEM or SEM instruments deployed in a production facility may be calibrated at different times. The drift rates exhibited by different instruments may also vary. For these reasons, it may be difficult to maintain good and sufficient consistency between instruments in a relatively large instrument fleet. The corresponding problem is sometimes referred to as "tool matching."

[0018] In one approach, tool matching is achieved by periodically and frequently calibrating the entire equipment fleet. However, after calibration, the equipment begins to drift in performance in different ways. The drift causes the deviations mentioned above, which are not corrected under this approach until the entire fleet is then calibrated and consistent again. In at least some use cases, this approach can be rather disruptive and / or time-consuming. The exemplary embodiments disclosed herein address this issue by monitoring multiple pieces of equipment and predicting expected deviations using the digital twins of each piece of equipment (e.g., digital twin 100). In some examples, the digital twins are beneficially used to determine what changes to each piece of equipment control parameters are needed for each piece of equipment to maintain consistency in performance across the fleet, without compromising measurement accuracy. The determined changes are then pushed to the physical equipment to beneficially reduce the frequency of calibrations and associated production line interruptions.

[0019] In general, tool matching can be said to be achieved when different instruments in an instrument fleet produce the "same" (within specified error limits) measurement results for the same experiment. Measurement / experiment types can vary depending on the use case. For example, if image intensities need to be matched, the corresponding experiment will be specifically configured to accurately measure intensity values, but some other characteristics (e.g., magnification, etc.) may not be strictly considered. Similarly, if feature sizes need to be matched, the corresponding experiment will be specifically configured to accurately measure effective magnification, but image intensity may not be a significant factor in that experiment.

[0020] 2 is a block diagram illustrating an equipment control system 200 that may be used for tool matching, according to some examples. The system 200 includes N measurement equipment 2101-2102. N where N is a positive integer greater than 1. In some examples, the number N ranges from 5 to 50. In some examples, the measuring devices 2101-210 N Each of is or includes an electron microscope.

[0021] Each measuring device 210 n each electronic controller 220 n , where n=1, 2, ..., N. Electronic Controller 220 n Measuring Instruments 210 n Each digital twin has operational control over its configuration parameters. n (See also FIG. 1) and each of the electronic controllers 2201-220. N Each of the communication links 228 to the master controller 230 n In the illustrated example, the master controller 230 is implemented using a suitable computing device (e.g., a networked server) 240. In operation, the electronic controllers 2201-220 Nand the master controller 230 communicates with the corresponding communication link 228 n , which allows the system 200 to communicate with the measurement instruments 2201-220, for example, as described in more detail below. N Tool matching can be performed.

[0022] For purposes of illustration, and without any implied limitations, consider an example where N=3 and the corresponding measurement instruments 2101-2103 are electron microscopes configured to perform metrology measurements on transistor dimensions. Assume that the transistor has a CD of 3.2 nm. For tool-matching purposes, it is desired that each of the electron microscopes 2101-2103, by performing automated measurements on that transistor, produce the same CD value of 3.2 nm within a specified tolerance of 0.05 nm (i.e., within 3.2 ± 0.05 nm). This result can be achieved, for example, by properly aligning and calibrating the electron microscopes 2101-2103 on a test sample having confirmed dimensions. The calibration generates three respective sets of configuration parameters for the electron microscopes 2101-2103.

[0023] The digital twins 1001-1003 receive the three respective sets of configuration parameters along with an indication that an acceptable tool matching condition was achieved during calibration using those parameters. In operation following calibration, each of the electron microscopes 2101-2103 transmits the acquired images and associated sensor information to the corresponding electronic controller 220. n Each electronic controller 220 n To extract the drift data, the received data and data twin 100 n(See also FIG. 1 ). Drift data is communicated by electronic controllers 2201-2203 to master controller 230 via communication links 2281-2283. Master controller 230 models the effects of these drifts as applied to a particular use case and extrapolates to determine the effect of the drifts on the tool matching state of the entire fleet of electron microscopes at a future time. If master controller 230 determines that one or more of the predicted deviations exceed the tool matching tolerances specified for this particular use case, master controller 230 operates to determine corrections to the respective sets of configuration parameters for electron microscopes 2101-2103 that will keep the entire fleet within the specified tool matching tolerances. The determined parameter corrections are then pushed to electron microscopes 2101-2103 via electronic controllers 2201-2203 to maintain the desired state of tool matching among the fleet without having to perform another calibration on the test sample at that time.

[0024] 3 is a flow diagram illustrating a tool matching method 300 implemented in the master controller 230 of the equipment control system 200, according to some examples. The method 300 begins (at block 302) when the master controller 230 matches the measurement instruments 2101-210. N and receiving drift data corresponding to each measurement device 210. n The drift data is sent from the detector to the corresponding electronic controller 220 n The sensor data extracted by and received from the equipment is then used to create the corresponding digital twin100 n and processed using communication link 228 as indicated above. n to the master controller 230 via

[0025] The method 300 also includes (at block 304) the master controller 230 determining whether the measurement instruments 2101-210 at a future time are in a predetermined time range. NIn some examples, the prediction is made (at block 304) by appropriately extrapolating the drift data received at block 302. The time increment for selecting the future time at block 304 is an algorithm parameter that depends on the use case. In various examples, the time increment can be in the range of minutes to hours.

[0026] The method 300 also includes (at decision block 306) determining whether the master controller 230 is configured to control the measurement instruments 2101-210. N The predetermined threshold is an algorithm parameter that is use case dependent and may be used to determine whether the predicted deviation for any of the different devices 210 across the equipment fleet exceeds a predetermined threshold. n This relates to the aforementioned tolerance for measurement variation between measurements. If the predicted deviation is less than the threshold ("No" at decision block 306), processing of method 300 returns to the operations of block 302. If the predicted deviation is greater than the threshold ("Yes" at decision block 306), processing of method 300 is directed to block 308.

[0027] The operation of block 308 is performed by the master controller 230 controlling the measuring instruments 2101 to 210. N The determination includes determining estimated configuration parameter changes for one or more of the measurement devices 2101-210. N The objective of the present invention is to find a set of fleet-wide configuration parameter changes that will bring the tool matching specifications for the fleet to their respective configurations that together satisfy the tool matching specifications for the fleet. In some examples, the configuration parameter changes are calculated by applying a vector having deviations as its components to the measurement devices 210 at future times in block 308. n The tool matching parameters are determined using a neural network that implements a fleet-wide model that maps deviations to a corresponding vector of expected parameter changes that will correct the deviations to an extent that will keep the entire fleet of measurement instruments 210 within tool matching specifications. nThe neural network may be trained using machine learning methods using previous drift and calibration data acquired for the entire fleet of measuring instruments 2101-210 and a suitably constructed loss function tailored to the use case. When the trained neural network is presented with the deviation vector constructed using the predicted deviations in block 304, the neural network can predict the deviations of the measuring instruments 2101-210. N In some other examples, other suitable fleet-wide models may also be used to estimate configuration parameter changes.

[0028] The method 300 also includes (at block 310) the master controller 230 transmitting the estimated configuration parameter changes to the electronic controllers 2201-220 for validation. N In some examples, the operation of block 310 includes (i) transmitting the estimated vector of configuration parameter changes determined in block 308 to different individual measurement devices 210. n and (ii) parsing the subset into a subset of configuration parameter changes corresponding to the n via the respective electronic controllers 220 n Upon receiving a respective subset of the configuration parameter changes from the master controller 230, each electronic controller 220 n is the corresponding digital twin 100 n By performing a configuration parameter change through the corresponding individual measurement device 210 n The electronic controller 220 then operates to determine the effect of such changes on measurements performed using the n The master controller 230 reports the determined effect back to the master controller 230. The method 300 begins (at block 312) when the master controller 230 reports the determined effect back to the master controller 230. N and receiving respective evaluation reports from the respective evaluation reports.

[0029] The method 300 also includes the master controller 230 determining (at decision block 314) whether the estimated parameter changes are acceptable. This determination is made by the electronic controllers 2201-220 at block 312. N In some examples, at least one of the evaluation reports is made at decision block 314 based on the evaluation reports received from the corresponding measurement device 210. n indicates that the respective subset of configuration parameter changes would not meet the tool matching specification if implemented therein, the estimated parameter changes are deemed unacceptable. If the estimated configuration parameter changes are deemed unacceptable (“No” at decision block 314), processing of method 300 is directed to decision block 316. If the estimated configuration parameter changes are deemed acceptable (“Yes” at decision block 314), processing of method 300 is directed to block 318.

[0030] The operation of block 316 includes the master controller 230 determining whether the fleet of equipment 210 can still achieve an acceptable tool matching state at a future time. In various examples, this determination may be made based on the number of iterations through block 308 and / or the magnitude of the deviation indicated in the evaluation report of block 312. For example, in some cases, once the number of iterations through block 308 reaches a fixed, predetermined number, the master controller 230 will determine that the equipment fleet cannot self-maintain an acceptable tool matching state without performing a fleet-wide service and / or calibration. In some other cases, if the magnitude of the deviation indicated in the validation report for two or more pieces of equipment exceeds a fixed threshold, the master controller 230 will determine that the equipment fleet cannot self-maintain an acceptable tool matching state without performing a fleet-wide service and / or calibration. If the master controller 230 determines that the equipment fleet can self-maintain an acceptable tool matching state without a fleet-wide service or calibration (“Yes” at decision block 316), processing of method 300 returns to block 308, where another attempt is made to find an acceptable vector of parameter changes using, for example, an updated input vector appropriately constructed using the evaluation report of block 312. If the master controller 230 determines that the equipment fleet cannot self-maintain an acceptable tool matching state without a fleet-wide service or calibration (“No” at decision block 316), processing of method 300 is directed to block 320.

[0031] The operation of block 318 is performed by the master controller 230 sending a configuration change command to the electronic controllers 2201-220. N The configuration change command is inferred by the master controller 230 in the last instance of block 308 and then transmitted to the electronic controllers 2201-2202 via blocks 310 and 312. NThe configuration change instructions are based on the vector of parameter changes verified by the electronic controllers 2201-220. N , processing of method 300 loops back to block 302.

[0032] The operation of block 320 is performed by the master controller 230 controlling the device 210. n

[0033] After the operations of block 320 are completed, method 300 ends.

[0033] FIG. 4 illustrates an individual electronic controller 220 of the equipment control system 200, according to some examples. n 4 is a flow diagram illustrating a tool matching method 400 implemented in the electronic controller 220. The method 400 is compatible with the method 300 and is n and the master controller 230, which executes the method 300.

[0034] The method 400 begins (at block 402) with the electronic controller 220 n However, measuring equipment 210 n In various examples, the drift data includes transmitting drift data corresponding to the electronic controller 220 to the master controller 230. n The sensor data is extracted from the detector by the n Using Equipment 210 n is received from

[0035] The method 400 also includes the electronic controller 220 n is in a wait mode for a control message from the master controller 230 (at decision block 404). nand is generated by the master controller 230 using blocks 308, 310 of the method 300. If a control message is not received ("No" at decision block 404), the electronic controller 220 n remains in standby mode and processing of method 400 loops through block 402. If a control message is received (“Yes” at decision block 404), processing of method 400 is directed to block 406.

[0036] The operation of block 406 is performed by the electronic controller 220 n includes evaluating the estimated configuration parameter changes communicated via the control messages received from the master controller 230 in block 404. In some examples, the evaluation may include evaluating the received configuration parameter changes in the digital twin 100. n and running the associated simulation using its associated model module to determine deviations corresponding to the received parameter changes. n includes reporting the calculated deviation to the master controller 230. The reported deviation is received by the master controller 230 in block 312.

[0037] The method 400 also includes (at block 408) controlling the electronic controller 220 nreceiving and executing a command from master controller 230. Depending on the type of command received, processing of method 400 may loop back to block 402 or 406, or may terminate after the received command is executed. For example, if the received command is to change a configuration parameter in response to a control message sent by master controller 230 in block 318 of method 300, processing of method 400 loops back to block 402 after the received command is executed. When the received command is the next installment of an estimated configuration parameter change sent by master controller 230 in the next instance of block 310 of method 300, processing of method 400 loops back to block 406. When the received command is a service notification sent by master controller 230 in block 320 of method 300, processing of method 400 terminates.

[0038] 5 is a block diagram illustrating a computing device 500, according to some examples. In various examples, the master controller 230 or the electronic controller 220 n may be implemented by a single computing device 500 or by multiple computing devices 500. In some examples, an instance of a computing device 500 may be configured to implement method 300 or method 400.

[0039] 5 is illustrated as having several components, any one or more of which may be omitted or duplicated as appropriate for the application and setting. In some embodiments, some or all of the components included in computing device 500 may be mounted on one or more motherboards and enclosed in a housing. In some embodiments, some of these components may be fabricated on a single system-on-a-chip (SoC) (e.g., an SoC may include one or more electronic processing devices 502 and one or more storage devices 504). 5, but may include interface circuitry for coupling to one or more components using any suitable interface (e.g., a Universal Serial Bus (USB) interface, a High-Definition Multimedia Interface (HDMI) interface, a Controller Area Network (CAN) interface, a Serial Peripheral Interface (SPI) interface, an Ethernet interface, a wireless interface, or any other suitable interface). For example, computing device 500 may not include display device 510, but may include display device interface circuitry (e.g., connector and driver circuitry) to which an external display device 510 may be coupled.

[0040] Computing device 500 includes processing device 502 (e.g., one or more processing devices). As used herein, the terms "electronic processor device" and "processing device" may interchangeably refer to any device or portion of a device that processes electronic data from registers and / or memory and converts the electronic data into other electronic data that may be stored in registers and / or memory. In various embodiments, processing device 502 may include one or more digital signal processors (DSPs), application-specific integrated circuits (ASICs), or other processors. integrated circuit (ASIC), central processing unit (CPU) The processing unit may include a CPU, a graphics processing unit (GPU), a server processor, or any other suitable processing device.

[0041] Computing device 500 also includes a storage device 504 (e.g., one or more storage devices). In various embodiments, storage device 504 is a random-access memory (RAM) device (e.g., static RAM). The storage device 504 may include one or more memory devices, such as static RAM (SRAM) devices, magnetic RAM (MRAM) devices, dynamic RAM (DRAM) devices, resistive RAM (RRAM) devices, or conductive-bridging RAM (CBRAM) devices, hard-drive-based memory devices, solid-state memory devices, network drives, cloud drives, or any combination of memory devices. In some embodiments, the storage device 504 may include memory that shares a die with the processing device 502. In such embodiments, the memory may be used as cache memory and includes, for example, embedded dynamic random-access memory (eDRAM) or spin transfer torque magnetic random-access memory (STT-MRAM). In some embodiments, the storage device 504 may include a non-transitory computer-readable medium having instructions that, when executed by one or more processing devices (e.g., processing device 502), cause the computing device 500 to perform any suitable of the methods, or portions of such methods, disclosed herein below.

[0042] Computing device 500 further includes an interface device 506 (e.g., one or more interface devices 506). In various embodiments, interface device 506 may include one or more communication chips, connectors, and / or other hardware and software to manage communications between computing device 500 and other computing devices. For example, interface device 506 may include circuitry to manage wireless communications for transferring data to and from computing device 500. The term "wireless" and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communication channels, etc. that may communicate data via modulated electromagnetic radiation over a non-solid medium. This term does not imply that the associated devices do not include any wiring, although in some embodiments they may not. The circuitry included in interface device 506 for managing wireless communications may implement any of a number of wireless standards or protocols, including, but not limited to, Wi-Fi (IEEE 802.11 family), Institute for Electrical and Electronic Engineers (IEEE) standards including the IEEE 802.16 standard, the Long-Term Evolution (LTE) project (e.g., the Advanced LTE project, the Ultramobile Broadband (UMB) project (also known as "3GPP®2"), etc.) with any amendments, updates, and / or revisions.In some embodiments, the circuitry included in the interface device 506 for managing wireless communications includes circuits for Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Evolved HSPA, and the like. In some embodiments, the circuitry included in the interface device 506 for managing wireless communications may operate in accordance with a GSM HSPA, E-HSPA, or LTE network. In some embodiments, the circuitry included in the interface device 506 for managing wireless communications may operate in accordance with an Enhanced Data for GSM Evolution (EDGE), GSM EDGE Radio Access Network (GERAN), Universal Terrestrial Radio Access Network (UTRAN), or Evolved UTRAN (E-UTRAN). In some embodiments, the circuitry included in the interface device 506 for managing wireless communications may operate in accordance with a Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), or LTE network. Division Multiple Access (TDMA), Digital Enhanced Cordless Telecommunications The interface device 506 may operate according to the Enhanced Cordless Telecommunication (DECT), Evolution-Data Optimized (EV-DO), and their derivatives, as well as any other wireless protocols designated as 3G, 4G, 5G, and beyond. In some embodiments, the interface device 506 may include one or more antennas (e.g., one or more antenna arrays) configured to receive and / or transmit wireless signals.

[0043] In some embodiments, interface device 506 may include circuitry for managing wired communications, such as electrical, optical, or any other suitable communications protocol. For example, interface device 506 may include circuitry to support communications according to Ethernet technology. In some embodiments, interface device 506 may support both wireless and wired communications, and / or may support multiple wired and / or wireless communications protocols. For example, a first set of circuits in interface device 506 may be dedicated to short-range wireless communications, such as Wi-Fi or Bluetooth, and a second set of circuits in interface device 506 may be dedicated to long-range wireless communications, such as global positioning system (GPS), EDGE, GPRS, CDMA, WiMAX, LTE, EV-DO, or others. In some other embodiments, a first set of circuits in interface device 506 may be dedicated to wireless communications, and a second set of circuits in interface device 506 may be dedicated to wired communications.

[0044] Computing device 500 also includes battery / power circuitry 508. In various embodiments, battery / power circuitry 508 may include one or more energy storage devices (e.g., batteries or capacitors) and / or circuitry for coupling components of computing device 500 to an energy source separate from computing device 500 (e.g., to AC line power).

[0045] Computing device 500 also includes a display device 510 (e.g., one or more individual display devices). In various embodiments, display device 510 may include any visual indicator, such as a heads-up display, a computer monitor, a projector, a touchscreen display, a liquid crystal display (LCD), a light-emitting diode display, or a flat panel display.

[0046] Computing device 500 also includes additional input / output (I / O) devices 512. In various embodiments, I / O devices 512 may include one or more data / signal transfer interfaces, audio I / O devices (e.g., a microphone or microphone array, a speaker, a headset, an earphone, an alarm, etc.), an audio codec, a video codec, a printer, sensors (e.g., a thermocouple or other temperature sensor, a humidity sensor, a pressure sensor, a vibration sensor, etc.), image capture devices (e.g., one or more cameras), human interface devices (e.g., a cursor control device such as a keyboard, a mouse, a stylus, a trackball, or a touchpad), etc.

[0047] Depending on the particular embodiment of system 100, various components of interface device 506 and / or I / O device 512 may be configured to send and receive suitable control messages, suitable control / telemetry signals, and data streams. In some examples, interface device 506 and / or I / O device 512 include one or more analog-to-digital converters (ADCs) for converting received analog signals into a digital format suitable for operations performed by processing device 502 and / or storage device 504. In some additional examples, interface device 506 and / or I / O device 512 include one or more digital-to-analog converters (DACs) for converting digital signals provided by processing device 502 and / or storage device 504 into an analog format suitable for communication to corresponding components of system 100.

[0048] According to one example disclosed above, e.g., in the Summary of the Invention section and / or with reference to any one or any combination of some or all of FIGS. 1-5, there is provided an automated tool matching method for a plurality of measurement devices, the method comprising: estimating, using a first controller, configuration parameter changes for the plurality of measurement devices based on device drift data received from a plurality of second controllers, the estimated parameter changes being directed to tool matching the plurality of measurement devices at a future time, each of the second controllers configured to support a respective digital twin of a corresponding one of the measurement devices, and and receiving, using a first controller, a plurality of reports from a plurality of second controllers evaluating the estimated configuration parameter changes, each report being generated using a respective digital twin based on a respective subset of the estimated configuration parameter changes of the plurality of measurement instruments. If the plurality of reports indicate effectiveness of the estimated configuration parameter changes for tool matching of the plurality of measurement instruments at a future time, instructing, using the first controller, the plurality of second controllers to implement the estimated configuration parameter changes.

[0049] In some examples of the above method, the estimating includes predicting deviations of each of the plurality of measurement devices at a future time based on the device drift data, comparing the respective deviations to a threshold, and determining estimated configuration parameter changes that are predicted to produce an acceptable tool matching condition for the plurality of measurement devices at the future time if at least one of the respective deviations exceeds the threshold.

[0050] In some examples of any of the above methods, the determining is performed using a neural network trained with previous drift and calibration data corresponding to a plurality of measurement devices.

[0051] In some examples of any of the above methods, the method further includes, if the plurality of reports indicate invalidity of estimated configuration parameter changes for tool matching of the plurality of measurement devices at a future time, determining, using the first controller, whether the plurality of measurement devices can be brought into an acceptable tool matching state at a future time without performing a maintenance service or a fleet-wide calibration.

[0052] In some examples of any of the above methods, the method further includes flagging the plurality of measurement devices for a maintenance service or a fleet-wide calibration if the determining generates a determination that the plurality of measurement devices cannot be brought into an acceptable tool matching state without performing a maintenance service or a fleet-wide calibration.

[0053] In some examples of any of the above methods, the method further includes, if determining produces a determination that the plurality of measurement devices can be brought into an acceptable tool matching state without a maintenance service or a fleet-wide calibration, performing a next iteration of estimating further based on the plurality of reports.

[0054] In some examples of any of the above methods, each of the measurement instruments is an electron microscope instrument or a focused ion beam instrument.

[0055] In some examples of any of the above methods, each digital twin includes multiple model modules representing different respective physical components of the electron microscope instrument or focused ion beam instrument, and the model modules undergo iterative parameter updates based on comparison of measurements with corresponding model simulations.

[0056] In some examples of any of the above methods, the plurality of measurement devices includes five or more measurement devices.

[0057] Another example provides a non-transitory computer-readable medium storing instructions that, when executed by a computing device, cause the computing device to perform operations including any of the methods described above.

[0058] In accordance with yet another example disclosed above, e.g., in the Summary of the Invention section and / or with reference to any one or any combination of some or all of FIGS. 1-5, there is provided a tool matching system comprising: a first controller; and a plurality of second controllers, each second controller configured to support a respective digital twin of a corresponding one of a plurality of measurement devices, the plurality of second controllers further configured to control configuration parameters of the corresponding one of the measurement devices, wherein the first controller is configured to estimate configuration parameter changes of the plurality of measurement devices based on device drift data received from the plurality of second controllers. wherein the estimated configuration parameter changes are directed to tool matching the plurality of measurement devices at a future time; receiving a plurality of reports from a plurality of second controllers evaluating the estimated configuration parameter changes, each report generated using a respective digital twin based on a respective subset of the estimated configuration parameter changes of the plurality of measurement devices; and instructing the plurality of second controllers to implement the estimated configuration parameter changes if the plurality of reports indicate effectiveness of the estimated configuration parameter changes for tool matching the plurality of measurement devices at a future time.

[0059] In some examples of the above system, to estimate configuration parameter changes, the first controller is configured to predict deviations of each of the multiple measurement devices at a future time based on the device drift data, compare the respective deviations to a threshold, and if at least one of the respective deviations exceeds the threshold, determine estimated configuration parameter changes that are predicted to produce an acceptable tool matching state for the multiple measurement devices at the future time.

[0060] In some examples of any of the above systems, the first controller is configured to determine the estimated configuration parameter changes using a neural network trained on previous drift and calibration data corresponding to the plurality of measurement devices.

[0061] In some examples of any of the above systems, if the plurality of reports indicate invalidity of estimated configuration parameter changes for tool matching of the plurality of measurement devices at a future time, the first controller is configured to determine whether the plurality of measurement devices can be brought into an acceptable tool matching state at a future time without performing a maintenance service or a fleet-wide calibration.

[0062] In some examples of any of the above systems, if a determination is made that the plurality of measurement devices cannot be brought into an acceptable tool matching state without a maintenance service or fleet-wide calibration, the first controller is configured to flag the plurality of measurement devices for a maintenance service or fleet-wide calibration.

[0063] In some examples of any of the above systems, if a determination is made that the plurality of measurement instruments can be brought into an acceptable tool matching state without a maintenance service or fleet-wide calibration, the first controller is configured to generate a revised set of configuration parameter changes based on the plurality of reports.

[0064] In some examples of any of the above systems, each of the measurement instruments is an electron microscope instrument or a focused ion beam instrument.

[0065] In some examples of any of the above systems, each digital twin includes multiple model modules representing different respective physical components of the electron microscope instrument or focused ion beam instrument, and the model modules undergo iterative parameter updates based on comparison of measurements with corresponding model simulations.

[0066] In some examples of any of the above systems, the plurality of measurement devices includes five or more measurement devices.

[0067] In some examples of any of the above systems, a second controller of the plurality of second controllers is configured to evaluate a respective subset of the estimated configuration parameter changes by inputting the respective subset into a respective digital twin and running a simulation to calculate a corresponding deviation, and to generate a respective one of a plurality of reports for the first controller based on the calculated deviation.

[0068] In some examples of any of the above systems, the second controller is further configured to perform an action in response to an instruction received from the first controller, the action being selected from the group consisting of: sending additional equipment drift data to the first controller; performing a next iteration of evaluating the estimated configuration parameter changes using the respective digital twins; implementing a respective subset of the estimated configuration parameter changes using a corresponding one of the plurality of measurement devices; and configuring the corresponding one of the plurality of measurement devices for maintenance service or fleet-wide calibration.

[0069] It should be understood that the above description is intended to be illustrative, and not limiting. Many implementations and applications other than the examples provided will become apparent upon reading the above description. The scope should not be determined with reference to the above description, but instead with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. It is anticipated and intended that future developments will occur in the art discussed herein, and that the disclosed systems and methods will be incorporated into such future examples. In short, it should be understood that this application is capable of modification and variation.

[0070] All terms used in the claims are intended to be given their broadest reasonable interpretation and their ordinary meaning as understood by one skilled in the art described herein, unless expressly indicated to the contrary herein. In particular, the use of singular articles such as "a," "the," "said," etc., should be read to recite one or more of the indicated elements, unless the claim describes an express limitation to the contrary.

[0071] The Abstract is provided to allow the reader to quickly ascertain the nature of the technical disclosure. The Abstract is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. Additionally, in the foregoing Detailed Description, it may be seen that various features are grouped together in various examples for the purpose of streamlining the disclosure. This method of disclosure should not be interpreted as reflecting an intention that the claimed subject matter incorporates more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed example. Accordingly, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as independently claimed subject matter.

[0072] Unless expressly stated otherwise, each numerical value and range should be construed as approximation as if the word "about" or "approximately" were before the value or range.

[0073] In the method claims that follow, elements, if any, are listed in a particular order with corresponding labeling, but unless the claim description otherwise suggests a particular order for implementing some or all of the elements, the elements are not necessarily intended to be limited to being implemented in that particular order.

[0074] Unless otherwise specified herein, the use of ordinal adjectives such as "first," "second," "third," etc. to refer to one object among a plurality of similar objects merely indicates that different instances of such similar objects are being referred to and is not intended to imply that the similar objects so referred to must be in a corresponding order or sequence, either temporally, spatially, in ranking, or in any other manner.

[0075] Unless otherwise specified herein, the conjunction "if" shall have the same meaning as "when" or "upon" or "in response to determining" or "in response to detecting," in addition to its plain meaning. "(a) detecting a stated condition or event; (b) detecting a stated condition or event; (c) detecting a stated condition or event; (d) detecting a stated condition or event; (e) detecting a stated condition or event;

[0076] Also, for purposes of this description, the terms "couple," "coupling," "coupled," "connecting," "connecting," or "connected" refer to any manner known or later developed in the art that allows energy to be transferred between two or more elements, where the intervening presence of one or more additional elements is contemplated but not required. Conversely, the terms "directly coupled," "directly connected," etc., imply the absence of such additional elements.

[0077] The functions of the various elements shown in the figures, including any functional blocks labeled "processor" and / or "controller," may be provided through the use of dedicated hardware as well as hardware capable of executing software in association with appropriate software. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by multiple individual processors, some of which may be shared. Furthermore, explicit use of the terms "processor" or "controller" should not be construed as referring exclusively to hardware capable of executing software, but may implicitly include, without limitation, digital signal processor (DSP) hardware, network processors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), read only memory (ROM) for storing software, random access memory (RAM), and non-volatile storage. Other hardware, conventional and / or custom, may also be included. Similarly, any switches shown in the figures are conceptual only. Those functions may be performed through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, with the particular technique being selectable by the implementer as more particularly understood from the context.

[0078] As used in this application, the terms “circuit” and “circuitry” may refer to one or more or all of the following: (a) hardware-only circuit implementations (such as implementations with only analog and / or digital circuitry); (b) (where applicable), (i) combinations of analog and / or digital hardware circuitry with software / firmware; and (ii) combinations of hardware circuitry and software, such as combinations of any portion of a hardware processor with software (including a digital signal processor), software, and memory that work together to cause a device such as a cell phone or server to perform various functions; and (c) hardware circuits and / or processors, such as a microprocessor or portion of a microprocessor, that require software (e.g., firmware) to operate, but the software may be absent when not required for operation. This definition of circuit applies to all uses of the term in this application, including any claims. As a further example, as used in this application, the term circuit also covers implementations of simply a hardware circuit or processor (or processors), or portions of a hardware circuit or processor, and its (or their) accompanying software and / or firmware. The term circuitry also covers, for example, baseband or processor integrated circuits for mobile devices, or similar integrated circuits in servers, cellular network devices, or other computing or network devices, where applicable to particular claim elements.

[0079] Those skilled in the art will appreciate that any block diagrams herein represent conceptual views of illustrative circuitry embodying the principles of the present disclosure. Similarly, any flowcharts, flow diagrams, state transition diagrams, pseudocode, or the like, may be substantially represented on a computer-readable medium and represent various processes that may be performed by such a computer or processor, whether or not a computer or processor is explicitly shown.

Claims

1. 1. A method for automatic tool matching for a plurality of metrology instruments, the method comprising: using a first controller to estimate configuration parameter changes for the plurality of measurement devices based on equipment drift data received from a plurality of second controllers, the estimated configuration parameter changes being directed to tool matching the plurality of measurement devices at a future time, each of the second controllers configured to support a respective digital twin of a corresponding one of the measurement devices and further configured to control configuration parameters of the corresponding one of the measurement devices; receiving, using the first controller, a plurality of reports from the plurality of second controllers evaluating the estimated configuration parameter changes, each of the reports generated using the respective digital twin based on a respective subset of the estimated configuration parameter changes of the plurality of measurement devices; and if the plurality of reports indicate the effectiveness of the estimated configuration parameter changes for the tool matching of the plurality of measurement devices at the future time, using the first controller to instruct the plurality of second controllers to implement the estimated configuration parameter changes.

2. The estimating step comprises: predicting deviations of each of the plurality of measurement instruments at the future time based on the instrument drift data; comparing each deviation to a threshold; and determining the estimated configuration parameter changes predicted to produce an acceptable tool matching condition for the plurality of measurement devices at the future time if at least one of the respective deviations exceeds the threshold.

3. The method of claim 2 , wherein said determining is performed using a neural network trained with previous drift and calibration data corresponding to said plurality of measurement devices.

4. if the reports indicate invalidity of the estimated configuration parameter changes for the tool matching of the plurality of measurement devices at the future time, determining, using the first controller, whether the plurality of measurement devices can be brought into an acceptable tool matching state at the future time without a maintenance service or a fleet-wide calibration; The method of claim 1 further comprising:

5. flagging the plurality of measurement devices for the maintenance service or the fleet-wide calibration if the determining produces a determination that the plurality of measurement devices cannot be brought into the acceptable tool matching state without performing the maintenance service or the fleet-wide calibration; The method of claim 4 further comprising:

6. performing a next iteration of the estimating further based on the plurality of reports if the determining produces a determination that the plurality of measurement devices can be brought into the acceptable tool matching state without performing the maintenance service or fleet-wide calibration; The method of claim 4 further comprising:

7. The method of claim 1 , wherein each of the measurement instruments is an electron microscope instrument or a focused ion beam instrument.

8. 8. The method of claim 7, wherein the respective digital twins include a plurality of model modules representing different respective physical components of the electron microscope instrument or the focused ion beam instrument, the model modules undergoing iterative parameter updates based on comparison of measurements with corresponding model simulations.

9. The method of claim 1 , wherein the plurality of measurement devices comprises five or more measurement devices.

10. 1. A tool matching system, comprising: a first controller; a plurality of second controllers, each second controller configured to support a respective digital twin of a corresponding one of a plurality of measurement devices and further configured to control configuration parameters of the corresponding one of the measurement devices; The first controller estimating configuration parameter changes for the plurality of measurement devices based on device drift data received from the plurality of second controllers, the estimated configuration parameter changes being directed to tool matching the plurality of measurement devices at a future time; and receiving a plurality of reports from the plurality of second controllers evaluating the estimated configuration parameter changes, each of the reports generated using the respective digital twin based on a respective subset of the estimated configuration parameter changes of the plurality of measurement devices; and if the plurality of reports indicate validity of the estimated configuration parameter changes for the tool matching of the plurality of measurement devices at the future time, instructing the plurality of second controllers to implement the estimated configuration parameter changes.

11. To estimate the configuration parameter change, the first controller predicting deviations of each of the plurality of measurement instruments at the future time based on the instrument drift data; comparing each deviation to a threshold; and determining estimated configuration parameter changes predicted to produce an acceptable tool matching condition for the plurality of measurement devices at the future time if at least one of the respective deviations exceeds the threshold.

12. 12. The system of claim 11, wherein the first controller is configured to determine the estimated configuration parameter changes using a neural network trained on previous drift and calibration data corresponding to the plurality of measurement devices.

13. 11. The system of claim 10, wherein if the plurality of reports indicate invalidity of the estimated configuration parameter changes for the tool matching of the plurality of measurement devices at the future time, the first controller is configured to determine whether the plurality of measurement devices can be brought into an acceptable tool matching state at the future time without a maintenance service or a fleet-wide calibration.

14. 14. The system of claim 13, wherein if a determination is made that the plurality of measurement devices cannot be brought into the acceptable tool matching state without performing the maintenance service or a fleet-wide calibration, the first controller is configured to flag the plurality of measurement devices for the maintenance service or the fleet-wide calibration.

15. 14. The system of claim 13, wherein if a determination is made that the plurality of measurement devices can be brought into the acceptable tool matching condition without the maintenance service or fleet-wide calibration, the first controller is configured to generate a revised set of configuration parameter changes based on the plurality of reports.

16. The system of claim 10 , wherein each of the measurement instruments is an electron microscope instrument or a focused ion beam instrument.

17. 17. The system of claim 16, wherein the respective digital twins include a plurality of model modules representing different respective physical components of the electron microscope instrument or the focused ion beam instrument, the model modules undergoing iterative parameter updates based on comparison of measurements with corresponding model simulations.

18. The system of claim 10 , wherein the plurality of measurement devices includes five or more measurement devices.

19. A second controller of the plurality of second controllers comprises: evaluating the respective subsets of estimated configuration parameter changes by inputting the respective subsets into the respective digital twin and running a simulation to calculate corresponding deviations; The system of claim 10 , configured to generate a respective one of the plurality of reports for the first controller based on the calculated deviation.

20. The second controller is further configured to perform an action in response to an instruction received from the first controller, the action comprising: sending additional instrument drift data to the first controller; performing a next iteration evaluating the estimated configuration parameter changes using the respective digital twins; implementing each subset of the estimated configuration parameter changes with the corresponding one of the plurality of measurement devices; and 20. The system of claim 19, wherein the step of configuring the corresponding one of the plurality of measurement devices for maintenance service or fleet-wide calibration is selected from the group consisting of:

Images