Tool matching using digital twin

Abstract

The invention relates to tool matching using a digital twin. In some embodiments, a tool matching system includes a first controller and a plurality of second controllers. Each of the second controllers is configured to support a digital twin and to control configuration parameters of a corresponding measuring instrument. The first controller is configured to estimate a configuration parameter change of the measurement instrument based on instrument drift data. The estimated parameter change is for tool matching of the measuring instrument at a future time. The first controller is further configured to receive a plurality of reports evaluating the estimated configuration parameter changes. Each of the reports is generated using a respective digital twin based on a respective subset of the estimated configuration parameter changes. The first controller is further configured to instruct the second controller to implement the estimated configuration parameter change when the report indicates validity of the estimated configuration parameter change for the tool match of the measurement instrument at the future time.

Description

Technical Field

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

[0002] Applied, technical, or industrial metrology involves the application of measurements to manufacturing or other industrial processes to ensure the suitability of measuring instruments, the calibration of such instruments, and quality control for their intended purposes. For example, obtaining good measurements is important in many industries because measurements often have a significant impact on the value and quality of the final product, as well as production costs. In some cases, traceability and correctability of instrument performance between calibrations are important capabilities for providing high confidence in measurement results and good agreement between multiple measuring instruments.

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

[0004] An example provides an automatic tool matching method for multiple measuring instruments, the method comprising: using a first controller, estimating configuration parameter changes of the multiple measuring instruments based on instrument drift data received from multiple second controllers, the estimated parameter changes being for tool matching of the multiple measuring instruments at a future time, each of the second controllers being configured to support a corresponding digital twin of a corresponding measuring instrument in the measuring instrument, and also being configured to control the configuration parameters of the corresponding measuring instrument in the measuring instrument; using the first controller, receiving multiple reports evaluating the estimated configuration parameter changes from the multiple second controllers, each of the reports being generated using the corresponding digital twin based on a corresponding subset of the estimated configuration parameter changes for the multiple measuring instruments; and using the first controller, instructing the multiple second controllers to implement the estimated configuration parameter changes when the multiple reports indicate the effectiveness of the estimated configuration parameter changes for the tool matching of the multiple measuring instruments at the future time.

[0005] Another example provides a tool matching system, which includes: a first controller; and multiple second controllers, each of the second controllers being configured to support a corresponding digital twin of a corresponding measuring instrument in a plurality of measuring instruments, and also being configured to control configuration parameters of the corresponding measuring instrument in the measuring instrument, wherein the first controller is configured to: estimate configuration parameter changes of the multiple measuring instruments based on instrument drift data received from the multiple second controllers, the estimated parameter changes being for tool matching of the multiple measuring instruments at a future time; receive multiple reports evaluating the estimated configuration parameter changes from the multiple second controllers, each of the reports being generated using the corresponding digital twin based on a corresponding subset of the estimated configuration parameter changes for the multiple measuring instruments; and when the multiple reports indicate the effectiveness of the estimated configuration parameter changes for the tool matching of the multiple measuring instruments at the future time, instruct the multiple second controllers to implement the estimated configuration parameter changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] The foregoing aspects of the present disclosure and many of the attendant advantages will become more readily understood when reference is made to the following detailed description taken in conjunction with the accompanying drawings, in which:

[0007] Figure 1 is a block diagram illustrating a processing pipeline of a digital twin according to some examples.

[0008] Figure 2 is a block diagram illustrating an instrument control system that may be used for tool matching according to some examples.

[0009] Figure 3 is an illustration based on some examples Figure 2 Flowchart of a tool matching method implemented at a main controller of an instrument control system.

[0010] Figure 4 is an illustration based on some examples Figure 2 A flow chart of a tool matching method implemented at a separate instrument controller of an instrument control system.

[0011] Figure 5 is a block diagram illustrating a computing device according to some examples. DETAILED DESCRIPTION

[0012] As semiconductor device patterns are scaled down during the semiconductor manufacturing process, control of the critical dimensions of such patterns is becoming a priority in many semiconductor manufacturing plants (commonly referred to as fabs). For example, the transistor-gate critical dimension (CD) is typically on the order of a few nanometers. Every nanometer deviation from the target gate length can affect the operating speed of the device. Additionally, when the post-etch gate CD is too small, threshold voltage shifts and leakage currents can render the corresponding semiconductor device inoperative. In an automated foundry environment, target gate CD can be achieved in several different ways. For example, using in-line process monitoring, lithography and etching tools can be tuned to improve the fab's CD performance and reduce the resulting inter-wafer CD variation.

[0013] An important component of online process monitoring involves the use of dimensional measurement instruments. Technical requirements for such instruments include high measurement accuracy and consistent performance. Example challenges associated with measurements performed using such instruments include, but are not limited to, improving the measurement accuracy of individual measuring instruments; reducing the variation in dimensional measurements between individual instruments within a production line; and reducing the temporal variation in dimensional measurements obtained by individual measuring instruments.

[0014] For example, electron microscopes such as scanning electron microscopes (SEMs) and / or transmission electron microscopes (TEMs) can be used in semiconductor manufacturing plants to measure various dimensions of semiconductor devices, including CD. A typical electron microscope is a sophisticated and technically complex instrument characterized by a relatively large number of configuration parameters. During operation, the electron microscope can be calibrated to establish a relationship between the values of the configuration parameters and certain specific physical measurements. Several different types of calibration can be involved in the process of placing the microscope in the proper working configuration.

[0015] One example calibration procedure is magnification calibration. More specifically, the projection system lens of an electron microscope has a specific current that results in a specific magnification of the specimen image 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 200mA may correspond to an image scale of 100nm per pixel. Other 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 the beam position on the sample plane. Stage shift calibration relates the magnitude of the stimulus applied to the motor drive of the microscope stage to changes in the position of the sample. Focus calibration relates the objective lens current to the focal position along the longitudinal axis of the electron beam (commonly referred to as defocus).

[0016] After calibrating a microscope, it often begins to exhibit performance drift, which results in a deviation from the expected performance. As used herein, the term "deviation" refers to the difference between the calibrated set point and the actual performance state or behavior of the instrument. For example, assume that during focus calibration, the behavior exhibited is such that at an objective current of 10 mA, the electron beam defocus is 1 nm. However, later, at the same objective current, the electron beam defocus becomes 10 nm. The 9-nm difference (= 10-1) is referred to as the deviation from the corresponding set point of the focus calibration. The gradual change of the set point 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 form of processed or unprocessed sensor signals and / or detector signals, based on which deviations from expected performance can be determined and / or quantified. In some examples, such sensor and / or detector signals are generated using one or more of: a direct current detection camera, a CCD camera, a high-speed digital camera, a video camera optically coupled to a fluorescent screen (such as a commercially available FluCam device), and a scanning transmission electron microscope (STEM) detector.

[0017] In some examples, the electron microscope is provided with a digital twin. As used herein, the term "digital twin" refers to a virtual model of the corresponding instrument. Such a model typically spans the life cycle of the instrument and uses data received from various sensors and / or detectors associated with the instrument to simulate the behavior of the instrument, monitor operation, and / or recommend configuration adjustments. For example, a digital twin can be used to observe the state of the instrument as needed. When sensors collect data from the instrument, the 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 and accurate representation of the properties and state of the instrument (including the drift described above). As used herein, the term "real-time" refers to a computer-based process that controls the corresponding environment by receiving data, processing the received data, and generating a response quickly enough to affect the environment without significant delay. Real-time response is generally understood to be on the order of milliseconds, or sometimes microseconds. In the context of digital twins, "real-time" updates mean that the digital twin represents the corresponding actual instrument accurately enough at any point in time.

[0018] For purposes of illustration and without any implicit limitation, example embodiments are described below with reference to electron microscopes. However, the various embodiments are not limited thereto. As used herein, the term "measuring instrument" should be interpreted to include 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 relevant art will be able to make and use various embodiments corresponding to various types of measuring instruments without undue experimentation.

[0019] Figure 1 is a block diagram illustrating a processing pipeline 101 of a digital twin 100 according to some examples. In the example shown, the digital twin 100 represents a TEM instrument (in Figure 1 1 (not explicitly shown in the figure), the digital twin 100 communicates with the TEM instrument via an interface 102. The processing pipeline 101 includes a plurality of model modules 110 to 126 corresponding to different respective physical components of the TEM instrument. As an example, the following model modules are shown: electron gun and accelerator model module 110; focusing lens model module 112; probe corrector model module 114; objective lens model module 116; sample model module 118; image corrector model module 120; projection lens system model module 122; viewing and recording device model module 124; and image filter and camera model module 126. In other examples, the processing pipeline 101 can also be implemented using a different set of model modules (other than that shown).

[0020] Different model modules among the model modules 110 to 126 may communicate with each other, for example, Figure 1, as indicated in . For example, the output of one model module of processing pipeline 101 can serve as an input to another model module of processing pipeline 101. In some examples, the cascade of model modules 110 to 126 is configured to provide a simulation of how various settings of an electron microscope column relate to signals detected using associated sensors or detectors. In some examples, the input to a model module comprises a wave function or ray diagram, and the corresponding output of the model module comprises 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 (such as lens currents, etc.) affect associated electron beam characteristics. The wave optics model module 130 and the geometrical optics model module 140 are configured to communicate appropriately with individual model modules of model modules 110 to 126 to support electron beam propagation simulation covering the optical path between the electron source (represented by the electron gun and accelerator model module 110) and the electron sink (represented by the image filter and camera model module 126) of the TEM instrument. The use of two modules 130 , 140 for such simulations enables the digital twin 100 to appropriately account for the effects of wave-particle duality exhibited by electrons in at least some components and / or configurations of the TEM instrument.

[0021] In operation, the model modules 110 to 126 are subject to 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 measurements received from the TEM instrument by the calibration and validation module 150 with corresponding model simulations performed using the model modules 110 to 126, 130, 140. For example, when the comparison reveals sufficiently large differences, one or more parameters used in the model modules 110 to 126 are updated so that various drifts occurring in the TEM instrument are appropriately reflected in the digital twin 100.

[0022] Different TEM or SEM instruments deployed at a production facility may be calibrated at different times. The drift rates exhibited by different instruments may also vary. For these reasons, maintaining good and sufficient agreement between instruments in a relatively large fleet of instruments can be challenging. This correspondence problem is sometimes referred to as "tool matching."

[0023] In one approach, tool matching is achieved by calibrating an entire fleet of instruments regularly and frequently. However, after calibration, the instruments begin to drift in performance in different ways. This drift results in the above-mentioned deviation, which is not corrected under this approach until the next time the entire fleet is calibrated and becomes consistent again. In at least some use cases, this approach can be quite disruptive and / or time consuming. The example embodiments disclosed herein address this problem by monitoring multiple instruments and using corresponding digital twins of the instruments (such as digital twin 100) to predict expected deviations. In some examples, the digital twins are beneficially used to determine the changes required in the corresponding instrument control parameters to maintain performance consistency across the fleet without compromising measurement accuracy. The determined changes are then pushed to the physical instruments to advantageously reduce the frequency of calibration and associated production line interruptions.

[0024] In general, it can be said that tool matching is achieved when different instruments in the fleet produce "identical" (within specified error limits) measurements for the same experiment. The measurement / experiment type can vary depending on the use case. For example, when the intensity of an image needs to be matched, the corresponding experiment is specifically configured to accurately measure the intensity value, while some other characteristics (e.g., such as magnification) may not be strictly considered. Similarly, when feature size needs to be matched, the corresponding experiment is specifically configured to accurately measure the effective magnification, while image intensity may not be an important factor in this experiment.

[0025] Figure 2 2 is a block diagram illustrating an instrument control system 200 that can be used for tool matching according to some examples. The system 200 includes N measurement instruments 2101 to 210 N , where N is a positive integer greater than 1. In some specific examples, the number N is in the range of 5 to 50. In some examples, the measuring instruments 2101 to 210 N Each measuring instrument is or includes an electron microscope.

[0026] 210 per measuring instrument n Connected to the corresponding electronic controller 220 n , where n=1, 2, ..., N. Electronic controller 220 n With measuring instruments 210 n The configuration parameters of the operation control and includes a digital twin 100 configured to support the corresponding n (See also Figure 1 ) of the corresponding computing device (in Figure 2 Not explicitly shown, see Figure 5 ). Electronic controllers 2201 to 220 N Each electronic controller has a corresponding communication link 228 to the main controller 230n In the example shown, the main controller 230 is implemented using a suitable computing device (e.g., a network-connected server) 240. In operation, the electronic controllers 2201 to 220 N and the main controller 230 via corresponding communication link 228 n Exchange control messages that enable the system 200 to perform operations for the measuring instruments 2201 to 220 N tool matching, for example, as described in more detail below.

[0027] For the purpose of illustration and without any implicit limitation, let us consider an example where N=3 and the corresponding measuring instruments 2101 to 2103 are electron microscopes configured to perform metrology measurements on transistor dimensions. Assume that we have a transistor with a CD of 3.2 nm. For the purpose of tool matching, we want each of the electron microscopes 2101 to 2103 to produce the same CD value of 3.2 nm within a specified tolerance of 0.05 nm (i.e., within ±0.05 nm of 3.2) by performing automatic measurements on the transistor. This result can be achieved, for example, by properly aligning and calibrating the electron microscopes 2101 to 2103 on a test sample with confirmed dimensions. The calibration will produce corresponding three sets of configuration parameters for the electron microscopes 2101 to 2103.

[0028] The digital twins 1001 to 1003 receive the corresponding three configuration parameter sets with an indication that an acceptable tool matching state has been achieved at the time of calibration using these parameters. In operation after calibration, each of the electron microscopes 2101 to 2103 continuously sends acquired images and related sensor information to the corresponding electronic controller 220. n Each electronic controller 220 n Using the received data and the data twin 100 n The corresponding model module (see also Figure 1) to extract drift data. The drift data is transmitted by the electronic controllers 2201 to 2203 to the main controller 230 via the communication links 2281 to 2283. The main controller 230 models the effects of those drifts applied to a particular use case to infer and determine the impact of the drift on the tool match state of the entire fleet of electron microscopes at a future time. When the main controller 230 determines that one or more of the predicted deviations exceeds the tool match tolerance specified for that particular use case, the main controller 230 operates to determine a correction to the corresponding set of configuration parameters of the electron microscopes 2101 to 2103 that will keep the entire fleet within the specified tool match tolerance. The determined parameter corrections are then pushed to the electron microscopes 2101 to 2103 via the electronic controllers 2201 to 2203 to maintain the desired state of tool match across the fleet without having to perform another calibration on the test sample at that time.

[0029] Figure 3 2 is a flow chart illustrating a tool matching method 300 implemented at the main controller 230 of the instrument control system 200 according to some examples. The method 300 includes the main controller 230 receiving a tool matching method corresponding to the measuring instruments 2101 to 210 N Drift data (in block 302). For each measuring instrument 210 n , the drift data is obtained by the corresponding electronic controller 220 n The sensor data extracted from the detector and received from the instrument is processed using the corresponding digital twin 100 n processed and transmitted via communication link 228 n Sent to the main controller 230, as indicated above.

[0030] The method 300 also includes the main controller 230 predicting that the measuring instruments 2101 to 210 N The deviation of each measuring instrument in the block 304 is predicted (in block 304). In some examples, the prediction (in block 304) is made by appropriately extrapolating the drift data received in block 302. The time increment used to select the future time for block 304 is an algorithm parameter that depends on the use case. In various examples, the time increment can range between minutes and hours.

[0031] The method 300 further includes the main controller 230 determining the measuring instruments 2101 to 210 N Whether the prediction deviation of any of the measuring instruments exceeds a predetermined threshold value (in decision block 306). The predetermined threshold value is an algorithm parameter that depends on the usage and is different from the above-mentioned threshold value for different instruments 210 in the entire fleet of instruments. nWhen the prediction deviation is less than the threshold ("No" at decision block 306), processing of method 300 is directed back to the operation of block 302. When the prediction deviation exceeds the threshold ("Yes" at decision block 306), processing of method 300 is directed to block 308.

[0032] The operation of block 308 includes the main controller 230 determining the measurement instruments 2101 to 210 N The determination is directed to finding a queued set of configuration parameter changes that cause the measuring instruments 2101 to 210 N In some examples, configuration parameter changes are determined in block 308 using a neural network implementing a fleet model that maps vectors having biases as components to corresponding vectors of parameter changes that are expected to correct the biases to the point where the fleet measurement instruments 210 are in their respective configurations that together satisfy the tool match specification for the fleet. n The degree to which the tool is kept within the matching specification. The neural network can use machine learning methods to take advantage of previous drift and for the entire fleet of measuring instruments 210 n When the trained neural network is provided with a bias vector constructed using the bias predicted in block 304, the neural network outputs a bias vector for measuring instruments 2101 to 2102. N In some other examples, other suitable team models can also be used to estimate configuration parameter changes.

[0033] The method 300 further includes the main controller 230 sending the estimated configuration parameter changes for verification to the electronic controllers 2201 to 220 N (In block 310 ). In some examples, the operations of block 310 include: (i) resolving the estimated vector of configuration parameter changes determined in block 308 into vectors corresponding to different individual measurement instruments 210 n and (ii) via communication link 228 n This subset is sent to the corresponding electronic controller 220 n Upon receiving a corresponding subset of configuration parameter changes from the master controller 230, each electronic controller 220 n Operate to pass the corresponding digital twin 100 n Run configuration parameter changes to determine the effect of such changes on the use of corresponding individual measurement instruments 210 n The electronic controller 220 performs the measurement of the effect. nThe determined impact is then reported back to the main controller 230. The method 300 also includes the main controller 230 sending the electronic controller 2201 to 220 N A corresponding assessment report is received (in block 312 ).

[0034] The method 300 also includes the main controller 230 determining whether the estimated parameter change is acceptable (in decision block 314). N The received assessment reports are determined in decision block 314. In some examples, the corresponding measurement instrument 210 is configured to be configured to implement a corresponding subset of configuration parameter changes when at least one of the assessment reports indicates that the corresponding subset of configuration parameter changes is implemented thereat. n When the tool fit specifications will not be met, the estimated parameter changes are deemed unacceptable. When the estimated configuration parameter changes are deemed unacceptable ("No" at decision block 314), processing of method 300 is directed to block 316. When the estimated configuration parameter changes are deemed acceptable ("Yes" at decision block 314), processing of method 300 is directed to block 318.

[0035] The operation at block 316 includes the master controller 230 determining whether the fleet of instruments 210 can still achieve an acceptable tool match state at a future time. In various examples, this determination can be made based on the number of iterations through block 308 and / or the magnitude of the deviations indicated in the evaluation report at block 312. For example, in some cases, when the number of iterations through block 308 reaches a fixed, predetermined number, the master controller 230 will determine that the fleet of instruments is unable to self-maintain an acceptable tool match state without fleet maintenance and / or calibration. In some other cases, when the magnitude of the deviations indicated in the validation reports for two or more instruments exceeds a fixed threshold, the master controller 230 will determine that the fleet of instruments is unable to self-maintain an acceptable tool match state without fleet maintenance and / or calibration. When the master controller 230 determines that the fleet of instruments is able to self-maintain an acceptable tool match state without fleet maintenance or calibration ("yes" at decision block 316), processing of the method 300 is directed back to block 308, where another attempt is made to find an acceptable vector of parameter changes, for example, using an updated input vector appropriately constructed using the evaluation report at block 312. When the master controller 230 determines that the instrument fleet cannot self-maintain an acceptable tool match status without fleet maintenance or calibration (“NO” at decision block 316 ), processing of the method 300 is directed to block 320 .

[0036] The operation of block 318 includes the main controller 230 sending a configuration change instruction to the electronic controllers 2201 to 220 NThe configuration change instructions are based on the vector of parameter changes estimated by the main controller 230 in the last instance of block 308 and then executed via blocks 310, 312 using the electronic controllers 2201 to 220 N After the configuration change command is sent to the electronic controller 2201 to 220 N Thereafter, processing of method 300 loops back to block 302 .

[0037] The operation of block 320 includes the main controller 230 sending the team of instruments 210 n Marking as unable to self-correct identifies when the system 100 will no longer be in an acceptable tool match state, and schedules maintenance service and / or fleet calibration accordingly. After completing the operations of block 320, the method 300 terminates.

[0038] Figure 4 is an illustration of a separate electronic controller 220 in the instrument control system 200 according to some examples. n Flowchart of tool matching method 400 implemented at. Method 400 is compatible with method 300 and is performed by electronic controller 220 n Communication is supported between the host controller 230 and the host controller 230 , wherein the latter executes the method 300 .

[0039] The method 400 includes the electronic controller 220 n will correspond to the measuring instrument 210 n The drift data is sent to the main controller 230 (in block 402). In various examples, the electronic controller 220 n Drift data is extracted from the detector and the digital twin 100 is used as indicated above. n From Instrument 210 n Receive sensor data.

[0040] The method 400 also includes the electronic controller 220 n In standby mode for control messages from the master controller 230 (in decision block 404). The expected control message is configured to provide for the measurement instrument 210 n The estimated configuration parameter changes are generated by the main controller 230 using blocks 308, 310 of the method 300. If no control message is received ("No" at decision block 404), the electronic controller 220 n Remaining in standby mode, wherein processing of method 400 loops through block 402. When a control message is received ("yes" at decision block 404), processing of method 400 is directed to block 406.

[0041] The operation of block 406 includes the electronic controller 220 nThe estimated configuration parameter changes transmitted via the control message received from the master controller 230 in block 404 are evaluated. In some examples, the received configuration parameter changes are input to the digital twin 100 n The evaluation is performed by running a related simulation using its related model module to determine the deviation corresponding to the received parameter change. The operation of block 406 also includes the electronic controller 220 n The calculated deviation is reported back to the master controller 230. In block 312, the reported deviation is received by the master controller 230.

[0042] The method 400 also includes the electronic controller 220 n An instruction from the master controller 230 is received and executed (in block 408). Depending on the type of instruction received, processing of method 400 may loop back to block 402 or 406, or may terminate after executing the received instruction. For example, when the received instruction 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 instruction is executed. When the received instruction is the next installation of the 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 instruction is a maintenance service notification sent by master controller 230 in block 320 of method 300, processing of method 400 terminates.

[0043] Figure 5 is a block diagram illustrating a computing device 500 according to some examples. In various examples, the main controller 230 or the electronic controller 220 n It may be implemented by a single computing device 500 or by multiple computing devices 500. In some examples, an instance of computing device 500 may be configured to implement method 300 or method 400.

[0044] Figure 5 The computing device 500 is illustrated as having multiple components, but any one or more of these components may be omitted or repeated depending on suitability for the application and settings. In some embodiments, some or all of the components included in the computing device 500 may be attached to one or more motherboards and encapsulated in a housing. In some embodiments, some of these components may be manufactured onto a single system on a chip (SoC) (e.g., the SoC may include one or more electronic processing devices 502 and one or more storage devices 504). Additionally, in various embodiments, the computing device 500 may not include Figure 5One or more of the illustrated components may include interface circuitry for coupling to the 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, the computing device 500 may not include the display device 510, but may include display device interface circuitry (e.g., a connector and a driver circuit) capable of coupling to the external display device 510.

[0045] The computing device 500 includes a processing device 502 (e.g., one or more processing devices). As used herein, the terms "electronic processing device" and "processing device" interchangeably refer to any device or portion of a device that processes electronic data from registers and / or memory to transform the electronic data into other electronic data that can be stored in registers and / or memory. In various embodiments, the processing device 502 may include one or more digital signal processors (DSPs), application-specific integrated circuits (ASICs), central processing units (CPUs), graphics processing units (GPUs), server processors, or any other suitable processing devices.

[0046] The computing device 500 also includes a storage device 504 (e.g., one or more storage devices). In various embodiments, the storage device 504 may include one or more memory devices, such as random access memory (RAM) devices (e.g., 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 serve as a cache memory and include, 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 thereon that, when executed by one or more processing devices (e.g., processing device 502), cause the computing device 500 to perform any appropriate method or portions of such methods disclosed herein below.

[0047] The computing device 500 also includes an interface device 506 (e.g., one or more interface devices 506). In various embodiments, the interface device 506 may include one or more communication chips, connectors, and / or other hardware and software to manage communications between the computing device 500 and other computing devices. For example, the interface device 506 may include circuitry for managing wireless communications for transmitting data to and from the computing device 500. The term "wireless" and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communication channels, etc. that can transmit data via modulated electromagnetic radiation through a non-solid medium. The term does not imply that the associated device does not contain any wires, although in some embodiments, it may not contain any wires. The circuitry included in the interface device 506 for managing wireless communications may implement any of a plurality of wireless standards or protocols, including, but not limited to, Institute of Electrical and Electronics Engineers (IEEE) standards, including Wi-Fi (IEEE 802.11 series), IEEE 802.16 standards, the Long Term Evolution (LTE) project, and any amendments, updates, and / or revisions (e.g., the LTE-Advanced project, the Ultra Mobile Broadband (UMB) project (also known as "3GPP2"), etc.). In some embodiments, the circuitry included in the interface device 506 for managing wireless communications may operate in accordance with a Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Evolved 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 Enhanced Data for GSM Evolution (EDGE), GSM Evolution 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 Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Digital Enhanced Cordless Telecommunications (DECT), Evolution-Data Optimized (EV-DO), and their derivatives, as well as any other wireless protocols designated as 3G, 4G, 5G, and higher. 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.

[0048] In some embodiments, interface device 506 can include a circuit for managing wired communication, such as electrical communication protocol, optical communication protocol or any other suitable communication protocol. For example, interface device 506 can include a circuit supporting the communication according to Ethernet technology. In some embodiments, interface device 506 can support both wireless and wired communication, and / or can support multiple wired communication protocols and / or multiple wireless communication protocols. For example, a first group of circuits of interface device 506 can be dedicated to the shorter range wireless communication such as Wi-Fi or Bluetooth, and a second group of circuits of interface device 506 can be dedicated to the longer range wireless communication such as global positioning system (GPS), EDGE, GPRS, CDMA, WiMAX, LTE, EV-DO etc. In some other embodiments, a first group of circuits of interface device 506 can be dedicated to wireless communication, and a second group of circuits of interface device 506 can be dedicated to wired communication.

[0049] 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).

[0050] Computing device 500 also includes a display device 510 (e.g., one or more separate display devices). In various embodiments, display device 510 can include any visual indicator, such as a heads-up display, a computer monitor, a projector, a touch screen display, a liquid crystal display (LCD), a light-emitting diode display, or a flat-panel display.

[0051] The computing device 500 also includes additional input / output (I / O) devices 512. In various embodiments, the I / O devices 512 may include one or more data / signal transmission interfaces, audio I / O devices (e.g., a microphone or microphone array, speakers, headphones, earbuds, alarms, etc.), audio codecs, video codecs, printers, sensors (e.g., thermocouples or other temperature sensors, humidity sensors, pressure sensors, vibration sensors, etc.), image capture devices (e.g., one or more cameras), human interface devices (e.g., a keyboard, a cursor control device such as a mouse, stylus, trackball, or trackpad), etc.

[0052] Depending on the particular implementation of system 100, the various components of interface device 506 and / or I / O device 512 can be configured to send and receive appropriate control messages, appropriate 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 digital form 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 analog form suitable for transmission to corresponding components of system 100.

[0053] According to the above, for example in the Summary section and / or with reference to Figures 1 to 5 An example disclosed in any one of the figures or any combination of some or all of the figures provides an automatic tool matching method for multiple measuring instruments, the method comprising: utilizing a first controller to estimate configuration parameter changes of multiple measuring instruments based on instrument drift data received from multiple second controllers, the estimated parameter changes being for tool matching of multiple measuring instruments at a future time, each of the second controllers being configured to support a corresponding digital twin of a corresponding measuring instrument in the measuring instruments, and also being configured to control configuration parameters of the corresponding measuring instrument in the measuring instruments; utilizing the first controller to receive multiple reports evaluating the estimated configuration parameter changes from the multiple second controllers, each of the reports being generated utilizing the corresponding digital twin based on a corresponding subset of the estimated configuration parameter changes for the multiple measuring instruments; and utilizing the first controller to instruct the multiple second controllers to implement the estimated configuration parameter changes when the multiple reports indicate the effectiveness of the estimated configuration parameter changes for the tool matching of the multiple measuring instruments at the future time.

[0054] In some examples of the above method, the estimating includes: predicting corresponding deviations of multiple measuring instruments at a future time based on instrument drift data; comparing the corresponding deviations with a threshold; and when at least one of the corresponding deviations exceeds the threshold, determining an estimated configuration parameter change, the estimated configuration parameter change being predicted to produce an acceptable tool matching state for the multiple measuring instruments at the future time.

[0055] In some examples of any of the methods above, the determining is performed using a neural network trained using previous drift and calibration data corresponding to the plurality of measurement instruments.

[0056] In some examples of any of the above methods, the method also includes: when multiple reports indicate the invalidity of the estimated configuration parameter changes for tool matching of multiple measuring instruments at a future time, using a first controller to determine whether the multiple measuring instruments can be placed in an acceptable tool matching state at a future time without maintenance service or fleet calibration.

[0057] In some examples of any of the above methods, the method further includes: when a determination is made that the multiple measuring instruments cannot be placed in an acceptable tool match state without maintenance service or full-fleet calibration, marking the multiple measuring instruments for maintenance service or for full-fleet calibration.

[0058] In some examples of any of the above methods, the method further includes: when the decision yields a determination that the plurality of measurement instruments can be placed in an acceptable tool fit state without maintenance service or fleet calibration, performing a next iteration of estimating further based on the plurality of reports.

[0059] In some examples of any of the methods above, each of the measurement instruments is an electron microscope instrument or a focused ion beam instrument.

[0060] In some examples of any of the above methods, the corresponding digital twin includes multiple model modules representing different corresponding physical components of the electron microscope instrument or the focused ion beam instrument, which are subjected to iterative parameter updates based on comparison of measurement results and corresponding model simulations.

[0061] In some examples of any of the methods above, the plurality of measurement instruments includes five or more measurement instruments.

[0062] 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.

[0063] According to the above, for example in the Summary section and / or reference Figures 1 to 5Yet another example disclosed in any one of the figures or any combination of some or all of the figures, provides a tool matching system comprising: a first controller; and multiple second controllers, each of the second controllers being configured to support a corresponding digital twin of a corresponding measuring instrument in a plurality of measuring instruments, and further configured to control configuration parameters of the corresponding measuring instrument in the measuring instrument, wherein the first controller is configured to: estimate configuration parameter changes of the multiple measuring instruments based on instrument drift data received from the multiple second controllers, the estimated parameter changes being for tool matching of the multiple measuring instruments at a future time; receive multiple reports evaluating the estimated configuration parameter changes from the multiple second controllers, each of the reports being generated using the corresponding digital twin based on a corresponding subset of the estimated configuration parameter changes for the multiple measuring instruments; and instruct the multiple second controllers to implement the estimated configuration parameter changes when the multiple reports indicate the effectiveness of the estimated configuration parameter changes for the tool matching of the multiple measuring instruments at the future time.

[0064] In some examples of the above systems, to estimate a configuration parameter change, the first controller is configured to: predict corresponding deviations of the multiple measuring instruments at the future time based on the instrument drift data; compare the corresponding deviations with a threshold; and when at least one of the corresponding deviations exceeds the threshold, determine an estimated configuration parameter change, the estimated configuration parameter change being predicted to produce an acceptable tool matching state for the multiple measuring instruments at the future time.

[0065] In some examples of any of the systems above, the first controller is configured to determine the estimated configuration parameter change using a neural network trained using previous drift and calibration data corresponding to the plurality of measurement instruments.

[0066] In some examples of any of the above systems, when the multiple reports indicate the invalidity of the estimated configuration parameter change for the tool match of the multiple measuring instruments at the future time, the first controller is configured to determine whether the multiple measuring instruments can be placed in an acceptable tool match state at the future time without maintenance service or fleet calibration.

[0067] In some examples of any of the above systems, when it is determined that the plurality of measuring instruments cannot be placed in the acceptable tool match state without the maintenance service or the fleet calibration, the first controller is configured to mark the plurality of measuring instruments for the maintenance service or for the fleet calibration.

[0068] In some examples of any of the above systems, when it is determined that the plurality of measurement instruments can be placed in the acceptable tool match state without the maintenance service or fleet calibration, the first controller is configured to generate a modified set of configuration parameter changes based on the plurality of reports.

[0069] In some examples of any of the systems above, each of the measurement instruments is an electron microscope instrument or a focused ion beam instrument.

[0070] In some examples of any of the above systems, the corresponding digital twin includes multiple model modules representing different corresponding physical components of the electron microscope instrument or the focused ion beam instrument, and the model modules undergo iterative parameter updates based on comparison of measurement results and corresponding model simulations.

[0071] In some examples of any of the systems above, the plurality of measurement instruments includes five or more measurement instruments.

[0072] In some examples of any of the above systems, a second controller among the multiple second controllers is configured to: evaluate the corresponding subset by inputting the corresponding subset of estimated configuration parameter changes into the corresponding digital twin and running a simulation to calculate corresponding deviations; and generate a corresponding report among the multiple reports for the first controller based on the calculated deviations.

[0073] 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 instrument drift data to the first controller; performing a next iteration of evaluating the estimated configuration parameter changes using the corresponding digital twin; implementing a corresponding subset of the estimated configuration parameter changes using a corresponding measuring instrument from the plurality of measuring instruments; and configuring a corresponding measuring instrument from the plurality of measuring instruments for maintenance service or fleet calibration.

[0074] It should be understood that the above description is intended to be illustrative and not restrictive. Upon reading the above description, many specific implementations and applications other than the examples provided will be apparent. The scope should not be determined with reference to the above description, but rather with reference to the appended claims and the full scope of equivalents to which such claims are entitled. It is anticipated and intended that future developments will occur in the technology discussed herein, and that the disclosed systems and methods will be incorporated into such future examples. In short, it should be understood that the present application is capable of modification and variation.

[0075] All terms used in the claims are intended to be given their broadest reasonable constructions and their ordinary meanings as understood by those skilled in the art described herein unless an explicit indication to the contrary is made herein. Specifically, use of singular articles such as "a," "an," "the," and the like should be understood to recite one or more of the indicated elements unless a claim recites an explicit limitation to the contrary.

[0076] The Abstract is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is to be understood that it will not be used to interpret or limit the scope or meaning of the claims. Additionally, in the foregoing Detailed Description, it can be seen that various features are grouped together in various examples for the purpose of streamlining the disclosure. This approach of disclosure should not be interpreted as reflecting an intention that the claimed subject matter includes more features than expressly recited in each claim. Rather, as reflected in the following claims, the subject matter of the present invention lies in fewer than all the 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 a separately claimed subject matter.

[0077] Unless expressly stated otherwise, each numerical value and range should be interpreted as approximate as if the value or range were preceded by the word "about" or "approximately."

[0078] Although elements in the following method claims, if any, are recited in a specific order with corresponding labels, these elements are not necessarily intended to be implemented in that specific order unless the claim recitation otherwise implies a specific order for implementing some or all of these elements.

[0079] Unless otherwise specified herein, the use of ordinal adjectives "first," "second," "third," etc. to refer to one of multiple similar objects indicates merely that different instances of such similar objects are being referred to, and is not intended to imply that the similar objects so referred to are necessarily in a corresponding order or sequence in time, space, rank, or in any other manner.

[0080] Unless otherwise indicated herein, in addition to its ordinary meaning, the conjunction "if" may also or alternatively be interpreted to mean "when" or "upon" or "in response to determining" or "in response to detecting," the interpretation of which may depend on the specific context. For example, the phrase "if it is determined" or "if [the condition] is detected" may be interpreted to mean "upon determination" or "in response to determination" or "upon detection of [the condition or event]" or "in response to detection of [the condition or event]."

[0081] Also for the purposes of this specification, the terms "coupled" and "connected" refer to any means known in the art or later developed in which energy is allowed to be transferred between two or more elements, and the intervening one or more additional elements is contemplated, although not required. In contrast, the terms "directly coupled," "directly connected," and the like imply the absence of such additional elements.

[0082] The functions of the various elements shown in the drawings, including any functional blocks marked as "processors" and / or "controllers", can be provided by using dedicated hardware and hardware capable of executing software in association with appropriate software. When provided by a processor, the functions can be provided by a single dedicated processor, by a single shared processor, or by multiple separate processors (some of which can be shared). In addition, the explicit use of the terms "processor" or "controller" should not be interpreted as exclusively referring to hardware capable of executing software, but can implicitly include but are not limited to digital signal processor (DSP) hardware, network processors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), read-only memories (ROMs), random access memories (RAMs), and non-volatile storage devices for storing software. Other conventional and / or customized hardware may also be included. Similarly, any switches shown in the figures are conceptual only. Their functions can be performed by the operation of program logic, by dedicated logic, by the interaction of program control and dedicated logic, or even manually, as more specifically understood from the context, and specific techniques can be selected by the implementer.

[0083] As used in this application, the term "circuitry" may refer to one or more or all of the following: (a) a hardware circuit implementation only (such as an implementation only in analog and / or digital circuitry); (b) a combination of hardware circuitry and software, such as (if applicable): (i) a combination of analog and / or digital hardware circuitry and software / firmware, and (ii) a hardware processor with software (including a digital signal processor), any portion of software and memory, which work together to cause a device such as a mobile phone or server to perform various functions; and (c) a hardware circuit and / or processor, such as a microprocessor or a portion of a microprocessor, which requires software (e.g., firmware) to operate, but may not be present when software is not required for operation. This definition of circuitry applies to all uses of the term in this application, including in any claims. As another example, as used in this application, the term circuitry also covers an implementation of a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and / or firmware. The term "circuitry" also covers (for example and if applicable to a particular claim element) a baseband integrated circuit or a processor integrated circuit for a mobile device or a similar integrated circuit in a server, cellular network device, or other computing or networking device.

[0084] Those skilled in the art will appreciate that any block diagrams herein represent conceptual diagrams of illustrative circuitry embodying the principles of the present disclosure. Similarly, it will be appreciated that any flow charts, flowcharts, state transition diagrams, pseudo-code, etc. represent various processes that can be substantially represented in a computer-readable medium and thus executed by a computer or processor, whether or not such computer or processor is explicitly shown.

Claims

1. An automatic tool matching method for multiple measuring instruments, the method comprising: estimating, by a first controller, configuration parameter changes of the plurality of measurement instruments based on instrument drift data received from a plurality of second controllers, the estimated parameter changes being for tool matching of 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, with the first controller, a plurality of reports evaluating the estimated configuration parameter changes from the plurality of second controllers, each of the reports being generated, with the respective digital twin, based on a respective subset of the estimated configuration parameter changes for the plurality of measurement instruments; as well as With the first controller, when the plurality of reports indicate the effectiveness of the estimated configuration parameter changes for the tool fit of the plurality of measurement instruments at the future time, instructing the plurality of second controllers to implement the estimated configuration parameter changes.

2. The method of claim 1 , wherein the estimating comprises: predicting respective biases of the plurality of measurement instruments at the future time based on the instrument drift data; comparing the corresponding deviation with a threshold; as well as When at least one of the respective deviations exceeds the threshold, an estimated configuration parameter change is determined, the estimated configuration parameter change being predicted to produce an acceptable tool match state for the plurality of measurement instruments at the future time. 3 . The method of claim 2 , wherein the determining is performed using a neural network trained using previous drift and calibration data corresponding to the plurality of measurement instruments.

4. The method according to claim 1, further comprising: When the multiple reports indicate the invalidity of the estimated configuration parameter changes for the tool match of the multiple measuring instruments at the future time, the first controller is used to determine whether the multiple measuring instruments can be placed in an acceptable tool match state at the future time without maintenance service or full calibration.

5. The method according to claim 4, further comprising: When the determining results in a determination that the plurality of measurement instruments cannot be placed in the acceptable tool match state without the maintenance service or the fleet calibration, the plurality of measurement instruments are marked for the maintenance service or for the fleet calibration.

6. The method according to claim 4, further comprising: When the determining results in a determination that the plurality of measurement instruments can be placed in the acceptable tool match state without the maintenance or fleet calibration, a next iteration of the estimating is performed further based on the plurality of reports. The method of claim 1 , wherein each of the measuring instruments is an electron microscope instrument or a focused ion beam instrument.

8. A method according to claim 7, wherein the corresponding digital twin includes multiple model modules representing different corresponding physical components of the electron microscope instrument or the focused ion beam instrument, and the model modules are subjected to iterative parameter updates based on the comparison of measurement results and corresponding model simulations.

9. The method of claim 1, wherein the plurality of measuring instruments comprises five or more measuring instruments.

10. A tool matching system, comprising: a first controller; and a plurality of second controllers, each of the second controllers being configured to support a respective digital twin of a corresponding measuring instrument of a plurality of measuring instruments and further configured to control a configuration parameter of the corresponding measuring instrument of the measuring instruments, The first controller is configured as follows: estimating configuration parameter changes for the plurality of measurement instruments based on instrument drift data received from the plurality of second controllers, the estimated parameter changes being tool matched for the plurality of measurement instruments at a future time; receiving a plurality of reports evaluating the estimated configuration parameter changes from the plurality of second controllers, each of the reports being generated using the respective digital twin based on a respective subset of the estimated configuration parameter changes for the plurality of measurement instruments; as well as When the plurality of reports indicate the effectiveness of the estimated configuration parameter changes for the tool fit of the plurality of measurement instruments at the future time, the plurality of second controllers are instructed to implement the estimated configuration parameter changes.

11. The system of claim 10, wherein to estimate the configuration parameter change, the first controller is configured to: predicting respective biases of the plurality of measurement instruments at the future time based on the instrument drift data; comparing the corresponding deviation to a threshold; and When at least one of the respective deviations exceeds the threshold, an estimated configuration parameter change is determined, the estimated configuration parameter change being predicted to produce an acceptable tool match state for the plurality of measurement instruments at the future time.

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

13. A system according to claim 10, wherein when the plurality of reports indicate the invalidity of the estimated configuration parameter change for the tool matching of the plurality of measuring instruments at the future time, the first controller is configured to determine whether the plurality of measuring instruments can be placed in an acceptable tool matching state at the future time without maintenance service or fleet calibration.

14. The system of claim 13, wherein when it is determined that the plurality of measuring instruments cannot be placed in the acceptable tool match state without the maintenance service or the fleet calibration, the first controller is configured to mark the plurality of measuring instruments for the maintenance service or for the fleet calibration.

15. The system of claim 13, wherein upon determining that the plurality of measurement instruments can be placed in the acceptable tool fit state without the maintenance service or fleet calibration, the first controller is configured to generate a modified 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. A system according to claim 16, wherein the corresponding digital twin includes multiple model modules representing different corresponding physical components of the electron microscope instrument or the focused ion beam instrument, and the model modules are subjected to iterative parameter updates based on the comparison of measurement results and corresponding model simulations.

18. The system of claim 10, wherein the plurality of measurement instruments comprises five or more measurement instruments.

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

20. The system of claim 19, wherein the second controller is further configured to perform an action in response to instructions received from the first controller, the action selected from the group consisting of: sending additional instrument drift data to the first controller; performing a next iteration of evaluating the estimated configuration parameter changes using the corresponding digital twin; implementing a respective subset of the estimated configuration parameter changes using the corresponding measurement instruments of the plurality of measurement instruments; as well as The corresponding measuring instrument of the plurality of measuring instruments is configured for maintenance service or fleet calibration.

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