Determining substrate characteristics by virtual substrate measurements

Abstract

A method includes receiving feature data defining a plurality of features of a virtual substrate. The method further includes preparing a virtual substrate for display on a graphical user interface (GUI). The method further includes receiving one or more first inputs through the GUI. The one or more first inputs are associated with one or more locations of a dummy substrate. The method further includes defining a three-dimensional measurement probe based on the one or more first inputs. The method further includes outputting a measurement result of the measurement probe. The measurement result is associated with a characteristic of the dummy substrate measured by the three-dimensional measurement probe.

Description

Technical Field

[0001] The present disclosure relates to methods associated with determining substrate characteristics, such as semiconductor substrate characteristics. More particularly, the present disclosure relates to methods for measuring a virtual substrate to determine substrate characteristics. Background Art

[0002] Products can be produced by performing one or more manufacturing processes using manufacturing equipment. For example, semiconductor manufacturing equipment can be used to produce substrates through semiconductor manufacturing processes. A product having specific properties suitable for a target application will be produced. The properties of the input substrate to a processing operation have an impact on the output of that processing operation. Measurements can be used to predict the results of a processing operation. Summary of the Invention

[0003] The following is a brief overview of the present disclosure to provide a basic understanding of some aspects of the present disclosure. This overview is not an exhaustive review of the present disclosure. It is neither intended to identify the important or critical elements of the present disclosure, nor to delineate any scope of any specific embodiments of the present disclosure or any scope of the claims. Its sole purpose is to present some concepts of the present disclosure in a brief form as a prelude to the more detailed description presented later.

[0004] In one aspect of the present disclosure, a method includes receiving feature data defining a plurality of features of a virtual substrate. The method further includes preparing the virtual substrate for display on a graphical user interface (GUI). The method further includes receiving, via the GUI, one or more first inputs. The one or more first inputs are associated with one or more locations of the virtual substrate. The method further includes defining a three-dimensional measurement probe based on the one or more first inputs. The method further includes outputting measurement results of the measurement probe. The measurement results are associated with characteristics of the virtual substrate measured by the three-dimensional measurement probe.

[0005] In another aspect of the present disclosure, a system includes a memory and a processing device coupled to the memory. The processing device is configured to receive feature data defining a plurality of features of a virtual substrate. The processing device is further configured to prepare the virtual substrate for display on the GUI. The processing device further receives, via the GUI, one or more first inputs. The one or more first inputs are associated with one or more locations of the virtual substrate. The processing device further defines a three-dimensional measurement probe based on the one or more first inputs. The processing device is further configured to output measurement results of the measurement probe. The measurement results are associated with characteristics of the virtual substrate measured by the three-dimensional measurement probe.

[0006] In another aspect of the present disclosure, a non - transitory machine - readable storage medium stores instructions that, when executed, cause a processing device to perform operations. The operations include receiving feature data defining a plurality of features of a virtual substrate. The operations further include preparing the virtual substrate for display on a GUI. The operations further include receiving one or more first inputs via the GUI. The one or more first inputs are associated with one or more positions of the virtual substrate. The operations further include defining a three - dimensional measurement probe based on the one or more first inputs. The operations further include outputting measurement results of the measurement probe. The measurement results are associated with characteristics of the virtual substrate measured by the three - dimensional measurement probe. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The present disclosure is illustrated by way of example and not limitation in the accompanying drawings.

[0008] Figure 1 is a block diagram illustrating an exemplary system architecture in accordance with some embodiments of the present disclosure.

[0009] Figure 2A is a flowchart of a method for extracting virtual substrate feature data in accordance with some embodiments of the present disclosure.

[0010] Figure 2B is a flowchart of a method for measuring characteristics of a virtual substrate in accordance with some embodiments of the present disclosure.

[0011] Figures 3A to 3C depicts an example graphical user interface for measuring characteristics of a virtual substrate in accordance with some embodiments of the present disclosure.

[0012] Figure 4A is a flowchart of a method for measuring characteristics of a virtual substrate in accordance with some embodiments of the present disclosure.

[0013] Figure 4B is a flowchart of a method for changing a probing probe for measuring a virtual substrate in accordance with some embodiments of the present disclosure.

[0014] Figure 5 depicts an example substrate including features in accordance with some embodiments of the present disclosure.

[0015] Figure 6 is a block diagram showing a computer system in accordance with some embodiments of the present disclosure. DETAILED DESCRIPTION

[0016] This document describes techniques for determining substrate characteristics through virtual substrate measurements. A manufacturing apparatus is used to produce products such as substrates (e.g., wafers, semiconductors). The manufacturing apparatus may include a manufacturing or processing chamber to isolate the substrate from the external environment. The properties of the produced substrate are desired to meet target values to facilitate specific functions. Manufacturing parameters are selected to produce a substrate that meets the target property values. Many manufacturing parameters (e.g., hardware parameters, processing parameters, etc.) contribute to the properties of the processed substrate. The manufacturing system can control the parameters by specifying a setpoint for the property value, receiving data from sensors set in the manufacturing chamber, and adjusting the manufacturing apparatus until the sensor readings match the setpoint.

[0017] A processing procedure (e.g., a method for manufacturing a substrate) may include many processing operations (e.g., processing steps). For example, a semiconductor wafer can be manufactured by operations such as adding materials to the substrate in one or more deposition operations, removing materials from the substrate in one or more etching operations, changing the properties of the substrate in one or more annealing operations, etc. The output of a processing operation depends on the input product on which the operation is performed. For example, the result of a deposition operation depends on the properties of the substrate on which the material is deposited.

[0018] Predicting the results of processing operations can be valuable. The results can be predicted through modeling, e.g., using one or more physics-based models to predict the results of processing operations. Physics-based models can include deposition models, etching models, and / or models for any type of processing performed on a substrate. The model can be provided with data indicating the input substrate and produce a prediction of the output substrate after performing the modeled processing operation. The substrate processing model includes simulation models that receive simulation parameters as inputs (such as etching or deposition rates, changes in etching or deposition rates over time, etc.). The substrate processing model can include models that receive processing parameters as inputs (such as gas flow rates, temperature, radio frequency, etc.). These substrate processing models can output a digital twin of the processed substrate. The digital twin or virtual substrate can have digital characteristics (e.g., such as critical dimensions) and / or substantially represent the characteristics of the substrate processed by a substrate processing system (e.g., in one or more substrate processing chambers).

[0019] It can be advantageous to measure the characteristics and / or properties of the virtual substrate to better predict the results of processing operations. Many conventional software-based systems do not provide the function to do so. For example, conventional systems may not be able to perform three-dimensional measurements.

[0020] The systems and methods of the present disclosure can address one or more of these deficiencies of the conventional methods. In some embodiments, feature data defining a plurality of features of a virtual substrate is received. The virtual substrate can be a digital twin of a substrate to be processed by a substrate processing system as described herein according to a processing recipe. The feature data may have been generated by digitally processing the virtual substrate using processing parameters as described above herein. In some embodiments, the processed virtual substrate is then prepared for display on a GUI. In some embodiments, a geometric modeling core (e.g., a CAD core) is used to generate a graphical representation of the virtual substrate on the GUI.

[0021] The GUI can include a plurality of user interaction features. For example, the GUI can include features configured to allow a user to measure features (e.g., critical dimensions) of the virtual substrate displayed on the GUI. Additionally, the GUI can include features configured to allow a user to manipulate the view of the virtual substrate displayed on the GUI. One or more inputs can be received via the GUI (e.g., from a user) to define one or more virtual probes (e.g., 3D virtual probes). In some embodiments, the input can be an input defining 3D virtual probes that cause measurements to be made to measure one or more features of the virtual substrate. Each of the inputs can be associated with a location on the virtual substrate. For example, a user can click on a location on the virtual substrate in the GUI. In some embodiments, the input is made in one or more views of the virtual substrate displayed on the GUI. For example, a user can click on a first location of the virtual substrate in a first view (e.g., a side view of the virtual substrate) on the GUI and can click on a second location of the virtual substrate in another view (e.g., a top view of the virtual substrate) on the GUI. The first and second selected locations can be 2D locations (e.g., 2D coordinates) and / or 3D locations (e.g., 3D coordinates). In some embodiments, the input can be made in a three-dimensional perspective view of the virtual substrate and / or a cross-sectional view of the virtual substrate.

[0022] In some embodiments, a three-dimensional measurement probe is defined based on the input. The measurement probe can be used to measure one or more features of the virtual substrate. For example, the measurement probe can measure the distance between two or more locations (e.g., along a line) on the virtual substrate. In another example, the measurement probe can measure the distance between two or more surfaces of the virtual substrate. In some embodiments, the three-dimensional measurement probe can be defined in three dimensions. One or more parameters of the measurement probe can be output for display on the GUI. For example, the coordinates of the endpoints of the measurement probe (e.g., x coordinate, y coordinate, z coordinate) can be displayed in a table on the GUI. In some embodiments, the parameters can be changed based on the input (e.g., by the user via the GUI).

[0023] In some embodiments, the measurement results of a three-dimensional measurement probe are output. The measurement results can be output for display on a GUI. The measurement results can be associated with the characteristics of a virtual substrate. The characteristics can be determined by the measurement results of the measurement probe. For example, the measurement can reflect the distance (e.g., critical dimension) between two surfaces (e.g., virtual surfaces) of the virtual substrate. In another example, the measurement results can reflect the depth of a virtual entity of the virtual substrate. Boolean operations (such as union or intersection) between such probes and the virtual substrate can also be used to extract other metrology data; including but not limited to, the critical dimensions of deposition or cutting processes or deformations (such as bending deformations, etc.) that can occur during processing. In some embodiments, the characteristics can be used to determine adjustments to a processing recipe based on the measurement results.

[0024] Aspects of the present disclosure provide technical advantages over conventional systems and methods. By providing features to define a three-dimensional measurement probe of a virtual substrate, features of the virtual substrate that cannot be measured in two dimensions can be measured. Additionally, aspects of the present disclosure can allow for faster measurement of the virtual substrate, thus saving time and cost. Furthermore, according to aspects of the present disclosure, when compared to conventional systems for measurement, the virtual substrate features can be measured more clearly and accurately. More accurate measurement of the virtual substrate can lead to better predictions regarding the processed substrate, resulting in a higher quality processed substrate that more closely meets the target specifications. More accurate measurement can also lead to improved processing recipes for processing the substrate. Improving a processing recipe or the like in view of virtual substrate measurements has the advantage of reducing the time, energy, chamber wear, chamber maintenance, chamber maintenance time, replacement parts, materials, and / or disposal costs associated with producing products that do not meet the target performance standards.

[0025] Figure 1 is a block diagram showing an exemplary system 100 (exemplary system architecture) according to some embodiments. System 100 includes a client device 120, a manufacturing device 124, a sensor 126, a metrology device 128, a geometric modeling core 132, and a data storage 140.

[0026] The sensor 126 can provide sensor data 142 associated with the manufacturing equipment 124 (e.g., associated with the production of corresponding products (such as substrates) by the manufacturing equipment 124). The sensor data 142 can be used to determine equipment health and / or product health (e.g., product quality). The manufacturing equipment 124 can produce products according to a recipe or execute a run over a period of time. In some embodiments, the sensor data 142 can include the values of one or more of the following: optical sensor data, spectral data, temperature (e.g., heater temperature), spacing (SP), pressure, high frequency radio frequency (HFRF), radio frequency (RF) matching voltage, RF matching current, RF matching capacitor position, voltage of an electrostatic chuck (ESC), actuator position, current, flow rate, power, voltage, etc. The sensor data (e.g., a portion of the sensor data 142) can be associated with the product currently being processed, recently processed products, the quantity of recently processed products, etc. The sensor data can include data stored in association with previously produced products. The sensor data 142 can include attribute data, markers of the state of the manufacturing equipment, etc. Examples of attribute data include markers of the manufacturing equipment ID or design, sensor ID, type, and / or location. Examples of markers of the state of the manufacturing equipment include current faults, service life, and so on.

[0027] Sensor data 142 may be associated with, regarding, and / or indicative of manufacturing parameters, such as hardware parameters of manufacturing equipment 124 or processing parameters of manufacturing equipment 124. Examples of hardware parameters include hardware settings or installed components, such as the size, type, etc. of installed components. Examples of processing parameters include heater settings, gas flow settings, pressure settings, and the like. Data associated with some hardware parameters and / or processing parameters may alternatively or additionally be stored as manufacturing parameters 150. Manufacturing parameters 150 may include historical manufacturing parameters (e.g., associated with historical processing runs) and current manufacturing parameters. Manufacturing parameters 150 may indicate input settings of the manufacturing apparatus (e.g., heater power, gas flow, etc.). Sensor data 142 and / or manufacturing parameters 150 may be provided when manufacturing equipment 124 performs a manufacturing process (e.g., equipment readings when processing a product). Sensor data 142 may vary for each product (e.g., each substrate). The substrate may have property values measured by metrology equipment 128. Examples of property values include film thickness, film strain, critical dimension, optical properties, electrical properties, etc. Property values may be measured at a stand-alone metrology facility, by an integrated or in-line metrology system, or the like. Metrology data 160 may be a component of data store 140. Metrology data 160 may include historical metrology data (e.g., metrology data associated with previously processed products).

[0028] In some embodiments, metrology data 160 may be provided without using a stand-alone metrology facility. For example, metrology data 160 may be in-situ metrology data (e.g., metrology or metrology proxy collected during processing), integrated metrology data (e.g., metrology or metrology proxy collected when the product is in a chamber or under vacuum but not during a processing operation), on-line metrology data (e.g., data collected after removing the substrate from vacuum), etc. Metrology data 160 may include current metrology data (e.g., metrology data associated with the current or most recent processed product). In some embodiments, metrology data 160 corresponds to historical property data of the product and prediction data 168 is associated with predicted property data. Historical property data of the product may include data of products processed using manufacturing parameters associated with historical sensor data and historical manufacturing parameters.

[0029] Metrology equipment 128 may include microscopy and / or imaging equipment. Metrology equipment 128 may include one or more devices for obtaining images of a substrate, a portion of the substrate, a feature of the substrate, or the like. Metrology equipment 128 may include SEM equipment, XSEM equipment, TEM equipment, and / or other forms of imaging and microscopy equipment. Metrology data 160 may include image data, microscopy data, and the like.

[0030] In some embodiments, sensor data 142, metrology data 160, or manufacturing parameters 150 may be processed (e.g., by client device 120). Processing sensor data 142 may include generating features (e.g., virtual features). In some embodiments, a feature is a pattern in sensor data 142, metrology data 160, and / or manufacturing parameters 150. Examples of such features include slope, width, height, peak, etc. In some embodiments, a feature is a combination of values from sensor data 142, metrology data, and / or manufacturing parameters. Examples of such features include power derived from voltage and current, etc.

[0031] Each instance (e.g., set) of sensor data 142 may correspond to a product (e.g., a substrate), a set of manufacturing equipment, a type of substrate produced by the manufacturing equipment, or the like. Each instance of metrology data 160 and manufacturing parameters 150 may similarly correspond to a product, a set of manufacturing equipment, a type of substrate produced by the manufacturing equipment, or the like. The data store may further store information associated with sets of different data types, e.g., information indicating that a set of sensor data, a set of metrology data, and a set of manufacturing parameters are all associated with the same product, manufacturing equipment, substrate type, etc.

[0032] Data store 140 may further include virtual substrate data 162. Virtual substrate data 162 may include data on simulations, syntheses, and / or virtual substrates. Virtual substrate data 162 may include measurements of features, parameters of features, images of features, etc. Various characteristics and representations of features may be stored as feature data 164. Virtual substrate data 162 may include measurements, parameters, and / or images of simulated substrates. Characteristics and representations of simulations and / or virtual substrates may be stored as substrate data 166. Substrate data 166 may include 2D and / or 3D feature arrays.

[0033] Client device 120, manufacturing equipment 124, sensors 126, metrology equipment 128, geometric modeling core 132, and data store 140 may be coupled to each other via network 130 for determining substrate characteristics through virtual substrate measurements. In some embodiments, network 130 may provide access to cloud-based services. Operations performed by client device 120, data store 140, etc. may be performed by virtual cloud-based devices.

[0034] In some embodiments, network 130 is a public network that provides client device 120 with access to prediction server 112, data store 140, and other publicly available computing devices. In some embodiments, network 130 is a private network that provides client device 120 with access to manufacturing equipment 124, sensors 126, metrology equipment 128, data store 140, and other privately available computing devices. Network 130 may include one or more wide area networks (WANs), local area networks (LANs), wired networks (e.g., Ethernet networks), wireless networks (e.g., 802.11 networks or Wi-Fi networks), cellular networks (e.g., Long Term Evolution (LTE) networks), routers, hubs, switches, server computers, cloud computing networks, and / or combinations thereof.

[0035] Client device 120 may include computing devices such as personal computers (PCs), laptop computers, mobile phones, smartphones, tablet computers, netbook computers, network-connected televisions (“smart TVs”), network-connected media players (e.g., Blu-ray players), set-top boxes, over-the-top (OTT) streaming devices, operator boxes, etc. Client device 120 may include substrate measurement component 122. Substrate measurement component 122 may receive user input indicative of a position on a virtual substrate displayed on GUI 123 (e.g., via graphical user interface (GUI) 123, which is displayed via client device 120). In some embodiments, substrate measurement component 122 determines measurement results associated with a three-dimensional measurement probe defined on a graphical representation of the virtual substrate based on user input (e.g., input via GUI 123).

[0036] In some embodiments, the graphical representation of the virtual substrate is presented on GUI 123 by geometric modeling core 132. Geometric modeling core 132 may be a computer-aided design package's solid modeling software component. In some embodiments, geometric modeling core 132 may access client device 120 via network 130. However, in some embodiments, geometric modeling core 132 may be included on client device 120. Geometric modeling core 132 may load a plurality of features of the virtual substrate (e.g., contained in virtual substrate data 162) and prepare the virtual substrate for display (e.g., on GUI 123). In some embodiments, the functions of client device 120 and geometric modeling core 132 may be performed by a cloud-based service.

[0037] In some embodiments, the user input (e.g., via GUI 123) includes an indication of one or more points on a graphical representation of a virtual substrate. For example, the user may "click" on one or more points (e.g., locations) on the virtual substrate via GUI 123. A line measurement probe may be defined as a line between two or more points indicated by the user. Similarly, a point measurement probe or a spot measurement probe may be defined by a single point indicated by the user. Each point may have coordinates (e.g., an x coordinate, a y coordinate, and / or a z coordinate) embodied in one or more parameters of the measurement probe. These parameters may be stored as measurement probe parameters 144 in data store 140. In some embodiments, the measurement probe parameters 144 may be displayed in a table via GUI 123. In some embodiments, the measurement probe parameters 144 may be changed (e.g., altered, adjusted, etc.) via GUI 123. The substrate measurement component 122 may output measurement data 146 based on the measurement probe. The measurement data 146 may contain one or more measurement results determined by the substrate measurement component. In some embodiments, the measurement data 146 may be stored in data store 140 for later access (e.g., via client device 120). The measurement results associated with the measurement probe may be output by the substrate measurement component 122 based on the measurement probe parameters 144. In some embodiments, a corrective action may be performed based on the measurement results (e.g., via client device 120). The corrective action may be an adjustment to a processing recipe executed by manufacturing device 124. For example, the corrective action may include updating (e.g., changing, adjusting, etc.) one or more manufacturing parameters 150. Updating the manufacturing parameters may include setting optimal manufacturing parameters for producing a product. System 100 may have the technical advantage of utilizing more favorable manufacturing parameters. The manufacturing parameters may include hardware parameters, processing parameters, input substrate properties, etc. System 100 may avoid the high-cost results of utilizing suboptimal manufacturing parameters.

[0038] Performing a manufacturing process that results in component failures of manufacturing equipment 124 can be costly in terms of downtime, product damage, equipment damage, expedited ordering of replacement parts, and the like. The systems and / or methods of the present disclosure can mitigate one or more such deficiencies. By inputting a virtual substrate based on measured characteristics into a model, receiving an output, and performing a corrective action, system 100 can have technical advantages over conventional systems. The virtual substrate can be based on metrology data 160. The virtual substrate can be generated based on one or more microscopy images. The corrective action can include predicted operational maintenance. The corrective action can include replacement, processing, cleaning, etc. of components. System 100 can have the technical advantage of avoiding the costs of unexpected component failures. System 100 can have the advantage of avoiding the costs of unscheduled downtime. System 100 can have the advantage of avoiding the costs of lost productivity due to equipment downtime. System 100 can have the advantage of avoiding the costs of product scrap. By utilizing the systems and / or methods of the present disclosure, system 100 can avoid further costs beyond these. The differences between the predicted and measured characteristics of the substrate can include indications of drift, aging, or equipment failure. Monitoring the performance of components (e.g., manufacturing equipment 124, sensors 126, metrology equipment 128, and the like) over time can provide indications of deteriorating components.

[0039] The corrective action can be associated with one or more types of process control. Process control can include Computational Process Control (CPC), Statistical Process Control (SPC), Advanced Process Control (APC), model-based process control, and the like. SPC can include controlling electronic components to determine process progress. SPC can include predicting the useful life of components. SPC can include comparing data with historical data, such as comparing trace data with historical data to determine if the trace data is within a 3-Σ window of the average value. The corrective action can relate to preventive operational maintenance, design optimization, updating manufacturing parameters, updating manufacturing recipes, feedback control, machine learning modifications, or the like.

[0040] In some embodiments, the corrective action includes providing a warning to the user. The warning may include an alert to stop or not perform the manufacturing process. A warning may be provided if the measurement data 146 indicates an anomaly. A warning may be provided if the measurement data 146 indicates an abnormal product, component, equipment, etc. In some embodiments, the execution of the corrective action includes causing an update to one or more manufacturing parameters 150. In some embodiments, performing the corrective action may include retraining a machine learning model associated with the manufacturing equipment 124. Performing the corrective action may include updating other types of models associated with the manufacturing equipment 124, such as tuning a physics-based model, a process model, a virtual model, or the like. In some embodiments, performing the corrective action may include training a new machine learning model and / or developing a new physics- or process-based model associated with the manufacturing equipment 124.

[0041] The manufacturing parameters 150 may include hardware parameters and / or process parameters. The hardware parameters may include information indicating which components are installed in the manufacturing system, an indication of the component age, an indication of the software version or update, etc. The process parameters may include temperature, pressure, gas flow rate, current, voltage, lift speed, etc. In some embodiments, the corrective action includes causing preventive operational maintenance. Preventive operational maintenance may include replacing, treating, cleaning components of the manufacturing system, etc. In some embodiments, the corrective action includes causing design optimization. Design optimization may include updating manufacturing parameters, updating the manufacturing process, and / or updating the manufacturing equipment to improve the performance of the manufacturing system. In some embodiments, the corrective action includes updating a recipe. Changing the recipe may include changing the timing of the manufacturing subsystem entering an idle or active mode, changing the setpoints of various property values, or the like.

[0042] The data storage 140 may be a memory (e.g., random access memory), a drive (e.g., hard disk, flash drive), a database system, a cloud-accessible memory system, or another type of component or device capable of storing data. The data storage 140 may include multiple storage components (e.g., multiple drives or multiple databases) that may span multiple computing devices (e.g., multiple server computers). The data storage 140 may store sensor data 142, manufacturing parameters 150, metrology data 160, virtual substrate data 162, measurement probe parameters 144, and / or measurement data 146.

[0043] Sensor data 142 may include historical sensor data and current sensor data. Sensor data may include a sensor data time trace over the duration of a manufacturing process, an association of the data with physical sensors, pre-processed data (such as averages and composite data), and data indicative of sensor performance over time (i.e., for many manufacturing processes). Manufacturing parameters 150 and metrology data 160 may contain similar characteristics. For example, metrology data 160 may include historical metrology data and current metrology data. Historical sensor data, historical metrology data, and historical manufacturing parameters may be historical data. Virtual substrate data 162 may include data related to generating virtual, synthetic, and / or digital substrates for providing to a process model to generate a process model output. Virtual substrate data 162 may include data indicative of features, feature characteristics, feature parameterizations, substrates, substrates including arrays of features, and the like.

[0044] Generating and utilizing virtual substrate data 162 has significant technical advantages over other methods. Developing an understanding of the relationship between process operation inputs and process operation results can improve process design, product design, operation design, process operation results, and the like. Improving process operation results can reduce the cost of the process in terms of: the proportion of defective products produced; the proportion of materials, time, energy, etc. dedicated to producing defective products; product performance; and the like. By comparing predicted results of a process operation with measured results, defects in models, process equipment components, process recipes, or the like can be discovered, diagnosed, and corrected. Accurately correcting defects can improve the performance of a manufacturing system, improve the predictive ability of one or more models, reduce unplanned maintenance events, and the like.

[0045] In some embodiments, a "user" may represent a single individual. However, other embodiments of the present disclosure cover a "user" being an entity controlled by multiple users and / or automated sources. For example, a collection of independent users united as a group of administrators may be considered a "user".

[0046] Some embodiments combine the use of virtual substrates with a GUI, which enables defining point and / or line probes to quickly and simply measure features such as critical dimensions of a virtual substrate. The GUI may include multiple views, such as a top view, a cross-sectional side view, an isometric view, a cross-sectional isometric view, and the like. In any view, points for line and / or point probes may be defined based on user interaction with the view of the virtual substrate. The line and / or point probes may then generate measurement results of the features of the virtual substrate and output the measurement results through the GUI.

[0047] Figure 2Ais a flowchart of a method 200A for extracting virtual substrate feature data according to some embodiments of the present disclosure. Method 200A may be executed by processing logic, which may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, processing devices, etc.), software (such as instructions running on a processing device, a general-purpose computer system, or a dedicated machine), firmware, microcode, or a combination thereof. In some embodiments, method 200A may be at least partially executed by Figure 1 the substrate measurement component 122. In some embodiments, a non-transitory machine-readable storage medium stores instructions that, when executed by a processing device (e.g., of the substrate measurement component 122), cause the processing device to execute method 200A.

[0048] For simplicity of explanation, method 200A is depicted and described as a series of operations. However, the operations according to the present disclosure may occur in various orders and / or simultaneously, and occur together with other operations not presented and described herein. In addition, not all of the illustrated operations may be executed to implement method 200A according to the disclosed objectives. In addition, those skilled in the art will understand and recognize that method 200A may alternatively be represented as a series of interrelated states by a state diagram or events.

[0049] In block 202, a substrate digital twin (e.g., a virtual substrate) is provided. In some embodiments, the substrate digital twin is generated by the methods described above herein. The substrate digital twin may correspond to a substrate prepared according to substrate specifications (e.g., a substrate recipe). The substrate digital twin may include a plurality of virtual features. The substrate digital twin may be referred to herein as a "virtual substrate".

[0050] In block 204, the virtual substrate undergoes virtual processing. In some embodiments, the virtual processing simulates processing a substrate by a substrate processing system. For example, the virtual substrate may be virtually processed using a virtual processing device to replicate processing a substrate by a processing device. The virtual substrate may undergo virtual etching, virtual deposition, etc. Models (e.g., physics-based models, statistical models, machine learning models, etc.) may be used to perform the virtual processing.

[0051] In block 206, the processed virtual substrate is output. The processed virtual substrate may include a plurality of virtual features. The features may have been virtually formed during the virtual processing. For example, the virtual features may include virtual pillars, virtual terraces, virtual valleys, etc. on the virtual substrate. The processed virtual substrate may thus represent a processed substrate with corresponding real features. In some embodiments, the processed virtual substrate is a digital twin of a substrate processed by a processing device (e.g., a processing chamber, etc.) of a substrate processing system according to a recipe.

[0052] In block 208, feature data (e.g., Figure 1The characteristic data 164) is extracted based on a virtual substrate. In some embodiments, the characteristic data is a plurality of characteristics of the virtual substrate. The characteristic data may be extracted such that a graphical representation of the virtual substrate can be constructed based on the characteristic data (e.g., by Figure 1 the geometric modeling core 132).

[0053] Figure 2B is a flowchart of a method 200B for measuring characteristics of a virtual substrate according to some embodiments of the present disclosure. The method 200B may be executed by processing logic, which may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, a processing device, etc.), software (such as instructions running on a processing device, a general-purpose computer system, or a dedicated machine), firmware, microcode, or a combination thereof. In some embodiments, the method 200B may be at least partially executed by Figure 1 the substrate measurement component 122. In some embodiments, a non-transitory machine-readable storage medium stores instructions that, when executed by a processing device (e.g., of the substrate measurement component 122), cause the processing device to execute the method 200B.

[0054] For simplicity of explanation, the method 200B is depicted and described as a series of operations. However, the operations according to the present disclosure may occur in various orders and / or simultaneously, and may occur together with other operations not presented and described herein. Moreover, implementing the method 200B for the disclosed objectives does not require performing all of the illustrated operations. Further, those skilled in the art will understand and recognize that the method 200B may alternatively be represented as a series of interrelated states by a state diagram or events.

[0055] At block 212, a data set containing a set of virtual substrate characteristics is received. In some embodiments, the data is extracted from the virtual substrate at block 208 of the method 200A. In some embodiments, the virtual substrate characteristic data includes data associated with a plurality of virtual substrate characteristics. The characteristics may include pads, valleys, pillars, trenches, holes, etc. on the virtual substrate.

[0056] At block 214, the geometric modeling core (e.g., Figure 1 the geometric modeling core 132) prepares a graphical representation of the virtual substrate for use in the GUI (e.g., Figure 1presented on the GUI 123). In some embodiments, the geometric modeling core may prepare multiple views of the graphical representation of the virtual substrate for display on the GUI. For example, the geometric modeling core may prepare a top view of the virtual substrate, a side view of the virtual substrate, a three-dimensional perspective view of the virtual substrate, and / or a cross-sectional view of the virtual substrate. In some embodiments, the geometric modeling core includes features that, through the GUI, allow the user to manipulate the representation of the virtual substrate. In some instances, the user may be able to select which view of the virtual substrate (i.e., the graphical representation of the virtual substrate) is displayed on the GUI. In further instances, the user may be able to zoom in on the view of the virtual substrate on the GUI (e.g., magnify and / or reduce). The user may additionally be able to manipulate the view of the virtual substrate in the GUI. For example, the user may pan, zoom, rotate the isometric view of the virtual substrate, etc.

[0057] In block 216, a measurement probe is defined to measure a feature of the virtual substrate. In some embodiments, the measurement probe is defined through the GUI (e.g., by the user). For example, one or more inputs may be made through the GUI to define the measurement probe. The input may be an indication or selection associated with the position of the virtual substrate / position on the virtual substrate (e.g., a click and / or gesture in the GUI). Each position may include an x coordinate, a y coordinate, and / or a z coordinate. In some instances, the input defines the endpoints of a line probe (e.g., a line measurement probe). In some instances, the input defines one or more point probes (e.g., one or more point measurement probes). A first input may define the first endpoint of the measurement probe and a second input may define the second endpoint of the measurement probe. In some embodiments, the measurement probe is a three-dimensional measurement probe (e.g., a measurement probe in an XYZ coordinate system, etc.). In some embodiments, the probe is a point probe (e.g., measuring the depth at a given x, y coordinate).

[0058] A measurement probe can be defined in one or more views of a virtual substrate displayed via a GUI based on user interaction with the GUI (e.g., based on clicks at one or more points on the virtual substrate). The measurement probe can also be defined based on input of x, y, and / or z values (e.g., by a user typing coordinates). In some embodiments, a first endpoint of the measurement probe can be defined in a first view of the virtual substrate, and a second endpoint of the measurement probe can be defined in a second view of the virtual substrate different from the first view. For example, a user can define (e.g., via the GUI) a first endpoint of a line measurement probe in a side view of the virtual substrate and can define a second endpoint of the line measurement probe in a top view of the virtual substrate. In another example, a user can define a first endpoint of a line measurement probe in a 3D perspective view of the virtual substrate and can define a second endpoint of the line measurement probe in a cross-sectional view of the virtual substrate. In some embodiments, the parameters of the measurement probe are output for display in a table on the GUI. In some embodiments, the parameters can include the coordinates of the measurement probe endpoints.

[0059] In some embodiments, the measurement probe is associated with a measurement result of a characteristic of the virtual substrate. The measurement probe can define the bounds of the measurement. For example, the measurement can be made along the measurement probe between the endpoints of the measurement probe. In some embodiments, (e.g., Figure 1 of the substrate measurement component 122) the processing logic measures the distance between the endpoints of the measurement probe. For example, the distance between two surfaces intersected by a line measurement probe can be measured.

[0060] In some embodiments, a point measurement probe can be defined by an indication on the surface of the virtual substrate displayed in the GUI. The point measurement probe can provide a measurement result along a path orthogonal to the indicated surface from the indicated surface to the next surface of the virtual substrate. The point measurement probe can also sample the virtual substrate at the indicated point.

[0061] In block 218, an array of measured substrate characteristics is output. The array of measured characteristics can include the measurement results of the measurement probes defined in block 216. In some embodiments, the array of measured characteristics can be used to evaluate the virtual processing of the virtual substrate and, in turn, can be used to predict the actual processing of an actual substrate by a substrate processing device (e.g., a substrate processing chamber, etc.).

[0062] Figures 3A to 3C An example graphical user interface (GUI) 300 for measuring characteristics of a virtual substrate in accordance with some embodiments of the present disclosure is depicted. The GUI 300 can correspond to Figure 1 the GUI 123. In some embodiments, the GUI 300 is prepared for use by a client device (e.g., Figure 1displayed by the client device 120). The GUI 300 can be displayed on a monitor (e.g., a monitor screen, a computer screen, a tablet screen, etc.) coupled to the client device.

[0063] In some embodiments, the GUI 300 includes a virtual substrate view 302 for displaying a graphical representation of a virtual substrate. A geometric modeling core (e.g., Figure 1 the geometric modeling core 132) can compile virtual substrate data (e.g., Figure 2A extracted feature data including the virtual substrate (e.g., extracted in Figure 1 the box 208) of the virtual substrate data 162) into a graphical representation. The representation of the virtual substrate can illustrate a plurality of virtual substrate features 310. The virtual substrate features 310 can be (e.g., in Figure 2A the box 204) features of the virtual substrate formed by virtual processing. In some embodiments, the virtual substrate features 310 can include holes, pillars, valleys, terraces, channels, trenches, etc.

[0064] See Figure 3A , which illustrates a three-dimensional perspective view of the virtual substrate. The perspective view can be at least a partial cross-sectional view. See Figure 3B , which illustrates a top view of the virtual substrate. See Figure 3C , which illustrates a cross-sectional side view of the virtual substrate. The GUI 300 can be configured to allow a user to select a top view, a side view, a three-dimensional perspective view, or a cross-sectional view of the virtual substrate for display in the virtual substrate view 302. In some embodiments, the view of the virtual substrate can be manipulated by one or more features of the GUI 300. For example, a user can rotate the virtual substrate in the displayed view and / or use the interactive controls of the GUI 300 to zoom in or out of the displayed view. In some embodiments, measurement probes can be defined in the virtual substrate view 302. The measurement probes can be a line probe 314 (e.g., a line measurement probe) or a point probe 312 (e.g., a point measurement probe).

[0065] In some embodiments, the line probe 314 is defined by two end points. A user can select the first end point by making a first indication (e.g., a click indication such as using a computer mouse) at a first position of the virtual substrate displayed in the virtual substrate view 302. A user can select the second end point by making a second indication at a second position of the virtual substrate displayed in the virtual substrate view 302. In some embodiments, the line probe 314 is formed between the first end point and the second end point. The line probe 314 can be used to measure the features of the virtual substrate with which the line probe 314 intersects. For example, the line probe 314 can measure the distance between two surfaces (e.g., of the virtual substrate feature 310) along the line probe 314.

[0066] In some embodiments, the line probe 314 can be defined by interacting with one or more views of the virtual substrate displayed in the virtual substrate view 302. For example, the first endpoint of the line probe 314 can be selected in the perspective view of the virtual substrate (e.g., as shown in Figure 3A ), and the second endpoint of the line probe can be selected in the top view (e.g., as shown in Figure 3B ). In some embodiments, the GUI 300 is configured to allow the user to switch from one view of the virtual substrate to another to define the line probe 314.

[0067] In some embodiments, the point probe 312 is defined by a single point. The user can select the point by indicating a location on the virtual substrate displayed in the virtual substrate view 302 (e.g., such as a click indication using a computer mouse). The point probe 312 can measure a feature of the virtual substrate corresponding to the point probe 312. For example, the point probe 312 can measure the distance along a path orthogonal to the first surface from the first surface corresponding to the indicated location to the next second surface of the virtual substrate.

[0068] In some embodiments, the parameters associated with each of the line probe 314 and / or the point probe 312 are output to the measurement probe table 306 displayed on the GUI 300. The measurement probe table 306 can display the parameters of the measurement probes. In some embodiments, the measurement probe table 306 displays the coordinates of the endpoints of one or more line probes 314. For example, the measurement probe table 306 can display the x coordinate, y coordinate, and z coordinate (e.g., x1, y1, z1) corresponding to the first endpoint of the line probe 314. The measurement probe table 306 can additionally display the x coordinate, y coordinate, and z coordinate (e.g., x2, y2, z2) corresponding to the second endpoint of the line probe 314.

[0069] The measurement probe table 306 can identify each of the measurement probes with identifiers (e.g., MP1, MP2, MP3, etc.). In some embodiments, the GUI 300 is configured to allow the user to change the parameters of the measurement probes displayed in the measurement probe table 306. In some instances, the user can make one or more inputs (e.g., such as clicking and editing via a computer mouse and keyboard) to change the endpoint coordinates of the line probe 314. In some instances, the user can change one or more line probe endpoint coordinates by selecting the measurement probe from the measurement probe table 306 (e.g., indicated by the identifier as described above) and typing new values for the endpoint coordinates. In some instances, the user can change the x coordinate, y coordinate, and / or z coordinate of the endpoint of the measurement probe. In some instances, the user can change one or more coordinates of the line probe 314 and / or one or more coordinates of the point probe 312.

[0070] In some embodiments, the GUI 300 includes a window that displays measurement probe settings 304. In some embodiments, the measurement probe settings 304 are user-interactive. For example, through the window of the measurement probe settings 304, a user can input one or more settings for managing the measurement probe. Such settings may include the spatial properties of one or more measurement probes or other qualifiers that determine what the measurement probe is to measure (e.g., the line width of the silicon phase of a virtual substrate while ignoring any silicon oxide of the virtual substrate, etc.). In some embodiments, one or more measurement probe settings 304 may determine how the measurement probe measures the characteristics of the virtual substrate. In some embodiments, the measurement results of the measurement probe are displayed in the measurement probe settings 304. For example, the measurement results corresponding to the line probe 314 (e.g., such as the distance between two surfaces along the line probe) may be displayed in the window of the GUI 300 corresponding to the measurement probe settings 304. In some embodiments, one or more measurement results of the measurement probe are output to determine adjustments to a recipe for processing a substrate (e.g., a substrate processing recipe, a virtual substrate processing recipe).

[0071] Figure 4A is a flowchart of a method 400A for measuring characteristics of a virtual substrate according to some embodiments of the present disclosure. The method 400A may be executed by processing logic, which may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, processing devices, etc.), software (such as instructions running on a processing device, a general-purpose computer system, or a dedicated machine), firmware, microcode, or a combination thereof. In some embodiments, the method 400A may be at least partially executed by Figure 1 the substrate measurement component 122. In some embodiments, a non-transitory machine-readable storage medium stores instructions that, when executed by a processing device (e.g., of the substrate measurement component 122), cause the processing device to execute the method 400A.

[0072] For simplicity of explanation, the method 400A is depicted and described as a series of operations. However, the operations according to the present disclosure may occur in various orders and / or simultaneously, and occur together with other operations not presented and described herein. In addition, it is not necessary to perform all the illustrated operations to implement the method 400A according to the disclosed objectives. In addition, those skilled in the art will understand and recognize that the method 400A may alternatively be represented as a series of interrelated states by a state diagram or events.

[0073] In block 410, the processing logic receives feature data that defines a plurality of features of the virtual substrate. In some embodiments, the features are formed by virtual processing of the virtual substrate.

[0074] At block 412, the processing logic prepares a virtual substrate for display on the GUI. In some embodiments, the geometric modeling core creates a graphical representation of the virtual substrate based on the feature data received at block 410. The geometric modeling core may prepare a graphical representation of the virtual substrate for display on the GUI.

[0075] At block 414, the processing logic receives, via the GUI (e.g., from the user), one or more inputs associated with one or more locations of the virtual substrate. In some embodiments, the user may indicate one or more locations of the virtual substrate in the view of the GUI that displays the virtual substrate. For example, the user may click (or otherwise indicate) one or more locations on the displayed virtual substrate. The locations may be indicated by the user in one or more display views of the virtual substrate. For example, the user may indicate a first location on the virtual substrate in a first view and may indicate a second location in a second view.

[0076] At block 416, the processing logic defines a three-dimensional measurement probe based on the input at block 414. In some embodiments, the three-dimensional measurement probe is a line probe or a point probe (e.g., Figures 3A to 3C the line probe 314 or the point probe 312). In some embodiments, the measurement probe is defined by interacting with the view of the virtual substrate displayed on the GUI (e.g., based on the input received at block 414). In some embodiments, the endpoints of the measurement probe (e.g., the endpoints of the line probe) correspond to the input received at block 414. For example, a first location on the virtual substrate may correspond to a first endpoint of the line probe and a second location on the virtual substrate may correspond to a second endpoint of the line probe. The measurement probe may be defined based on the endpoints indicated at block 414.

[0077] At block 418, the processing logic outputs the measurement results of the three-dimensional measurement probe. In some embodiments, the measurement results are associated with the characteristics of the virtual substrate measured by the three-dimensional measurement probe. For example, the measurement results may reflect the dimensions of the virtual substrate features measured by the measurement probe. The measurement results may be used to update the processing recipe for processing the substrate.

[0078] Figure 4B is a flowchart of a method 400B for changing a measurement probe for measuring a virtual substrate according to some embodiments of the present disclosure. Method 400B may be executed by processing logic that may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, processing devices, etc.), software (such as instructions running on a processing device, a general-purpose computer system, or a dedicated machine), firmware, microcode, or a combination thereof. In some embodiments, method 400B may be at least partially performed by Figure 1The substrate measurement component 122 performs. In some embodiments, the non-transitory machine-readable storage medium stores instructions that, when executed by a processing device (e.g., the substrate measurement component 122), cause the processing device to perform method 400B.

[0079] For simplicity of explanation, method 400B is depicted and described as a series of operations. However, the operations in accordance with the present disclosure can occur in various orders and / or simultaneously, and occur together with other operations not presented and described herein. In addition, it is not necessary to perform all the operations shown to implement method 200B according to the disclosed objectives. In addition, those skilled in the art will understand and recognize that method 400B can alternatively be represented as a series of interrelated states by a state diagram or events.

[0080] In block 420, the processing logic determines one or more parameters of the measurement probe. In some embodiments, the one or more parameters include the coordinates of one or more points associated with the measurement probe. In some instances, the one or more parameters include the x coordinate, y coordinate, and / or z coordinate of the endpoint of the measurement probe. In some embodiments, the one or more parameters are determined based on inputs (e.g., user inputs via the GUI) that correspond to the position (e.g., points) of the virtual substrate displayed in the GUI.

[0081] In block 422, the processing logic prepares at least one of the one or more parameters for a table (e.g., Figures 3A to 3C the measurement probe table 306) to be displayed on the GUI. In some embodiments, the table is used to display the endpoint coordinates corresponding to one or more measurement probes.

[0082] In block 424, the processing logic receives one or more inputs to adjust the values of one or more parameters in the table. In some embodiments, the one or more inputs can adjust one or more coordinate values corresponding to the endpoints of one or more measurement probes. In some instances, the user can input to the table (via the GUI) to change the endpoints of the line measurement probe.

[0083] In block 426, the processing logic changes one or more endpoints of the measurement probe based on the one or more inputs. The new measurement results of the measurement probe can be determined based on the changed endpoints.

[0084] Figure 5 An example substrate 500 including features is depicted in accordance with some embodiments. Substrate 500 can be a physical substrate. Substrate 500 can be a virtual substrate. Substrate 500 can be similar to a microscopic image of a device, e.g., an XSEM or TEM image. Aspects of the present disclosure include providing data indicative of substrate properties to a processing model corresponding to one or more processing operations. Substrate 500 can be a substrate that has not undergone the corresponding processing operation. Substrate 500 can be a substrate that has undergone the corresponding processing operation.

[0085] The substrate 500 includes several features. The substrate 500 includes nominally identical features 580 and 582. Device features can include multiple components defined by multiple characteristics, etc. A portion of feature 580 resides on top of the pedestal 570. The device can include a feature having a gate 572. The gate can be surrounded by a spacer 574 and covered by a mask 576. A deposited material 578 can be disposed on top of the mask 576. Other devices, other designs, etc. are within the scope of the present disclosure.

[0086] The process model can be an etch model, a deposition model, or another model configured to predict the result of one or more process operations. For example, the process model can predict the result of a process operation that results in the deposition of the deposited material 578. The measurement of features 580 and 582 can be performed before or after the deposition of the deposited material 578. Some measurement techniques (such as XSEM) are capable of measuring the properties and / or profiles of features that exist before a process operation is performed. For example, an XSEM metrology system can provide data from which the shape of feature 580 before deposition can be extracted and provided to the process model.

[0087] The characteristics of a feature can include radius of curvature, slope, distance, and other properties. For example, the bending radius of curvature of the pedestal 570, the slopes of the respective edges of components (such as the spacer 574 and / or the gate 572), etc. can be characteristics of feature 580. The characteristics of feature 580 can be parameterized, for example, based on the variation between the characteristics of feature 580 and feature 582, based on the variation between the characteristics of feature 580 and other features of the substrate 500, based on the variation between the characteristics of feature 580 and other features of other substrates, etc.

[0088] Figure 6FIG. 0 is a block diagram illustrating a computer system 600 in accordance with some embodiments. In some embodiments, the computer system 600 may be connected (e.g., via a network such as a Local Area Network (LAN), an intranet, an extranet, or the Internet) to other computer systems. The computer system 600 may operate as a server or a client computer in a client-server environment, or as a peer computer in a peer-to-peer or distributed network environment. The computer system 600 may be provided by a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a cellular phone, an Internet appliance, a server, a network router, a switch or bridge, or any device capable of executing a set of instructions (sequentially or otherwise) that specify actions to be taken by that device. Additionally, the term "computer" shall include any collection of computers that independently or jointly execute a set of instructions (or multiple sets of instructions) to perform any one or more of the methods described herein.

[0089] In a further aspect, the computer system 600 may include a processing device 602, a volatile memory 604 (e.g., Random Access Memory (RAM)), a non-volatile memory 606 (e.g., Read-Only Memory (ROM) or Electrically-Erasable Programmable ROM (EEPROM)), and a data storage device 618 that may communicate with each other via a bus 608.

[0090] The processing device 602 may be provided by one or more processors, such as a general-purpose processor (such as, for example, a Complex Instruction Set Computing (CISC) microprocessor, a Reduced Instruction Set Computing (RISC) microprocessor, a Very Long Instruction Word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of various types of instruction sets) or a special-purpose processor (such as, for example, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), or a network processor).

[0091] The computer system 600 may further include a network interface device 622 (e.g., coupled to the network 674). The computer system 600 may also include a video display unit 610 (e.g., an LCD), an alphanumeric input device 612 (e.g., a keyboard), a cursor control device 614 (e.g., a mouse), and a signal generation device 620.

[0092] In some embodiments, the data storage device 618 may include a non-transitory computer-readable storage medium 624 (e.g., a non-transitory machine-readable storage medium storing instructions), on which any one or more of the instructions 626 encoding the methods or functions described herein may be stored, including Figure 1 the instruction encoding components (such as the substrate measurement component 122, etc.), and for implementing the methods described herein.

[0093] The instructions 626 may also reside, in whole or in part, within the volatile memory 604 and / or within the processing device 602 during their execution by the computer system 600. Thus, the volatile memory 604 and the processing device 602 may also constitute a machine-readable storage medium.

[0094] Although the computer-readable storage medium 624 is shown as a single medium in the illustrative example, the term "computer-readable storage medium" should include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) that store one or more sets of executable instructions. The term "computer-readable storage medium" should also include any tangible medium that is capable of storing or encoding a set of instructions for execution by a computer, which instructions cause the computer to perform any one or more of the methods described herein. The term "computer-readable storage medium" should include, but is not limited to, solid state memory, optical media, and magnetic media.

[0095] The methods, components, and features described herein may be implemented by discrete hardware components or may be integrated into the functionality of other hardware components, such as an ASIC, FPGA, DSP, or similar device. Additionally, the methods, components, and features may be implemented through firmware modules or functional circuitry within a hardware device. Further, the methods, components, and features may be implemented by any combination of a hardware device and computer program components, or by a computer program.

[0096] Unless otherwise specifically stated, terms such as "receive", "execute", "provide", "define", "obtain", "cause", "access", "determine", "add", "use", "train", "reduce", "generate", "change", "output", "prepare", or the like refer to actions and processes performed or implemented by a computer system that manipulate and transform data represented as a physical (electronic) quantity within a register and memory of the computer system into other data similarly represented as a physical quantity within a memory or register of the computer system or other such information storage, transmission, or display device. Additionally, as used herein, the terms "first", "second", "third", "fourth", etc. mean labels for distinguishing among different elements and may not have an ordinal meaning according to their numerical designation.

[0097] The examples described herein are also directed to an apparatus for performing the methods described herein. This apparatus may be specially constructed to perform the methods described herein, or may include a general-purpose computer system selectively programmed by a computer program stored in the computer system. Such a computer program may be stored in a computer-readable tangible storage medium.

[0098] The methods and illustrative examples described herein are not inherently related to any particular computer or other device. Various general-purpose systems may be used in accordance with the teachings described herein, or it may prove convenient to construct a more specialized device to perform each of the methods and / or their individual functions, routines, subroutines, or operations described herein. Examples of structures for various such systems are set forth in the foregoing description.

[0099] The foregoing description is intended to be illustrative and not restrictive. Although the present disclosure has been described with reference to specific illustrative examples and embodiments, it will be recognized that the present disclosure is not limited to the described examples and embodiments. The scope of the present disclosure should be determined with reference to the appended claims, along with the full scope of equivalents to such claims.

Claims

1. A method, the method comprising: Receiving feature data defining a plurality of features of a virtual substrate; Preparing the virtual substrate for display on a graphical user interface (GUI); Receiving, via the GUI, one or more first inputs, wherein the one or more first inputs are associated with one or more positions of the virtual substrate; Defining a three-dimensional measurement probe based on the one or more first inputs; and Outputting measurement results of the measurement probe, wherein the measurement results are associated with characteristics of the virtual substrate measured by the three-dimensional measurement probe.

2. The method of claim 1, wherein preparing the virtual substrate for display on the GUI comprises: preparing one or more of a top view of the virtual substrate, a first side view of the virtual substrate, a three-dimensional perspective view of the virtual substrate, or a cross-sectional view of the virtual substrate.

3. The method of claim 2, wherein the measurement probe is defined in one or more of the top view, the first side view, the three-dimensional perspective view, or the cross-sectional view based on the one or more first inputs.

4. The method of claim 3, wherein the measurement probe comprises a line measurement probe, wherein a first end of the line measurement probe is defined by interacting with a first view selected from the top view, the first side view, the three-dimensional perspective view, and the cross-sectional view, and wherein a second end of the line measurement probe is defined by interacting with a second view selected from the top view, the first side view, the three-dimensional perspective view, and the cross-sectional view, the second view being different from the first view.

5. The method of claim 3, wherein the measurement probe comprises a point measurement probe, and wherein the point measurement probe is defined by interacting with a view selected from the top view, the side view, the three-dimensional perspective view, and the cross-sectional view.

6. The method of claim 1, the method further comprising: Determining one or more parameters of the measurement probe, the one or more parameters comprising coordinates of one or more points associated with the measurement probe; and Preparing a table for display on the GUI, wherein the table contains at least one of the one or more parameters of the measurement probe.

7. The method of claim 6, the method further comprising: Receiving one or more second inputs in the table, the one or more second inputs adjusting values of the one or more parameters; and Changing one or more endpoints of the measurement probe based on the one or more second inputs.

8. The method of claim 1, wherein the one or more first inputs comprise one or more indications associated with the one or more positions of the virtual substrate, wherein each of the one or more positions comprises an X coordinate, a Y coordinate, and a Z coordinate.

9. The method according to claim 8, wherein a first end point of the measurement probe is defined by a first indication among the one or more indications, a second end point of the measurement probe is defined by a second indication among the one or more indications, and the measurement result of the measurement probe includes a distance between the first end point and the second end point.

10. The method according to claim 1, wherein the virtual substrate is prepared for display on the GUI by a geometric modeling core.

11. The method according to claim 1, wherein the virtual substrate includes a digital twin of a substrate that has been or will be processed by a substrate processing system according to a recipe.

12. The method according to claim 11, the method further comprising: Determining an adjustment to the recipe based on the measurement result of the measurement probe.

13. A system, the system comprising a memory and a processing device coupled to the memory, wherein the processing device is configured to: Receive feature data defining a plurality of features of a virtual substrate; Prepare the virtual substrate for display on a graphical user interface (GUI); Receive one or more first inputs via the GUI, wherein the one or more first inputs are associated with one or more positions of the virtual substrate; Define a three-dimensional measurement probe based on the one or more first inputs; And Output a measurement result of the measurement probe, wherein the measurement result is associated with a characteristic of the virtual substrate measured by the three-dimensional measurement probe.

14. The system according to claim 13, wherein the measurement probe includes a line measurement probe, wherein a first end of the line measurement probe is defined by interacting with a first view selected from a top view, a first side view, and a three-dimensional perspective view, and a second end of the line measurement probe is defined by interacting with a second view selected from the top view, the first side view, and the three-dimensional perspective view.

15. The system according to claim 13, wherein the processing device is further configured to: Determine one or more parameters of the measurement probe, the one or more parameters including coordinates of one or more points associated with the measurement probe; Prepare a table for display on the GUI, wherein the table contains at least one of the one or more parameters of the measurement probe; Receive one or more second inputs in the table, the one or more second inputs adjusting values of the one or more parameters; And Changing one or more end points of the measurement probe based on the one or more second inputs.

16. The system according to claim 13, wherein the one or more first inputs include one or more indications associated with the one or more positions of the virtual substrate, wherein each of the one or more positions includes an X coordinate, a Y coordinate, and a Z coordinate.

17. The system of claim 16, wherein a first endpoint of the measurement probe is defined by a first indication among the one or more indications, a second endpoint of the measurement probe is defined by a second indication among the one or more indications, and wherein the measurement result of the measurement probe comprises a distance between the first endpoint and the second endpoint.

18. A non-transitory machine-readable storage medium storing instructions that, when executed, cause a processing device to perform operations, the operations comprising: Receiving feature data defining a plurality of features of a virtual substrate; Preparing the virtual substrate for display on a graphical user interface (GUI); Receiving, via the GUI, one or more first inputs, wherein the one or more first inputs are associated with one or more locations of the virtual substrate; Defining a three-dimensional measurement probe based on the one or more first inputs; And Outputting a measurement result of the measurement probe, wherein the measurement result is associated with a characteristic of the virtual substrate measured by the three-dimensional measurement probe.

19. The non-transitory machine-readable storage medium of claim 18, wherein the one or more first inputs comprise one or more indications associated with the one or more locations of the virtual substrate, wherein each of the one or more locations comprises an X coordinate, a Y coordinate, and a Z coordinate.

20. The non-transitory machine-readable storage medium of claim 18, wherein the virtual substrate comprises a digital twin of a substrate that is being processed or will be processed by a substrate processing system according to a recipe.

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