Optimizing PCB Layout Using Board Measurements

Abstract

A computer-readable medium includes instructions that, when executed by a processing device, cause the processing device to perform operations. The operations include processing a first substrate in a process chamber while the first substrate is supported by a substrate support at a first placement location. The first substrate includes a first surface profile after processing. The operations further include generating a first surface profile map of the first surface profile using a substrate measurement system. The operations further include determining a plurality of etch rates corresponding to a plurality of locations on the first substrate. The operations further include processing data associated with the plurality of etch rates using a model, the model to output one or more estimated surface profiles associated with the one or more estimated placement locations. The operations further include determining a recommended placement for the substrate on the substrate support based on the one or more estimated placement locations.

Description

[Technical Field]

[0001] Embodiments of the present disclosure relate generally to optimizing the placement of a substrate in a processing chamber, and particularly to generating maps and / or numerical profiling of substrates processed using the chamber and optimizing the placement of the substrate in the processing chamber based on the maps and / or numerical profiling of the substrates. [Background technology]

[0002] Substrate processing can include a series of processes that fabricate electrical circuits in semiconductors according to a circuit design. These processes can be performed in a series of process chambers. The successful operation of modern semiconductor manufacturing facilities can be aimed at facilitating the movement of a steady stream of wafers from one chamber to another as electrical circuits are formed in the wafers to form products. In the process of performing many substrate processes, conditions in the processing chambers can change, which can result in processed substrates that do not meet target conditions and results. Summary of the Invention [Problem to be solved by the invention]

[0003] Substrates are placed in process chambers for processing, and placement of the substrate relative to chamber components can result in variations in the quality of products fabricated using the process chamber and / or scrap of substrates processed using the process chamber. [Means for solving the problem]

[0004] The following is a simplified summary of the present disclosure to provide a basic understanding of some aspects of the disclosure. This summary is not an extensive overview of the disclosure. It is not intended to identify key or critical elements of the disclosure, nor to delineate the scope or claims of particular embodiments of the disclosure. Its sole purpose is to present some concepts of the disclosure in a simplified form as a prelude to the more detailed description that is presented later.

[0005] In an exemplary embodiment, a computer-readable medium includes instructions that, when executed by a processing device, cause the processing device to perform operations. The operations include processing a first substrate in a process chamber of a substrate processing system while the first substrate is supported by a substrate support at first placement locations on the substrate support. The first substrate includes a first surface profile after processing. The operations further include generating a first surface profile map of the first surface profile using a substrate measurement system. The operations further include determining first multiple etch rates corresponding to the first multiple locations on the first substrate based on the first surface profile map. The operations further include processing data associated with the first multiple etch rates using a model. The model is for outputting one or more estimated surface profiles associated with one or more estimated placement locations on the substrate support based on the first multiple etch rates. The operations further include determining a recommended placement for the substrate on the substrate support based on the one or more estimated placement locations.

[0006] In an exemplary embodiment, a system includes a process chamber including a substrate support. The system further includes a substrate measurement tool, a memory, and a processing device operably coupled to the memory. The processing device is for processing a first substrate in the process chamber while the first substrate is supported by the substrate support at a first location on the substrate support. The first substrate includes a first surface profile after processing. The processing device is further for generating a first surface profile map of the first surface profile using the substrate measurement tool. The processing device is further for determining a first plurality of etch rates corresponding to the first plurality of locations on the first substrate based on the first surface profile map. The processing device is further for processing data associated with the first plurality of etch rates using a model. The model is for outputting one or more estimated surface profiles associated with one or more estimated location locations on the substrate support based on the first plurality of etch rates. The processing device is further for determining a recommended location for the substrate on the substrate support based on the one or more estimated location locations.

[0007] In an exemplary embodiment, a method includes processing a first substrate in a process chamber while the first substrate is supported by a substrate support at first placement locations on the substrate support. The first substrate includes a first surface profile after processing. The method further includes generating a first surface profile map of the first surface profile using a substrate measurement system. The method further includes determining a first plurality of etch rates corresponding to the first plurality of locations on the first substrate based on the first surface profile map. The method further includes processing data associated with the first plurality of etch rates using a model. The model is for outputting one or more estimated surface profiles associated with the one or more estimated placement locations on the substrate support based on the first plurality of etch rates. The method further includes determining a recommended placement for the substrate on the substrate support based on the one or more estimated placement locations.

[0008] The present disclosure is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings, in which like references indicate similar elements. It should be noted that various references in this disclosure to "an embodiment" or "one embodiment" do not necessarily refer to the same embodiment, but rather that such references mean at least one. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 illustrates an exemplary computer system architecture according to aspects of the present disclosure. [Figure 2A] FIG. 1 is a schematic top view of an exemplary manufacturing system according to aspects of the present disclosure. [Figure 2B] 2B is a schematic cross-sectional side view of a substrate measurement system included within the exemplary manufacturing system of FIG. 2A, according to an embodiment of the present disclosure. [Figure 2C] FIG. 2 is a schematic cross-sectional side view of a substrate measurement subsystem according to aspects of the present disclosure. [Figure 3]FIG. 1 illustrates an exemplary system architecture for substrate placement prediction for a processing chamber, in accordance with aspects of the present disclosure. [Figure 4] FIG. 1 illustrates a model training and model application workflow for substrate placement determination, according to one embodiment. [Figure 5] 1 is a flowchart of a method for generating a training dataset for training a machine learning model, according to an aspect of the present disclosure. [Figure 6] 1 is a flow diagram of a method for training a machine learning model to determine substrate placement, according to one embodiment. [Figure 7] 1 is a flow diagram of a method for determining a recommended substrate placement according to an aspect of the present disclosure. [Figure 8] 1 is a flow diagram of a method for comparing a first estimated substrate placement and a second estimated substrate placement according to an aspect of the present disclosure. [Figure 9] 1 is a diagram of a profile map (eg, heat map) of a processed substrate according to an aspect of the present disclosure. [Figure 10A] 1 is a flow diagram of a method for determining optimal substrate placement according to an aspect of the present disclosure. [Figure 10B] 1 is a flow diagram of a method for determining optimal substrate placement according to an aspect of the present disclosure. [Figure 11A] FIG. 1 is an exemplary plot of an experimental substrate layout design, according to aspects of the present disclosure. [Figure 11B] FIG. 10 illustrates an exemplary radial plot of substrate etch rate versus θ, according to aspects of the present disclosure. [Figure 12A] FIG. 10 illustrates an exemplary plot of substrate etch rate versus θ, according to aspects of the present disclosure. [Figure 12B] FIG. 10 illustrates an exemplary plot of normalized substrate etch rate versus θ, according to aspects of the present disclosure. [Figure 13A] FIG. 10 illustrates an exemplary plot of normalized substrate etch rates for experimental substrate layout designs, according to aspects of the present disclosure. [Figure 13B]FIG. 1 is an exemplary plot of an experimental substrate design, according to aspects of the present disclosure. [Figure 13C] FIG. 10 illustrates an exemplary plot of a linear fit to substrate etch rate, according to aspects of the present disclosure. [Figure 14] 1 is a flow diagram of a method for determining optimal substrate placement according to an aspect of the present disclosure. [Figure 15] 1 is a diagrammatic representation of a machine, an exemplary form of computing device, capable of executing a set of instructions to cause the machine to perform any one or more of the techniques discussed herein. DETAILED DESCRIPTION OF THE INVENTION

[0010] Embodiments of the present disclosure are directed to systems and methods for optimizing substrate placement within a process chamber using substrate measurements. Process results of a manufacturing process depend on many factors, including the process recipe, chamber parameter settings, chamber component conditions, and substrate placement within the process chamber. For example, process results can vary across the surface of a substrate based on the placement of the substrate relative to a process kit ring. The gap between the edge of the substrate and the process kit ring is often related to the impact of substrate processing near the edge of the substrate. In addition, process results can vary across the surface of a substrate based on showerhead conditions, substrate support conditions that support the substrate, chamber liner conditions, pump and / or valve conditions, etc. Optimized substrate placement can be affected by the conditions of the process chamber components described herein. For example, any changes over time in substrate supports, such as electrostatic chucks, clamps, vacuum chucks, heaters, supports that include pockets with lips at the edges of the supports, and / or substrate supports that include one or more embedded features (e.g., heaters, cold plates, electrical elements, etc.), can slowly impact substrate results through temperature changes across the surface of the processed substrate, radio frequency (RF) fields at the edge of the substrate, etc. These effects on substrate results slowly affect the optimized substrate placement for best process results, and therefore the optimal placement of the substrate on the substrate support may tend to change over time.

[0011] Substrate results can be particularly affected by substrate placement near the edges of processed substrates. For example, factors that contribute to substrate etch rate (e.g., temperature, gas flow, etc.) can cause tilt near the edges of processed substrates. "Tilt" refers to the tendency of substrate features (e.g., valleys, walls, pillars, mesas, etc.) not to be perpendicular to the substrate surface. Excessive tilt can result in substrates of low quality and / or being discarded. Tilt often affects substrates near their edges, especially near their extreme edges (e.g., within 5 millimeters of the substrate edge). Variations in substrate placement relative to substrate support components (e.g., process kit rings, etc.) can affect substrate processing. For example, differences in the gap between the edge of the substrate and the process kit ring (e.g., the gap surrounding the wafer) can result in tilt greater than a threshold amount of acceptable tilt, leading to the substrate being discarded.

[0012] Embodiments described herein provide a mechanism for determining an optimized placement of a substrate for processing in a process chamber. Some embodiments can be used to determine a recommended position for a substrate on a substrate support for processing in a process chamber. The recommended position can be used to determine an offset for a substrate handling robot (e.g., a transfer chamber robot) to position the substrate in the process chamber.

[0013] In some embodiments, a substrate is processed in a process chamber according to a recipe. The substrate can be processed to deposit or etch a film layer and / or one or more features (e.g., measurable features) on a surface of the substrate. The substrate can be a bare substrate or a test substrate that does not include a product. Alternatively, the substrate can be a product substrate. The features can include features distributed across the surface of the substrate, such as mesas, dots, structures, valleys, walls, lines, trenches, grooves, fiducials, etc. In some examples, the substrate can be supported by a substrate support (e.g., an electrostatic chuck, etc.) in the process chamber during processing. In some examples, after processing, the substrate can have a surface profile (e.g., a thickness profile, a slope profile, etc.). In an example of an etch process recipe, the surface profile can indicate an etch rate across the surface of the substrate during the etch process (e.g., an etch rate profile). In some examples, the etch rate can be correlated to a slope on the substrate surface, particularly at locations near the edge of the substrate.

[0014] After the films and / or features are deposited or etched on the substrate, in some embodiments, the substrate can be removed from the process chamber and placed into a substrate metrology system. In some embodiments, the substrate metrology system generates a profile map of the substrate based on the surface profile of the substrate. The substrate metrology system can be, for example, a reflected light metrology system or other metrology system that measures the film thickness of the films and / or features at multiple locations on the substrate. The thickness information can be used to generate a profile map for the substrate. Alternatively, one or more other profile maps of the substrate (e.g., of optical constants, roughness, particle count, etc.) can be generated from other measurement data.

[0015] A model (e.g., a trained machine learning model, a physics-based model, a statistical model, and / or an image processor, etc.) can then be used to process the film and / or feature thickness information (e.g., a thickness profile map) or other film and / or feature information (e.g., other profile maps, such as optical constant profile maps, particle number profile maps, etc.) to identify variations in one or more film properties. The model can output estimated substrate placement values ​​for placement of the substrate relative to one or more components (e.g., an electrostatic chuck, a process kit ring, etc.) within the process chamber. In embodiments, the model can output a recommended placement position for the substrate, and can output one or more predicted film properties associated with the recommended placement position (e.g., a predicted profile map of a substrate processed from the recommended placement position), and so on.

[0016] In some embodiments, etch rates at multiple locations on the surface of the substrate are determined from the feature thickness information. In some embodiments, a model can be used to process data associated with the etch rates to determine an estimated placement location for the substrate on the substrate support for processing. For example, a numerical model can process normalized etch rates for multiple test substrates (each test substrate processed with a different substrate placement) at each specified location on the substrate to determine the effect of different substrate placements. The model can output estimated substrate placements and / or estimated substrate placement values ​​for one or more components within the process chamber.

[0017] In some embodiments, a recommended placement for the substrate on the substrate support is determined based on the estimated substrate placement value. In some examples, the recommended placement is an optimized placement location on the substrate support (e.g., electrostatic chuck, etc.) for processing the substrate to meet target substrate specifications (e.g., having less than a threshold amount of tilt near the substrate edge). In some examples, the optimized location of the substrate on the substrate support is such that a uniform gap exists around the substrate between the edge of the substrate and the inner diameter of the process kit ring. However, in some examples, the optimized location of the substrate does not provide a uniform gap. In such examples, the optimized location of the substrate takes into account process chamber variables, such as conditions of one or more chamber components (e.g., process kit ring conditions, showerhead conditions, electrostatic chuck conditions, etc.). In some embodiments, robot arm settings for placing the substrate at the recommended placement location are determined, and these settings are used to place the substrate on the substrate support.

[0018] Thus, the embodiments described herein add new detection capabilities to process chambers without increasing the cost to those process chambers. In some embodiments, these new detection capabilities can be leveraged to increase the quality and / or quantity of processed substrates that meet threshold specifications (e.g., threshold tilt specifications for processed substrates, etc.). Additionally or alternatively, the embodiments described herein can be used to reduce the amount of wasted product (e.g., discarded substrates) due to discarded product that does not meet threshold specifications. The embodiments described herein can be used to automatically optimize certain process variables (e.g., substrate placement during processing) to process substrates of improved quality more quickly and more efficiently compared to conventional substrate processing systems. In particular, the embodiments described herein can be used to produce substrates with better edge characteristics (e.g., tilt, etc.) compared to substrates produced by conventional systems, again resulting in higher quality substrates, less wasted product, etc.

[0019] FIG. 1 illustrates an exemplary computer system architecture 100 according to aspects of the present disclosure. The computer system architecture 100 includes a client device 120, a manufacturing device 122, a substrate metrology system 126, a prediction server 112 (e.g., for generating prediction data, providing model adaptation, using a knowledge base, etc.), and a data store 150. The prediction server 112 may be part of a prediction system 110. The prediction system 110 may further include server machines 170 and 180. In some embodiments, the computer system architecture 100 may include or be part of a manufacturing system for processing substrates, such as the manufacturing system 200 of FIG. 2A . In additional or alternative embodiments, the computer system architecture 100 may include or be part of a substrate placement prediction system (e.g., evaluating the condition of one or more chamber parts in a process chamber). Further details regarding the substrate placement prediction system are provided with respect to FIGS. 3-4 .

[0020] Components of client devices 120, manufacturing equipment 122, substrate metrology system 126, prediction system 110, and / or data store 150 can be coupled to each other via network 140. In some embodiments, network 140 is a public network that provides client devices 120 access to prediction server 112, data store 150, and other publicly available computing devices. In some embodiments, network 140 is a private network that provides client devices 120 access to manufacturing equipment 122, substrate metrology system 126, data store 150, and / or other privately available computing devices. Network 140 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.

[0021] The client device 120 may include computing devices such as a personal computer (PC), a laptop, a mobile phone, a smartphone, a tablet computer, a netbook computer, a network-connected television ("smart TV"), a network-connected media player (e.g., a Blu-ray player), a set-top box, an over-the-top (OTT) streaming device, an operator box, etc.

[0022] The manufacturing equipment 122 can fabricate products according to a recipe. In some embodiments, the manufacturing equipment 122 can include or be part of a manufacturing system that includes one or more stations (e.g., process chambers, transfer chambers, load locks, factory interfaces, etc.) configured to perform various operations on substrates.

[0023] The substrate metrology system 126 may be a component of a manufacturing system that can be used to measure substrates before and / or after they are processed in one or more process chambers. The substrate metrology system 126 may be configured to generate optical emission spectroscopy data, reflectance measurement data, and / or other metrology data. The substrate metrology system 126 may include one or more components configured to collect and / or generate measurement data associated with one or more portions of the profile of the surface of the substrate after the substrate is removed from a process chamber.

[0024] In some embodiments, the substrate metrology system 126 can be configured to generate metrology data associated with substrates processed by other manufacturing equipment 122. The metrology data can include one or more values ​​of film property data (e.g., wafer-spatial film properties such as thickness), dimensions (e.g., thickness, height, etc.), dielectric constant, dopant concentration, density, defects, etc. The metrology data can be of finished or semi-finished products or test substrates such as blanket wafers. Some embodiments are discussed with reference to using reflected light measurement data and thickness profile maps to determine the condition of chamber components. However, it should be understood that the principles and embodiments described herein with respect to reflected light measurement data and thickness profile maps also apply to other types of metrology data. For example, measurements can be made of particle counts, optical constants of coatings, surface roughness of coatings, material composition of coatings, etc. Such measurements can be made for many regions on the substrate and used to generate profile maps of particle counts, optical constants, surface roughness, material composition, etc. across the measured substrate.

[0025] The substrate metrology system 126 can be configured to generate metrology data associated with a substrate before and / or after substrate processing. The substrate metrology system 126 can be integrated with a station of a manufacturing system, including the manufacturing equipment 122. In some embodiments, the substrate metrology system 126 can be coupled to or part of a station (e.g., a process chamber, a transfer chamber, etc.) of a process tool that is maintained in a vacuum environment. Such a substrate metrology system 126 can be referred to as an on-board metrology instrument. Thus, the substrate can be measured by the substrate metrology system 126 while the substrate is in a vacuum environment. For example, after a substrate process (e.g., an etch process, a deposition process, etc.) is performed on the substrate, the substrate metrology system 126 can generate metrology data for the processed substrate without the processed substrate being removed from the vacuum environment. In other or similar embodiments, the substrate metrology system 126 can be coupled to or part of a manufacturing system that is not maintained in a vacuum environment (e.g., a factory interface module, etc.). Such a substrate metrology system 126 can be referred to as an on-board metrology instrument.

[0026] As an alternative to the substrate metrology system 126 being included within the manufacturing system (e.g., mounted to a factory interface or transfer chamber), the substrate metrology system 126 may be a device separate from (i.e., external to) the manufacturing equipment 122. For example, the substrate metrology system 126 may be a stand-alone piece of equipment that is not coupled to any station of the manufacturing equipment 122. To obtain measurements for a substrate using the detached substrate metrology system 126, a user of the manufacturing system (e.g., a technician, an operator) can remove a substrate processed on the manufacturing equipment 122 from the manufacturing equipment 122 and transfer it to the substrate metrology system 126 for measurement. In some embodiments, the substrate metrology system 126 can transfer metrology data generated for the substrate over the network 140 to a client device 120 coupled to the substrate metrology system 126 (e.g., for presentation to a manufacturing user, such as an operator or technician). In other or similar embodiments, a manufacturing system user can obtain metrology data for a substrate from the substrate metrology system 126 and provide the metrology data to a computer system architecture via a graphical user interface (GUI) of the client device 120.

[0027] Data store 150 can be a memory (e.g., random access memory), a drive (e.g., a hard drive, a flash drive), a database system, or another type of component or device capable of storing data. Data store 150 can include multiple storage components (e.g., multiple drives or multiple databases) that can span multiple computing devices (e.g., multiple server computers). Data store 150 can store profile maps (e.g., generated from reflected light measurement data, spectral data, etc.), such as film thickness profile maps and / or other substrate profile maps. Film thickness profile maps and / or other substrate profile maps can include historical maps and / or current maps.

[0028] One or more portions of data store 150 can be configured to store data that is not accessible to users of the manufacturing system. In some embodiments, all data stored in data store 150 can be inaccessible to manufacturing system users. In other or similar embodiments, a portion of the data stored in data store 150 is inaccessible to users, while another portion of the data stored in data store 150 is accessible to users. In some embodiments, the inaccessible data stored in data store 150 is encrypted using an encryption mechanism unknown to the users (e.g., the data is encrypted using a private encryption key). In other or similar embodiments, data store 150 can include multiple data stores, with data that is inaccessible to users stored in a first data store and data that is accessible to users stored in a second data store.

[0029] In some embodiments, prediction system 110 includes server machine 170 and server machine 180. Server machine 170 includes a training set generator 172 capable of generating a training data set (e.g., a set of data inputs and a set of target outputs) for training, validating, and / or testing a machine learning model 190 or a set of machine learning models 190. Some operations of training set generator 172 are described in more detail below with respect to FIG. 4. In some embodiments, training set generator 172 can partition the training data into a training set, a validation set, and a test set.

[0030] The server machine 180 may include a training engine 182. An engine may refer to hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, a processing device, etc.), software (e.g., instructions executed on a processing device, a general-purpose computer system, or a dedicated machine), firmware, microcode, or a combination thereof. The training engine 182 may be capable of training one machine learning model 190 or a set of machine learning models 190. A machine learning model 190 may refer to a model artifact resulting from the training engine 182 using training data including training inputs and corresponding target outputs (correct responses for each training input). The training engine 182 may discover patterns in the training data that map the training inputs to target outputs (predicted responses) and provide a machine learning model 190 that captures these patterns. The machine learning model 190 may include a linear regression model, a partial least squares regression model, a Gaussian regression model, a random forest model, a support vector machine model, a neural network, a ridge regression model, etc.

[0031] The training engine 182 may also be capable of validating the trained machine learning models 190 using a corresponding set of features of the validation set from the training set generator 172. In some embodiments, the training engine 182 may assign a performance grade to each of the set of trained machine learning models 190. The performance grade may correspond to the accuracy of each trained model, the speed of each model, and / or the efficiency of each model. The training engine 182 may select trained machine learning models 190 having a performance grade that satisfies performance criteria to be used by the prediction engine 114, according to embodiments described herein. Further details regarding the training engine 182 are provided with respect to FIG. 5.

[0032] The prediction server 112 includes a prediction engine 114, which can provide data from the substrate metrology system 126 (e.g., a film thickness profile map) as input to a trained machine learning model 190 and run the trained model 190 on the input to obtain one or more outputs. In some embodiments, the trained model 190 executed by the prediction engine 114 is selected by the training engine 182 as having a performance grade that meets the performance criteria. As described further with respect to FIG. 6 , in some embodiments, the prediction engine 114 processes the input data using the model 190 to evaluate a substrate placement for processing a substrate in a process chamber.

[0033] It should be noted that in some other embodiments, the functionality of server machines 170 and 180 and prediction server 112 may be provided by more or fewer machines. For example, in some embodiments, server machines 170 and 180 may be integrated into a single machine, while in some other or similar embodiments, server machines 170 and 180 and prediction server 112 may be integrated into a single machine. In general, functionality described in one embodiment as being performed by server machine 170, server machine 180, and / or prediction server 112 may also be performed on client device 120. Additionally, functionality attributed to particular components may also be performed by different or multiple components operating together.

[0034] In embodiments, a "user" may be represented as a single individual. However, other embodiments of the present disclosure encompass "users" that are entities controlled by multiple users and / or automated sources. For example, a set of individual users united as a group of administrators may be considered a "user."

[0035] 2A is a schematic top view of an exemplary manufacturing system 200 according to aspects of the present disclosure. The manufacturing system 200 can perform one or more processes on a substrate 202. The substrate 202, according to aspects of the present disclosure, can be any suitably rigid, dimensionally fixed, planar object suitable for fabricating electronic devices or circuit components thereon, such as, for example, a silicon-containing disk or wafer, a patterned wafer, a glass plate, etc. In some embodiments, the manufacturing system 200 can include or be part of a computer system architecture 110 according to embodiments described with respect to FIG. 1.

[0036] The manufacturing system 200 may include a process tool 204 and a factory interface 206 coupled to the process tool 204. The process tool 204 may include a housing 208 having a transfer chamber 210 therein. The transfer chamber 210 may include one or more processing chambers (also referred to as process chambers) 214, 216, 218 arranged around and coupled to the transfer chamber 210. The processing chambers 214, 216, 218 may be coupled to the transfer chamber 210 through respective ports, such as slit valves. The transfer chamber 210 may also include a transfer chamber robot 212 configured to transfer the substrate 202 between the process chambers 214, 216, 218, a load lock 220, or the like. The transfer chamber robot 212 may include one or more arms, each including one or more end effectors at the end of each arm. The end effectors may be configured to handle specific objects, such as wafers.

[0037] In some embodiments, the transfer chamber 210 also can include metrology equipment, such as a substrate metrology system 126, mounted to the transfer chamber 210. The substrate metrology system 126 can be configured to generate metrology data associated with the substrate 202 before or after substrate processing while the substrate is maintained in the vacuum environment. As shown in FIG. 2A , the substrate metrology system 126 can be mounted to or located within the transfer chamber 210. When the substrate metrology system 126 is located within or coupled to the transfer chamber 210, metrology data associated with the substrate 202 can be generated without the substrate 202 being removed from the vacuum environment (e.g., transferred to the factory interface 206).

[0038] The process chambers 214, 216, 218 can be adapted to perform any number of processes on the substrate 202. The same or different substrate processes can be performed in each processing chamber 214, 216, 218. Substrate processes can include atomic layer deposition (ALD), physical vapor deposition (PVD), chemical vapor deposition (CVD), etching, annealing, hardening, pre-cleaning, metal or metal oxide removal, etc. Other processes can also be performed on the substrates herein.

[0039] The load lock 220 may also be coupled to the housing 208 and the transfer chamber 210. The load lock 220 may be configured to interface with and be coupled to the transfer chamber 210 on one side and to the factory interface 206 on the other side. In some embodiments, the load lock 220 may have an environmentally controlled atmosphere that can be changed from a vacuum environment (where substrates may be transferred to and from the transfer chamber 210) to an atmospheric (or near atmospheric) inert gas environment (where substrates may be transferred to and from the factory interface 206).

[0040] The factory interface 206 may be any suitable enclosure, such as a front-end equipment module (EFEM). The factory interface 206 may be configured to receive substrates 202 from substrate carriers 222 (e.g., front-opening integrated pods (FOUPs)) docked to various load ports 224 of the factory interface 206. A factory interface robot 226 (shown in dashed lines) may be configured to transfer substrates 202 between the substrate carriers 222 (also referred to as containers) and the load locks 220. In other and / or similar embodiments, the factory interface 206 may be configured to receive replacement parts from replacement part storage containers.

[0041] In some embodiments, the manufacturing system 200 may include a substrate metrology system 126 attached to the factory interface 206. The substrate metrology system 126 attached to the factory interface may be configured to generate metrology data associated with the substrate 202 before the substrate 202 is placed in the vacuum environment (e.g., transferred to the load lock 220) and / or after the substrate 202 is removed from the vacuum environment (e.g., removed from the load lock 220).

[0042] The manufacturing system 200 may also be connected to a client device (e.g., client device 120 of FIG. 1 ) configured to provide information about the manufacturing system 200 to a user (e.g., an operator). In some embodiments, the client device may provide information to a user of the manufacturing system 200 via one or more graphical user interfaces (GUIs). For example, the client device may provide information about one or more chamber condition metrics of the processing chambers 214, 216, 218 (e.g., while performing a substrate process) via the GUI.

[0043] The manufacturing system 200 may also include or be coupled to a system controller 228. The system controller 228 may be and / or include a computing device such as a personal computer, a server computer, a programmable logic controller (PLC), a microcontroller, etc. The system controller 228 may include one or more processing devices, which may be general-purpose processing devices such as a microprocessor, a central processing unit, etc. More specifically, the processing device may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or a combination of instruction sets. The processing device may also be one or more special-purpose processing devices such as an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), a network processor, etc. The system controller 228 may include a data storage device (e.g., one or more disk drives and / or solid-state drivers), a main memory, a static memory, a network interface, and / or other components. The system controller 228 may execute instructions to implement any one or more of the techniques and / or embodiments described herein. In some embodiments, the system controller 228 may execute instructions to perform one or more operations in the manufacturing system 200 in accordance with a process recipe. These instructions may be stored on a computer-readable storage medium, which may include a main memory, a static memory, a secondary storage, and / or a processing device (during execution of the instructions).

[0044] In some embodiments, the system controller 228 may receive data from the substrate measurement system 126 based on measurements of substrates processed by the process chambers 214, 216, 218. The data received by the system controller 228 may include spectral data, reflected light measurement data, and / or other data for all or a portion of the substrate 202. The data received from the substrate measurement system 126 may be stored in a data store 250. The data store 250 may be included as a component within the system controller 228 or may be a separate component from the system controller 228. In some embodiments, the data store 250 may be or may include a portion of the data store 150, as described with respect to FIG. 1 .

[0045] 2B shows one embodiment of a substrate metrology system 251 that can be used to measure processed substrates. The substrate metrology system 251 can be an integrated measurement and / or imaging system (e.g., an integrated reflectance metrology (IR) system) configured to measure film properties (e.g., thickness, etc.) across the surface of the substrate 264 after the substrate 264 has been processed in a processing chamber. Reflectance metrology is a measurement technique that uses measured changes in light reflected from an object to determine the geometric and / or material properties of the object. A reflectance spectrometer measures the intensity of the reflected light over a range of wavelengths. For dielectric films, these intensity variations can be used to determine the thickness of the film.

[0046] Process results, including film thickness, can be monitored across one or more substrates for etching and deposition processes, for example, using substrate metrology system 251. An integrated metrology and / or imaging system can be used to measure the surface of substrate 264 while the substrate 264 is still within a device manufacturing system. In some embodiments, substrate metrology system 251 can correspond to substrate metrology system 126. Substrate metrology system 251 can be connected to a factory interface or a transfer chamber. Alternatively, substrate metrology system 251 can be located inside a factory interface or a transfer chamber. Substrate metrology system 251 can also be a stand-alone system not connected to a manufacturing system. Substrate metrology system 251 can be mechanically isolated from the factory interface and the external environment to protect substrate metrology system 251 from external vibrations. In some embodiments, substrate metrology system 251 and the components included therein can provide analytical measurements (e.g., thickness measurements) that can provide a uniformity profile across the surface of substrate 264, referred to herein as a profile map. A computing device can process the data from substrate metrology system 251 and provide feedback to a user. The substrate metrology system 251 can be an assembly capable of measuring film thickness and / or other film properties such as optical constants, particle count, roughness, etc., on a portion of a substrate or across the entire substrate after the substrate has been processed in the chamber. The results of the measurements can be used to determine when to perform maintenance on the process chamber, when to perform further tests on the substrate, when to flag the substrate as out of specification, the placement of the substrate during substrate processing, etc.

[0047] When the substrate 264 is lowered onto the substrate support 256 (e.g., a chuck) and secured to the substrate support 256, the center of the substrate 264 may be offset from the center of the chuck. A processing device of the substrate measurement system 251 may determine one or more coordinate transformations between the center of the substrate 264 and the center of the chuck 256 (the center of the chuck corresponds to the axis of rotation about which the chuck rotates) and apply the one or more coordinate transformations to correct for the offset, as described in more detail below.

[0048] The substrate measurement system 251 may include a rotary actuator 252 and a linear actuator 254. The rotary actuator 252 may be a motor, a rotary actuator (e.g., an electric rotary actuator), etc. The linear actuator 254 may be an electric linear actuator that may convert rotational motion in the motor into linear or linear motion along an axis. The substrate measurement system 251 may include a substrate support 256, a camera 258, a sensor 260, and a processing device 262.

[0049] The substrate support 256 can be a vacuum chuck, an electrostatic chuck, a magnetic chuck, a mechanical chuck (e.g., a four-jaw chuck, a three-jaw chuck, an edge / ring clamp chuck, etc.), or other type of chuck. The substrate support 256 can secure a substrate 264 (e.g., a wafer). The rotary actuator 252 can rotate the substrate support 256 about a first axis 253. The rotary actuator 252 can be controlled by a servo controller and / or a servo motor, which can enable precise control of the rotational position, velocity, and / or acceleration of the rotary actuator and thus the substrate support 256. The linear actuator 254 can move the substrate support 256 linearly along a second axis 255. The linear actuator 254 can be controlled by a servo controller and / or a servo motor 272, which can enable precise control of the linear position, velocity, and acceleration of the linear actuator 254 and thus the substrate support 256.

[0050] The camera 258 can be disposed on the substrate support 256 and can generate one or more images of a substrate 264 held by the substrate support 256. The camera 258 can be an optical camera, an infrared camera, or any other suitable type of camera. The sensor 260 can also be disposed on the substrate support 256 and can measure at least one target location on the substrate at a time (e.g., can generate reflected light or other measurements of the target locations). The camera 258 and the sensor 260 can be fixed in a stationary position on the substrate measurement system 251, and the substrate support 256 can be moved in an rθ motion by the rotary actuator 252 and the linear actuator 254.

[0051] In some embodiments, the processing device 262 can determine that the substrate 264 is not centered on the substrate support 256 based on one or more images of the substrate 264 generated by the camera 258. The substrate 264 may not be centered on the substrate support 256 when initially placed on the substrate support 256. A robot blade 270 can place the substrate 264 on a transfer station 268 (e.g., a set of lift pins). The substrate support 256 can be moved in a first direction along the second axis 255 so that the substrate support 256 is placed on the transfer station 268. The transfer station 268 can be located on (or be a set of lift pins), and the lift mechanism 266 can move the transfer station 268 up and down in a vertical direction (orthogonal to the second axis 255 and parallel to the first axis 253). The substrate 264 can be received by the substrate support 256 while the substrate support 256 is placed on the transfer station 268. The substrate 264 may not be centered on the substrate support 256. The substrate support 256 may be moved in a second direction along the second axis 255 until the sensor 260 detects that the edge of the substrate 264 is at the target position.

[0052] The substrate support can be rotated 360 degrees, and images can be generated while the substrate support is rotating. One or more of the measurements and / or images can be obtained with the chuck at various different θ values, and the detected location of the edge can vary. A change in the detected edge can indicate that the substrate (which can be a circular substrate) is off-center. Additionally, the determined change in the detected edge can be used to calculate the amount of offset.

[0053] In one embodiment, the parameters (r, θ) determine the offset of the substrate relative to the stage. These parameters enable the motion system to generate forward and inverse transformations that convert the (r, θ) coordinates of the stage to the (r, θ) coordinates of the substrate. The motion system can then calculate the trajectory of the substrate in space while sending commands to move the motors attached to the substrate support 256. In one embodiment, the motion system runs real-time control software connected (e.g., through an EtherCAT network) to the motion drivers of the linear and rotary actuators, so that it can calculate any trajectory in space. The processing device 262 can calculate the corrected trajectory and transmit commanded positions to the motion drivers in real time (e.g., at a rate of 1 kHz).

[0054] In some embodiments, rotation of the substrate support 256 by the rotational actuator 252 for measuring the target position causes the substrate 264 to be not centered on the substrate support 256, resulting in an offset between the field of view of the sensor 260 and the target position on the substrate 264. In this case, the linear actuator 254 can move the substrate support 256 linearly along a second axis to correct the offset. The sensor 260 can then measure the target position on the substrate 264. After measurements of all target points on the substrate have been measured, the processing device 262 can determine a uniformity profile across the surface of the substrate 264 based on the measurements.

[0055] In some embodiments, the processing device 262 can determine one or more coordinate transformations between the center of the substrate support 256 (corresponding to the first axis 253 about which the substrate support 256 rotates) and the center of the substrate 264 that are applied during rotation of the substrate support 256 to correct for the offset.

[0056] 2C is a schematic cross-sectional side view of a substrate measurement subsystem 282 according to aspects of the present disclosure. The substrate measurement subsystem 282 may be configured to obtain measurements on one or more portions of a substrate, such as the substrate 202 of FIG. 2A, before or after processing of the substrate 202 in a processing chamber. In an embodiment, the substrate measurement subsystem 282 may correspond to the substrate measurement system 126 of FIG. 2A. The substrate measurement subsystem 282 may obtain measurements on a portion of the substrate 202 by generating data associated with the portion of the substrate 202. In some embodiments, the substrate measurement subsystem 282 may be configured to generate spectral data, positional data, and / or other characteristic data associated with the substrate 202.

[0057] The substrate measurement subsystem 282 can be configured to generate one or more types of data for a substrate, including spectral data, positional data, substrate property data, etc. The substrate measurement subsystem 282 can generate data for a substrate in response to a request to obtain one or more measurements for the substrate before or after the substrate is processed in the manufacturing system. The substrate measurement subsystem 282 can include one or more components that facilitate generating data for the substrate. For example, the substrate measurement subsystem can include a spectral sensing component for sensing one or more spectra from a portion of the substrate to generate spectral data for the substrate. In some embodiments, the spectral sensing component can be a replaceable component and can be configurable based on the type of process performed in the manufacturing system or the target type of measurement to be obtained by the substrate measurement subsystem. For example, one or more pieces of the spectral sensing component can be replaced in the substrate measurement subsystem to enable collection of reflectometry spectral data, ellipsometry spectral data, hyperspectral imaging data, chemical imaging (e.g., X-ray photoelectron spectroscopy (XPS), energy dispersive X-ray spectroscopy (EDX), X-ray fluorescence (XRF), etc.) data, etc.

[0058] The substrate measurement subsystem 282 may include a controller 283 configured to execute one or more instructions to generate data associated with a portion of the substrate 202. The substrate measurement subsystem 282 may include a substrate sensing component 284 configured to detect when the substrate 202 has been transferred to the substrate measurement subsystem 282. The substrate sensing component 284 may include any component configured to detect when the substrate 202 has been transferred to the substrate measurement subsystem 282. For example, the substrate sensing component 284 may include an optical sensing component that transmits a light beam across an entrance to the substrate measurement subsystem 282. When the substrate 202 is positioned within the substrate measurement subsystem 282, the substrate sensing component 284 may detect that the substrate 202 has been transferred to the substrate measurement subsystem 282 in response to the substrate 202 interrupting the light beam transmitted across the entrance to the substrate measurement subsystem 282. In response to detecting that the substrate 202 has been transferred to the substrate measurement subsystem 282, the substrate sensing component 284 may transmit an indication to the controller 283 indicating that the substrate 202 has been transferred to the substrate measurement subsystem 282.

[0059] In some embodiments, the substrate sensing component 284 can be further configured to detect identification information associated with the substrate 202. In some embodiments, the substrate 202 can be embedded in a substrate carrier (not shown) when the substrate 202 is transferred to the substrate measurement subsystem 282. The substrate carrier can include one or more registration features that enable identification of the substrate 202. For example, the optical sensing component of the substrate sensing component 284 can detect when the substrate 202 embedded in the substrate carrier interrupts a light beam transmitted across an entrance to the substrate measurement subsystem 282. The optical sensing component can further detect one or more registration features included on the substrate carrier. In response to detecting the one or more registration features, the optical sensing component can generate an optical signature associated with the one or more registration features. The substrate sensing component 284 can transmit the optical signature generated by the optical sensing component to the controller 283 along with an indication that the substrate has been placed in the substrate measurement subsystem 282. In response to receiving the optical signature from the sensing component 284, the controller 283 can analyze the optical signature to determine identification information associated with the substrate 202. The identification information associated with the substrate 202 may include an identifier for the substrate 202, an identifier for the process for the substrate 202 (e.g., a batch number or process run number), an identifier for a certain type of substrate 202 (e.g., a wafer, etc.), and the like.

[0060] The substrate measurement subsystem 282 may include one or more components configured to determine the position and / or orientation of the substrate 202 within the substrate measurement subsystem 282. The position and / or orientation of the substrate 202 may be determined based on the identification of a reference location on the substrate 202. The reference location may be a portion of the substrate 202 that includes an identifying feature associated with a unique portion of the substrate 202. For example, the substrate 202 may have a reference tag embedded in a center of the substrate 202. In another example, the substrate 202 may have one or more structural features on a surface of the substrate 202 that are included in the center of the substrate 202. The controller 283 may determine the identifying feature associated with the unique portion of the substrate 202 based on the determined identification information for the substrate 202. For example, in response to determining that the substrate 202 is a wafer, the controller 283 may determine one or more identifying features generally included in a portion of a wafer.

[0061] The controller 283 can identify a reference location relative to the substrate 202 using one or more camera components 285 configured to capture image data for the substrate 202. The camera components 285 can generate image data for one or more portions of the substrate 202 and transmit the image data to the controller 283. The controller 283 can analyze the image data to identify an identifying feature associated with the reference location relative to the substrate 202. The controller 283 can further determine a position and / or orientation of the substrate 202 shown in the image data based on the identified identifying feature of the substrate 202. The controller 283 can determine the position and / or orientation of the substrate 202 based on the identified identifying feature of the substrate 202 and the determined position and / or orientation of the substrate 202 shown in the image data.

[0062] In response to determining the position and / or orientation of the substrate 202, the controller 283 can generate position data associated with one or more portions of the substrate 202. In some embodiments, the position data can include one or more coordinates (e.g., Cartesian coordinates, polar coordinates, etc.), each coordinate associated with a portion of the substrate 202, and each coordinate determined based on a distance from a reference location relative to the substrate 202. For example, in response to determining the position and / or orientation of the substrate 202, the controller 283 can generate first position data associated with a portion of the substrate 202 that includes the reference location, the first position data including a Cartesian coordinate of (0,0). The controller 283 can generate second position data associated with a second portion of the substrate 202 relative to the reference location. For example, a portion of the substrate 202 located approximately 2 nanometers (nm) due east of the reference location can be assigned a Cartesian coordinate of (0,1). In another example, a portion of the substrate 202 located 5 nm due north of the reference location can be assigned a Cartesian coordinate of (1,0).

[0063] The controller 283 can determine one or more portions of the substrate 202 to measure based on the position data determined for the substrate 202. In some embodiments, the controller 283 can receive one or more operations of a process recipe associated with the substrate 202. In such embodiments, the controller 283 can further determine one or more portions of the substrate 202 to measure based on one or more operations of the process recipe. For example, the controller 283 can receive an indication that an etching process has been performed on the substrate 202 if several structural features have been etched onto the surface of the substrate 202. As a result, the controller 283 can determine one or more structural features to measure and the expected locations of such features at various portions of the substrate 202.

[0064] The substrate measurement subsystem 282 may include one or more measurement components for measuring the substrate 202. In some embodiments, the substrate measurement subsystem 282 may include one or more spectral sensing components 287 configured to generate spectral data for one or more portions of the substrate 202. As previously discussed, the spectral data may correspond to the intensity (i.e., strength or amount of energy) of the detected energy waves for each wavelength of the detected waves.

[0065] In one embodiment, the reflected energy waves received by the substrate measurement subsystem 282 may include multiple wavelengths. Each reflected energy wave may be associated with a different portion of the substrate 202. In some embodiments, an intensity may be measured for each reflected energy wave received by the substrate measurement subsystem 282. Each intensity may be measured for each wavelength of the reflected energy waves received by the substrate measurement subsystem 282. The association between each intensity and each wavelength may be the basis for forming spectral data. In some embodiments, one or more wavelengths may be associated with intensity values ​​outside of an expected range of intensity values. In such embodiments, intensity values ​​outside of the expected range of intensity values ​​may be an indication that a defect exists in a portion of the substrate 202.

[0066] The measurement components for measuring the substrate 202 may also include non-spectral sensing components configured to collect and generate non-spectral data. For example, the measurement components may include eddy current sensors or capacitive sensors. While some embodiments of this description may refer to collecting and using spectral data for the substrate 202, embodiments of this description may also be applicable to non-spectral data collected for the substrate 202.

[0067] The spectral sensing component 287 can be configured to detect energy waves reflected from a portion of the substrate 202 and generate spectral data associated with the detected waves. The spectral sensing component 287 can include a wave generator 288 and a reflected wave receiver 291. In some embodiments, the wave generator 288 can be a light wave generator configured to generate a light beam toward the portion of the substrate 202. In such embodiments, the reflected wave receiver 291 can be configured to receive the light beam reflected from the portion of the substrate 202. The wave generator 288 can be configured to generate an energy flow 289 (e.g., a light beam) and transmit the energy flow 289 toward the portion of the substrate 202. The reflected energy wave 290 can be reflected from the portion of the substrate 202 and can be received by the reflected wave receiver 291. Although FIG. 2C shows a single energy wave reflected from the surface of the substrate 202, multiple energy waves can be reflected from the surface of the substrate 202 and can be received by the reflected wave receiver 291.

[0068] In response to reflected wave receiver 291 receiving reflected energy waves 290 from the portion of substrate 202, spectral sensing component 287 can measure the wavelength of each wave included in reflected energy waves 289. Spectral sensing component 287 can further measure the intensity of each measured wavelength. In response to measuring each wavelength and each wavelength intensity, spectral sensing component 287 can generate spectral data for the portion of substrate 202. Spectral sensing component 287 can transmit the generated spectral data to controller 283. In response to receiving the generated spectral data, controller 283 can generate a mapping between the received spectral data and position data for the measured portion of substrate 202.

[0069] The substrate measurement subsystem 282 can be configured to generate a specific type of spectral data based on the type of measurement to be obtained by the substrate measurement subsystem 282. In some embodiments, the spectral sensing component 287 can be a first spectral sensing component configured to generate one type of spectral data. For example, the spectral sensing component 287 can be configured to generate reflectometry spectral data, ellipsometry spectral data, hyperspectral imaging data, chemical imaging data, thermal spectral data, or conductance spectral data. In such embodiments, the first spectral sensing component can be removed from the substrate measurement subsystem 282 and replaced with a second spectral sensing component configured to generate a different type of spectral data (e.g., reflectometry spectral data, ellipsometry spectral data, hyperspectral imaging data, or chemical imaging data).

[0070] The controller 283 can determine the type of data (i.e., spectral data, non-spectral data) to be generated for the substrate 202 based on the type of measurements to be obtained for one or more portions of the substrate 202. In some embodiments, the controller 283 can determine the type or types of measurements based on notifications received from the system controller 228 of FIG. 2A . In other or similar embodiments, the controller 283 can determine the type or types of measurements based on instructions to generate measurements for portions of the substrate 202. In response to determining the type or types of measurements to be obtained, the controller 283 can determine the type of data to be generated for the substrate 202. For example, the controller 283 can determine that spectral data should be generated for the substrate 202 and that the second spectral sensing component is the best-suited sensing component for obtaining the determined type of measurements for one or more portions of the substrate 202. In response to determining that the second sensing component is the optimal sensing component, the controller 283 may transmit a notification to the system controller indicating that the first spectral sensing component should be replaced with the second spectral sensing component and that the second spectral sensing component should be used to obtain one or more types of measurements on one or more portions of the substrate 202. The system controller 128 may transmit the notification to a client device connected to the manufacturing system, and the client device may provide the notification to a user (e.g., an operator) of the manufacturing system via a GUI.

[0071] In other or similar embodiments, the spectral sensing component 287 can be configured to generate multiple types of spectral data. In such embodiments, the controller 283, according to the previously described embodiments, can cause the spectral sensing component 287 to generate a specific type of spectral data based on the type of measurement to be taken for one or more portions of the substrate 202. In response to determining the type of measurement to be taken, the controller 283 can determine that a first type of spectral data should be generated by the spectral sensing component 287. Based on the determination that the first type of spectral data should be generated by the spectral sensing component 287, the controller 283 can cause the spectral sensing component 287 to generate the first type of spectral data for one or more portions of the substrate 202.

[0072] As described above, the controller 283 can determine one or more portions of the substrate 202 for measurement by the substrate measurement subsystem 282. In some embodiments, one or more measurement components, such as the spectral sensing component 287, can be stationary components within the substrate measurement subsystem 282. In such embodiments, the substrate measurement subsystem 282 can include one or more position components 295 configured to modify the position and / or orientation of the substrate 202 relative to the spectral sensing component 287. In some embodiments, the position components 295 can be configured to translate the substrate 202 relative to the spectral sensing component 287 along a first axis and / or a second axis. In other or similar embodiments, the position components 295 can be configured to rotate the substrate 202 relative to the spectral sensing component 287 about a third axis.

[0073] Once the spectral sensing component 287 generates spectral data for one or more portions of the substrate 202, the position component 295 can modify the position and / or orientation of the substrate 202 according to the one or more determined portions to be measured for the substrate 202. For example, before the spectral sensing component 287 generates spectral data for the substrate 202, the position component 295 can position the substrate 202 at Cartesian coordinate (0,0), and the spectral sensing component 287 can generate first spectral data for the substrate 202 at the Cartesian coordinate (0,0). In response to the spectral sensing component 287 generating the first spectral data for the substrate 202 at the Cartesian coordinate (0,0), the positioning component 240 can translate the substrate 202 along the first axis, such that the spectral sensing component 287 is configured to generate second spectral data for the substrate 202 at the Cartesian coordinate (0,1). In response to the spectral sensing component 287 generating the second spectral data for the substrate 202 at Cartesian coordinate (0,1), the controller 283 can rotate the substrate 202 along a second axis such that the spectral sensing component 287 is configured to generate third spectral data for the substrate 202 at Cartesian coordinate (1,1). This process can be performed multiple times until spectral data has been generated for each determined portion of the substrate 202.

[0074] In some embodiments, the surface of the substrate 202 may include one or more layers 297 of material. The one or more layers 297 may include an etching material, a photoresist material, a mask material, a deposition material, etc. In some embodiments, the one or more layers 297 may include an etching material to be etched according to an etching process performed in a processing chamber. In such embodiments, spectral data may be collected for one or more portions of the unetched etching material of the layer 297 deposited on the substrate 202 according to previously disclosed embodiments. In other or similar embodiments, the one or more layers 297 may include an etching material that has already been etched by an etching process in a processing chamber. In such embodiments, one or more structural features (e.g., lines, posts, openings, etc.) may be etched into the one or more layers 297 of the substrate 202. In such embodiments, spectral data may be collected for one or more structural features etched into the one or more layers 297 of the substrate 202.

[0075] In some embodiments, the substrate measurement subsystem 282 may include one or more additional sensors configured to capture additional data for the substrate 202. For example, the substrate measurement subsystem 282 may include additional sensors configured to determine the thickness of the substrate 202, the thickness of a film deposited on the surface of the substrate 202, etc. Each sensor may be configured to transmit the captured data to the controller 283.

[0076] In response to receiving at least one of spectral data, positional data, or characteristic data for the substrate 202, the controller 283 can transmit the received data to the system controller 228 for processing and analysis according to embodiments described herein.

[0077] In some embodiments, the substrate measurement subsystem 282 includes one or more image capture devices 299, such as a camera (e.g., including a complementary metal-oxide semiconductor (CMOS) sensor or a charge-coupled device (CCD) sensor), connected to the controller 283. The image capture devices 299 can generate images (e.g., two-dimensional (2D) color images, infrared (IR) images, near-infrared images, etc.). In embodiments, these images, along with the spectral data generated by the spectral sensing component 287, can be processed by one or more trained machine learning models to make a determination regarding one or more chamber components.

[0078] 3 illustrates an exemplary system architecture 300 for predicting or evaluating substrate placement for a processing chamber in accordance with aspects of the present disclosure. In some embodiments, system architecture 300 may include or be a part of one or more components of computer architecture 100 and / or manufacturing system 200. System architecture 300 may include one or more components of manufacturing equipment 122 (e.g., substrate metrology system 126), server machine 320, and server machine 350.

[0079] As previously described, the manufacturing equipment 122 may perform operations according to a recipe or over a period of time to create a product. The manufacturing equipment 122 may include a process chamber 310 configured to perform a substrate process on a substrate according to a substrate process recipe. In some embodiments, the recipe may be a blanket wafer recipe that deposits a film on a test substrate. In some embodiments, the recipe may be a blanket wafer recipe that etches a surface of a test substrate. In some embodiments, the process chamber 310 may be any of the process chambers 214, 218, 218 described with respect to FIG. 2A . The manufacturing equipment 122 may also include a substrate metrology system 126, as described herein.

[0080] Manufacturing equipment 122 can be coupled to server machine 320. Server machine 320 can include a processing device 322 and / or a data store 332. In some embodiments, processing device 322 can be configured to execute one or more instructions to perform operations at manufacturing equipment 122. For example, processing device 322 can include or be part of system controller 228 described with respect to FIG. 2A . In some embodiments, data store 332 can include or be part of data store 150 and / or data store 250.

[0081] The processing device 322 can be configured to receive data from one or more components of the manufacturing equipment 122 (i.e., over a network). For example, the processing device 322 can receive surface profile data (e.g., wafer map, thickness profile data, etc.) 336 collected by the substrate metrology system 126 after the substrate is processed in a process chamber. In another example, the processing device 322 can receive metrology data collected by other metrology instruments before and / or after a substrate process for that substrate. The metrology data can include metrology measurements generated for the substrate by integrated metrology instruments. In some embodiments, the processing device 322 can store the received spectral data, film thickness profile data, substrate thickness profile data, and / or received metrology data in the data store 332.

[0082] The processing device 352 may include a substrate placement engine 330. The substrate placement engine 330 in the processing device 322 may be configured to determine a recommended placement (e.g., recommended location) for a substrate being processed in the process chamber 310. The process chamber 310 may be the process chamber in which the measured substrate was processed. The substrate placement engine 330 may determine one or more substrate placement metrics for one or more substrates processed in the process chamber 310 in which the substrate was processed from the substrate surface profile data 336. In some embodiments, the substrate placement metric may include a set of values ​​(e.g., a vector, a matrix, etc.) that indicates correlation of a particular data combination, correlation, pattern, and / or relationship present in the sensor data. For example, the substrate placement metric may include a feature vector that includes binary values ​​that indicate the presence or absence of a particular feature in the data.

[0083] The substrate placement metrics can be compared to known patterns and / or combinations of substrate placement metrics (e.g., target substrate placement metrics). The target substrate placement metrics can be associated with ideal substrate placement for optimal processing of the substrate. In response to determining that the substrate placement metrics for one or more substrate placement locations satisfy one or more substrate placement criteria (e.g., the conditions for the processed substrate satisfy a threshold specification, such as a threshold slope specification), the substrate placement engine 330 can determine a recommended placement for the substrate to be processed and / or provide an alert to a user. In some embodiments, the substrate placement criteria can include a specified combination of values ​​indicated by the substrate placement metrics. After the recommended substrate placement is determined, the substrate placement engine 330 can output instructions for the robot arm to use when placing the substrate on the substrate support according to the coordinates of the recommended substrate placement using specific settings (e.g., x setting and / or y setting).

[0084] 3 , the processing device 322, in some embodiments, may include a training set generator 324 and / or a training engine 326. In some embodiments, the training set generator 324 may correspond to the training set generator 172, and / or the training engine 326 may correspond to the training engine 182, as described with respect to FIG. 1 . The training set generator 324 may be configured to generate a training set 340 for training a machine learning model 334 or a set of machine learning models 334. For example, the training set generator 324 may generate training inputs based on historical substrate surface profile data 336. The substrate surface profile data 336 (e.g., a thickness profile map) may be associated with etch rates and / or slopes across the surfaces of one or more substrates processed in the processing chamber. In some embodiments, the training set generator 324 may retrieve historical substrate surface profile data 336 (e.g., substrate thickness profile data) from the data store 332 to generate the training inputs. The training set generator 324 can generate target outputs indicating estimated substrate placement values ​​(e.g., substrate placement metrics) for the training inputs based on the historical substrate surface profile data 336. The training set generator 324 can include the generated training inputs and the generated target outputs in a training set 340. The training set 340 can further include an indicator of substrate placement (e.g., on a substrate support, such as an electrostatic chuck, relative to the inner diameter of a process kit ring) for each historical substrate thickness profile. Further details regarding generating the training set 340 are provided with respect to FIG. 5.

[0085] The training engine 326 can be configured to train, validate, and / or test the machine learning model 334 or sets of machine learning models 334. The training engine 326 can provide a training set 340 to train the machine learning model 334 and store the trained machine learning model 334 in the data store 332. In some embodiments, the training engine 326 can validate the trained machine learning model 334 using a validation set 342. The validation set 342 can include surface profile data 336 and associated chamber component condition metrics. The training set generator 324 and / or the training engine 326 can generate the validation set 342 based on historical surface profile data 336. In some embodiments, the validation set 342 can include historical surface profile data 336 that differs from the historical surface profile data 336 included in the training set 340.

[0086] The training engine 326 can provide the historical surface profile data 336 as input to the trained machine learning model 334 and can extract one or more substrate placement metrics for processing a substrate in the process chamber from one or more outputs of the trained model 334. The input can further include the age of one or more components of the process chamber and / or one or more images of the substrate used to generate the historical surface profile data 236. The training engine 326 can assign a performance score to the trained model 334 based on the accuracy of the substrate placement metrics. The training engine 326 can select the trained model 334 to be used to evaluate substrate placement for processing a substrate in the process chamber based on the surface profile data for substrates processed by the process chamber.

[0087] As previously discussed, the training set generator 324 and / or the training engine 326 may, in some embodiments, be components of the processing device 322 residing on the server 320. In additional or alternative embodiments, the training set generator 324 and / or the training engine 326 may also be components of the processing device 352 residing on the server 350. The server 350 may include, or be part of, a computing system separate from the manufacturing system 200. As previously discussed, the server 320 may, in some embodiments, include, or be part of the system controller 228 described with respect to FIG. 2A . In such embodiments, the server 350 may include, or be part of, a computing system coupled to, but separate from, the system controller 228 (i.e., via a network). For example, users of the manufacturing system 200 may be provided with access to data stored in one or more portions of the data store 332 or to one or more processes running on the processing device 322. However, users of manufacturing system 200 may not be provided with access to any data stored in one or more portions of data store 354 or to any processes executed on processing device 352.

[0088] Processing device 352, like processing device 322, can be configured to execute training set generator 324 and / or training engine 326. In some embodiments, server 350 can be coupled to manufacturing equipment 122 and / or in-line metrology equipment 130 via a network. Accordingly, processing device 352 can obtain surface profile data 336 and substrate placement metrics corresponding to surface profile data 336 to be used by training set generator 324 and / or training engine 326 to generate training set 340 and validation set 342, according to embodiments described with respect to processing device 322. In other or similar embodiments, server 350 is not coupled to manufacturing equipment 122 and / or external metrology equipment 130. Accordingly, processing device 352 can obtain surface profile data 336 from processing device 322.

[0089] The training set generator 324 on the processing device 352 can generate the training set 340 according to the previously described embodiments. The training engine 326 on the processing device 352 can train and / or validate the machine learning model 334 according to the previously described embodiments. In some embodiments, the server 350 can be coupled to other manufacturing equipment and / or other server machines different from the manufacturing equipment 122 and / or the server machine 320. The processing device 352 can acquire surface profile data 336 from the other manufacturing equipment and / or server machines according to embodiments described herein. In some embodiments, the training set 340 and / or the validation set 342 can be generated based on the surface profile data 336 acquired for substrates processed in the process chamber 310 as well as other surface profile data acquired for other substrates processed in process chambers in other manufacturing systems.

[0090] In response to the training engine 326 selecting the trained model 334 to be used, the processing device 352 may transmit the trained model 334 to the processing device 322. The substrate placement engine 330 may use the trained model 334 to provide substrate placement recommendations, as described above.

[0091] 4 illustrates a model training workflow 405 and a model application workflow 417 for substrate placement determination according to one embodiment. The model training workflow 405 and the model application workflow 417 may be performed by processing logic executed by a processor of a computing device. One or more of these workflows 405, 417 may be performed, for example, by one or more machine learning models implemented on the processing device and / or other software and / or firmware executing on the processing device.

[0092] The model training workflow 405 is for training one or more machine learning models (e.g., deep learning models) to determine optimal substrate placement for processing a substrate in a process chamber. The model application workflow 417 is for applying the one or more trained machine learning models to perform substrate placement assessment. Each of the profile maps 412 can be associated with a thickness profile or other surface profile of a processed substrate. For example, each of the profile maps 412 can reflect the thickness of the corresponding substrate after a processing operation (e.g., an etch operation, etc.) performed on the substrate.

[0093] Various machine learning outputs are described herein. Specific numbers and arrangements of machine learning models are described and illustrated. However, it should be understood that the number and types of machine learning models used, as well as the arrangements of such machine learning models, can be modified to achieve the same or similar end results. Therefore, the arrangements of the machine learning models described and illustrated are merely examples and should not be construed as limiting.

[0094] In some embodiments, one or more machine learning models are trained to perform one or more substrate placement estimation tasks. Each task can be performed by a separate machine learning model. Alternatively, a single machine learning model can perform each of the tasks or portions of the tasks. For example, a first machine learning model can be trained to determine substrate placement (e.g., a location on a substrate support for substrate processing), and a second machine learning model can be trained to determine substrate handoff offsets (e.g., substrate handling robot handoff offsets). Additionally or alternatively, different machine learning models can be trained to perform different combinations of tasks. In one example, one or several machine learning models can be trained, and the trained machine learning (ML) model is a single shared neural network with multiple shared layers and multiple separate higher-level output layers, each output layer outputting a different prediction, classification, identification, etc. For example, a first higher-level output layer can determine substrate placement relative to a first type of chamber part (e.g., an electrostatic chuck), and a second higher-level output layer can determine substrate placement relative to a second type of chamber part (e.g., a process kit ring).

[0095] One type of machine learning model that can be used to perform some or all of the above tasks is an artificial neural network, such as a deep neural network. An artificial neural network generally includes a feature representation component with a classifier or recurrent layer that maps features to a target output space. A convolutional neural network (CNN), for example, hosts multiple layers of convolutional filters. Pooling can be performed to address nonlinearities in lower layers, and a multilayer perceptron is typically attached on top to map upper-layer features extracted by the convolutional layers to a decision (e.g., classification output). Deep learning is a type of machine learning algorithm that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. Deep neural networks can be trained in a supervised (e.g., classification) and / or unsupervised (e.g., pattern analysis) manner. Deep neural networks include a hierarchy of multiple layers, with different layers learning different levels of representation corresponding to different levels of abstraction. In deep learning, each level learns to transform its input data into a slightly more abstract and complex representation. In particular, the deep learning process can learn which features naturally best fit into which levels. The "deep" in "deep learning" refers to the number of layers through which data is transformed. More precisely, a deep learning system has substantial contribution allocation path (CAP) depth. A CAP is a chain of input-to-output transformations. A CAP describes the potentially causal connections between inputs and outputs. For forward neural networks, the CAP depth can be the depth of the network, which can be the number of hidden layers plus one. For recurrent neural networks, where a signal can propagate through a layer more than once, the CAP depth is potentially unlimited.

[0096] Training a neural network can be accomplished in a supervised learning manner, which involves feeding a training data set of labeled inputs through the network, observing its outputs, defining an error (by measuring the difference between the output and the label value), and using techniques such as deep gradient descent and backpropagation to adjust the mass of the network across all of its layers and nodes to minimize the error. In many applications, repeating this process across many labeled inputs in the training data set results in a network that can provide the correct output when presented with inputs different from those present in the training data set.

[0097] For the model training workflow 405, a training dataset including hundreds, thousands, tens of thousands, hundreds of thousands, or more profile maps 412 of a single substrate should be used to form the training dataset. The data may include, for example, uniformity profiles determined using a given number of measurements, each associated with a particular target location. This data may be processed to generate one or more training datasets 436 for training one or more machine learning models. The training data items in the training dataset 436 may include the profile maps 412, substrate configurations used to process the substrates measured to generate the profile maps, and / or one or more images of those substrates.

[0098] To achieve training, processing logic inputs a training data set 436 to one or more untrained machine learning models. Before inputting a first input to the machine learning models, the machine learning models can be initialized. Processing logic trains the untrained machine learning models based on the training data set to generate one or more trained machine learning models that perform the various operations described above. Training can be performed by inputting input data, such as one or more profile maps 412 (e.g., thickness profile map, spectral profile map, roughness profile map, particle count profile map, optical constant profile map, etc.), component images and / or age information, into the machine learning model one at a time.

[0099] A machine learning model processes this input to generate an output. An artificial neural network includes an input layer consisting of values ​​in the data points. The next layer is called the hidden layer, and each node in the hidden layer receives one or more of the input values. Each node includes parameters (e.g., masses) to apply to the input values. Thus, each node essentially inputs the input values ​​into a multivariate function (e.g., a nonlinear mathematical transformation) to produce an output value. The next layer can be another hidden layer or an output layer. In either case, the nodes in the next layer receive output values ​​from the nodes in the previous layer, and each node applies masses to those values ​​and then generates its own output value. This can be done at each layer. The final layer is the output layer, where there is one node for each class, prediction, and / or output that the machine learning model can produce.

[0100] Thus, the output can include one or more predictions or inferences (e.g., an estimate of substrate placement in a process chamber for substrate processing in the process chamber in which the measured substrate was processed). Processing logic can compare the output estimated substrate placement to historical substrate placements. Processing logic determines an error (i.e., classification error) based on the difference between the estimated substrate placement and the target substrate placement. Processing logic adjusts the mass of one or more nodes in the machine learning model based on the error. An error term or delta can be determined for each node in the artificial neural network. Based on this error, the artificial neural network adjusts one or more of its parameters (masses for one or more inputs of the node) for one or more of its nodes. Parameters can be updated using a backpropagation method, such that nodes in the highest layer are updated first, followed by nodes in the next layer, and so on. An artificial neural network includes multiple layers of "neurons," each layer receiving as input values ​​from neurons in the previous layer. The parameters for each neuron include a mass associated with values ​​received from each of the neurons in the previous layer. Accordingly, adjusting the parameters may include adjusting masses assigned to each of the inputs to one or more neurons in one or more layers within the artificial neural network.

[0101] After the model parameters are optimized, model validation can be performed to determine whether the model has improved and to determine the current accuracy of the deep learning model. After one or more training rounds, the processing logic can determine whether a stopping criterion has been met. The stopping criterion can be a target accuracy level, a target number of processed images from the training dataset, a target amount of change to the parameters relative to one or more previous data points, combinations thereof, and / or other criteria. In one embodiment, the stopping criterion is met when at least a minimum number of data points have been processed and at least a threshold accuracy has been achieved. The threshold accuracy can be, for example, 70%, 40%, or 90% accuracy. In one embodiment, the stopping criterion is met when the accuracy of the machine learning model stops improving. If the stopping criterion is not met, further training is performed. If the stopping criterion is met, training can be completed. After the machine learning model is trained, the model can be tested using a reserved portion of the training dataset. After one or more trained machine learning models 438 are generated, they can be stored in model storage 445 and added to the substrate placement engine 330.

[0102] According to one embodiment, for the model application workflow 417, the input data 462 can be input to one or more substrate placement determiners 467, each of which can include a trained neural network or other model. Additionally or alternatively, the one or more substrate placement determiners 467 can apply image processing algorithms to determine chamber component conditions. The input data can include a profile map (e.g., of a polymer layer on a substrate measured using an integrated reflected light measurement device or substrate metrology system). The input data can further include one or more images of the substrate. Based on the input data 462, the substrate placement determiner 467 can output one or more estimated substrate placements 469. The estimated substrate placements 469 can include a substrate placement relative to a substrate support structure for processing the substrate in the process chamber (e.g., which may be an offset from the center of a substrate support, such as an electrostatic chuck). In some examples, a substrate support-based coordinate system (e.g., based on a center point of the substrate support) can be used to correlate the estimated substrate placement (e.g., position) relative to the substrate support.

[0103] The action determiner 472 can determine one or more actions 470 to take based on the substrate placement 469. In one embodiment, the action determiner 472 compares the substrate placement estimates to one or more substrate placement thresholds. If one or more of the substrate placement estimates meet or exceed the substrate placement thresholds, the action determiner 472 can determine that updating the substrate placement is recommended for future substrate placements and can output a recommendation or notification to update the substrate placement parameters. In some embodiments, the action determiner 472 automatically updates the substrate placement metrics based on the substrate placement 469 meeting one or more criteria. In some examples, the substrate placement 469 can include an estimated position where the substrate will be positioned for processing relative to the substrate support (e.g., an electrostatic chuck) and / or a component of the substrate support (e.g., a process kit ring). The estimated position can be an optimized position for the substrate to minimize tilt within the processed substrate, particularly near the edge of the substrate. The substrate placement 469 can include a coordinate position relative to the center point of the substrate support relative to the center point of the substrate. In some examples, the substrate placement 469 may reflect an offset between the center of the substrate and the center of the substrate support, which in some embodiments may be used to determine one or more offsets for the robot to handle the substrate (e.g., updated positions for the robot to place and / or "hand off" the substrate, etc.).

[0104] 5 is a flowchart of a method 500 for generating a training dataset for training a machine learning model to perform substrate placement assessment, according to an aspect of the present disclosure. Method 500 is performed by processing logic, which may include hardware (circuitry, dedicated logic, etc.), software (such as running on a general-purpose computer system or a dedicated machine), firmware, or some combination thereof. In one embodiment, method 500 may be performed by a computer system, such as computer system architecture 100 of FIG. 1. In other or similar embodiments, one or more operations of method 500 may be performed by one or more other machines not shown. In some aspects, one or more operations of method 500 may be performed by training set generator 324 of server machine 320 or server machine 350, as described with respect to FIG. 3.

[0105] At block 510, processing logic initializes a training set T to an empty set (e.g., {}). At block 512, processing logic obtains substrate surface data associated with a substrate processed in a process chamber of the manufacturing system (e.g., reflected light measurement data of the surface of a film on the substrate, such as a film thickness profile map or wafer map).

[0106] At block 514, processing logic obtains substrate placement information for a substrate processed by the process chamber. As mentioned above, the substrate placement information may include a coordinate position of the substrate relative to the substrate support and / or relative to components of the substrate support.

[0107] At block 516, processing logic generates training inputs based on the sensor data acquired for the substrate at block 512. In some embodiments, the training inputs may include a normalized set of sensor data (e.g., including surface reflectometer data as described herein).

[0108] At block 518, processing logic may generate target outputs based on the substrate placement information obtained at block 514. The target outputs may correspond to substrate placement metrics (data indicative of placement for one or more substrates processed in the process chamber) for substrates processed in the process chamber.

[0109] At block 520, processing logic generates an input / output mapping. The input / output mapping refers to training inputs that include or are based on data for a substrate, and target outputs for the training inputs, where the target outputs identify substrate configurations, and the training inputs are associated with (or mapped to) the target outputs. At block 522, processing logic adds the input / output mapping to a training set T.

[0110] At block 524, processing logic determines whether training set T includes a sufficient amount of training data for training the machine learning model. Note that in some embodiments, training set T may be determined to be sufficient based solely on the number of input / output mappings in the training set, while in some other embodiments, training set T may be determined to be sufficient based on one or more other criteria (e.g., the degree of diversity of the training examples) in addition to or instead of the number of input / output mappings. In response to determining that training set T includes a sufficient amount of training data for training the machine learning model, processing logic provides training set T for training the machine learning model. In response to determining that the training set does not include a sufficient amount of training data for training the machine learning model, method 500 returns to block 512.

[0111] At block 526, processing logic provides a training set T for training the machine learning model. In some embodiments, the training set T is provided to the training engine 326 of the server machine 320 and / or the server machine 350 to perform the training. In the case of a neural network, for example, input values ​​of a given input / output mapping (e.g., spectral data and / or chamber data for a previous substrate) are input to the neural network, and output values ​​of the input / output mapping are stored in output nodes of the neural network. The connection mass in the neural network is then adjusted according to a learning algorithm (e.g., backpropagation, etc.), and the procedure is repeated for other input / output mappings in the training set T. After block 526, the machine learning model 190 can be used to provide substrate placement (e.g., substrate placement metrics) for substrates processed in the process chamber.

[0112] 6 is a flow chart illustrating one embodiment of a method 600 for training a machine learning model to predict a substrate placement for processing a substrate in a process chamber. Method 600 is performed by processing logic, which may include hardware (circuitry, dedicated logic, etc.), software (such as running on a general-purpose computer system or a dedicated machine), firmware, or some combination thereof. In one embodiment, method 600 may be performed by a computer system, such as computer system architecture 100 of FIG. 1. In other or similar embodiments, one or more operations of method 600 may be performed by one or more other machines not shown. In some aspects, one or more operations of method 600 may be performed by training engine 326 for server machine 320 or server machine 350 described with respect to FIG. 3.

[0113] At block 602 of method 600, processing logic assembles a training dataset, which may include data from multiple substrate profile maps (e.g., a film thickness profile map indicating polymer film thickness for multiple locations on a substrate, a substrate surface profile map, a substrate thickness profile map, etc.). Each data item in the training dataset may include one or more labels. Data items in the training dataset may include input-level (e.g., image-level) labels indicating the presence or absence of one or more substrate features associated with a substrate configuration. For example, some data items may include labels for gradients present in a processed substrate. In some embodiments, each data item includes a substrate map, which may be an image of the substrate (e.g., a heat map of the substrate). Colors in the heat map may indicate substrate thickness and / or other parameter values ​​(e.g., gradient, etc.). Alternatively, actual thickness values ​​may be used for each of many coordinates on the surface of the substrate (e.g., of the substrate).

[0114] At block 604, data items from the training data set are input to an untrained machine learning model. At block 606, the machine learning model is trained to generate a trained machine learning model that classifies or predicts one or more substrate configurations for processing substrates in a process chamber based on the training data set. The machine learning model can also be trained to output one or more other types of predictions, coordinate-level classifications, decisions, etc.

[0115] In one embodiment, at block 610, training data item inputs are input to the machine learning model. The input may include data from a substrate profile map (e.g., a substrate surface profile map) indicating one or more surface characteristics across the substrate (e.g., thickness, optical constants, grain count, roughness, material properties, etc.). The data may, in embodiments, be input as an image or a feature vector. At block 612, the machine learning model processes the input to generate an output. The output may include one or more substrate placements (e.g., substrate placement values, etc.). The substrate placements may be recommended partial placements for future substrate placements on the substrate support. The output may additionally or alternatively include one or more substrate handoff offsets for the robot arm. For example, the handoff offsets may correlate an initial robot handoff orientation to an updated robot handoff orientation to match a predicted substrate placement for substrate processing.

[0116] At block 614, processing logic compares the output probabilities and / or values ​​of the substrate placement metrics to a known optimal substrate placement associated with the input. At block 616, processing logic determines an error based on the difference between the output and the known placement. At block 618, processing logic adjusts the mass of one or more nodes in the machine learning model based on the error.

[0117] At block 620, processing logic determines whether a stopping criterion has been met. If the stopping criterion has not been met, the method returns to block 610, where another training data item is input to the machine learning model. If the stopping criterion has been met, the method proceeds to block 625, where training of the machine learning model is complete.

[0118] In one embodiment, one or more ML models are trained to be applied across multiple process chambers, which may be of the same type or model. The trained ML models can then be further tuned for use with a specific instance of a process chamber. Further tuning can be performed by using additional training data items, including surface profile maps of substrates processed by the process chamber. Such tuning can account for chamber mismatches between chambers and / or specific hardware process kits for some process chambers. Additionally, in some embodiments, further training is performed to tune the ML models for a process chamber after maintenance on the process chamber and / or one or more changes to the process chamber hardware.

[0119] 7 is a flow diagram of a method 700 for determining a recommended substrate placement according to aspects of the present disclosure. Method 700 is performed by processing logic, which may include hardware (circuitry, dedicated logic, etc.), software (such as running on a general-purpose computer system or a dedicated machine), firmware, or some combination thereof. In one embodiment, method 700 may be performed by a computer system, such as computer system architecture 100 of FIG. 1. In other or similar embodiments, one or more operations of method 700 may be performed by one or more other machines not shown. In some aspects, one or more operations of method 700 may be performed by substrate placement engine 330 of server machine 320 described with respect to FIG. 3.

[0120] At block 706, processing logic (e.g., of a processing device) determines a center of a substrate support (e.g., an electrostatic chuck) in a process chamber of a substrate processing system (also referred to as a manufacturing system). In some embodiments, the center of the substrate support is determined from data collected by a multi-function wafer. For example, a multi-function wafer (e.g., a camera wafer) can collect images of the electrostatic chuck in the process chamber. The processing device can process these images to determine the center of the electrostatic chuck. The center of the substrate support can correspond to a reference point (e.g., a (0,0) point) of the substrate support.

[0121] At block 708, the substrate is aligned with the center of the substrate support. In some embodiments, processing logic causes a robot arm (e.g., of a substrate handling robot) to place the substrate on the substrate support with the center of the substrate aligned with the center of the substrate support. In some embodiments, the substrate is center aligned on the substrate support to obtain a baseline measurement for the placement of the substrate. In some embodiments, the substrate is a proxy substrate (e.g., a test substrate, etc.).

[0122] At block 710, the process chamber processes the substrate. For example, the process chamber may perform an etching process to partially remove a film on the surface of the substrate or a film deposition process to deposit a film on the substrate. In some embodiments, the film is a polymer. In other embodiments, the film is a ceramic (e.g., a metal oxide). The processing of the substrate may be performed according to a recipe (e.g., an etching process recipe, a deposition process recipe, etc.). During processing, the substrate may be supported by a substrate support (e.g., an electrostatic chuck) in the process chamber. The substrate may be placed in an initial position within the process chamber before processing (e.g., block 708) and may remain in the initial position during processing. After processing, in some embodiments, the substrate includes a surface profile (e.g., a thickness profile, etc.).

[0123] At block 712, one or more robots transfer the substrate from the process chamber to a substrate measurement system. If a substrate measurement system is connected to or contained within the transfer chamber, the transfer chamber robot can remove the substrate from the process chamber and insert the substrate into the substrate measurement system. If a substrate measurement system is connected to or contained within the factory interface, the transfer chamber robot can remove the substrate from the process chamber and place the substrate into a load lock. A factory interface robot can then remove the substrate from the load lock and insert the substrate into the substrate measurement system. The substrate measurement system can be any of the substrate measurement systems described above, such as an integrated reflected light measurement (IR) device.

[0124] At block 714, the substrate measurement system generates measurements of many locations on the surface of the substrate, each location may have a unique set of coordinates.

[0125] At block 716, the substrate measurement system and / or computing device may generate a profile map of the surface profile of the substrate (e.g., a substrate surface profile map) based on the measurements of the substrate measurement system. The profile map may be or may include an image, where each pixel in the image corresponds to a coordinate on the substrate. Each pixel may have an intensity value corresponding to a measurement value (e.g., a thickness value) at the substrate coordinate associated with the pixel. In some embodiments, the profile map indicates a thickness profile, where the thickness profile indicates an etch rate profile. For example, the profile map may reflect the thickness profile of a substrate subjected to an etching process over a predetermined amount of time. In some embodiments, the profile map indicates substrate surface defects such as slope. In some embodiments, the slope is correlated to the etch rate. In some embodiments, the profile may be or may include a feature vector, where each entry in the feature vector is associated with a coordinate of the substrate, and each entry may have a value that represents a value of the surface profile at the coordinate of the substrate. In some embodiments, processing logic (e.g., of the measurement system and / or computing device) may determine an etch rate profile map corresponding to an etch rate near the edge of the substrate.

[0126] At block 718, the computing device uses the model to process data from the profile map (e.g., thickness profile map, grain map, optical constant map, roughness map, etch rate profile map, etc.). In some embodiments, the computing device uses one or more trained machine learning models to process data from the profile map. In some embodiments, the trained machine learning models were trained using data collected from multiple substrates processed according to the recipe. For example, a set of substrates are processed according to the recipe at various configurations (e.g., positions) on the substrate support. Profile maps of the processed substrates can be generated. The profile maps and corresponding substrate configurations can be input as training data for training the machine learning model.

[0127] In some embodiments, the model is a physics-based model or a statistical model. In some embodiments, the model outputs an estimated substrate placement value (e.g., an estimated substrate placement metric) for the placement of the substrate relative to one or more components of the substrate support. In some examples, the model outputs a placement metric (e.g., a coordinate position of the substrate on the substrate support) corresponding to the placement relative to the substrate support (e.g., an electrostatic chuck) and / or one or more components of the substrate support (e.g., a process kit ring). In some embodiments, the output of the model is based on the etch rate profile map determined in block 716. In some embodiments, the output of the model indicates that the substrate was not optimally positioned for processing. In one embodiment, instead of or in addition to using a trained ML model, one or more computer vision algorithms are used to process the profile map.

[0128] At block 720, processing logic (e.g., of a computing device) determines a recommended placement for the substrate on the substrate support based on the estimated placement values ​​(e.g., output by the model at block 718). In some examples, the computing device may use the estimated substrate placement values ​​to determine a location on the substrate support for placement of the substrate. Specifically, the computing device may determine a coordinate position based on the value(s) output from the model at block 718. In some embodiments, the computing device may determine a gap that exists between the edge of the substrate and the inner diameter of the process kit ring. In some embodiments, the gap is not uniform around the substrate. For example, the gap may be larger on one side of the substrate than on the other side of the substrate. Thus, the gap may be correlated to the placement of the substrate (e.g., on an electrostatic chuck, within the inner diameter of the process kit ring, etc.). The processing logic may then place another substrate in the process chamber (e.g., via a substrate handling robot) according to the recommended placement, as described later herein.

[0129] FIG. 8 is a flow diagram of a method 800 for comparing a first estimated substrate placement with a second estimated substrate placement in accordance with aspects of the present disclosure. In some embodiments, method 800 is performed in conjunction with method 700 described herein above. For example, method 700 can correspond to processing a first substrate, and method 800 can correspond to processing a second substrate and comparing the results of the first substrate with the results of the second substrate. Method 800 is performed by processing logic, which may include hardware (circuitry, dedicated logic, etc.), software (such as running on a general-purpose computer system or a dedicated machine), firmware, or some combination thereof. In one embodiment, method 800 can be performed by a computer system, such as computer system architecture 100 of FIG. 1. In other or similar embodiments, one or more operations of method 800 can be performed by one or more other machines not shown. In some aspects, one or more operations of method 800 can be performed by substrate placement engine 330 of server machine 320 described with respect to FIG. 3.

[0130] At block 808, a substrate (e.g., a second substrate) is placed in a process chamber according to a recommended orientation (e.g., determined in block 720 of method 700). In some embodiments, processing logic causes a robot to place the substrate on a substrate support (e.g., an electrostatic chuck) in the process chamber. The robot can place the substrate in accordance with the recommended orientation such that the center of the substrate is offset from the center of the substrate support. For example, the recommended orientation can specify that the center of the substrate is offset from the center of the electrostatic chuck by a specified amount in a specified direction. The amount and direction of the offset can be a difference between a first robot transfer orientation and a second robot transfer orientation. In some examples, the first robot transfer orientation is a baseline (e.g., default, etc.) orientation, while the second robot transfer orientation is an updated orientation based on the offset. The robot can place the substrate on the electrostatic chuck offset by the specified offset amount from the center of the electrostatic chuck in the offset direction.

[0131] At block 810, the substrate is processed in a process chamber according to a recipe (e.g., the recipe of block 710 of method 700). The recipe may be an etch process recipe and / or a deposition process recipe. As with the substrate processed at block 710 of method 700, the substrate includes a surface profile after processing. The surface profile may be a thickness profile.

[0132] At block 812, the substrate is transferred (eg, by one or more transfer robots) from the process chamber to a substrate measurement system (eg, similar to block 712 of method 700).

[0133] At block 814, the substrate measurement system measures the surface of the substrate (eg, similar to block 714 of method 700).

[0134] At block 816, the substrate measurement system and / or computing device may generate a profile map of the surface profile of the substrate based on the measurements of the substrate measurement system (e.g., similar to block 716 of method 700). In some embodiments, the profile may be or include an image. The profile map may indicate an etch rate profile.

[0135] At block 818, the computing device processes data from the profile map (e.g., thickness profile map, grain map, optical constant map, roughness map, etc.) using a model (e.g., a trained machine learning model, a physics-based model, a statistical model, etc.). In some embodiments, the model outputs an estimated substrate placement value (e.g., an estimated substrate placement metric, etc.).

[0136] At block 820, processing logic (e.g., of a computing device) compares the estimated substrate placement value output from the model at block 818 with another estimated substrate placement value (e.g., the estimated substrate placement value of block 718 of method 700). The processing logic may determine whether the recommended placement should be updated based on the comparison. For example, in response to the estimated substrate placement values ​​being the same (e.g., substantially the same, within a threshold difference of each other, etc.), the processing logic may determine that the recommended placement is sufficient. In response to the estimated substrate placement values ​​being different (e.g., outside a threshold difference of each other, etc.), the processing logic may determine that the recommended placement should be updated.

[0137] At block 822, processing logic updates the recommended placement based on the comparison at block 820.

[0138] FIG. 9 illustrates a profile map 900 of a processed substrate according to an embodiment of the present disclosure. The profile map 900 is a heat map showing temperatures at different locations on a substrate during processing in a process chamber. The temperatures can be based on the thicknesses of films deposited or etched at different locations during processing. A key 902 is provided to illustrate how to interpret the profile map 900. As shown, a hot spot 905 is not uniform around the edge of the substrate. In some embodiments, methods described herein can provide a substrate without hot spots (e.g., substantially uniform temperature, substantially no hot spots, etc.). The non-uniform hot spot can indicate an increased etch rate, which correlates with increased substrate tilt. The substrate may be positioned too close to a process kit ring (e.g., of a substrate support in a process chamber) on the side proximate to the hot spot 905 during processing. For example, the gap between the edge of the substrate and the inner diameter of the process kit ring may be too small or too large near the hot spot 905. Non-uniform hot spots 905 around the edge of the substrate may indicate excessive tilt in the processed substrate features near the edge of the substrate, and therefore, according to some embodiments described herein, the orientation of the substrate for processing should be altered.

[0139] 10A-10B are flow diagrams of methods for determining optimal substrate placement according to aspects of the present disclosure. FIG. 10A illustrates a method 1000A for determining optimal substrate placement according to some embodiments. FIG. 10B illustrates a method 1000B for determining optimal substrate placement according to some embodiments. In some embodiments, methods 1000A and / or 1000B may be methods for implementing an all-angle-fit (AAF) algorithm to recommend wafer placement adjustments. In some embodiments, methods 1000A and / or 1000B are for optimizing etch profile uniformity, particularly near the extreme edges of the substrate.

[0140] 10A , a design of experiments (DOE) is performed in block 1002. In some embodiments, multiple substrates (e.g., test substrates) are processed (e.g., etched) at various locations on a substrate support in a process chamber. In some embodiments, a first substrate is placed at a first location (e.g., a first position) on the substrate support. The first substrate may be processed, and the processed first substrate may have a first surface profile. In some embodiments, the first surface profile is measured using a substrate measurement system described herein above. In some embodiments, a second substrate is placed at a second location on the substrate support that is different from the first location. The second substrate is then processed, and the surface profile of the processed second substrate is measured. A third substrate may be processed at a third location, and the surface profile of the processed third substrate may then be measured.

[0141] Referring to FIG. 11A, an exemplary plot 1100A of a substrate placement DOE is shown. Referring to FIG. 13A, an exemplary plot 1300A illustrating substrate placement in a DOE is shown. In some embodiments, plot 1100A can correspond to plot 1300A. In some embodiments, to perform the DOE, the substrate can be placed at various locations on the substrate support. Referring again to FIG. 11A, in some embodiments, a substrate is placed and processed at each of placement locations 1102-1118. In some embodiments, location 1118 can correspond to the center of the substrate support. In some embodiments, a first substrate can be placed and processed at first location 1102, a second substrate can be placed and processed at second location 1104, a third substrate can be placed and processed at third location 1106, and so on. While FIG. 11A shows nine possible placement locations for processing substrates, the DOE can also be performed with fewer than nine processed substrates located at different placement locations. In some embodiments, no more than two locations can be located on the same line in two-dimensional space. In some embodiments, at least one location of the DOE is located away from the straight line formed between the other two locations. In some embodiments, the DOE can be performed with 10 or more processed substrates located at different locations. Including a larger number of processed substrates at various locations can increase the accuracy of the DOE. Increased accuracy can be obtained when the DOE locations cover more azimuthal angles. For example, the locations shown in FIG. 13A form an "X"-shaped pattern. Adding additional locations to the DOE that form another "X"-shaped pattern with a different angle (e.g., a shallower or deeper angle) between the legs of the "X" can make the prediction more accurate. In some embodiments, the DOE can be performed with as few as three processed substrates located at three different locations.

[0142] Referring again to FIG. 10A , data processing is performed at block 1004. In some embodiments, the etch rate near the edge of each processed substrate is determined (e.g., at block 1002) by the DOE. For example, the etch rate can be determined by subtracting the processed thickness of the substrate from the unprocessed thickness and dividing by the processing time. In some embodiments, the etch rate is determined at a radial position near the edge of the first processed substrate for several azimuthal angles. Referring to FIG. 11B , a radial plot of substrate etch rate versus azimuthal angle θ is shown. In some embodiments, the etch rate is determined for all azimuthal angles around the center of the processed substrate. In some embodiments, the etch rate is determined for several azimuthal angles around the center of the processed substrate such that the determined etch rate represents the entire etch rate profile. The determined etch rate can represent only all azimuthal angles. For example, the etch rate can be determined near the edge of the substrate at azimuthal angles of 0°, 15°, 30°, 45°, etc. around the processed substrate. However, the etch rate can be determined at various azimuthal angles on a scale different from that shown in Figure 11B. For example, the etch rate can be determined at 0°, 10°, 20°, 30°, etc., or at 0°, 30°, 60°, 95°, etc. Figure 11B shows the etch rate of a processed substrate at various azimuthal angles around the center of the substrate near the edge of the substrate.

[0143] Referring again to FIG. 10A , in block 1004A, the etch rate may be normalized. Normalizing the etch rate can help account for differences in etch chamber conditions. Normalizing the etch rate can help account for substrate differences, such as slight differences in composition or defects. Substrate differences can lead to inconsistencies in substrate processing and therefore inconsistent data. Normalizing the etch rate can help remove effects from the data set that are outside of defects in the substrate or processing defects and / or differences. In some embodiments, the etch rate is normalized using an average etch rate. In some examples, a first average etch rate is determined for a first etch rate profile (e.g., the etch rate profile shown in FIG. 11B or FIG. 12A ). Each value in the first etch rate profile is divided by the first average etch rate to determine a first normalized etch rate profile. In some similar examples, a second average etch rate is determined for a second etch rate profile. Each value in the second etch rate profile is divided by the second average etch rate to determine a second normalized etch rate profile. Other methods of normalizing the etch rate data may also be used, and further details regarding data normalization are discussed herein below with respect to Figure 10B.

[0144] Referring to FIG. 12A, an exemplary plot of substrate etch rate versus azimuth angle θ is shown. An etch rate profile 1202A corresponding to the etch rate of a first substrate near the edge of the first substrate is shown. An etch rate profile 1204A corresponding to the etch rate of a second substrate near the edge of the second substrate is shown. Referring to FIG. 12B, an exemplary plot of normalized substrate etch rate versus azimuth angle θ is shown. Normalized etch rate profile 1202B can correspond to etch rate profile 1202A in FIG. 12A, and normalized etch rate profile 1204B can correspond to etch rate profile 1204A in FIG. 12A. Normalizing the etch rate profiles allows comparisons of etch rate values ​​for discrete azimuth angles (e.g., discrete values ​​of θ) by removing from the data at least some of the inconsistency between processed substrates.

[0145] At block 1004B, a linear fit is performed using corresponding values ​​of normalized etch rates for substrates processed as part of the DOE (e.g., normalized etch rate values ​​for substrates processed at the same azimuth angle). Referring to FIG. 13B, an exemplary graphical representation 1300B of normalized substrate etch rates for substrates processed at various locations is shown. For a particular azimuth angle 1354 (45° shown in FIG. 13B), a normalized etch rate is extracted from the normalized etch rate profile 1352 for each of the processed substrates. Referring to FIG. 13C, a plot 1300C of a linear fit to the normalized substrate etch rate is shown. The normalized etch rate 1372 for each processed substrate at azimuth angle 1354 is plotted versus differential position along vector 1310 of FIG. 13A. Further details regarding vector 1310 of FIG. 13A are provided later in this specification. The plotted normalized etch rates 1372 form a linear fit 1380. A linear fit 1380 can represent the predicted etch rate of the processed substrate at various positions along the vector 1310 and at the corresponding azimuthal angles. A linear fit can be determined for the normalized etch rate at each azimuthal angle.

[0146] Referring again to FIG. 10A , in block 1006, an optimal substrate placement location is determined using the linear fit data determined in block 1004B. In some embodiments, the linear fit data can be used to predict an etch rate profile for any substrate placement. By predicting the etch rate profile at various placement locations, an optimal location that satisfies one or more metrics can be determined. For example, a placement location that minimizes the etch rate range (e.g., the etch rate range near the edge of the substrate) or the etch rate standard deviation (e.g., the etch rate standard deviation near the edge of the substrate) can be determined. Minimizing the etch rate range and / or the etch rate standard deviation can result in more consistently processed substrates, especially near the edge of the substrate. After the optimal substrate placement location is determined, the optimal placement is recommended to the substrate processing system. The substrate can be placed on a substrate support in the process chamber according to the recommended placement.

[0147] Referring to FIG. 10B, a method 1000B for determining optimal substrate placement for processing in a process chamber is shown. Method 1000B is described with reference to an etch process, but can be equally implemented for a deposition process. Thus, any description referring to etching, etch rate, etch rate profile, etc., can also apply to deposition, deposition rate, deposition rate profile, etc. Method 1000B can also be applied to any other metric that exhibits a linear or higher-order response to substrate placement at a particular azimuthal angle. For example, an etch rate gradient for a blanket substrate (e.g., etch rate difference from a 146 mm radius and a 148 mm radius) and an etch slope profile for a patterned substrate can demonstrate a similar response to substrate placement. At block 1010, a substrate is placed at a placement location on a substrate support. The substrate can be a test substrate processed as part of a DOE. The substrate can be placed at one of locations 1102-1118 shown in FIG. 11A. At block 1020, an etching operation can be performed on the substrate to remove material from the substrate (or a deposition operation can be performed to add material to the substrate). At block 1030, a substrate measurement system can be used to measure the etch or deposition rate near the edge of the substrate at multiple radial distances from the center of the substrate at multiple azimuthal angles. In some examples, the etch or deposition rate is measured around the central axis of the substrate at a distance of approximately 140 mm to 150 mm from the center of the substrate. The measured etch or deposition rates at various azimuthal angles can be used to construct an etch or deposition rate profile (e.g., as shown in FIG. 11B for a single substrate and in FIG. 12A for two substrates). At block 1040, the etch or deposition rate profile is normalized. In some embodiments, the etch or deposition rate profile is normalized using an average etch rate for the etch rate profile or an average deposition rate for the deposition rate profile.For example, referring to Figure 12A, for an etch rate profile 1202A, an average etch rate can be determined from an azimuth angle of 0° to an azimuth angle of 360°. Each etch rate value in the etch rate profile can be divided by the average etch rate to calculate a normalized etch rate profile 1202B in Figure 12B.

[0148] Referring again to FIG. 10B , blocks 1010-1040 can be repeated multiple times for multiple substrates processed as part of a DOE. Each time block 1010 is repeated, the associated substrate is positioned at a different location on the substrate support. In some embodiments, blocks 1010-1040 are repeated nine times to process nine test substrates at nine different locations. In some embodiments, blocks 1010-1040 are repeated only three times to process only three test substrates at only three different locations. Including more or fewer substrates in a DOE (and thus repeating blocks 1010-1040 more or fewer times) depends on the accuracy threshold of the DOE. For example, if the accuracy threshold is higher, more test substrates are processed. Similarly, if the accuracy threshold is lower, fewer test substrates can be processed.

[0149] At block 1050, normalized etch rate profiles are compiled for each of the substrates processed at blocks 1010-1040 (e.g., during each iteration of blocks 1010-1040) at various substrate orientations. Referring to FIG. 13B, a graphical representation 1300B of the normalized etch rate profiles is shown. The normalized etch rate profile 1352 may represent the etch rate profile for substrates processed at each orientation location 1102-118 shown in FIG. 11A and / or each orientation location 1302 shown in FIG. 13A. Referring again to FIG. 10B, at block 1055, a normalized etch rate at each azimuthal angle for each processed substrate is determined using the normalized etch rate profile compiled at block 1050. Referring to FIG. 13B, the normalized etch rate is determined from the corresponding normalized etch rate profile 1352 at an azimuthal angle 1354. The azimuthal angle 1354 is shown at 45°. However, in some embodiments, depending on how deep the substrate etch rate sampling map is, several normalized etch rates for each processed substrate are determined from the normalized etch rate profile 1352 at all azimuthal angles.

[0150] Referring again to FIG. 10B, at block 1060, a linear fit is performed for each azimuth angle for which a normalized etch rate was determined in block 1055. The linear fit can be performed using a variety of linear and nonlinear higher-order fitting methods known to those skilled in the art. Referring to FIG. 13A, an exemplary plot 1300A illustrating substrate placement in a DOE is shown. A substrate can be processed while placed at each of substrate placement locations 1302 on a substrate support. Vector 1310 can correspond to azimuth angle 1354 in FIG. 13B. Vector 1310 can be at an angle with respect to the X-axis equivalent to the value of azimuth angle 1354. Placement location 1302 can be projected onto vector 1310, and each location of the projected placement location on vector 1310 is converted to a value y′. As shown, y′ is at an angle of 45° from the X-axis of plot 1300A, and azimuth angle 1354 is also at a corresponding angle θ of 45°. 13C , the normalized etch rate 1372 at azimuth angle 1354 is plotted against dy′. The normalized etch rate 1372 is plotted for each of the processed substrates. A linear fit 1380 is calculated to fit the plotted normalized etch rate 1372. The process of projecting location 1302 onto vector 1310, converting each of the projected location locations onto vector 1310 to y′, and plotting normalized etch rate 1372 against dy′ can be repeated for each of the data corresponding to each of several azimuth angles.

[0151] Referring again to FIG. 10B , at block 1070, a predicted etch rate profile is calculated for the predicted substrate placement location using the one or more linear fits calculated at block 1060. For example, the etch rate data and / or the linear fit may be used to determine predicted locations that can produce substrates that meet one or more threshold criteria. The threshold criteria may include a threshold etch rate profile range, a threshold etch rate profile minimum, a threshold etch rate profile maximum, and / or a threshold etch rate profile standard deviation. A predicted value of the etch rate at a specific azimuthal angle may be calculated for the predicted substrate placement location using the linear fit 1380 corresponding to the specific azimuthal angle. After the predicted etch rates at all azimuthal angles have been determined, the predicted etch rates may be combined to form a predicted etch rate profile. At block 1080, an optimal substrate placement location is determined for creating an optimized etch rate profile. In some embodiments, processing logic searches all substrate placement locations to determine which location has the optimal etch rate profile (e.g., meets one or more threshold criteria described above). The optimal placement location may be determined through experimentation. In some embodiments, the optimal placement location can be determined using a machine learning model described herein. The machine learning model can be trained with historical data, such as historical etch rate profiles and / or historical substrate placement locations, to determine the optimal placement for producing a substrate with an optimized etch rate profile. At block 1090, the substrate can be placed at the optimal placement location for processing.

[0152] 12A , an exemplary plot 1200A of substrate etch rate versus azimuth angle θ is shown, according to aspects of the present disclosure. In some embodiments, etch rate profile 1202A is the etch rate profile of a substrate processed at an optimal location on the substrate support. The optimal location can be determined using one or more methods described herein. In contrast, etch rate profile 1204A is the etch rate profile of a substrate processed at a non-optimal location on the substrate support.

[0153] 12B , an exemplary plot 1200B of normalized etch rate versus azimuth angle θ is shown, according to aspects of the present disclosure. In some embodiments, normalized etch rate profile 1202B is the normalized etch rate profile of a substrate processed at an optimal location on the substrate support. In contrast, normalized etch rate profile 1204B is the normalized etch rate profile of a substrate processed at a non-optimal location on the substrate support. As shown by plot 1200B, normalized etch rate profile 1202B has a more consistent normalized etch rate across all azimuth angles θ. Thus, the etch rate of a substrate processed at the optimal location is more consistent, and processing a substrate at the optimal location results in a better substrate than processing a substrate at a non-optimal location.

[0154] 14 is a flow diagram of a method 1400 for determining optimal substrate placement according to aspects of the present disclosure. Method 1400 may be performed by processing logic that may include hardware (circuitry, dedicated logic, etc.), software (such as running on a general-purpose computer system or a dedicated machine), firmware, or some combination thereof. In one embodiment, method 1400 may be performed by a computer system such as computer system architecture 100 of FIG. 1. In other or similar embodiments, one or more operations of method 1400 may be performed by one or more other machines not shown.

[0155] At block 1410, a first substrate is processed in a process chamber. For example, the process chamber may perform an etching process to partially remove a film on a surface of the substrate or a film deposition process to deposit a film on the substrate. In some embodiments, the film is a polymer. In other embodiments, the film is a ceramic (e.g., a metal oxide). The processing of the substrate may be performed according to a recipe (e.g., an etching process recipe, a deposition process recipe, etc.). During processing, the substrate may be supported by a substrate support (e.g., an electrostatic chuck) in the process chamber. The substrate may be placed in an initial position in the process chamber before processing and may remain in the initial position during processing. After processing, in some embodiments, the substrate includes a surface profile (e.g., a thickness profile, etc.).

[0156] At block 1412, one or more robots transfer the substrate from the process chamber to a substrate measurement system. If the substrate measurement system is connected to or contained within the transfer chamber, the transfer chamber robot can remove the substrate from the process chamber and insert the substrate into the substrate measurement system. If the substrate measurement system is connected to or contained within the factory interface, the transfer chamber robot can remove the substrate from the process chamber and place the substrate into a load lock. The factory interface robot can then remove the substrate from the load lock and insert the substrate into the substrate measurement system. The substrate measurement system can be any of the substrate measurement systems described above, such as an integrated reflected light measurement (IR) device.

[0157] At block 1414, the substrate measurement system generates measurements for many locations on the surface of the substrate, each location may have a unique set of coordinates.

[0158] At block 1416, the substrate measurement system and / or computing device may generate a profile map of the surface profile of the substrate (e.g., a substrate surface profile map) based on the measurements of the substrate measurement system. The profile map may be or may include an image, where each pixel in the image corresponds to a coordinate on the substrate. Each pixel may have an intensity value corresponding to a measurement value (e.g., a thickness value) at the substrate coordinate associated with the pixel. In some embodiments, the profile map indicates a thickness profile, where the thickness profile indicates an etch rate profile. For example, the profile map may reflect a thickness profile of a substrate subjected to an etching process over a predetermined amount of time. In some embodiments, the profile map indicates substrate surface defects such as slope. In some embodiments, the slope is correlated to the etch rate. In some embodiments, the profile may be or may include a feature vector, where each entry in the feature vector is associated with a coordinate of the substrate, and each entry may have a value that represents a value of the surface profile at the coordinate of the substrate. In some embodiments, processing logic (e.g., of the measurement system and / or computing device) may determine an etch rate profile map corresponding to an etch rate near the edge of the substrate.

[0159] At block 1418, the computing device may determine a plurality of etch rates corresponding to a plurality of locations on the substrate. The computing device may use the surface profile map generated at block 1416 to determine an etch rate profile of the etch rate near the edge of the substrate at several azimuthal angles around the center of the substrate.

[0160] At block 1420, processing logic processes data associated with the plurality of etch rates determined at block 1418. In some embodiments, the computing device processes the data using a model. The model may include a trained machine learning model, a mathematical model, a linear fit model, and / or a statistical model. The model may output one or more estimated surface profiles associated with one or more estimated placement locations on the substrate support. For example, the model may use a numerical method to estimate a predicted surface profile for a substrate processed at the estimated placement locations. In some embodiments, the model normalizes the etch rate profile (e.g., determined at block 1418) to determine a normalized etch rate for various values ​​of the azimuth angle θ. In some embodiments, the model performs a linear fit using the normalized etch rates from several processed substrates for each value of the azimuth angle θ. Using the linear fit, the model may determine one or more estimated placement locations on the substrate support for processing a substrate with an optimized etch rate profile.

[0161] At block 1422, processing logic (e.g., of a computing device) determines a recommended placement for the substrate on the substrate support based on one or more estimated placement locations (e.g., output by the model at block 1420). In some examples, the computing device may use the estimated placement locations to determine a location on the substrate support for placement of the substrate. Specifically, the computing device may determine a coordinate location based on the value(s) output from the model at block 1420. The processing logic may then cause another substrate to be placed in the process chamber (e.g., via a substrate handling robot) according to the recommended placement, as described herein.

[0162] 15 shows a diagrammatic representation of a machine, in the exemplary form of a computing device 1500, capable of executing a set of instructions to cause the machine to perform any one or more of the techniques discussed herein. In alternative embodiments, the machine may be connected (e.g., networked) to other machines in a local area network (LAN), an intranet, an extranet, or the Internet. The machine may operate in the capacity of a server or a client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet computer, a set-top box (STB), a personal digital assistant (PDA), a cellular phone, a web appliance, a server, a network router, switch, or bridge, or any machine capable of executing (sequentially or otherwise) a set of instructions that specify actions to be taken by the machine. Further, while only a single machine is shown, the term "machine" should also be taken to include any group of machines (e.g., computers) that individually or jointly execute a set (or sets) of instructions to perform any one or more of the techniques discussed herein. In some embodiments, computing device 1500 may correspond to one or more of server machine 170, server machine 180, prediction server 112, system controller 228, server machine 320, or server machine 350, as described herein.

[0163] The exemplary computing device 1500 includes a processing device 1502, a main memory 1504 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM), such as synchronous DRAM (SDRAM)), a static memory 1506 (e.g., flash memory, static random access memory (SRAM)), and a secondary memory (e.g., a data storage device 1528), which communicate with each other via a bus 1508.

[0164] The processing device 1502 may represent one or more general-purpose processors, such as a microprocessor, a central processing unit, or the like. More specifically, the processing device 1502 may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor implementing other instruction sets, or a processor implementing a combination of instruction sets. The processing device 1502 may also be one or more special-purpose processing devices, such as an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), a network processor, or the like. The processing device 1502 may also be or include a system-on-chip (SoC), a programmable logic controller (PLC), or other type of processing device. The processing device 1502 is configured to execute processing logic for performing the operations discussed herein.

[0165] The computing device 1500 may further include a network interface device 1522 for communicating with a network 1564. The computing device 1500 may also include a video display unit 1510 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device 1512 (e.g., a keyboard), a cursor control device 1514 (e.g., a mouse), and a signal generation device 1520 (e.g., a speaker).

[0166] The data storage device 1528 may include a machine-readable storage medium (or more specifically, a non-transitory computer-readable storage medium) 1524 having stored thereon one or more sets of instructions 1526 that implement any one or more of the techniques or functions described herein. For example, the instructions 1526 may include instructions for the substrate placement engine 330. A non-transitory storage medium refers to a storage medium other than a carrier wave. The instructions 1526 may also reside completely or at least partially within the main memory 1504 and / or the processing device 1502 during execution thereof by the computing device 1500, the main memory 1504, and the processing device 1502, which also constitute computer-readable storage media.

[0167] While the exemplary embodiment shows computer-readable storage medium 1524 as a single medium, the term "computer-readable storage medium" should be interpreted to include a single medium or multiple media (e.g., centralized or distributed databases, and / or associated caches and servers) that store one or more sets of instructions. The term "computer-readable storage medium" should also be interpreted to include any medium capable of storing or encoding a set of instructions for execution by a machine that cause the machine to perform any one or more of the techniques of this disclosure. Thus, the term "computer-readable storage medium" should be interpreted to include, but is not limited to, solid-state memory and optical and magnetic media.

[0168] The above description sets forth numerous specific details, such as examples of specific systems, components, methods, etc., to provide a thorough understanding of some embodiments of the present disclosure. However, it will be apparent to those skilled in the art that at least some embodiments of the present disclosure can be practiced without these specific details. In other instances, well-known components or methods have not been described in detail or have been presented in simple block diagram form to avoid unnecessarily obscuring the present disclosure. Thus, the specific details described are merely exemplary. It is contemplated that particular embodiments may vary from these exemplary details and still be within the scope of the present disclosure.

[0169] Throughout this specification, a reference to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with that embodiment is included in at least one embodiment. Thus, the appearances of the phrase "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. In addition, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." When the term "about" or "approximately" is used herein, it is intended to mean that the stated nominal value is accurate to within ±10%.

[0170] Although the method operations herein are illustrated and described in a particular order, the order of the method operations may be changed, such that certain operations may be performed in reverse order, and certain operations may be performed at least partially concurrently with other operations. In alternative embodiments, instructions or sub-operations of separate operations may be performed intermittently and / or alternately.

[0171] It is understood that the above description is intended to be illustrative, and not limiting. Many other embodiments will be apparent to those skilled in the art upon reading and understanding the above description. Accordingly, the scope of the present disclosure should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

Claims

1. processing a first substrate in a process chamber of a substrate processing system according to a recipe while the first substrate is supported by a substrate support of the process chamber, the first substrate including a first surface profile after the processing; generating a first profile map of the first surface profile of the first substrate using a substrate metrology system of the substrate processing system; processing data from the first profile map using a model, the model outputting a first estimated substrate placement value for a placement of the first substrate relative to one or more components of the substrate support; determining a recommended placement for the substrate on the substrate support based on the first estimated substrate placement value; and A method comprising:

2. further comprising placing a second substrate in the process chamber according to the recommended placement, wherein the one or more components of the substrate support include a process kit ring, and the second substrate is positioned within an inner diameter of the process kit ring according to the recommended placement. The method of claim 1.

3. processing the second substrate in the process chamber according to the recipe, the second substrate including a second surface profile after the processing; generating a second profile map of the second surface profile using the substrate metrology system of the substrate processing system; processing data from the second profile map using the model, the model outputting a second estimated substrate placement value; comparing the first estimated substrate placement value and the second estimated substrate placement value; updating the recommended placement based on the comparison; and The method of claim 2 further comprising:

4. determining a center of the substrate support; aligning the first substrate with the center of the substrate support prior to the processing of the first substrate; The method of claim 1 further comprising:

5. The method of claim 4 , wherein the recommended placement for the substrate causes the center of the substrate to be offset from the center of the substrate support.

6. 2. The method of claim 1, further comprising determining a substrate handoff offset based on the recommended placement, the substrate handoff offset being an offset relative to a robot arm from a first robot handoff orientation to a second robot handoff orientation.

7. The method of claim 1 , wherein the first surface profile comprises a first thickness profile.

8. determining a first etch rate profile of the first substrate relative to an etch rate profile near an edge of the first substrate based on the first profile map, wherein the model outputs the first estimated substrate placement value based on the first etch rate profile. The method of claim 1.

9. The method of claim 1 , wherein the model comprises at least one of a trained machine learning model, a physics-based model, or a statistical model.

10. the model comprises a trained machine learning model, and the method comprises:

10. The method of claim 1, further comprising training a machine learning model to create the trained machine learning model, wherein the machine learning model is trained using data from a plurality of processed substrates processed according to the recipe.

11. a process chamber; a substrate measurement tool; Memory and a processing device coupled to the memory, the processing device comprising: processing a first substrate in the process chamber according to a recipe while the first substrate is supported by a substrate support of the process chamber, the first substrate including a first surface profile after the processing; generating a first profile map of the first surface profile of the first substrate using the substrate metrology tool; processing data from the first profile map using a model, the model outputting a first estimated substrate placement value for a placement of the first substrate relative to one or more components of the substrate support; determining a recommended placement for the substrate on the substrate support based on the first estimated substrate placement value. system.

12. the processing device further comprising:

12. The system of claim 11, for placing a second substrate in the process chamber according to the recommended placement, wherein the one or more components of the substrate support include a process kit ring, and wherein the second substrate is positioned within an inner diameter of the process kit ring according to the recommended placement.

13. the processing device further comprising: processing the second substrate in the process chamber according to the recipe, the second substrate comprising a second surface profile after the processing; generating a second profile map of the second surface profile using the substrate metrology tool; processing data from the second profile map using the model, the model outputting a second estimated substrate placement value; comparing the first estimated substrate placement value and the second estimated substrate placement value; and updating the recommended placement based on the comparison.

14. the processing device further comprising: determining a center of the substrate support; and aligning the first substrate with the center of the substrate support prior to the processing of the first substrate. The system of claim 11.

15. The system of claim 11 , wherein the recommended placement for the substrate causes the center of the substrate to be offset from the center of the substrate support.

16. the model comprises a trained machine learning model, and the processing device further:

12. The system of claim 11, for training a machine learning model to create the trained machine learning model, wherein the machine learning model is trained using data from a plurality of processed substrates processed according to the recipe.

17. 1. A computer-readable medium containing instructions that, when executed by a processing device, cause the processing device to perform an operation, the operation comprising: processing a first substrate in a process chamber according to a recipe while the first substrate is supported by a substrate support of the process chamber, the first substrate including a first surface profile after the processing; generating a first profile map of the first surface profile of the first substrate using a substrate metrology system of a substrate processing system; processing data from the first profile map using a model, the model outputting a first estimated substrate placement value for a placement of the first substrate relative to one or more components of the substrate support; determining a recommended placement for the substrate on the substrate support based on the first estimated substrate placement value.

18. The operation is 20. The computer-readable medium of claim 17, further comprising: disposing a second substrate in the process chamber according to the recommended placement, wherein the one or more components of the substrate support include a process kit ring, and wherein the second substrate is positioned within an inner diameter of the process kit ring according to the recommended placement.

19. The operation is processing the second substrate in the process chamber according to the recipe, the second substrate comprising a second surface profile after the processing; generating a second profile map of the second surface profile using the substrate measurement system; and processing data from the second profile map using the model, the model outputting a second estimated substrate placement value; comparing the first estimated substrate placement value and the second estimated substrate placement value; and updating the recommended placement based on the comparison.

20. the model comprises a trained machine learning model, and the operation comprises:

20. The computer-readable medium of claim 17, further comprising training a machine learning model to create the trained machine learning model, wherein the machine learning model is trained using data from a plurality of processed substrates processed according to the recipe.

21. 1. A computer-readable medium containing instructions that, when executed by a processing device, cause the processing device to perform an operation, the operation comprising: processing a first substrate in a process chamber of a substrate processing system while the first substrate is supported by a substrate support at a first location on the substrate support, the first substrate including a first surface profile after the processing; generating a first surface profile map of the first surface profile using a substrate measurement system; determining a first plurality of etch rates corresponding to a first plurality of locations on the first substrate based on the first surface profile map; processing data associated with the first plurality of etch rates using a model, the model to output one or more estimated surface profiles associated with one or more estimated placement locations on the substrate support based on the first plurality of etch rates; and determining a recommended placement for the substrate on the substrate support based on the one or more estimated placement locations.

22. The operation is 22. The computer-readable medium of claim 21, further comprising positioning a second substrate in the process chamber on the substrate support according to the recommended positioning within an inner diameter of a process kit ring.

23. The operation is processing a second substrate in the process chamber while the second substrate is supported by the substrate support at a second location on the substrate support, the second substrate including a second surface profile after the processing; generating a second surface profile map of the second surface profile using the substrate measurement system; determining a second plurality of etch rates corresponding to a second plurality of locations on the second substrate based on the second surface profile map; and 22. The computer-readable medium of claim 21, further comprising: using the model to process data associated with the second plurality of etch rates, wherein the one or more estimated surface profiles associated with the one or more estimated placement locations on the substrate support are further based on the second plurality of etch rates.

24. 22. The computer-readable medium of claim 21, wherein the recommended placement is off-center of the substrate support.

25. The computer-readable medium of claim 1 , wherein the model comprises at least one of a trained machine learning model, a linear fit model, or a statistical model.

26. the model comprises the trained machine learning model, and the operation comprises:

26. The computer-readable medium of claim 25, further comprising training a machine learning model to create the trained machine learning model, wherein the machine learning model is trained using data from a plurality of processed substrates processed in the process chamber.

27. 22. The computer-readable medium of claim 21, wherein the first plurality of locations on the first substrate correspond to locations spaced radially at a plurality of azimuthal angles from a center of the first substrate.

28. 22. The computer-readable medium of claim 21, wherein processing data associated with the first plurality of etch rates includes generating a linear fit of at least one first etch rate of the first plurality of etch rates corresponding to at least one first location of the first plurality of locations, and wherein the one or more estimated surface profiles are based on the linear fit.

29. 22. The computer-readable medium of claim 21, wherein processing data associated with the first plurality of etch rates comprises normalizing each of the first plurality of etch rates based on an average etch rate of the first plurality of etch rates.

30. 22. The computer-readable medium of claim 21, wherein the recommended placement corresponds to optimized placement locations on the substrate support for fabricating a second substrate having a second plurality of etch rates at a second plurality of locations on the second substrate that match one or more values ​​of one or more metrics.

31. 22. The computer-readable medium of claim 21, wherein the first surface profile comprises a first thickness profile.

32. a process chamber including a substrate support; a substrate measurement tool; Memory and a processing device operably coupled to the memory, the processing device comprising: processing a first substrate in the process chamber while the first substrate is supported by the substrate support at a first location on the substrate support, the first substrate including a first surface profile after the processing; generating a first surface profile map of the first surface profile using the substrate metrology tool; determining a first plurality of etch rates corresponding to a first plurality of locations on the first substrate based on the first surface profile map; processing data associated with the first plurality of etch rates using a model, the model to output one or more estimated surface profiles associated with one or more estimated placement locations on the substrate support based on the first plurality of etch rates; determining a recommended placement for the substrate on the substrate support based on the one or more estimated placement locations. system.

33. the processing device further comprising:

33. The system of claim 32, for positioning a second substrate in the process chamber on the substrate support according to the recommended positioning within an inner diameter of a process kit ring.

34. the processing device further comprising: processing a second substrate in the process chamber while the second substrate is supported by the substrate support at a second location on the substrate support, the second substrate including a second surface profile after the processing; generating a second surface profile map of the second surface profile using the substrate metrology tool; determining a second plurality of etch rates corresponding to a second plurality of locations on the second substrate based on the second surface profile map; and 33. The system of claim 32, wherein the system is for using the model to process data associated with the second plurality of etch rates, wherein the one or more estimated surface profiles associated with the one or more estimated placement locations on the substrate support are further based on the second plurality of etch rates.

35. the model comprises a trained machine learning model, and the processing device further:

33. The system of claim 32, further comprising: training a machine learning model to create the trained machine learning model, the machine learning model being trained using data from a plurality of processed substrates processed in the process chamber.

36. processing data associated with the first plurality of etch rates; normalizing each of the first plurality of etch rates based on an average etch rate of the first plurality of etch rates; and generating a linear fit of at least one first etch rate of the first plurality of etch rates corresponding to at least one first location of the first plurality of locations, wherein the one or more estimated surface profiles are based on the linear fit.

37. 33. The system of claim 32, wherein the recommended placement corresponds to optimized placement locations on the substrate support for fabricating a second substrate having a second plurality of etch rates at a second plurality of locations on the second substrate that match one or more values ​​of one or more metrics.

38. processing a first substrate in a process chamber while the first substrate is supported by a substrate support at a first location on the substrate support, the first substrate including a first surface profile after the processing; generating a first surface profile map of the first surface profile using a substrate measurement system; determining a first plurality of etch rates corresponding to a first plurality of locations on the first substrate based on the first surface profile map; processing data associated with the first plurality of etch rates using a model, the model to output one or more estimated surface profiles associated with one or more estimated placement locations on the substrate support based on the first plurality of etch rates; determining a recommended placement for the substrate on the substrate support based on the one or more estimated placement locations; A method comprising:

39. placing a second substrate in the process chamber on the substrate support according to the recommended placement within an inner diameter of a process kit ring; 39. The method of claim 38, further comprising:

40. 39. The method of claim 38, wherein processing data associated with the first plurality of etch rates comprises generating a linear fit of at least one first etch rate of the first plurality of etch rates corresponding to at least one first location of the first plurality of locations, and wherein the one or more estimated surface profiles are based on the linear fit.

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