Substrate placement optimization using substrate measurements

Abstract

A computer readable medium includes instructions that, when executed by a processing device, cause the processing device to perform an operation. The operations include processing a first substrate in a processing chamber while the first substrate is supported by a substrate support in a first placement position. 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 the substrate measurement system. The operations further include determining a plurality of etch rates corresponding to the plurality of locations on the first substrate. The operations also include processing data associated with the plurality of etch rates using a model that outputs one or more estimated surface profiles associated with the one or more estimated placement locations. The operations also include determining a suggested placement of the substrate on the substrate support based on the one or more estimated placement locations.

Description

[0001] This application is a divisional application of the invention patent application with the application number 2023800566219. Technical Field

[0002] Embodiments of the present disclosure generally relate to optimizing the placement of substrates in a processing chamber, and more particularly to generating a map and / or numerical profiling of substrates to be processed using the chamber and optimizing the placement of substrates in the processing chamber based on the map and / or numerical profiling of the substrates. Background Art

[0003] Substrate processing may include a series of processes for fabricating electronic circuits in a semiconductor according to a circuit design. These processes may be carried out in a series of processing chambers. The successful operation of modern semiconductor manufacturing facilities may aim to facilitate the stable movement of wafers from one chamber to another during the process of forming circuits in the wafers to form products. During the implementation of many substrate processes, the conditions of the processing chamber may change and may cause the processed substrates to fail to meet the target conditions and results.

[0004] Substrates are placed in a processing chamber for processing. The placement of the substrate relative to the chamber components may result in variations in the quality of the products produced using the processing chamber and / or the scrapping of the substrates processed using the processing chamber. Summary of the Invention

[0005] The following is a simplified summary of the present disclosure to provide a basic understanding of some aspects of the present disclosure. This summary is not an extensive overview of the present disclosure. It is neither intended to identify the key or critical elements of the present disclosure nor to depict the scope of any particular implementation of the present disclosure or the scope of any claims. Its sole purpose is to present some concepts of the present disclosure in a simplified form as a prelude to the more detailed description presented later.

[0006] 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 processing chamber of a substrate processing system while the first substrate is supported by a substrate support at a first placement position 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 a plurality of first etch rates corresponding to a plurality of first positions on the first substrate based on the first surface profile map. The operations further include processing data associated with the plurality of first etch rates using a model. The model outputs one or more estimated surface profiles associated with one or more estimated placement positions on the substrate support based on the plurality of first etch rates. The operations further include determining a recommended placement of the substrate on the substrate support based on the one or more estimated placement positions.

[0007] In an exemplary embodiment, a system includes a processing chamber that includes a substrate support. The system also includes a substrate measurement tool, a memory, and a processing device operatively coupled to the memory. The processing device is configured to cause a first substrate to be processed in the processing chamber while the first substrate is supported by the substrate support at a first placement position on the substrate support. The first substrate includes a first surface profile after processing. The processing device is further configured to generate a first surface profile map of the first surface profile using the substrate measurement tool. The processing device is further configured to determine a plurality of first etch rates corresponding to a plurality of first positions on the first substrate based on the first surface profile map. The processing device also processes data associated with the plurality of first etch rates using a model. The model outputs one or more estimated surface profiles associated with one or more estimated placement positions on the substrate support based on the plurality of first etch rates. The processing device is further configured to determine a recommended placement of the substrate on the substrate support based on the one or more estimated placement positions.

[0008] In an exemplary embodiment, a method includes processing a first substrate in a processing chamber while the first substrate is supported by a substrate support at a first placement position 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 plurality of first etch rates corresponding to a plurality of first positions on the first substrate based on the first surface profile map. The method further includes processing data associated with the plurality of first etch rates using a model. The model outputs one or more estimated surface profiles associated with one or more estimated placement positions on the substrate support based on the plurality of first etch rates. The method further includes determining a recommended placement of the substrate on the substrate support based on the one or more estimated placement positions. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The present disclosure is illustrated by way of example and not limitation, in the figures of the accompanying drawings, wherein like reference numerals indicate like elements. It should be noted that the different references to "an" or "one" embodiment in the present disclosure are not necessarily to the same embodiment, and such references mean at least one.

[0010] Figure 1 Depicts an illustrative computer system architecture in accordance with aspects of the present disclosure.

[0011] Figure 2A Is a top schematic view of an exemplary manufacturing system in accordance with aspects of the present disclosure.

[0012] Figure 2B Is included in an exemplary manufacturing system in accordance with aspects of the present disclosure Figure 2A A cross-sectional schematic side view of a substrate measurement system in the exemplary manufacturing system.

[0013] Figure 2C Is a schematic side cross-sectional view of a substrate measurement subsystem according to an aspect of the present disclosure.

[0014] Figure 3 Depicts an illustrative system architecture for substrate placement prediction for a processing chamber according to an aspect of the present disclosure.

[0015] Figure 4 Illustrates a model training workflow and a model application workflow for substrate placement determination according to one embodiment.

[0016] Figure 5 Is a flowchart of a method for generating a training data set for training a machine learning model according to an aspect of the present disclosure.

[0017] Figure 6 Illustrates a flowchart of a method for training a machine learning model to determine substrate placement according to one embodiment.

[0018] Figure 7 Is a flowchart of a method for determining a proposed substrate placement according to an aspect of the present disclosure.

[0019] Figure 8 Is a flowchart of a method for comparing a second estimated substrate placement with a first estimated substrate placement according to an aspect of the present disclosure.

[0020] Figure 9 Is a contour map (e.g., heat map) of a processed substrate according to an aspect of the present disclosure.

[0021] Figure 10A - 10B Is a flowchart of a method for determining an optimal substrate placement according to an aspect of the present disclosure.

[0022] Figure 11A Is an example plot of an experimental substrate placement design according to an aspect of the present disclosure.

[0023] Figure 11B Is an example radial plot of substrate etch rate versus θ according to an aspect of the present disclosure.

[0024] Figure 12A Is an example plot of substrate etch rate versus θ according to an aspect of the present disclosure.

[0025] Figure 12B Is an example plot of normalized substrate etch rate versus θ according to an aspect of the present disclosure.

[0026] Figure 13A Is an example plot of normalized substrate etch rate of an experimental substrate placement design according to an aspect of the present disclosure.

[0027] Figure 13BAn example drawing of an experimental substrate design according to an aspect of the present disclosure is shown.

[0028] Figure 13C An example drawing of a linear fit of the substrate etch rate according to an aspect of the present disclosure is shown.

[0029] Figure 14 A flowchart of a method for determining optimal substrate placement according to an aspect of the present disclosure is shown.

[0030] Figure 15 A diagrammatic representation of a machine in the exemplary form of a computing device is shown, in which a set of instructions can be executed to cause the machine to perform any one or more of the methods discussed herein. Detailed Description

[0031] Embodiments of the present disclosure relate to systems and methods for optimizing substrate placement in a processing chamber using substrate measurements. The processing results of a manufacturing process depend on many factors, including the processing recipe, chamber parameter settings, chamber component conditions, and substrate placement within the processing chamber. For example, based on the placement of the substrate relative to the processing kit ring, the processing results may vary across the substrate surface. The gap between the substrate edge and the processing kit ring is typically related to the effect of substrate processing adjacent to the substrate edge. Additionally, based on the condition of the showerhead, the substrate support that holds the substrate, the condition of the chamber liner, the condition of the pumps and / or valves, etc., the processing results may vary across the surface of the entire substrate. The optimal placement of the substrate may be affected by the conditions of the processing chamber components described herein. For example, any change over time to the substrate support, such as an electrostatic chuck, a clamp, a vacuum chuck, a heater, a support including a bag having a lip on the support edge, and / or a substrate support including one or more embedded functions (such as heaters, cold plates, electronic components, etc.), will gradually affect the substrate results, such as through temperature variations across the processed substrate surface, radio frequency (RF) fields at the edges of the substrate, etc. These effects on the substrate results will gradually affect the optimal substrate placement for the best processing results. Thus, over time, the optimal placement of the substrate on the substrate support will tend to change.

[0032] Substrate results can be particularly affected by the placement of the substrate near the edge of the processed substrate. For example, factors that affect the substrate etch rate (e.g., temperature, gas flow, etc.) can cause tilting near the edge of the processed substrate. "Tilting" refers to the tendency of substrate features (e.g., valleys, walls, pillars, mesa, etc.) not to be orthogonal to the substrate surface. Excessive tilting can result in poor substrate quality and / or rejection. Tilting often affects the substrate near the edge of the substrate, and particularly near the outermost edge of the substrate (e.g., within five millimeters of the substrate edge). Variations in the placement of the substrate relative to components of the substrate support (e.g., a process kit ring, etc.) can affect the processing of the substrate. For example, variations in the gap between the substrate edge and the process kit ring (e.g., the gap around a wafer) can result in a threshold amount of tilting beyond what is allowed, causing the substrate to be rejected.

[0033] The embodiments described herein provide a mechanism for determining an optimized placement of a substrate to be processed in a processing chamber. Some embodiments can be used to determine a recommended position of the substrate on a substrate support for processing in a processing chamber. The recommended position can be used to determine an offset of a substrate handling robot (e.g., a transfer chamber robot, etc.) to place the substrate in the processing chamber.

[0034] In some embodiments, a substrate is processed in a processing chamber according to a recipe. The substrate can be processed to deposit or etch a thin film layer and / or one or more features (e.g., measurable features) on the 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. Features can include features distributed on the surface of the substrate, such as mesa, dots, structures, valleys, walls, lines, trenches, grooves, fiducials, etc. In some examples, during processing, the substrate can be supported by a substrate support of the processing chamber (e.g., an electrostatic chuck, etc.). In some examples, after processing, the substrate can have a surface profile (e.g., a thickness profile, a tilt profile, etc.). In an example of an etch processing recipe, the surface profile can indicate the 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 related to tilting, particularly at locations on the substrate surface near the edge of the substrate.

[0035] In some embodiments, after depositing or etching a film and / or features on the substrate, the substrate can be removed from the processing chamber and input into a substrate measurement system. In some embodiments, a contour map of the substrate is generated by the substrate measurement system based on the surface profile of the substrate. The substrate measurement system can be, for example, a reflectometry system or other measurement system that measures the film thickness and / or features at multiple locations on the substrate. Thickness information can be used to generate a contour map of the substrate. Alternatively, one or more other contour maps of the substrate (e.g., optical constants or roughness, particle count, etc.) can be generated from other measurement data.

[0036] 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) or other film and / or feature information (e.g., other profiles, such as an optical constant profile, a particle count profile, etc.) to identify changes in one or more properties of the film. The model can output an estimated substrate placement value for placing the substrate relative to one or more elements in the processing chamber (e.g., an electrostatic chuck, a processing kit ring, etc.). In an embodiment, the model can output a suggested placement location of the substrate, can output one or more predicted film properties associated with the suggested placement location (e.g., a predicted profile of the substrate processed from the suggested placement location), and so on.

[0037] In some embodiments, the etch rate at a plurality of locations on the substrate surface is determined based on the feature thickness information. In some embodiments, a model can be used to process data associated with the etch rate to determine an estimated placement location of the substrate on the substrate support for processing. For example, a numerical model can process the normalized etch rates of a plurality of test substrates (each test substrate is processed at a different substrate placement) at specified locations on each substrate to determine the effect of different substrate placements. The model can output an estimated substrate placement and / or an estimated substrate placement value relative to one or more elements in the processing chamber.

[0038] In some embodiments, a suggested placement of the substrate on the substrate support is determined based on the estimated substrate placement value. In some examples, the suggested placement is an optimized placement location on the substrate support (e.g., an electrostatic chuck, etc.) for processing the substrate to meet target substrate specifications (e.g., the tilt near the substrate edge is less than a threshold amount, etc.). In some examples, the optimized location of the substrate on the substrate support results in a uniform gap around the substrate between the edge of the substrate and the inner diameter of the processing 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 processing chamber variables, such as the condition of one or more chamber elements (e.g., the condition of the processing kit ring, the condition of the showerhead, the condition of the electrostatic chuck, etc.). In some embodiments, the robot settings are determined to place the substrate at the suggested placement location, and those settings are used to place the substrate onto the substrate support.

[0039] Accordingly, the embodiments described herein add new detection capabilities to a processing chamber without adding to the cost of those processing chambers. In some embodiments, these new detection capabilities can be utilized to improve the quality and / or quantity of processed substrates that meet threshold specifications (e.g., threshold tilt specifications of processed substrates, etc.). Additionally or alternatively, the embodiments described herein can be used to reduce the amount of scrap products (e.g., scrap substrates) resulting from scrapping products that do not meet the threshold specifications. The embodiments described herein can be used to automatically optimize certain processing variables (e.g., substrate placement during processing) to process higher quality substrates quickly and more efficiently when compared to conventional substrate processing systems. Specifically, the embodiments described herein can be used to produce substrates that have better edge characteristics (e.g., tilt, etc.) when compared to substrates produced by conventional systems. This, in turn, can lead to higher quality substrates, fewer scrap products, etc.

[0040] Figure 1 Illustrative computer system architecture 100 in accordance with aspects of the present disclosure is depicted. Computer system architecture 100 includes client device 120, fabrication equipment 122, substrate measurement system 126, prediction server 112 (e.g., for generating prediction data, for providing model tuning, for using a knowledge base, etc.), and data storage 150. Prediction server 112 can be part of prediction system 110. Prediction system 110 can further include server machines 170 and 180. In some embodiments, computer system architecture 100 can include or can be part of a manufacturing system (such as Figure 2A manufacturing system 200) for processing substrates. In additional or alternative embodiments, computer system architecture 100 can include or be part of a substrate placement prediction system (e.g., which evaluates the condition of one or more chamber elements in a processing chamber). Further details regarding the substrate placement prediction system are provided in Figures 3 - 4 provided.

[0041] Elements of the client device 120, manufacturing equipment 122, substrate measurement system 126, prediction system 110, and / or data storage 150 may be coupled to each other via the network 140. In some embodiments, the network 140 is a public network that provides the client device 120 access to the prediction server 112, data storage 150, and other publicly available computing devices. In some embodiments, the network 140 is a private network that provides the client device 120 access to the manufacturing equipment 122, substrate measurement system 126, data storage 150, and / or other privately available computing devices. The network 140 may include one or more wide area networks (WANs), local area networks (LANs), wired networks (e.g., Ethernet), 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.

[0042] The client device 120 may include a computing device such as a personal computer (PC), laptop, mobile phone, smartphone, tablet, notebook computer, Internet-connected television (“smart TV”), Internet-connected media player (e.g., Blu-ray player), set-top box, over-the-top (OTT) streaming device, operation box, etc.

[0043] The manufacturing equipment 122 may produce products according to a recipe. In some embodiments, the manufacturing equipment 122 may include or be part of a manufacturing system that includes one or more stations (e.g., processing chambers, transfer chambers, load locks, factory interfaces, etc.) configured to perform different operations on a substrate.

[0044] The substrate measurement system 126 may be an element of the manufacturing system that may be used to measure substrates before and / or after processing in one or more processing chambers. The substrate measurement system 126 may be configured to generate optical emission spectroscopy data, reflectance measurement data, and / or other metrology data. The substrate measurement system 126 may include one or more elements configured to collect and / or generate measurement data associated with one or more portions of the profile of the substrate surface after the substrate has been removed from the processing chamber.

[0045] In some embodiments, the substrate measurement system 126 may be configured to generate metrology data associated with substrates processed by other manufacturing equipment 122. The metrology data may include values of one or more of film property data (e.g., wafer-level film properties such as thickness), dimensions (e.g., thickness, height, etc.), dielectric constant, dopant concentration, density, defects, and the like. The metrology data may be data of a finished or semi-finished product, or may be data of a test substrate (e.g., a blanket wafer). Some embodiments are discussed with reference to using reflectance measurement data and thickness profiles to determine the condition of chamber components. However, it should be understood that the principles and embodiments described herein in connection with reflectance measurement data and thickness profiles are also applicable to other types of metrology data. For example, particle counting, optical constants of coatings, surface roughness of coatings, material composition of coatings, etc. may be measured. Such measurements may be made on many regions of the substrate and may be used to generate profiles of particle counts, optical constants, surface roughness, material composition, etc. on the measured substrate.

[0046] The substrate measurement system 126 may be configured to generate metrology data associated with a substrate before and / or after substrate processing. The substrate measurement system 126 may be integrated with a station of a manufacturing system that includes the manufacturing equipment 122. In some embodiments, the substrate measurement system 126 may be coupled to a station of a processing tool (e.g., a processing chamber, a transfer chamber, etc.) maintained in a vacuum environment or may be part of a station of the processing tool. Such a substrate measurement system 126 may be referred to as on-board metrology equipment. Thus, when the substrate is in a vacuum environment, the substrate may be measured through the substrate measurement system 126. For example, after substrate processing (e.g., an etching process, a deposition process, etc.) is performed on the substrate, metrology data of the processed substrate may be generated through the substrate measurement system 126 without removing the processed substrate from the vacuum environment. In other or similar embodiments, the substrate measurement system 126 may be coupled to a manufacturing system (e.g., a factory interface module, etc.) that is not maintained in a vacuum environment or may be part of the manufacturing system. Such a substrate measurement system 126 may be referred to as on-board metrology equipment.

[0047] Instead of being included in the manufacturing system (e.g., attached to a factory interface or transfer chamber), the substrate measurement system 126 can be a device separate (i.e., external) from the manufacturing equipment 122. For example, the substrate measurement system 126 can be a stand-alone device that is not coupled to any station of the manufacturing equipment 122. To obtain measurement results of a substrate using the separate substrate measurement system 126, a user of the manufacturing system (e.g., an engineer, an operator) can cause the substrate being processed at the manufacturing equipment 122 to be removed from the manufacturing equipment 122 and transferred to the substrate measurement system 126 for measurement. In some embodiments, the substrate measurement system 126 can transmit metrology data generated for the substrate to a client device 120 coupled to the substrate measurement system 126 via a network 140 (e.g., for presentation to a user of the manufacturing, such as an operator or an engineer). In other or similar embodiments, a user of the manufacturing system can obtain metrology data of the substrate from the substrate measurement system 126 and provide the metrology data to a computer system architecture via a graphical user interface (GUI) of the client device 120.

[0048] The data storage 150 can be a memory (e.g., a random access memory), a drive (e.g., a hard disk drive, a flash drive), a database system, or another type of element or device capable of storing data. The data storage 150 can include multiple storage elements (e.g., multiple drives or multiple databases) that can span multiple computing devices (e.g., multiple server computers). The data storage 150 can store profiles (e.g., generated from reflectometry data, from spectroscopic data, etc.), such as film thickness profiles and / or other substrate profiles. The film thickness profiles and / or other substrate profiles can include historical profiles and / or current profiles.

[0049] One or more portions of the data storage 150 can be configured to store data that is not accessible to a user of the manufacturing system. In some embodiments, a user of the manufacturing system may not have access to all of the data stored at the data storage 150. In other or similar embodiments, a portion of the data stored at the data storage 150 is not accessible to the user, while another portion of the data stored at the data storage 150 is accessible to the user. In some embodiments, the inaccessible data stored at the data storage 150 is encrypted using an encryption mechanism unknown to the user (e.g., encrypting the data using a private key). In other or similar embodiments, the data storage 150 can include multiple data storages, where the data that is not accessible to the user is stored in a first data storage, and the data that is accessible to the user is stored in a second data storage.

[0050] In some embodiments, the prediction system 110 includes server machines 170 and 180. Server machine 170 includes a training set generator 172 that is capable of generating a training data set (e.g., a set of data inputs and a set of target outputs) to train, validate, and / or test a machine learning model 190 or a collection of machine learning models 190. Some operations of the training set generator 172 are described in detail below with reference to Figure 4 In some embodiments, the training set generator 172 may split the training data into a training set, a validation set, and a test set.

[0051] Server machine 180 may include a training engine 182. An engine may refer to hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, processing devices, etc.), software (e.g., instructions running on a processing device, a general-purpose computer system, or a dedicated machine), firmware, microcode, or a combination thereof. The training engine 182 is capable of training a machine learning model 190 or a collection of machine learning models 190. The machine learning model 190 may refer to a model artifact established by the training engine 182 using training data including training inputs and corresponding target outputs (correct answers for the corresponding training inputs). The training engine 182 may find patterns in the training data that map the training inputs to the target outputs (answers to be predicted) 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.

[0052] The training engine 182 is also capable of validating the trained machine learning model 190 using the corresponding feature set of the validation set of the training set generator 172. In some embodiments, the training engine 182 may assign a performance rating to each of a collection of trained machine learning models 190. The performance rating may correspond to the accuracy of the corresponding trained model, the speed of the corresponding model, and / or the efficiency of the corresponding model. According to the embodiments described herein, the training engine 182 may select a trained machine learning model 190 having a performance rating that meets the performance criteria used by the prediction engine 114. Further details regarding the training engine 182 are provided with reference to Figure 5 Provided.

[0053] The prediction server 112 includes a prediction engine 114 that is capable of providing data (e.g., a film thickness profile) from the substrate measurement system 126 as an input to the trained machine learning model 190 and running the trained model 190 on the input to obtain one or more outputs. In some embodiments, the trained model 190 run by the prediction engine 114 is selected by the training engine 182 to have a performance rating that meets the performance criteria. As described with respect to Figure 6As further described, in some embodiments, the prediction engine 114 processes input data using the model 190 to evaluate substrate placement for processing a substrate in a processing chamber.

[0054] It should be noted that in some other implementations, the functions of the server machines 170 and 180 and the prediction server 112 may be provided by a larger or smaller number of machines. For example, in some embodiments, the server machines 170 and 180 may be integrated into a single machine, and in some other or similar embodiments, the server machines 170 and 180 and the prediction server 112 may be integrated into a single machine. Generally, functions described in one implementation as being performed by the server machine 170, the server machine 180, and / or the prediction server 112 may also be performed on the client device 120. Additionally, the functions attributed to a particular element may be performed by different or multiple elements operating together.

[0055] In an embodiment, a "user" may be represented as a single individual. However, other embodiments of the present disclosure cover a "user" as an entity controlled by multiple users and / or automated sources. For example, a group of individual users united as a group of administrators may be considered a "user".

[0056] FIG. 2 is a top schematic view of an exemplary manufacturing system 200 in accordance with aspects of the present disclosure. The manufacturing system 200 may perform one or more processes on a substrate 202. In accordance with aspects of the present disclosure, the substrate 202 may be any suitable rigid, fixed-size planar article, such as a silicon-containing disk or wafer, a patterned wafer, a glass plate, etc., suitable for manufacturing electronic devices or circuit elements thereon. In some embodiments, in accordance with the embodiments described with respect to Figure 1 the described embodiments, the manufacturing system 200 may comprise or be part of the computer system architecture 110.

[0057] The manufacturing system 200 may include a processing tool 204 and a factory interface 206 coupled to the processing tool 204. The processing 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 processing rooms) 214, 216, 218 disposed around and coupled thereto. The processing chambers 214, 216, 218 may be coupled to the transfer chamber 210 through corresponding 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 processing chambers 214, 216, 218, the load lock 220, etc. The transfer chamber robot 212 may include one or more arms, each arm including one or more end effectors at the end of each arm. The end effectors may be configured to manipulate a particular object, such as a wafer.

[0058] In some embodiments, the transfer chamber 210 may also include a metrology device attached thereto, such as the substrate metrology system 126. The substrate metrology system 126 may be configured to generate metrology data associated with the substrate 202 before or after substrate processing while the substrate is maintained in a vacuum environment. As Figure 2A shown, the substrate metrology system 126 may be attached to or disposed within the transfer chamber 210. If the substrate metrology system 126 is disposed within or coupled to the transfer chamber 210, metrology data associated with the substrate 202 may be generated without removing the substrate 202 from the vacuum environment (e.g., transferring it to the factory interface 206).

[0059] The processing chambers 214, 216, 218 may be adapted to perform any number of processes on the substrate 202. The same or different substrate processing may be performed in each of the processing chambers 214, 216, 218. Substrate processing may include atomic layer deposition (ALD), physical vapor deposition (PVD), chemical vapor deposition (CVD), etching, annealing, curing, pre-cleaning, metal or metal oxide removal, etc. Other processes may be performed on the substrates therein.

[0060] 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 dock and couple with the transfer chamber 210 on one side and dock and couple with the factory interface 206 on the other side. The load lock 220 may have an environmentally controlled atmosphere that, in some embodiments, may change from a vacuum environment (wherein substrates may be transferred to and from the transfer chamber 210) to an inert gas environment at atmospheric pressure (or near atmospheric pressure) (wherein substrates may be transferred to and from the factory interface 206).

[0061] The factory interface 206 may be any suitable enclosure, such as an equipment front end module (EFEM). The factory interface 206 may be configured to receive the substrate 202 from a substrate carrier 222 (e.g., a front opening unified pod (FOUP)) docked at various load ports 224 of the factory interface 206. The factory interface robot 226 (shown in dashed lines) may be configured to transfer the substrate 202 between the substrate carrier 222 (also referred to as a container) and the load lock 220. In other and / or similar embodiments, the factory interface 206 may be configured to receive replacement parts from a replacement part storage container.

[0062] In some embodiments, the manufacturing system 200 may include a substrate measurement system 126 attached to the factory interface 206. The substrate measurement 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 a 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).

[0063] The manufacturing system 200 may also be connected to a client device (e.g., Figure 1 client device 120) that is 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 via the GUI about one or more chamber condition metrics (e.g., during substrate processing) of the processing chambers 214, 216, 218.

[0064] 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 implemented instruction sets. The processing device may also be one or more dedicated 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 data storage devices (e.g., one or more disk drives and / or solid state drives), main memory, static memory, a network interface, and / or other elements. The system controller 228 may execute instructions to perform any one or more of the methods and / or embodiments described herein. In some embodiments, the system controller 228 may execute instructions to perform one or more operations at the manufacturing system 200 according to a processing recipe. The instructions may be stored on a computer-readable storage medium, which may include main memory, static memory, secondary storage, and / or the processing device (during execution of the instructions).

[0065] In some embodiments, the system controller 228 may receive data from the substrate measurement system 126 based on measurements of substrates that have been processed in the processing chambers 214, 216, 218. The data received by the system controller 228 may include spectral data, reflectance 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 the data storage 250. The data storage 250 may be included as an element within the system controller 228, or may be an element separate from the system controller 228. In some embodiments, the data storage 250 may be or include a portion of the data storage 150 as described with respect to Figure 1 as described.

[0066] Figure 2B An embodiment of a substrate measurement system 251 that may be used to measure a processed substrate is shown. The substrate measurement system 251 may be an integrated measurement and / or imaging system (e.g., an integrated reflectance (IR) measurement system) that is configured to measure film properties (e.g., such as thickness) across the surface of a substrate 264 after the substrate 264 has been processed in a processing chamber. Reflectance measurement is a measurement technique that uses changes in the light reflected from an object that is measured to determine the geometric and / or material properties of the object. A reflectance spectrometer measures the intensity of reflected light over a range of wavelengths. For dielectric films, these intensity changes can be used to determine the film thickness.

[0067] For example, a substrate measurement system 251 can be used to monitor the processing results on one or more substrates for etching and deposition processes, including film thickness. An integrated measurement and / or imaging system can be used to measure the surface of substrate 264 while the substrate 264 is still in the device manufacturing system. In some embodiments, the substrate measurement system 251 can correspond to the substrate measurement system 126. The substrate measurement system 251 can be connected to a factory interface or a transfer chamber. Alternatively, the substrate measurement system 251 can be located inside the factory interface or the transfer chamber. The substrate measurement system 251 can also be an independent system not connected to the manufacturing system. The substrate measurement system 251 can be mechanically isolated from the factory interface and the external environment to protect the substrate measurement system 251 from external vibrations. In some embodiments, the substrate measurement system 251 and its components can provide analytical measurements (e.g., thickness measurements) that can provide a uniformity profile (referred to herein as a profile map) across the surface of substrate 264. A computing device can process the data from the substrate measurement system 251 and provide feedback to the user. The substrate measurement system 251 can be a component capable of measuring film thickness and / or other film properties (e.g., optical constants, particle count, roughness, etc.) on a part or the entire substrate after the substrate has been processed in a chamber. The measurement results can be used to determine when to perform maintenance on the processing chamber, when to perform further tests on the substrate, when to mark the substrate as non - compliant, the placement of the substrate during substrate processing, etc.

[0068] When the substrate 264 is lowered and fixed to the substrate support 256 (e.g., a chuck), the center of the substrate 264 can be offset from the center of the chuck. The processing device of the substrate measurement system 251 can determine one or more coordinate transformations between the center of the substrate 264 and the center of the chuck (the center of the chuck corresponds to the axis of rotation about which the chuck rotates) 256 and apply one or more coordinate transformations to correct the offset, as described in more detail below.

[0069] The substrate measurement system 251 can include a rotary actuator 252 and a linear actuator 254. The rotary actuator 252 can be a motor, a rotary actuator (e.g., an electric rotary actuator), etc. The linear actuator 254 can be an electric linear actuator that can convert the rotary motion in the motor into a linear or straight - line motion along an axis. The substrate measurement system 251 can include a substrate support 256, a camera 258, a sensor 260, and a processing device 262.

[0070] 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 chuck, etc.), or other types of chucks. The substrate support 256 can hold the substrate 264 (e.g., a wafer). The rotation actuator 252 can rotate the substrate support 256 about the first axis 253. The rotation actuator 252 can be controlled by a servo controller and / or a servo motor, which can allow precise control of the rotational position, speed, and / or acceleration of the rotation actuator and thus can allow precise control of the rotational position, speed, and / or acceleration of the substrate support 256. The linear actuator 254 can linearly move the substrate support 256 along the second axis 255. The linear actuator 254 can be controlled by a servo controller and / or a servo motor 272, which can allow precise control of the linear position, speed, and acceleration of the linear actuator 254 and thus can allow precise control of the linear position, speed, and acceleration of the substrate support 256.

[0071] The camera 258 can be positioned above the substrate support 256 and can generate one or more images of the substrate 264 held by the substrate support 256. The camera 258 can be an optical camera, an infrared camera, or other suitable types of cameras. The sensor 260 can also be positioned above the substrate support 256 and can measure at least one target position on the substrate at a time (e.g., can generate a reflection measurement or other measurement of the target position). The camera 258 and the sensor 260 can be fixed at fixed positions on the substrate measurement system 251, while the substrate support 256 can move in an r-θ motion through the rotation actuator 252 and the linear actuator 254.

[0072] 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. When the substrate 264 is initially placed on the substrate support 256, the substrate 264 may not be located at the center of the substrate support 256. The robotic blade 270 can place the substrate 264 on the transfer station 268 (e.g., a set of lift pins). The substrate support 256 can move in a first direction along the second axis 255 such that the substrate support 256 is positioned at the transfer station 268. The transfer station 268 can be located on the lift mechanism 266 (or can be a set of lift pins), which can move the transfer station 268 up and down in the vertical direction (i.e., perpendicular to the second axis 255 and parallel to the first axis 253). When the substrate support 256 is positioned at the transfer station 268, the substrate 264 can be received by the substrate support 256. The substrate 264 may not be located at the center of the substrate support 256. The substrate support 256 can move 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.

[0073] The substrate support can rotate 360 degrees and an image can be generated during the rotation of the substrate support. One or more measurements and / or image generations can be performed at various different θ values using a suction cup, and the detected edge positions can vary. The variation in the detected edges can indicate that the substrate (which can be a circular substrate) is off-center. Additionally, the determined variation in the detected edges can be used to calculate the offset.

[0074] In one embodiment, the parameters (r, θ) determine the offset of the substrate relative to the platform. Using these parameters, the motion system can establish forward and inverse transforms that convert the (r, θ) coordinates of the platform to the (r, θ) coordinates of the substrate. Then, the motion system can calculate the trajectory in the space of the substrate while sending commands to move the motor attached to the substrate support 256. In one embodiment, the motion system can calculate the trajectory in any space because it runs (e.g., via an Ethernet network) the real-time control software of a motion driver connected to linear and rotary actuators. The processing device 262 can calculate the correction trajectory and transmit the commanded position to the motion driver in real time (e.g., at a rate of 1 kHz).

[0075] In some embodiments, the rotation of the rotary actuator 252 for measuring the target position causes an offset between the field of view of the sensor 260 and the target position on the substrate 264 due to the substrate 264 not being centered on the substrate support 256. In this case, the linear actuator 254 can linearly move the substrate support 256 along a second axis to correct the offset. Then, the sensor 260 can measure the target position on the substrate 264. Once the measurements of all the target points on the substrate have been made, the processing device 262 can determine the uniformity profile across the surface of the substrate 264 based on the measurements.

[0076] In some embodiments, the processing device 262 can determine one or more coordinate transforms between the center of the substrate support 256 (corresponding to the first axis 253 about which the substrate support 256 rotates) applied during the rotation of the substrate support 256 and the center of the substrate 264 to correct the offset.

[0077] Figure 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 can be configured to obtain measurements of one or more portions of a substrate, such as the substrate 202, before or after processing the substrate 202 at a processing chamber. The substrate measurement subsystem 282 can correspond to Figure 2A in the embodiments. Figure 2Asubstrate measurement system 126. The substrate measurement subsystem 282 can obtain measurements of a portion of the substrate 202 by generating data associated with a portion of the substrate 202. In some embodiments, the substrate measurement subsystem 282 can be configured to generate spectral data, position data, and / or other attribute data associated with the substrate 202.

[0078] The substrate measurement subsystem 282 can be configured to generate one or more types of data of the substrate, including spectral data, position data, substrate attribute data, etc. The substrate measurement subsystem 282 can generate substrate data in response to a request to obtain one or more measurements of the substrate before or after processing the substrate at the manufacturing system. The substrate measurement subsystem 282 can include one or more components that facilitate the generation of substrate data. For example, the substrate measurement subsystem can include a spectral sensing component for sensing a spectrum or a spectrum from a portion of the substrate and generating spectral data of the substrate. In some embodiments, the spectral sensing component can be an interchangeable component that can be configured based on the type of processing implemented at the manufacturing system or the target type of measurement to be obtained at the substrate measurement subsystem. For example, one or more components of the spectral sensing component can be interchanged at the substrate measurement subsystem to enable the collection of reflectance spectral data, ellipsometric 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.

[0079] The substrate measurement subsystem 282 can include a controller 283 that is configured to execute one or more instructions for generating data associated with a portion of the substrate 202. The substrate measurement subsystem 282 can include a substrate sensing component 284 that is configured to detect when the substrate 202 is transferred to the substrate measurement subsystem 282. The substrate sensing component 284 can include any component that is configured to detect when the substrate 202 is transferred to the substrate measurement subsystem 282. For example, the substrate sensing component 284 can include an optical sensing component that transmits a light beam through an entrance to the substrate measurement subsystem 282. When the substrate 202 is placed within the substrate measurement subsystem 282, the substrate sensing component 284 can detect that the substrate 202 has been transferred to the substrate measurement subsystem 282 in response to the substrate 202 blocking the light beam transmitted through 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 can transmit an indication to the controller 283 indicating that the substrate 202 has been transferred to the substrate measurement subsystem 282.

[0080] In some embodiments, the substrate sensing element 284 may be further configured to detect identification data associated with the substrate 202. In some embodiments, when the substrate 202 is transferred to the substrate measurement subsystem 282, the substrate 202 may be embedded within a substrate carrier (not shown). The substrate carrier may include one or more registration features capable of identifying the substrate 202. For example, the optical sensing element of the substrate sensing element 284 may detect that the substrate 202 embedded within the substrate carrier has blocked a light beam transmitted through an entrance to the substrate measurement subsystem 282. The optical sensing element may 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 element may generate an optical signature associated with the one or more registration features. The substrate sensing element 284 may transmit the optical signature generated by the optical sensing element, along with an indication that the substrate has been placed within the substrate measurement subsystem 282, to the controller 283. In response to receiving the optical signature from the sensing element 284, the controller 283 may 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 of the substrate 202, an identifier of the processing of the substrate 202 (e.g., lot number or process run number), an identifier of the type of the substrate 202 (e.g., wafer, etc.), and the like.

[0081] The substrate measurement subsystem 282 may include one or more elements 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 position of the substrate 202. The reference position may be a portion of the substrate 202 that includes identification features associated with a particular portion of the substrate 202. For example, the substrate 202 may have a reference label embedded within a central portion of the substrate 202. In another example, the substrate 202 may have one or more structural features included on the surface of the substrate 202 at a central portion of the substrate 202. The controller 283 may determine the identification features associated with a particular portion of the substrate 202 based on the determined identification information of the substrate 202. For example, in response to determining that the substrate 202 is a wafer, the controller 283 may determine one or more identification features that are typically included in a portion of the wafer.

[0082] The controller 283 can identify a reference position of the substrate 202 using one or more camera elements 285 configured to capture image data of the substrate 202. The camera element 285 can generate image data of 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 identification features associated with the reference position of the substrate 202. The controller 283 can further determine the position and / or orientation of the substrate 202 as depicted in the image data based on the identified identification features of the substrate 202. The controller 283 can determine the position and / or orientation of the substrate 202 based on the identified identification features of the substrate 202 and the determined position and / or orientation of the substrate 202 as depicted in the image data.

[0083] 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, where each coordinate is determined based on the distance from the reference position of 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 position, where the first position data includes Cartesian coordinates of (0, 0). The controller 283 can generate second position data associated with a second portion of the substrate 202 relative to the reference position. For example, a portion of the substrate 202 that is approximately 2 nanometers (nm) due east of the reference position can be assigned Cartesian coordinates of (0,1). In another example, a portion of the substrate 202 that is 5 nm due north of the reference position can be assigned Cartesian coordinates of (1, 0).

[0084] The controller 283 can determine one or more portions of the substrate 202 to be measured based on the position data determined for the substrate 202. In some embodiments, the controller 283 can receive one or more operations of a processing recipe associated with the substrate 202. In such embodiments, the controller 283 can further determine one or more portions of the substrate 202 to be measured based on the one or more operations of the processing recipe. For example, the controller 283 can receive an indication to perform an etching process on the substrate 202, where several structural features are etched onto the surface of the substrate 202. As a result, the controller 283 can determine one or more structural features to be measured and the expected positions of these features in various portions of the substrate 202.

[0085] The substrate measurement subsystem 282 may include one or more measurement elements for measuring the substrate 202. In some embodiments, the substrate measurement subsystem 282 may include one or more spectral sensing elements 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 of the detected energy wave (i.e., the intensity or amount of energy) for each wavelength of the detected wave.

[0086] In one embodiment, multiple wavelengths may be included in the reflected energy waves received by the substrate measurement subsystem 282. Each reflected energy wave may be associated with a different portion of the substrate 202. In some embodiments, the intensity of each reflected energy wave received by the substrate measurement subsystem 282 may be measured. 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 the spectral data. In some embodiments, one or more wavelengths may be associated with intensity values outside an expected range of intensity values. In such embodiments, intensity values outside the expected range of intensity values may be an indication of the presence of a defect at a portion of the substrate 202.

[0087] The measurement elements for measuring the substrate 202 may also include non-spectral sensing elements configured to collect and generate non-spectral data. For example, the measurement elements may include eddy current sensors or capacitance sensors. Although some embodiments of this description may refer to collecting and using spectral data of the substrate 202, the embodiments of this description may be applicable to non-spectral data collected for the substrate 202.

[0088] The spectral sensing element 287 may be configured to detect an energy wave reflected from a portion of the substrate 202 and generate spectral data associated with the detected wave. The spectral sensing element 287 may include a wave generator 288 and a reflected wave receiver 291. In some embodiments, the wave generator 288 may be an optical wave generator configured to generate a light beam toward a portion of the substrate 202. In such an embodiment, the reflected wave receiver 291 may be configured to receive the reflected light beam from that portion of the substrate 202. The wave generator 288 may be configured to generate an energy stream 289 (e.g., a light beam) and transmit the energy stream 289 to a portion of the substrate 202. The reflected energy wave 290 may be reflected from that portion of the substrate 202 and received by the reflected wave receiver 291. Although Figure 2C a single energy wave reflected from the surface of the substrate 202 is shown, multiple energy waves may be reflected from the surface of the substrate 202 and received by the reflected wave receiver 291.

[0089] In response to the reflection wave receiver 291 receiving the reflected energy wave 290 from a portion of the substrate 202, the spectral sensing element 287 can measure the wavelength of each wave included in the reflected energy wave 289. The spectral sensing element 287 can further measure the intensity of each measured wavelength. In response to measuring each wavelength and the intensity of each wavelength, the spectral sensing element 287 can generate spectral data for the portion of the substrate 202. The spectral sensing element 287 can transmit the generated spectral data to the controller 283. The controller 283 can, in response to receiving the generated spectral data, generate a mapping between the received spectral data and the position data of the measured portion of the substrate 202.

[0090] 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 at the substrate measurement subsystem 282. In some embodiments, the spectral sensing element 287 can be a first spectral sensing element configured to generate one type of spectral data. For example, the spectral sensing element 287 can be configured to generate reflectance spectral data, ellipsometric spectral data, hyperspectral imaging data, chemical imaging data, thermal spectral data, or conductive spectral data. In such an embodiment, the first spectral sensing element can be removed from the substrate measurement subsystem 282 and replaced with a second spectral sensing element configured to generate a different type of spectral data (e.g., reflectance spectral data, ellipsometric spectral data, hyperspectral imaging data, or chemical imaging data).

[0091] 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 measurement obtained for one or more portions of the substrate 202. In some embodiments, the controller 283 can determine one or more types of measurements based on a notification received from Figure 2A the system controller 228. In other or similar embodiments, the controller 283 can determine one or more types of measurements based on instructions to generate a measurement of a portion of the substrate 202. In response to determining one or more 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 to generate spectral data for the substrate 202, and the second spectral sensing element is the best sensing element for obtaining the determined type of measurement of one or more portions of the substrate 202. In response to determining that the second sensing element is the best sensing element, the controller 283 can send a notification to the system controller indicating that the first spectral sensing element should be replaced with the second spectral sensing element and that the second spectral sensing element should be used to obtain one or more types of measurements of one or more portions of the substrate 202. The system controller 128 can transmit the notification to a client device connected to the manufacturing system, where the client device can provide the notification to a user (e.g., an operator) of the manufacturing system via the GUI.

[0092] In other or similar embodiments, the spectral sensing element 287 may be configured to generate multiple types of spectral data. In such embodiments, according to the previously described embodiments, the controller 283 may cause the spectral sensing element 287 to generate a specific type of spectral data based on the type of measurement to be obtained for one or more portions of the substrate 202. In response to determining the type of measurement to be obtained, the controller 283 may determine that the spectral sensing element 287 is to generate a first type of spectral data. Based on determining that the first type of spectral data will be generated by the spectral sensing element 287, the controller 283 may cause the spectral sensing element 287 to generate the first type of spectral data for one or more portions of the substrate 202.

[0093] As previously described, the controller 283 may determine one or more portions of the substrate 202 to be measured at the substrate measurement subsystem 282. In some embodiments, one or more measurement elements, such as the spectral sensing element 287, may be fixed elements within the substrate measurement subsystem 282. In such embodiments, the substrate measurement subsystem 282 may include one or more positioning elements 295 that are configured to modify the position and / or orientation of the substrate 202 relative to the spectral sensing element 287. In some embodiments, the positioning element 295 may be configured to translate the substrate 202 along a first axis and / or a second axis relative to the spectral sensing element 287. In other or similar embodiments, the positioning element 295 may be configured to rotate the substrate 202 about a third axis relative to the spectral sensing element 287.

[0094] When the spectral sensing element 287 generates spectral data for one or more portions of the substrate 202, the positioning element 295 may modify the position and / or orientation of the substrate 202 according to one or more determined portions of the substrate 202 to be measured. For example, before the spectral sensing element 287 generates spectral data for the substrate 202, the positioning element 295 may position the substrate 202 at the Cartesian coordinates (0, 0), and the spectral sensing element 287 may generate first spectral data for the substrate 202 at the Cartesian coordinates (0, 0). In response to the spectral sensing element 287 generating the first spectral data for the substrate 202 at the Cartesian coordinates (0, 0), the positioning element 240 may translate the substrate 202 along the first axis such that the spectral sensing element 287 is configured to generate second spectral data for the substrate 202 at the Cartesian coordinates (0, 1). In response to the spectral sensing element 287 generating the second spectral data for the substrate 202 at the Cartesian coordinates (0, 1), the controller 283 may rotate the substrate 202 along the second axis such that the spectral sensing element 287 is configured to generate third spectral data for the substrate 202 at the Cartesian coordinates (1, 1). This process may occur multiple times until spectral data is generated for each determined portion of the substrate 202.

[0095] In some embodiments, one or more layers 297 of material may be included on the surface of substrate 202. The one or more layers 297 may include etch materials, photoresist materials, mask materials, deposition materials, etc. In some embodiments, the one or more layers 297 may include an etch material that is etched according to an etch process implemented in the processing chamber. In such embodiments, spectral data may be collected for one or more portions of the unetched etch material of layer 297 deposited on substrate 202, according to the previously disclosed embodiments. In other or similar embodiments, the one or more layers 297 may include an etch material that has been etched according to an etch process in the processing chamber. In such embodiments, one or more structural features (e.g., lines, pillars, openings, etc.) may be etched into one or more of the layers 297 of substrate 202. In such embodiments, spectral data may be collected for one or more of the structural features etched into one or more of the layers 297 of substrate 202.

[0096] In some embodiments, the substrate measurement subsystem 282 may include one or more additional sensors configured to capture additional data of substrate 202. For example, the substrate measurement subsystem 282 may include additional sensors configured to determine the thickness of substrate 202, the thickness of a film deposited on the surface of substrate 202, etc. Each sensor may be configured to transmit the captured data to the controller 283.

[0097] According to the embodiments described herein, in response to receiving at least one of spectral data, position data, or property data of substrate 202, the controller 283 may transmit the received data to the system controller 228 for processing and analysis.

[0098] In some embodiments, the substrate measurement subsystem 282 includes one or more image capture devices 299 connected to the controller 283, such as cameras (e.g., including complementary metal oxide semiconductor (CMOS) sensors or charge coupled device (CCD) sensors). The image capture devices 299 may produce images (e.g., two-dimensional (2D) color images, infrared (IR) images, near-infrared images, etc.). In an embodiment, the images may be processed by one or more trained machine learning models together with the spectral data generated by the spectral sensing element 287 to make decisions regarding one or more chamber components.

[0099] Figure 3Illustrates an illustrative system architecture 300 for predicting substrate placement for processing a chamber in accordance with aspects of the present disclosure. In some embodiments, system architecture 300 may include one or more elements of computer architecture 100 and / or manufacturing system 200 or may be a part of one or more elements of computer architecture 100 and / or manufacturing system 200. System architecture 300 may include one or more elements of manufacturing device 122 (e.g., substrate measurement system 126), server machine 320, and server machine 350.

[0100] As previously described, manufacturing device 122 may produce products in accordance with a recipe or by performing operations over a period of time. Manufacturing device 122 may include processing chamber 310, which is configured to perform substrate processing on a substrate according to a substrate processing recipe. In some embodiments, the recipe may be a blanket wafer recipe for depositing a film on a test substrate. In some embodiments, the recipe may be a blanket wafer recipe for etching the surface of a test substrate. In some embodiments, processing chamber 310 may be any of the processing chambers 214, 216, 218 described in reference Figure 2A Manufacturing device 122 may also include substrate measurement system 126, as described herein.

[0101] Manufacturing device 122 may be coupled to server machine 320. Server machine 320 may include processing device 322 and / or data storage 332. In some embodiments, processing device 322 may be configured to execute one or more instructions to perform operations at manufacturing device 122. For example, processing device 322 may include the system controller 228 described in reference Figure 2A or may be a part of system controller 228. In some embodiments, data storage 332 may include data storage 150 and / or data storage 250 or may be a part of data storage 150 and / or data storage 250.

[0102] Processing device 322 may be configured to receive data from one or more elements of manufacturing device 122 (i.e., via a network). For example, processing device 322 may receive surface profile data (e.g., wafer maps, thickness profile data, etc.) 336 collected by substrate measurement system 126 after a substrate has been processed in the processing chamber. In another example, processing device 322 may receive metrology data collected by other metrology devices before and / or after performing substrate processing on a substrate. The metrology data may include metrology measurements generated for the substrate by an integrated metrology device. In some embodiments, processing device 322 may store the received spectral data, film thickness profile data, substrate thickness profile data, and / or the received metrology data at data storage 332.

[0103] The processing device 352 may include a substrate placement engine 330. The substrate placement engine 330 at the processing device 322 may be configured to determine a proposed placement (e.g., proposed location) for a substrate to be processed in the processing chamber 310. The processing chamber 310 may be a processing chamber for processing a measured substrate. The substrate placement engine 330 may determine one or more substrate placement metrics for one or more substrates to be processed in the processing chamber 310 based on substrate surface profile data 336. In some embodiments, the substrate placement metric may include a series of values (e.g., vectors, matrices, etc.) indicating the correlation of specific data combinations, correlations, patterns, and / or relationships present in the sensor data. For example, the substrate placement metric may include a feature vector that includes binary values indicating the presence or absence of specific features in the data.

[0104] The substrate placement metric may be compared with known patterns and / or combinations of substrate placement metrics (e.g., target substrate placement metrics). The target substrate placement metric may be associated with an ideal substrate placement for optimal processing of the substrate. In response to determining that the substrate placement metric for one or more substrate placement positions meets one or more substrate placement criteria (e.g., conditions related to processed substrates that meet a threshold specification such as a threshold tilt specification), the substrate placement engine 330 may determine a proposed placement of the substrate for processing and / or may provide an alert to the user. In some embodiments, the substrate placement criteria may include a specified combination of values indicated by the substrate placement metric. Once the proposed substrate placement is determined, the substrate placement engine 330 may output instructions for the robotic arm to use specific settings (e.g., x settings and / or y settings) when placing the substrate on the substrate support at the coordinates according to the proposed substrate placement.

[0105] As Figure 3 shown, in some embodiments, the processing device 322 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 referenced Figure 1As described. The training set generator 324 can be configured to generate a training set 340 to train a machine learning model 334 or a set of machine learning models 334. For example, the training set generator 324 can generate training inputs based on historical substrate surface profile data 336. The substrate surface profile data 336 (e.g., thickness profile) can be associated with the etch rate and / or tilt on the surface of one or more substrates processed in the processing chamber. In some embodiments, the training set generator 324 can retrieve historical substrate surface profile data 336 (e.g., substrate thickness profile data) from the data store 332 to generate training inputs. The training set generator 324 can generate a target output indicative of an estimated substrate placement value (e.g., substrate placement metric) of the training input based on the generation of the historical substrate surface profile data 336. The training set generator 324 can include the generated training inputs and the generated target outputs in the training set 340. For each historical substrate thickness profile, the training set 340 can additionally include an indicator of substrate placement (e.g., relative to the inner diameter of the process kit ring on a substrate support such as an electrostatic chuck). Reference Figure 5 Further details regarding the generation of the training set 340 are provided.

[0106] The training engine 326 can be configured to train, validate, and / or test a machine learning model 334 or a collection of machine learning models 334. The training engine 326 can provide the training set 340 to train the machine learning model 334 and store the trained machine learning model 334 at the data store 332. In some embodiments, the training engine 326 can use a validation set 342 to validate the trained machine learning model 334. The validation set 342 can include surface profile data 336 and associated chamber element condition metrics. The training set generator 324 and / or the training engine 326 can generate the validation set 342 based on the historical surface profile data 336. In some embodiments, the validation set 342 can include historical surface profile data 336 that is different from the historical surface profile data 336 included in the training set 340.

[0107] The training engine 326 can provide the historical surface profile data 336 as an input to the trained machine learning model 334 and can extract one or more substrate placement metrics for processing a substrate in the processing chamber from one or more outputs of the trained model 334. The input can additionally include the lifetimes of one or more elements of the processing 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 an efficacy 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 the substrate placement for processing a substrate in the processing chamber based on the surface profile data of the substrate processed by the processing chamber.

[0108] As previously discussed, in some embodiments, the training set generator 324 and / or the training engine 326 can be elements of the processing device 322 at the server 320. In additional or alternative embodiments, the training set generator 324 and / or the training engine 326 can be elements of the processing device 352 at the server 350. The server 350 can include or can be part of a computing system separate from the manufacturing system 200. In some embodiments, as referenced Figure 2A as described, the server 320 can contain or can be part of the system controller 228. In such an embodiment, the server 350 can include or can be part of a computing system that is coupled to (i.e., via a network) but separate from the system controller 228. For example, a user of the manufacturing system 200 can be provided access to data stored at one or more portions of the data storage 332 or to one or more processes executed at the processing device 322. However, a user of the manufacturing system 200 can not be provided access to any data stored at one or more portions of the data storage 354 or to any processes executed at the processing device 352.

[0109] The processing device 352 can be configured to execute the training set generator 324 and / or the training engine 326 in a manner similar to the processing device 322. In some embodiments, the server 350 can be coupled via a network to the manufacturing equipment 122 and / or the in-line metrology equipment 130. Accordingly, in accordance with the embodiments described with respect to the processing device 322, the processing device 352 can obtain the surface profile data 336 and the substrate placement metrics corresponding to the surface profile data 336 for use by the training set generator 324 and / or the training engine 326 to generate the training set 340 and the validation set 342. In other or similar embodiments, the server 350 is not coupled to the manufacturing equipment 122 and / or the external metrology equipment 130. Accordingly, the processing device 352 can obtain the surface profile data 336 from the processing device 322.

[0110] The training set generator 324 at the processing device 352 can generate the training set 340 in accordance with the previously described embodiments. In accordance with the previously described embodiments, the training engine 326 at the processing device 352 can train and / or validate the machine learning model 334. 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. In accordance with the embodiments described herein, the processing device 352 can obtain the surface profile data 336 from the other manufacturing equipment and / or server machines. In some embodiments, the training set 340 and / or the validation set 342 can be generated based on the surface profile data 336 obtained for the substrates processed at the processing chamber 310 and other surface profile data obtained for other substrates processed at the processing chambers of other manufacturing systems.

[0111] In response to the training engine 326 selecting a trained model 334 to use, the processing device 352 can transfer the trained model 334 to the processing device 322. The substrate placement engine 330 can use the trained model 334 to provide substrate placement suggestions as previously described.

[0112] Figure 4 A model training workflow 405 and a model application workflow 417 for substrate placement determination according to one embodiment are shown. The model training workflow 405 and the model application workflow 417 can be implemented by processing logic executed by a processor of a computing device. One or more of these workflows 405, 417 can be implemented, for example, through one or more machine learning models implemented on a processing device and / or other software and / or firmware executed on the processing device.

[0113] The model training workflow 405 is used to train one or more machine learning models (e.g., deep learning models) to determine an optimal substrate placement for processing a substrate in a processing chamber. The model application workflow 417 is used to apply one or more trained machine learning models to perform substrate placement evaluation. Each contour map 412 can be associated with a thickness contour or other surface contour of the substrate being processed. For example, each of the contour maps 412 can reflect the thickness of the corresponding substrate after performing a processing operation (e.g., an etching operation, etc.) on the substrate.

[0114] Various machine learning outputs are described herein. The specific number and arrangement of machine learning models are described and shown. However, it should be understood that the number and type of machine learning models used and the arrangement of such machine learning models can be modified to achieve the same or similar end results. Therefore, the arrangement of the machine learning models described and shown is merely an example and should not be construed as limiting.

[0115] 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 task or a subset of tasks. For example, a first machine learning model can be trained to determine substrate placement (e.g., a position on a substrate support for substrate processing), and a second machine learning model can be trained to determine substrate switching offsets (e.g., substrate handling robot switching offsets). Additionally or alternatively, different machine learning models can be trained to perform different combinations of tasks. In an example, one or several machine learning models can be trained, where the trained machine learning (ML) model is a single shared neural network having multiple shared layers and multiple higher-level different output layers, where each output layer outputs different predictions, classifications, identifications, etc. For example, a first higher-level output layer can determine substrate placement relative to a first type of chamber element (e.g., an electrostatic chuck), and a second higher-level output layer can determine substrate placement relative to a second type of chamber element (e.g., a process kit ring).

[0116] 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 element having a classifier or regression layer that maps features to an output space of a target. For example, a convolutional neural network (CNN) hosts multiple layers of convolutional filters. Pooling is performed in the lower layers, and non-linear problems can be solved. Generally, multiple layers of perceptrons are attached on top of the lower layers to map the top-level features extracted by the convolutional layer to a decision (e.g., a classification output). Deep learning is a category of machine learning algorithms that uses a cascade of multiple layers of non-linear processing units for feature extraction and transformation. Each successive layer uses the output of the previous layer as input. Deep neural networks can learn in a supervised (e.g., classification) and / or unsupervised (e.g., pattern analysis) manner. Deep neural networks include a hierarchy of layers, where different layers learn different levels of representations corresponding to different levels of abstraction. In deep learning, each level learns to transform its input data into a slightly more abstract and composite representation. It should be noted that deep learning processing can itself learn which features are best placed at which level. The "depth" in "deep learning" refers to the number of layers of data transformation. More precisely, a deep learning system has a relatively large credit assignment path (CAP) depth. The CAP is a chain of transformations from input to output. The CAP describes the potential causal relationship between the input and the output. For a feedforward neural network, the depth of the CAP can be the depth of the network and can be the number of hidden layers plus one. For a recurrent neural network where a signal can propagate through a layer more than once, the CAP depth is potentially infinite.

[0117] The training of a neural network can be achieved in a supervised learning manner, which involves feeding a training data set consisting of labeled inputs through the network, observing its output, defining an error (by measuring the difference between the output and the label values), and using techniques such as deep gradient descent and backpropagation to adjust the weights of the network at all its layers and nodes to minimize the error. In many applications, repeating this process over many labeled inputs in the training data set results in a network that can produce a correct output when the input is different from the inputs that exist in the training data set.

[0118] For the model training workflow 405, a training data set should be formed using a training data set that includes contour maps 412 of hundreds, thousands, tens of thousands, hundreds of thousands, or more substrates. The data can include, for example, uniformity contours determined using a given number of measurements, each measurement associated with a specific target location. This data can be processed to generate one or more training data sets 436 for training one or more machine learning models. The training data items in the training data set 436 can include the contour map 412, the substrate placement for the substrate being measured to generate the contour map, and / or one or more images of the substrate.

[0119] To implement the training, the processing logic inputs the training data set 436 into one or more untrained machine learning models. The machine learning model can be initialized before the first input is input into the machine learning model. The processing logic trains the untrained machine learning model based on the training data set to produce one or more trained machine learning models that perform the various operations as described above. The training can be implemented by inputting the input data into the machine learning one by one, such as one or more contour maps 412 (e.g., thickness contour map, spectral contour map, roughness contour map, particle count contour map, optical constant contour map, etc.), images of components, and / or lifetime information.

[0120] The machine learning model processes the input to produce an output. An artificial neural network includes an input layer consisting of values in data points. The next layer is called the hidden layer, and each node in the hidden layer receives one or more input values. Each node contains parameters (e.g., weights) applied to the input values. Thus, each node essentially inputs the input values into a multivariate function (e.g., a non-linear mathematical transformation) to produce an output value. The next layer can be another hidden layer or the output layer. In either case, the nodes in the next layer receive the output values from the nodes in the previous layer, and each node applies weights to these values and then produces its own output value. This can be implemented at each layer. The last layer is the output layer, where there is a node for each category, prediction, and / or output that the machine learning model can produce.

[0121] Accordingly, the output may include one or more predictions or inferences (e.g., an estimate of substrate placement in a processing chamber for substrate processing in the processing chamber where a measured substrate has been processed). The processing logic may compare the estimated substrate placement of the output with the historical substrate placement. The processing logic determines an error (i.e., a classification error) based on the difference between the estimated substrate placement and the target substrate placement. The processing logic adjusts the weights of one or more nodes in the machine learning model according to the error. An error term or increment may be determined for each node in the artificial neural network. Based on this error, the artificial neural network adjusts one or more of its one or more parameters (the weights of one or more inputs of the node). The parameters may be updated in a backpropagation manner such that the nodes in the highest layer are updated first, followed by the nodes in the next layer, and so on. The artificial neural network includes multiple layers of "neurons", where each layer receives values from the neurons in the previous layer as inputs. The parameters of each neuron include the weights associated with the values received from each neuron in the previous layer. Accordingly, adjusting the parameters may include adjusting the weights assigned to each input of one or more neurons in one or more layers of the artificial neural network.

[0122] Once 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 rounds of training, the processing logic may determine whether a stopping criterion has been met. The stopping criterion may be a target accuracy level, a target number of processed images from the training data set, a target amount of parameter change of one or more previous data points, a combination 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 may be, for example, 70%, 40%, or 90% accuracy. In one embodiment, the stopping criterion is met if the accuracy of the machine learning model has stopped improving. If the stopping criterion has not been met, further training is performed. If the stopping criterion is met, the training may be completed. Once the machine learning model is trained, a held-out portion of the training data set may be used to test the model. Once one or more trained machine learning models 438 are generated, they may be stored in the model storage 445 and may be added to the substrate placement engine 330.

[0123] For a model application workflow 417, according to one embodiment, input data 462 can be input into one or more substrate placement estimators 467, each of which may include a trained neural network or other model. Additionally or alternatively, one or more substrate placement estimators 467 can apply image processing algorithms to determine chamber element conditions. The input data can include contour maps (e.g., contour maps of polymer layers on a substrate measured using an integrated reflectometry device or a substrate measurement system). The input data can additionally optionally include one or more images of the substrate. Based on the input data 462, the substrate placement estimators 467 can output one or more estimated substrate placements 469. The estimated substrate placement 469 can include the substrate placement relative to a substrate support structure for processing the substrate in a processing chamber (e.g., selectively as an offset from the center of a substrate support such as an electrostatic chuck). In some examples, a coordinate system based on the substrate support (e.g., based on the center point of the substrate support) can be used to correlate the estimated substrate placement (e.g., position) relative to the substrate support.

[0124] The action determiner 472 can determine one or more actions 470 to be performed based on the substrate placement 469. In one embodiment, the action determiner 472 compares the substrate placement estimate with one or more substrate placement thresholds. If one or more of the substrate placement estimates meet or exceed the substrate placement threshold, the action determiner 472 can determine that it is recommended to update the substrate placement 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 metric based on the substrate placement 469 meeting one or more criteria. In some examples, the substrate placement 469 can include the estimated position of the substrate to be placed relative to the substrate support (e.g., electrostatic chuck) and / or relative to an element of the substrate support (e.g., a process kit ring) for processing. The estimated position can be the optimized position of the substrate to minimize tilt in the processed substrate, especially near the edges of the substrate. The substrate placement 469 can include the coordinate position correlating the center point of the substrate support with the center point of the substrate. In some examples, the substrate placement 469 can reflect the offset between the center of the substrate and the center of the substrate support. In some embodiments, the offset can be used to determine one or more offsets for a robot for manipulating the substrate (e.g., updated positions for robot placement and / or "switching" of substrates, etc.).

[0125] Figure 5is a flowchart of a method 500 for generating a training dataset for training a machine learning model to perform substrate placement evaluation according to aspects of the present disclosure. The method 500 is implemented by processing logic that may include hardware (circuits, dedicated logic, etc.), software (e.g., running on a general-purpose computer system or a dedicated machine), firmware, or a combination thereof. In one implementation, the method 500 may be implemented by a computer system such as Figure 1 computer system architecture 100. In other or similar implementations, one or more operations of the method 500 may be implemented by one or more other machines not depicted in the figure. In some aspects, one or more operations of the method 500 may be implemented by the training set generator 324 of the server machine 320 or the server machine 350 described with respect to Figure 3 the server machine 320 or the server machine 350.

[0126] At block 510, the processing logic initializes the training set T to an empty set (e.g., {}). At block 512, the processing logic obtains substrate surface data associated with a substrate being processed in a processing chamber of a manufacturing system (e.g., reflectance measurement data of the surface of a film on the substrate, such as a film thickness profile or a wafer map).

[0127] At block 514, the processing logic obtains substrate placement information for the substrate being processed by the processing chamber. As previously described, the substrate placement information may include the coordinate position of the substrate relative to the substrate support and / or relative to elements of the substrate support.

[0128] At block 516, the processing logic generates a training input based on the sensor data obtained for the substrate at block 512. In some embodiments, the training input may include a normalized sensor data set (e.g., including surface reflectometer data as described herein).

[0129] At block 518, the processing logic may generate a target output based on the substrate placement information obtained at block 514. The target output may correspond to a substrate placement metric for the substrate being processed in the processing chamber (data indicating the placement of one or more substrates being processed in the processing chamber).

[0130] At block 520, the processing logic generates an input / output mapping. The input / output mapping refers to a training input that includes or is based on data of the substrate, and a target output for the training input, where the target output identifies the substrate placement, and where the training input is associated with (or mapped to) the target output. At block 522, the processing logic adds the input / output mapping to the training set T.

[0131] At block 524, processing logic determines whether training set T includes a sufficient amount of training data to train a machine learning model. Note that in some embodiments, the sufficiency of training set T can be simply determined based on the number of input / output mappings in the training set, while in some other embodiments, the sufficiency of training set T can be determined based on one or more other criteria (e.g., a measure of the diversity of training examples, etc.) 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 to train a machine learning model, the processing logic provides training set T to train the machine learning model. In response to determining that the training set does not include a sufficient amount of training data to train a machine learning model, method 500 returns to block 512.

[0132] At block 526, the processing logic provides training set T to train the machine learning model. In some embodiments, training set T is provided to the training engine 326 of server machine 320 and / or server machine 350 to perform the training. In the case of a neural network, for example, the input values of the input / output mapping (e.g., spectral data and / or chamber data of a previous substrate) are input into the neural network, and the output values of the input / output mapping are stored in the output nodes of the neural network. The connection weights in the neural network are then adjusted according to a learning algorithm (e.g., backpropagation, etc.), and this procedure is repeated for other input / output mappings in training set T. After block 526, machine learning model 190 can be used to provide a substrate placement (e.g., a substrate placement metric) of a substrate being processed in the processing chamber.

[0133] Figure 6 is a flow chart illustrating an embodiment of method 600 for training a machine learning model to estimate a substrate placement for processing a substrate in a processing chamber. Method 600 is performed by processing logic that may include hardware (circuits, dedicated logic, etc.), software (e.g., running on a general-purpose computer system or a dedicated machine), firmware, or a combination thereof. In one embodiment, method 600 may be performed by a computer system such as Figure 1 computer system architecture 100. In other or similar embodiments, one or more operations of method 600 may be performed by one or more other machines not depicted in the figure. In some aspects, one or more operations of method 600 may be performed by the training engine 326 of server machine 320 or server machine 350 as described with respect to Figure 3

[0134] ​At block 602 of method 600, processing logic collects a training data set, which may include data from multiple substrate profiles (e.g., film thickness profiles indicating the film thickness of a polymer film at multiple locations on a substrate, substrate surface profiles, substrate thickness profiles, etc.). Each data item in the training data set may include one or more labels. The data items in the training data set may include input-level (e.g., image-level) labels that indicate the presence or absence of one or more substrate features associated with substrate placement. For example, some data items may include a label for the presence of a tilt in the 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). The colors in the heat map may indicate substrate thickness and / or other parameter values (e.g., such as tilt). Alternatively, actual thickness values may be used for each of a number of coordinates on the surface of the substrate (e.g., thickness values of the substrate).

[0135] At block 604, data items from the training data set are input into an untrained machine learning model. At block 606, the machine learning model is trained based on the training data set to produce a trained machine learning model that classifies or estimates one or more substrate placements for processing a substrate in a processing chamber. The machine learning model may also be trained to output one or more other types of predictions, coordinate-level classifications, decisions, etc.

[0136] In one embodiment, at block 610, the input of the training data item is input into the machine learning model. The input may include data from a substrate profile (e.g., a substrate surface profile) that indicates one or more surface characteristics across the substrate (e.g., thickness, optical constants, particle count, roughness, material properties, etc.). In an embodiment, the data may be input as an image or as a feature vector. At block 612, the machine learning model processes the input to produce an output. The output may include one or more substrate placements (e.g., substrate placement values, etc.). The substrate placement may be a proposed substrate placement for placing a future substrate on a substrate support. The output may additionally or alternatively include one or more substrate switching offsets for a robotic arm. For example, the switching offset may associate an initial robotic switching orientation with an updated robotic switching orientation to correspond to a predicted substrate placement for substrate processing.

[0137] At block 614, the processing logic compares the output probability and / or value of the substrate placement metric with a known best substrate placement associated with the input. At block 616, the processing logic determines an error based on the difference between the output and the known placement. At block 618, the processing logic adjusts the weights of one or more nodes in the machine learning model based on the error.

[0138] At block 620, the processing logic determines whether a stop criterion is met. If the stop criterion has not been satisfied, the method returns to block 610 and inputs another training data item into the machine learning model. If the stop criterion is satisfied, the method proceeds to block 625 and completes the training of the machine learning model.

[0139] In one embodiment, one or more ML models are trained for applications across multiple processing chambers, which may be of the same type or model. The trained ML model can then be further adjusted for a particular instance of a processing chamber. The further adjustment can be effected by using additional training data items that include surface contour maps of substrates processed by the processing chamber in question. Such adjustment can address chamber mismatches between the chamber and / or particular hardware processing kits of some processing chambers. Additionally, in some embodiments, after maintenance is performed on a processing chamber and / or after another change is made to the hardware of the processing chamber, further training is performed to adjust the ML model of the processing chamber.

[0140] Figure 7 is a flow diagram of a method 700 for determining a proposed substrate placement in accordance with aspects of the present disclosure. Method 700 is implemented by processing logic that may include hardware (circuits, specialized logic, etc.), software (e.g., running on a general-purpose computer system or a dedicated machine), firmware, or a combination thereof. In one implementation, method 700 may be implemented by a computer system such as Figure 1 a computer system architecture 100 of the like. In other or similar implementations, one or more operations of method 700 may be implemented by one or more other machines not depicted in the figures. In some aspects, one or more operations of method 700 may be implemented by a substrate placement engine 330 with respect to Figure 3 the server machine 320 described.

[0141] At block 706, the processing logic (e.g., of a processing apparatus) determines the center of a substrate support (e.g., an electrostatic chuck) of a processing chamber of a substrate processing system (also referred to as a manufacturing system). In some embodiments, the center of the substrate support is determined based on data collected by a multi-functional wafer. For example, a multi-functional wafer (e.g., a camera wafer) can collect an image of the electrostatic chuck in the processing chamber. The processing apparatus can process the image to determine the center of the electrostatic chuck. The center of the substrate support may correspond to a reference point (e.g., the (0, 0) point) of the substrate support.

[0142] At block 708, the substrate is centered with respect to the center of the substrate support. In some embodiments, the processing logic causes a robotic arm (e.g., of a substrate handling robot) to place the substrate on the substrate support such that the center of the substrate is aligned with the center of the substrate support. In some embodiments, the substrate is centered on the substrate support to obtain a baseline measurement regarding substrate placement. In some embodiments, the substrate is a proxy substrate (e.g., a test substrate, etc.).

[0143] At block 710, the processing chamber processes the substrate. For example, the processing chamber may perform an etch process to partially remove a film on the surface of the substrate, or may perform 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 etch process recipe, a deposition process recipe, etc.). During processing, the substrate may be supported by a substrate support (e.g., an electrostatic chuck) of the processing chamber. The substrate may be placed in an initial position in the processing chamber (e.g., at block 708) prior to processing and may remain in the initial position during processing. In some embodiments, after processing, the substrate includes a surface profile (e.g., a thickness profile, etc.).

[0144] At block 712, one or more robots transfer the substrate from the processing chamber to the substrate measurement system. If the substrate measurement system is connected to a transfer chamber or is included in a transfer chamber, the transfer chamber robot may remove the substrate from the processing chamber and insert the substrate into the substrate measurement system. If the substrate measurement system is connected to a factory interface or is included in a factory interface, the transfer chamber robot may remove the substrate from the processing chamber and place the substrate in a load lock. The factory interface robot may then remove the substrate from the load lock and insert the substrate into the substrate measurement system. The substrate measurement system may be any of the aforementioned substrate measurement systems, such as an integrated reflectometer (IR) device.

[0145] At block 714, the substrate measurement system generates measurement results at a plurality of locations on the surface of the substrate. Each location may have a unique set of coordinates.

[0146] At block 716, the substrate measurement system and / or the computing device may generate a contour map (e.g., a substrate surface contour map) of the surface profile of the substrate based on measurements of the substrate measurement system. The contour map may be or 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 coordinate of the substrate associated with the pixel. In some embodiments, the contour map indicates a thickness profile, and the thickness profile indicates an etch rate profile. For example, the contour map may reflect the thickness profile of a substrate that has undergone an etch process for a predetermined amount of time. In some embodiments, the contour map indicates substrate surface defects, such as tilts. In some embodiments, the tilt is related to the etch rate. In some embodiments, the contour may be or include a feature vector, where each entry in the feature vector is associated with a coordinate of the substrate, and where each entry may have a value that represents the value of the surface profile at the coordinate of the substrate. In some embodiments, the processing logic (e.g., of the measurement system and / or the computing device) may determine an etch rate contour map corresponding to the etch rate near the edge of the substrate.

[0147] At block 718, the computing device processes data from the contour map (e.g., a thickness contour map, a particle map, an optical constant map, a roughness map, an etch rate contour map, etc.) using a model. In some embodiments, the computing device uses one or more trained machine learning models to process data from the contour map. In some embodiments, the trained machine learning models are trained using data collected from a plurality of substrates processed according to a recipe. For example, a set of substrates are processed according to a recipe at various placements (e.g., positions) on a substrate support. Contour maps of the processed substrates may be generated. The contour maps and the corresponding substrate placements may be input as training data to train the machine learning models.

[0148] In some embodiments, the model is a physics-based model or a statistical model. In some embodiments, the model outputs estimated substrate placement values (e.g., estimated substrate placement metrics) for placing the substrate relative to one or more elements of the substrate support. In some examples, the model outputs a substrate placement metric (e.g., the coordinate position of the substrate on the substrate support) corresponding to the placement of the substrate relative to the substrate support (e.g., an electrostatic chuck) and / or one or more elements of the substrate support (e.g., a process kit ring). In some embodiments, the output of the model is based on the etch rate contour map determined at block 716. In some embodiments, the output of the model indicates that the substrate is not placed in the optimal position for processing. In one embodiment, instead of using or in addition to using the trained ML model, one or more computer vision algorithms are used to process the contour map.

[0149] At block 720, processing logic (e.g., of a computing device) determines a proposed placement of a substrate on a substrate support based on an estimated placement value (e.g., output by a model at block 718). In some examples, the computing device may use the estimated substrate placement value to determine a location on the substrate support for placing the substrate. Specifically, at block 718, the computing device may determine a coordinate location based on the value (or values) output from the model. In some embodiments, the computing device may determine a gap that exists between an edge of the substrate and an inner diameter of a process kit ring. In some embodiments, the gap around the substrate is non-uniform. For example, the gap on one side of the substrate may be larger than the gap on the opposite side of the substrate. Thus, the gap may be related to the placement of the substrate (e.g., on an electrostatic chuck, within the inner diameter of a process kit ring, etc.). The processing logic may then cause another substrate to be placed in the processing chamber (e.g., via a substrate handling robot) according to the proposed placement as described below.

[0150] Figure 8 is a flow diagram of a method 800 for comparing a second estimated substrate placement with a first estimated substrate placement in accordance with aspects of the present disclosure. In some embodiments, method 800 is implemented in conjunction with method 700 described above. For example, method 700 may correspond to processing a first substrate, while method 800 may correspond to processing a second substrate and comparing the results of the second substrate with the results of the first substrate. Method 800 is implemented by processing logic that may include hardware (circuits, dedicated logic, etc.), software (e.g., running on a general-purpose computer system or a dedicated machine), firmware, or a combination thereof. In one implementation, method 800 may be implemented by a computer system such as Figure 1 computer system architecture 100. In other or similar implementations, one or more operations of method 800 may be implemented by one or more other machines not depicted in the figures. In some aspects, one or more operations of method 800 may be implemented by a substrate placement engine 330 with respect to Figure 3 the server machine 320 described.

[0151] At block 808, the substrate (e.g., the second substrate) is placed in the processing chamber according to the recommended placement (e.g., determined at block 720 of method 700). In some embodiments, the processing logic causes the robot to place the substrate on a substrate support (e.g., an electrostatic chuck) within the processing chamber. According to the recommended placement, the robot can place the substrate such that the center of the substrate is offset from the center of the substrate support. For example, the recommended placement can specify that the center of the substrate will be offset by a specified amount in a specified direction from the center of the electrostatic chuck. The amount and direction of the offset can be the difference between a first robot switching orientation and a second robot switching orientation. In some examples, the first robot switching orientation is a baseline (e.g., preset, etc.) orientation, while the second robot switching orientation is an updated orientation based on the offset. The robot can place the substrate on the electrostatic chuck at a specified offset from the center of the electrostatic chuck along the offset direction.

[0152] At block 810, the substrate is processed in the processing chamber according to a recipe (e.g., the recipe of block 710 of method 700). The recipe can be an etch processing recipe and / or a deposition processing recipe. Similar to the substrate processed at block 710 of method 700, the substrate includes a processed surface profile. The surface profile can be a thickness profile.

[0153] At block 812, the substrate is transferred from the processing chamber (e.g., via one or more transfer robots) to a substrate measurement system (e.g., similar to block 712 of method 700).

[0154] At block 814, the substrate measurement system measures the surface of the substrate (e.g., similar to block 714 of method 700).

[0155] At block 816, the substrate measurement system and / or the computing device can generate a contour 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 contour can be an image or can include an image. The contour map can indicate an etch rate profile.

[0156] At block 818, the computing device processes data from the contour map (e.g., thickness contour map, particle 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.).

[0157] 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 can determine whether to update the proposed placement 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 can determine that the proposed placement is appropriate. In response to the estimated substrate placement values being different (e.g., outside a threshold difference of each other, etc.), the processing logic can determine that the proposed placement should be updated.

[0158] At block 822, the processing logic updates the proposed placement based on the comparison at block 820.

[0159] Figure 9 Is a profile map 900 of a processed substrate according to aspects of the present disclosure. The profile map 900 is a heat map that shows the temperature at different locations on the substrate during processing in a processing chamber. The temperature can be based on the thickness of the film deposited or etched at different locations during processing. A key 902 is provided to show how to interpret the profile map 900. As shown, the hot spots 905 are non-uniform around the edge of the substrate. In some embodiments, the methods described herein can provide a substrate without hot spots (e.g., substantially no hot spots, substantially uniform temperature, etc.). Non-uniform hot spots may indicate an increase in the etching rate, which is related to an increase in substrate tilt. During processing, on the side near the hot spot 905, the substrate may be too close to the processing kit ring (e.g., of the substrate support in the processing chamber). For example, near the hot spot 905, the gap between the edge of the substrate and the inner diameter of the processing kit ring may be too small or too large. The non-uniform hot spots 905 around the edge of the substrate can indicate an excessive tilt in the features of the processed substrate near the edge of the substrate. Therefore, according to some embodiments described herein, the placement of the substrate for processing should be changed.

[0160] Figure 10A - 10B Is a flowchart of a method for determining an optimal substrate placement according to aspects of the present disclosure. Figure 10A Illustrates a method 1000A for determining an optimal substrate placement according to some embodiments. Figure 10B Illustrates a method 1000B for determining an optimal substrate placement according to some embodiments. In some embodiments, methods 1000A and / or 1000B can be methods for performing a full-angle fitting (AAF) algorithm for adjusting a proposed wafer placement. In some embodiments, methods 1000A and / or 1000B are used to optimize etching profile uniformity, particularly near the outermost edge of the substrate.

[0161] Referring to Figure 10A, an experiment design (DOE) is performed at block 1002. In some embodiments, multiple substrates (e.g., test substrates) are processed (e.g., etched) at different positions on a substrate support in a processing chamber. In some embodiments, a first substrate is placed at a first placement position (e.g., the first position) on the substrate support. The first substrate can be processed, and the processed first substrate can have a first surface profile. In some embodiments, the first surface profile is measured using the substrate measurement system described above. In some embodiments, a second substrate is placed at a second placement position different from the first placement position on the substrate support. Then the second substrate is processed, and the surface profile of the processed second substrate is measured. A third substrate can be processed at a third placement position, and subsequently, the surface profile of the processed third substrate can be measured.

[0162] Referring to Figure 11A , an example plot 1100A showing substrate placement DOE is illustrated. Referring to Figure 13A , an example plot 1300A showing substrate placement in the illustrated DOE is shown. In some embodiments, plot 1100A can correspond to plot 1300A. In some embodiments, substrates can be placed at different positions on the substrate support to perform DOE. Referring again to Figure 11A , in some embodiments, substrates are placed and processed at each placement position 1102 - 1118. In some embodiments, position 1118 can correspond to the center of the substrate support. In some embodiments, a first substrate can be placed at the first position 1102 and processed, a second substrate can be placed at the second position 1104 and processed, a third substrate can be placed at the third position 1106 and processed, and so on. Although Figure 11A shows nine possible placement positions for processing substrates, DOE can be performed with fewer than nine processed substrates at different placement positions. In some embodiments, in a two-dimensional space, no more than two placement positions can be on the same line. In some embodiments, at least one placement position of the DOE is offset from a straight line formed between two other placement positions. In some embodiments, DOE can be performed with more than nine processed substrates at different placement positions. Including a greater number of processed substrates at different placement positions can improve the accuracy of the DOE. Higher accuracy can be obtained if the DOE placement positions cover more azimuth angles. For example, Figure 13A the shown placement positions form an "X" - shaped pattern. Adding additional placement positions in the DOE to form another "X" - shaped pattern with different angles between the "X" legs (e.g., shallower or deeper angles) may make the prediction more accurate. In some embodiments, DOE can be performed with as few as three processed substrates at three different placement positions.

[0163] Referring again toFigure 10A , data processing is performed at block 1004. In some embodiments, the etch rate at positions near the edge of each substrate being processed by DOE (e.g., at block 1002) is determined. For example, the etch rate can be determined by subtracting the processed thickness of the substrate from the unprocessed thickness and then dividing by the processing time. In some embodiments, the etch rate is determined for positions at multiple azimuth angles at a radial position adjacent to the edge of the first processed substrate. Referring to Figure 11B , a radial plot of the substrate etch rate versus the azimuth angle θ is shown. In some embodiments, the etch rate is determined for all azimuth angles around the center of the processed substrate. In some embodiments, the etch rate is determined for several azimuth angles around the center of the processed substrate such that the determined etch rate represents the entire etch rate profile. The determined etch rate may only represent all azimuth angles. For example, the etch rate can be determined at azimuth angles such as 0°, 15°, 30°, 45°, etc. around the processed substrate near the substrate edge. However, the etch rate can be determined at various azimuth angles with scales different from those Figure 11B shown. For example, the etch rate can be determined at 0°, 10°, 20°, 30°, etc. or 0°, 30°, 60°, 95°, etc. Figure 11B shows the etch rate of the processed substrate at various azimuth angles around the center of the substrate near the substrate edge.

[0164] Referring back to Figure 10A , at block 1004A, the etch rate can be normalized. Normalizing the etch rate can help explain differences in etch chamber conditions. Normalizing the etch rate can help explain differences in substrates, such as minor differences in composition or defects. Substrate differences can lead to inconsistencies in substrate processing, which may lead to data inconsistencies. Normalization of the etch rate can help eliminate the effects of outlying defects in the substrate or defects and / or variations in processing from the dataset. In some embodiments, the etch rate is normalized using the average etch rate. In some examples, for a first etch rate profile (e.g., Figure 11B or Figure 12A the etch rate profile shown in), a first average etch rate is determined. Each value of 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, for a second etch rate profile, a second average etch rate is determined. Each value of the second etch rate profile is divided by the second average etch rate to determine a second normalized etch rate profile. Other methods for normalizing etch rate data can be used. More details regarding data normalization are discussed below with reference to Figure 10B .

[0165] Referring to Figure 12A, shows an example plot of the substrate etch rate versus the azimuth angle θ. An etch rate profile 1202A corresponding to the etch rate of the first substrate near the first substrate edge is shown. An etch rate profile 1204A corresponding to the etch rate of the second substrate near the second substrate edge is shown. Referring to Figure 12B , shows an example plot of the normalized substrate etch rate versus the azimuth angle θ. The normalized etch rate profile 1202B can correspond to Figure 12A the etch rate profile 1207A, and the normalized etch rate profile 1204B can correspond to Figure 12A the etch rate profile 1204A. By the normalized etch rate profiles, the etch rate values at discrete azimuth angles (e.g., discrete values of θ) can be compared by eliminating at least some of the inconsistencies between the processed substrates from the data.

[0166] At block 1004B, a linear fit is performed using the corresponding values of the normalized etch rates of the substrates processed as part of the DOE (e.g., the normalized etch rate values of the processed substrates at the same azimuth angle). Referring to Figure 13B , shows an example graphical representation 1300B of the normalized substrate etch rates of the substrates processed at different placement positions. For a specific azimuth angle 1354 ( Figure 13B the 45° shown in Figure 13C ), the normalized etch rate is retrieved from the normalized etch rate profiles 1352 of each processed substrate. Referring to Figure 13A , shows a plot 1300C of the linear fit of the normalized substrate etch rate. The normalized etch rates 1372 of each processed substrate at the azimuth angle 1354 are plotted relative to different positions along Figure 13A the vector 1310. The vector 1310 in

[0167] will be described in detail below. A linear fit 1380 is formed by the plotted normalized etch rates 1372. The linear fit 1380 can represent the predicted etch rates of the processed substrates at different positions along the vector 1310 at the corresponding azimuth angles. A linear fit of the normalized etch rate can be determined at each azimuth angle. Figure 10A, at block 1006, using the linear fit data determined at block 1004B, the optimal substrate placement position is determined. In some embodiments, the linear fit data can be used to predict the etch rate profile for any substrate placement. By predicting the etch rate profiles at different placement positions, the optimal position that meets one or more metrics can be determined. For example, the placement position that minimizes the etch rate range (e.g., the etch rate range near the substrate edge) or the standard deviation of the etch rate (e.g., the standard deviation of the etch rate near the substrate edge) can be determined. Minimizing the etch rate range and / or the standard deviation of the etch rate can result in more consistent processed substrates, especially near the substrate edge. Once the optimal substrate placement position is determined, the optimal placement position is recommended to the substrate processing system. The substrate can be placed on the substrate support in the processing chamber according to the recommended placement.

[0168] Referring Figure 10B , method 1000B for determining the optimal substrate placement for processing in a processing chamber is shown. Method 1000B is described with reference to an etch process, but can equally be used for deposition processes. Thus, any description regarding etch, etch rate, etch rate profile, etc. also applies to deposition, deposition rate, deposition rate profile, etc. Method 1000B is also applicable to any other metric that shows a linear or higher-order response to substrate placement at a particular azimuth. For example, the etch rate gradient across the substrate (e.g., the difference in etch rate between a 146 mm radius and a 148 mm radius) and the etch tilt profile of a patterned substrate can also exhibit a similar response to substrate placement. At block 1010, the placement position of the substrate on the substrate support. The substrate can be a test substrate being processed as part of a DOE. The substrate can be placed at Figure 11A one of the positions 1102 - 1118 shown. At block 1020, an etch 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, using a substrate measurement system, the etch rate or deposition rate can be measured near the substrate edge at a radial distance from the substrate center at multiple azimuths. In some examples, the etch rate or deposition rate is measured about the central axis of the substrate at distances of approximately 140 mm and 150 mm from the substrate center. The etch rate or deposition rate measured at various azimuths can be used to construct an etch rate profile or a deposition rate profile (e.g., as Figure 11B shown for a single substrate, as Figure 12A shown for two substrates). At block 1040, the etch rate profile or the deposition rate profile is normalized. In some embodiments, the etch rate profile or the deposition rate profile is normalized using the average etch rate of the etch rate profile or the average deposition rate of the deposition rate profile. For example, referring to Figure 12A, for the etch rate profile 1202A, the average etch rate from azimuth angle 0° to azimuth angle 360° can be determined. Each etch rate value on the etch rate profile can be divided by the average etch rate to calculate Figure 12B the normalized etch rate profile 1202B.

[0169] Referring again to Figure 10B , for the multiple substrates processed as part of the DOE, blocks 1010 - 1040 can be repeated multiple times. Each time block 1010 is repeated, the associated substrate is placed at a different placement position on the substrate support. In some embodiments, blocks 1010 - 1040 are repeated nine times to process nine test substrates at nine different placement positions. In some embodiments, blocks 1010 - 1040 are repeated as few as three times to process as few as three test substrates at as few as three different placement positions. Including more or fewer substrates in the DOE (and thus repeating blocks 1010 - 1040 more or fewer times) depends on the precision threshold of the DOE. For example, if the precision threshold is higher, more test substrates are processed. Similarly, if the precision threshold is lower, fewer test substrates can be processed.

[0170] At block 1050, the normalized etch rate profiles of each substrate processed at various substrate placements (e.g., during each repetition of blocks 1010 - 1040) are compiled. Referring to Figure 13B , a graphical representation 1300B of the normalized etch rate profile is shown. The normalized etch rate profile 1352 can represent the etch rate profile of the substrate processed at each placement position 1102 - 1118 shown in Figure 11A and / or each placement position 1302 shown in Figure 13A . Referring again to Figure 10B , at block 1055, using the normalized etch rate profiles compiled at block 1050, the normalized etch rate of each processed substrate at each azimuth angle is determined. Referring to Figure 13B , the normalized etch rate is determined according to the corresponding normalized etch rate profile 1352 at azimuth angle 1354. The azimuth angle 1354 is shown as 45°. However, in some embodiments, depending on the density of the substrate etch rate sampling map, several normalized etch rates of each processed substrate are determined according to the normalized etch rate profiles 1352 at all azimuth angles.

[0171] Referring again to Figure 10B , at block 1060, for each azimuth angle at which the normalized etch rate is determined at block 1055, a linear fit is performed. Various linear and non - linear higher - order fitting methods known to those skilled in the art can be used for the linear fit. Referring to Figure 13A, showing an example drawing 1300A of substrate placement in a DOE. The substrate can be processed at each substrate placement position 1302 where it is placed on the substrate support. Vector 1310 can correspond to Figure 13B the azimuth angle 1354. The angle between vector 1310 and the X-axis can be equal to the value of the azimuth angle 1354. The placement position 1302 can be projected onto vector 1310, and the position of each projected placement position on vector 1310 is converted to a value y'. As shown, y' makes a 45° angle with the X-axis of drawing 1300A, and the corresponding angle θ where the azimuth angle 1354 is located is also 45°. Referring to Figure 13C , the normalized etch rate 1372 at the azimuth angle 1354 is shown relative to dy'. The normalized etch rate 1372 is plotted for each processed substrate. A linear fit 1380 is calculated to fit the plotted normalized etch rate 1372. For each data corresponding to each of several azimuth angles, the process of projecting the placement position 1302 onto vector 1310, converting the position of each projected placement position to y' of vector 1310, and plotting the normalized etch rate 1372 relative to dy' can be repeated.

[0172] Referring again to Figure 10B , at block 1070, using one or more linear fits calculated at block 1060, a predicted etch rate profile is calculated for the estimated substrate placement position. For example, using the etch rate data and / or the linear fit, an estimated position of the substrate that can produce a substrate satisfying one or more threshold criteria can be determined. The threshold criteria can 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. The linear fit 1380 corresponding to a specific azimuth angle can be used to calculate a predicted value of the etch rate at the specific azimuth angle for the estimated substrate placement position. Once the predicted etch rates for all azimuth angles are determined, the predicted etch rates can be combined to form a predicted etch rate profile. At block 1080, the best substrate placement position that produces the best etch rate profile is determined. In some embodiments, the processing logic searches all substrate placement positions to determine which position has the best etch rate profile (e.g., satisfying one or more of the above threshold criteria). The best placement position can be determined experimentally. In some embodiments, the machine learning model described herein can be used to determine the best placement position. The machine learning model can be trained with historical data (such as historical etch rate profiles and / or historical substrate placement positions) to determine the best placement, thereby producing a substrate with the best etch rate profile. At block 1090, the substrate can be placed at the best placement position for processing.

[0173] Referring again to Figure 12A, in accordance with aspects of the present disclosure, an exemplary plot 1200A of substrate etch rate versus azimuthal angle θ is shown. In some embodiments, the etch rate profile 1202A is the etch rate profile of a substrate processed at an optimal location on a substrate support. The optimal location can be determined using one or more of the methods described herein. In contrast, the etch rate profile 1204A is the etch rate profile of a substrate processed at a non-optimal location on the substrate support.

[0174] Referring Figure 12B , in accordance with aspects of the present disclosure, an exemplary plot 1200B of normalized etch rate versus azimuthal angle θ is shown. In some embodiments, the normalized etch rate profile 1202B is the normalized etch rate profile of a substrate processed at an optimal location on a substrate support. In contrast, the 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 in plot 1200B, the normalized etch rate profile 1202B has a more consistent normalized etch rate at all azimuthal angles θ. Thus, the etch rate of a substrate processed at an optimal location is more consistent, and processing the substrate at an optimal location will result in a better substrate than processing the substrate at a non-optimal location.

[0175] Figure 14 is a flow chart of a method 1400 for determining optimal substrate placement in accordance with aspects of the present disclosure. Method 1400 can be executed by processing logic including hardware (circuits, application specific logic, etc.), software (e.g., running on a general purpose computer system or a dedicated machine), firmware, or some combination thereof. In one implementation, method 1400 can be executed by a computer system of a computer system architecture 100 such as Figure 1 . In other or similar implementations, one or more operations of method 1400 can be executed by one or more other machines not shown in the figures.

[0176] At block 1410, a first substrate is processed in a processing chamber. For example, the processing chamber can perform an etch process to partially remove a film on the surface of the substrate, or perform 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., metal oxide). The processing of the substrate can be according to a recipe (e.g., an etch process recipe, a deposition process recipe, etc.). During processing, the substrate can be supported by a substrate support (e.g., an electrostatic chuck) of the processing chamber. The substrate can be placed in an initial position in the processing chamber before processing and can be held in the initial position during processing. After processing, in some embodiments, the substrate includes a surface profile (e.g., a thickness profile, etc.).

[0177] In block 1412, one or more robots transfer a substrate from a processing chamber to a substrate measurement system. If the substrate measurement system is connected to or included in a transfer chamber, the transfer chamber robot can retrieve the substrate from the processing chamber and insert the substrate into the substrate measurement system. If the substrate measurement system is connected to or included in a factory interface, the transfer chamber robot can retrieve the substrate from the processing chamber and place the substrate in a load lock. Then, the factory interface robot can retrieve the substrate from the load lock and insert the substrate into the substrate measurement system. The substrate measurement system can be any of the above substrate measurement systems, such as an integrated reflectometer (IR) device.

[0178] In block 1414, the substrate measurement system generates measurements at a number of locations on the substrate surface. Each location can have a unique set of coordinates.

[0179] In block 1416, the substrate measurement system and / or a computing device can generate a contour map (e.g., a substrate surface contour map) of the surface profile of the substrate based on the measurements of the substrate measurement system. The contour map can be or include an image, where each pixel in the image corresponds to a coordinate on the substrate. Each pixel can have an intensity value corresponding to a measurement value (e.g., a thickness value) at the coordinate of the substrate associated with the pixel. In some embodiments, the contour map indicates a thickness profile, and the thickness profile indicates an etch rate profile. For example, the contour map can reflect the thickness profile of a substrate that has undergone an etch process for a predetermined amount of time. In some embodiments, the contour map indicates substrate surface defects, such as tilts. In some embodiments, the tilt is related to the etch rate. In some embodiments, the contour can be or include a feature vector, where each entry in the feature vector is associated with a coordinate of the substrate, and where each entry can have a value representing a surface profile value at the substrate coordinate. In some embodiments, the processing logic (e.g., of the measurement system and / or the computing device) can determine an etch rate contour map corresponding to the etch rate near the substrate edge.

[0180] In block 1418, the computing device can determine a number of etch rates corresponding to a number of locations on the substrate. The computing device can use the surface contour map generated in block 1416 to determine an etch rate contour of the etch rate near the substrate edge at several azimuthal angles around the substrate center.

[0181] At block 1420, processing logic processes data associated with the multiple etch rates determined at block 1418. In some embodiments, a computing device uses a model to process the data. The model can include a trained machine learning model, a mathematical model, a linear fitting model, and / or a statistical model. The model can output one or more estimated surface profiles associated with one or more estimated placement locations on the substrate support. For example, the model can use numerical methods to estimate the predicted surface profile of the substrate processed at the estimated placement location. In some embodiments, the model normalizes the etch rate profile (e.g., determined at block 1418) and determines the normalized etch rates 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 can determine one or more estimated placement locations on the substrate support to process a substrate with an optimal etch rate profile.

[0182] At block 1422, the processing logic (e.g., of the computing device) determines a recommended placement of the substrate on the substrate support based on the one or more estimated placement locations (e.g., output by the model at block 1420). In some examples, the computing device can use the estimated placement locations to determine the locations on the substrate support for placing the substrate. Specifically, the computing device can determine the coordinate locations based on the value (or values) output from the model at block 1420. The processing logic can then place another substrate in the processing chamber (e.g., via a substrate handling robot) according to the recommended placement described herein.

[0183] Figure 15FIG. depicts a pictorial representation of a machine in the exemplary form of a computing device 1500 within which instructions may be executed to cause the machine to perform any one or more of the methods 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 telephone, a web appliance, a server, a network router, a switch or bridge, or any other machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, although only a single machine is shown, the term “machine” shall also be taken to include a collection of machines (e.g., computers) that individually or collectively execute a set (or multiple sets) of instructions to perform one or more of the methods discussed herein. In an embodiment, the computing device 1500 may correspond to one or more of the server machines 170, server machines 180, prediction server 112, system controller 228, server machines 320, or server machines 350 as described herein.

[0184] 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), etc.), a static memory 1506 (e.g., flash memory, static random access memory (SRAM), etc.), and a secondary memory (e.g., data storage device 1528), which communicate with each other via a bus 1508.

[0185] 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 dedicated 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 processing device 1502 may also be or include a system on a chip (SoC), a programmable logic controller (PLC), or other types of processing devices. The processing device 1502 is configured to execute processing logic for performing the operations discussed herein.

[0186] The computing device 1500 may further include a network interface device 1522 for communicating with the 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)), a character input device 1512 (e.g., a keyboard), a cursor control device 1514 (e.g., a mouse), and a signal generating device 1520 (e.g., a speaker).

[0187] The data storage device 1528 may include a machine-readable storage medium (or more specifically a non-transitory computer-readable storage medium) 1524, on which one or more sets of instructions 1526 are stored, the one or more sets of instructions implementing any one or more of the methods 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 within the processing device 1502 during execution by the computing device 1500, and the main memory 1504 and the processing device 1502 also constitute a computer-readable storage medium.

[0188] Although the computer-readable storage medium 1524 is shown as a single medium in the exemplary embodiment, the term "computer-readable storage medium" should be considered to include a single medium or multiple media (e.g., a central or distributed database, and / or associated caches and servers) storing one or more sets of instructions. The term "computer-readable storage medium" should also be considered to include any medium that is capable of storing or encoding a set of instructions that are executable by a machine and that cause the machine to perform one or more of the methods of the present disclosure. The term "computer-readable storage medium" should therefore be considered to include, but not be limited to, solid state memories, and optical and magnetic media.

[0189] The foregoing description sets forth numerous specific details, such as examples of specific systems, components, methods, etc., in order to provide a good understanding of many 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 may 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 a simple schematic format, in order to avoid unnecessarily obscuring the present disclosure. Accordingly, the specific details set forth are merely exemplary. Specific implementations may vary from these example details and still be contemplated as within the scope of the present disclosure.

[0190] Throughout the specification, reference to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" throughout the specification are not necessarily all referring to the same embodiment. Additionally, 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 given nominal value is precisely within ±10%.

[0191] Although the operations of the methods herein are shown and described in a particular order, the order of operation of each method may be altered, such that certain operations may be performed in the reverse order, and such that certain operations may be performed, at least in part, concurrently with other operations. In another embodiment, the instructions or sub-operations of different operations may be performed in an intermittent and / or alternating manner.

[0192] It should be understood that the foregoing description is intended to be illustrative and not restrictive. Many other embodiments will be apparent to those of skill in the art upon reading and understanding the foregoing description. Accordingly, the scope of the present disclosure should be determined with reference to the appended claims and the full scope of equivalents to which the claims are entitled.

Claims

1. A computer-readable medium includes instructions that, when executed by a processing device, cause the processing device to perform operations, the operations including: While a first substrate is supported by a substrate support at a first placement position on the substrate support, causing the first substrate to be processed in a processing chamber of a substrate processing system, wherein the first substrate includes a first surface profile after processing; Generating a first surface profile map of the first surface profile using a substrate measurement system; Determining a plurality of first etch rates corresponding to a plurality of first positions on the first substrate based on the first surface profile map; Processing data associated with the plurality of first etch rates using a model, wherein the model outputs one or more estimated surface profiles associated with one or more estimated placement positions on the substrate support based on the plurality of first etch rates; and Determining a recommended placement of the substrate on the substrate support based on the one or more estimated placement positions.

2. The computer-readable medium of claim 1, wherein the operations further include: Placing a second substrate on the substrate support in the processing chamber within an inner diameter of a processing kit ring according to the recommended placement.

3. The computer-readable medium of claim 1, wherein the operations further include: While a second substrate is supported by the substrate support at a second placement position on the substrate support, causing the second substrate to be processed in the processing chamber, wherein the second substrate includes a second surface profile after processing; Generating a second surface profile map of the second surface profile using the substrate measurement system; Determining a plurality of second etch rates corresponding to a plurality of second positions on the second substrate based on the second surface profile map; and Processing data associated with the plurality of second etch rates using the model, wherein the one or more estimated surface profiles associated with the one or more estimated placement positions on the substrate support are further based on the plurality of second etch rates.

4. The computer-readable medium of claim 1, wherein the recommended placement is offset from the center of the substrate support.

5. The computer-readable medium of claim 1, wherein the model includes at least one of a trained machine learning model, a linear fitting model, or a statistical model.

6. The computer-readable medium of claim 5, wherein the model includes the trained machine learning model, and the operations further include: Training a machine learning model to produce the trained machine learning model, wherein the machine learning model is trained using data of a plurality of processed substrates processed in the processing chamber.

7. The computer-readable medium of claim 1, wherein the plurality of first positions on the first substrate correspond to positions at a radial distance from the center of the first substrate at a plurality of azimuth angles.

8. The computer-readable medium according to claim 1, wherein processing data associated with the plurality of first etch rates includes generating a linear fit of at least a first etch rate of the plurality of first etch rates corresponding to at least a first position among the plurality of first positions, and wherein the one or more estimated surface profiles are based on the linear fit.

9. The computer-readable medium according to claim 1, wherein processing data associated with the plurality of first etch rates includes normalizing each of the plurality of first etch rates based on an average etch rate of the plurality of first etch rates.

10. The computer-readable medium according to claim 1, wherein the proposed placement corresponds to an optimal placement position on the substrate support, the optimal placement position being for generating a second substrate having a plurality of second etch rates with values conforming to one or more metrics at a plurality of second positions on the second substrate.

11. The computer-readable medium according to claim 1, wherein the first surface profile includes a first thickness profile.

12. A system comprising: a processing chamber including a substrate support; a substrate measurement tool; a memory; and a processing device operably coupled to the memory, the processing device for: while a first substrate is supported by the substrate support at a first placement position on the substrate support, causing the first substrate to be processed in the processing chamber, wherein the first substrate includes a first surface profile after processing; using the substrate measurement tool to generate a first surface profile map of the first surface profile; determining a plurality of first etch rates corresponding to a plurality of first positions on the first substrate based on the first surface profile map; using a model to process data associated with the plurality of first etch rates, wherein the model outputs one or more estimated surface profiles associated with one or more estimated placement positions on the substrate support based on the plurality of first etch rates; and determining a proposed placement of the substrate on the substrate support based on the one or more estimated placement positions.

13. The system according to claim 12, wherein the processing device is further for: placing a second substrate on the substrate support in the processing chamber within an inner diameter of a processing kit ring according to the proposed placement.

14. The system according to claim 12, wherein the processing device is further for: while a second substrate is supported by the substrate support at a second placement position on the substrate support, causing the second substrate to be processed in the processing chamber, wherein the second substrate includes a second surface profile after processing; using the substrate measurement tool to generate a second surface profile map of the second surface profile; determining a plurality of second etch rates corresponding to a plurality of second positions on the second substrate based on the second surface profile map; and using the model to process data associated with the plurality of second etch rates, wherein the one or more estimated surface profiles associated with the one or more estimated placement positions on the substrate support are further based on the plurality of second etch rates.

15. The system according to claim 12, wherein the model comprises a trained machine learning model, and the processing device is further configured to: Train a machine learning model to generate the trained machine learning model, wherein the machine learning model is trained using data of a plurality of processed substrates processed in the processing chamber.

16. The system according to claim 12, wherein processing data associated with the plurality of first etch rates comprises: Normalizing each of the plurality of first etch rates based on an average etch rate of the plurality of first etch rates; and Generating a linear fit of at least a first etch rate of the plurality of first etch rates corresponding to at least a first position of the plurality of first positions, wherein the one or more estimated surface profiles are based on the linear fit.

17. The system according to claim 12, wherein the proposed placement corresponds to an optimal placement position on the substrate support, and the optimal placement position is used to produce a second substrate having a plurality of second etch rates with values conforming to one or more metrics at a plurality of second positions on the second substrate.

18. A method comprising: Processing a first substrate in a processing chamber while the first substrate is supported by a substrate support at a first placement position on the substrate support, wherein the first substrate comprises a first surface profile after processing; Generating a first surface profile map of the first surface profile using a substrate measurement system; Determining a plurality of first etch rates corresponding to a plurality of first positions on the first substrate based on the first surface profile map; Processing data associated with the plurality of first etch rates using a model, wherein the model is configured to output one or more estimated surface profiles associated with one or more estimated placement positions on the substrate support based on the plurality of first etch rates; and Determining a proposed placement of the substrate on the substrate support based on the one or more estimated placement positions.

19. The method according to claim 18, further comprising: Placing a second substrate on the substrate support in the processing chamber within an inner diameter of a processing kit ring according to the proposed placement.

20. The method according to claim 18, wherein processing data associated with the plurality of first etch rates comprises generating a linear fit of at least a first etch rate of the plurality of first etch rates corresponding to at least a first position of the plurality of first positions, and wherein the one or more estimated surface profiles are based on the linear fit.

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