Controlling substrate stress uniformity based on overlay error measurement.

Abstract

The method includes obtaining first data indicating the overlay error of a substrate using a processing device. The method further includes generating second data indicating the first stress uniformity of the substrate based on the first data. The method further includes performing corrective actions based on the second data.

Description

Technical Field

[0001]

[0001] This disclosure relates to the management of substrate stress uniformity for substrate generation procedures. In particular, this disclosure relates to the management of substrate stress uniformity based on overlay errors for substrate generation procedures.

Background Art

[0002]

[0002] Products can be manufactured by performing one or more manufacturing processes using manufacturing equipment. For example, semiconductor manufacturing equipment can be used to manufacture substrates through semiconductor manufacturing processes. Products are manufactured to have specific characteristics suitable for the intended use. Understanding and controlling the characteristics within the manufacturing chamber helps in the consistent production of products. The relationship between substrate generation parameters and substrate characteristics can be utilized in the design or improvement of substrate generation procedures. When a processing step is executed, the characteristics of the substrate change. The characteristics of the substrate include those with a direct physical meaning and easy to control / optimize (e.g., substrate stress), and those with a weak physical meaning and difficult to control (e.g., in-plane distortion).

Summary of the Invention

[0003]

[0003] What is shown below is a simplified summary of this disclosure to provide a basic understanding of some aspects of this disclosure. This summary is not an exhaustive summary of this disclosure. It is not intended to identify the main or important elements of this disclosure, nor is it intended to indicate any scope of specific embodiments of this disclosure or any scope of the claims. The sole purpose of this summary is to present some concepts of this disclosure in a simplified form as an introduction to the more detailed description presented later.

[0004]

[0004] In one aspect of the present disclosure, the method includes obtaining first data indicating an overlay error of a substrate by a processing device. The method further includes generating second data indicating a first stress uniformity of the substrate based on the first data. The method further includes performing corrective actions based on the second data.

[0005]

[0005] In another aspect of the present disclosure, a non-temporary machine-readable storage medium stores an instruction. When executed, the instruction causes a processing device to perform a step which includes acquiring first data indicating an overlay error of a substrate. The step further includes generating second data indicating a first stress uniformity of the substrate based on the first data. The step further includes performing a corrective action based on the second data.

[0006]

[0006] In another aspect of the present disclosure, the system includes a memory and a processing device coupled to the memory. The processing device is configured to acquire first data indicating an overlay error of a substrate. The processing device is further configured to generate second data indicating stress on the substrate based on the first data. The processing device is further configured to perform corrective actions based on the second data.

[0007]

[0007] The present disclosure is shown in the drawings of the attached drawings as examples, not as limitations. [Brief explanation of the drawing]

[0008] [Figure 1]

[0008] This block diagram shows an exemplary system architecture according to several embodiments. [Figure 2]

[0009] This document shows a flowchart for utilizing stress calculations for substrate processing analysis, based on several embodiments. [Figure 3]

[0010] Several embodiments of in-plane strain data and corresponding stress maps are shown. [Figure 4]

[0011] This is a flowchart of a method for performing corrective actions based on stress data generated from overlay error data, according to several embodiments. [Figure 5]

[0012] This is a block diagram showing computer systems in several embodiments. [Modes for carrying out the invention]

[0009]

[0013] Techniques related to identifying substrate stress based on overlay error are described herein. This substrate stress may be further used in the implementation of corrective actions associated with the substrate processing procedure. Manufacturing equipment may be used to manufacture products such as substrates (e.g., wafers, semiconductors, displays, solar power, etc.). Manufacturing equipment (e.g., manufacturing tools) often includes a processing chamber that isolates the substrate to be processed from the external environment. The characteristics of the manufactured substrate need to meet target characteristic values ​​to promote performance, function, etc. Anomalies, drift, or other differences in the processing environment may result in substrates with suboptimal performance, such as semiconductors that cannot function as intended, or inefficiencies in manufacturing (e.g., further expenditure of time, materials, energy, etc.). The processing environment may be quantified by various sensors associated with the processing chamber, such as pressure gauges, temperature sensors, power sensors (e.g., voltmeters, etc.), gas flow meters, etc.

[0010]

[0014] The manufacturing of a substrate may involve various processes that alter the properties of the substrate. For example, the substrate manufacturing process may involve depositing one or more films on the surface of the substrate. Film deposition can cause changes in stress within the substrate. Stress caused by the process may result in substrate deflection or warping, out-of-plane distortion, in-plane distortion, overlay errors, and so on.

[0011]

[0015] The distortion of the substrate can be measured and analyzed. For example, a vector map of in-plane distortion associated with several locations on the substrate can be generated. This map shows the magnitude and direction of the in-plane distortion (e.g., overlay error) at those locations. Substrate distortion can be an indicator of potential improvements that may be made to the substrate manufacturing procedure, for example, to reduce manufacturing variations between substrates or to reduce variations in distortion across the entire substrate.

[0012]

[0016] In-plane strain of a substrate can be measured, for example, based on the overlay error of features in different layers of the substrate. Measuring in-plane strain can be difficult or inconvenient for translating it into root causes and using it for corrective actions. For example, in-plane strain may involve a map of two-dimensional vectors that can be inconvenient to interpret and inconvenient to provide as input to a model (e.g., a large number of inputs can be inconvenient for physics-based or machine learning models). Methods for generating data that is easier to use to generate insights into the manufacturing process can have difficulties. For example, the complexity of data generation, the challenge of finding correlations between simplified data and corrective actions, and the reduced usefulness of simplified data. Some methods for reducing the dimensionality of in-plane strain data may be physically meaningless and fail to indicate the root causes of manufacturing problems.

[0013]

[0017] The methods and systems of this disclosure can address one or more drawbacks of conventional methods. In some embodiments, data corresponding to in-plane strain or overlay error of the substrate is generated. This data may include a vector map, which shows the magnitude and direction of in-plane strain in various regions of the substrate. A mathematical transformation can be applied to the in-plane strain data to generate a stress map of the substrate. Generating the stress map may include calculating the divergence of the in-plane strain.

[0014]

[0018] In some embodiments, the divergence of in-plane strain correlates with the stress in the substrate. This stress may be related to one or more films deposited on the substrate. This stress may be caused by depositing one or more films on the substrate. The substrate stress map may have one or more advantages compared to conventional analysis methods.

[0015]

[0019] The stress on the substrate is physically significant. By providing data that shows a map of substrate stresses, corrective actions to adjust the stresses caused by film deposition within the substrate can be recommended and / or implemented. For example, a target stress profile to achieve target substrate characteristics can be achieved by adjusting one or more parameters related to substrate manufacturing in terms of substrate stress data.

[0016]

[0020] In some embodiments, further processing of the stress data may lead to additional insights. For example, the stress data can be decomposed. In a first embodiment, a polynomial approximation of the stress data may be generated. Various orders of the approximation (e.g., zero-order, first-order, second-order, etc.) may be associated with the higher-order wafer alignment (HOWA) of the overlay error. This HOWA indicates the root cause of each manufacturing defect, associated corrective actions, etc. The stress data may be decomposed into a planar portion, a radial portion, and a residual portion. These may each indicate one or more root causes of the manufacturing defect, corrective actions, etc.

[0017]

[0021] In some embodiments, stresses associated with multiple types of films can be calculated. For example, the properties of films made of various materials (e.g., oxide films, nitride films, etc.) can be understood by generating stress data associated with the films of those materials. The stress fluctuations of a target laminated substrate can be predicted from a stress map that shows the properties of the types of films contained within the substrate.

[0018]

[0022] In some embodiments, one or more corrective actions may be performed based on substrate stress map data. Corrective actions may include providing the user with a stress map. Corrective actions may include providing the user with an exploded stress map. Corrective actions may include providing the user with warnings, for example, warnings indicating that the stress profile does not meet one or more threshold criteria, warnings indicating that the stress profile is outside the thresholds or unexpected occurrences within the substrate (e.g., wafer arcing, localized spots of plasma density during deposition). Corrective actions may include updating the process recipe, for example, adjustments to the deposition process (e.g., adjusting gas flow, temperature, processing time, etc.) may be performed to improve the substrate stress profile. Corrective actions may include recommending or performing maintenance (e.g., a large planar component in the stress map indicates that some leveling of the system is needed). Corrective actions may include recommending changes to components of the processing system.

[0019]

[0023] The methods and systems of this disclosure offer technical advantages compared to conventional solutions. Conventionally, generating stress maps may involve performing more difficult, time-consuming, and / or expensive measurement techniques. Generating stress maps from overlay error data can provide the benefits of complex substrate stress measurement without the difficulty of performing the measurements. Generating stress maps from overlay error data can provide the benefits of substrate stress measurement using calculations based on previously performed overlay or in-plane strain measurements. Performing corrective actions based on stress data may improve the manufacturing process more than performing corrective actions based on in-plane strain data. Performing corrective actions based on stress data generated from overlay error data can be done without expensive substrate stress measurement. This can lead to increased throughput and reduced costs in substrate manufacturing, among other things.

[0020]

[0024] In one aspect of the present disclosure, a method includes obtaining, by a processing device, first data indicative of an overlay error of a substrate. The method further includes generating, based on the first data, second data indicative of a first stress uniformity of the substrate. The method further includes performing a corrective action based on the second data.

[0021]

[0025] In another aspect of the present disclosure, a non - transitory machine - readable storage medium stores instructions that, when executed, cause a processing device to perform a process that includes obtaining first data indicative of an overlay error of a substrate. The process further includes generating, based on the first data, second data indicative of a first stress uniformity of the substrate. The process further includes performing a corrective action based on the second data.

[0022]

[0026] In another aspect of the present disclosure, a system includes a memory and a processing device coupled to the memory. The processing device is configured to obtain first data indicative of an overlay error of a substrate. The processing device is further configured to generate, based on the first data, second data indicative of a stress of the substrate. The processing device is further configured to perform a corrective action based on the second data.

[0023]

[0027] FIG. 1 is a block diagram illustrating an exemplary system 100 (e.g., an exemplary system architecture) according to some embodiments. System 100 includes a client device 120, a substrate generation system 170, and a data store 140.

[0024]

[0028] The substrate generation system 170 includes components for performing processes such as substrate generation, substrate characteristic data generation, and numerical data generation. The substrate generation system 170 includes a manufacturing apparatus 124, a sensor 126, a measuring apparatus 128, and a substrate generation control unit 129. The substrate generation control unit 129 may include one or more processing devices, memory devices, etc. configured to execute processes associated with the substrate generation system 170, such as providing control over the manufacturing apparatus 124, the sensor 126, the measuring apparatus 128, etc. The substrate generation control unit 129 may include one or more computing devices, such as a personal computer (PC), a laptop, a mobile phone, a smartphone, a tablet computer, a notebook computer, a network-connected television (“smart TV”), a network-connected media player (e.g., a Blu-ray player), a set-top box, an over-the-top (OTT) streaming device, an operator box, etc.

[0025]

[0029] The manufacturing apparatus 124 may include one or more process tools, process chambers, process devices, etc. for manufacturing a physical substrate. The manufacturing apparatus may be provided with instructions for processing a substrate such as a semiconductor wafer and may execute the instructions. The manufacturing apparatus may be provided with instructions for processing a substrate in the form of a process recipe. The process recipe may provide instructions for executing a process procedure that may include one or more process steps.

[0026]

[0030] The substrate manufacturing system 170 includes a sensor 126. The sensor 126 may provide sensor data 142 associated with the manufacturing apparatus 124 (for example, associated with the manufacturing apparatus 124 manufacturing corresponding products such as substrates). The sensor data 142 may be used to determine the health of the apparatus and / or the health of the product (e.g., product quality). The manufacturing apparatus 124 may manufacture products by following a recipe or by running it over a period of time. In some embodiments, the sensor data 142 may include one or more values ​​from among temperature (e.g., heater temperature), spacing (SP), pressure, high-frequency radio frequency (HFRF), radio frequency (RF) matching voltage, RF matching current, RF matching capacitor position, electrostatic chuck (ESC) voltage, actuator position, current, flow, power, voltage, etc.

[0027]

[0031] Sensor data 142 may be associated with or indicate manufacturing parameters, such as hardware parameters of the manufacturing apparatus 124 (e.g., settings or components, e.g., size, type, etc.) or process parameters of the manufacturing apparatus 124. Data associated with some hardware parameters may be stored instead or further as manufacturing parameters 150. Manufacturing parameters 150 may indicate input settings for a manufacturing device (e.g., heater power, gas flow, etc.). Sensor data 142 and / or manufacturing parameters 150 may be provided while the manufacturing apparatus 124 is performing a manufacturing process (e.g., device readings generated during substrate processing). Sensor data 142 may differ for each product (e.g., each substrate). The substrate may have characteristic values ​​measured by the measuring device 128 (e.g., film thickness, film strain, etc.). Measurement data 160 may be some kind of data stored in the data store 140.

[0028]

[0032] The substrate manufacturing system 170 includes a measuring device 128. The measuring device 128 measures one or more characteristics of the substrate and provides measurement data 160 based on the measurements. In some embodiments, the measurement data 160 may include measured values ​​of substrate strain. The measuring device 128 may include a device configured to measure substrate strain. The measuring device 128 may include a device configured to determine in-plane strain and / or overlay error of the substrate.

[0029]

[0033] In-plane strain can include measurements of substrate warping. Excessive in-plane strain can be problematic during manufacturing processes such as photolithography and wafer bonding. In-plane strain can be measured via an interferometer, for example, during optical scanning interferometry or laser scanning. An interferometer directs a beam of monochromatic light (often from a laser) onto the substrate and then analyzes the interference pattern that occurs when this light is coupled with a reference beam. Based on the interference pattern, strain and misalignment between films on the substrate can be identified.

[0030]

[0034] The measuring device 128 may include a scatterometry-based overlay error measurement tool. Overlay error may include misalignment of pattern layers on the substrate. In modern integrated circuits with many layers, precise alignment of multiple layers can be difficult to achieve, yet essential for the performance of the substrate. Scatterometry techniques utilize light scattering from periodic structures, as well as scattering from diffraction gratings. The intensities of various scattering components, such as various diffraction orders, can be used to identify positional differences in the structure of various layers of the film. Positional differences or displacements can provide the overlay error.

[0031]

[0035] The measuring device 128 may include one or more imaging-based overlay error measuring tools. The measuring device 128 may include one or more interference pattern-based overlay error measuring tools, or any other tools for measuring in-plane distortion of the substrate, overlay error of the substrate, etc. The data store 140 may store the overlay error data as measurement data 160. The data store 140 may store the overlay error data as a vector map showing the direction and magnitude of in-plane distortion at several locations on the substrate.

[0032]

[0036] In some embodiments, sensor data 142, measurement data 160, and / or manufacturing parameters 150 may be processed (e.g., by a client device 120). Processing of sensor data 142, measurement data 160, and / or manufacturing parameters 150 may include generating features. In some embodiments, features are patterns of sensor data 142, measurement data 160, and / or manufacturing parameters 150 (e.g., gradient, width, length, peak, etc.), or combinations of values ​​from sensor data 142, measurement data 160, and / or manufacturing parameters 150 (e.g., power derived from voltage and current, etc.). Sensor data 142 may include features, which may be used by the client device 120 to perform signal processing and / or to obtain predictive data 168 for performing corrective actions.

[0033]

[0037] Each instance (e.g., set) of sensor data 142 may correspond to a product (e.g., a substrate), a set of manufacturing equipment, the type of substrate produced by the manufacturing equipment, a substrate production recipe, etc. Similarly, each instance of measurement data 160 and manufacturing parameter 150 may correspond to a product, a set of manufacturing equipment, the type of substrate produced by the manufacturing equipment, etc. The data store 140 may further store information relating sets of different data types, for example, information indicating that a set of sensor data, a set of measurement data, and a set of manufacturing parameters are all associated with the same product, manufacturing equipment, substrate type, etc.

[0034]

[0038] The client device 120, the components of the substrate manufacturing system 170, and the data store 140 can be connected to each other via the network 130 to generate predictive data 168 for performing corrective actions.

[0035]

[0039] The client device 120 includes a corrective action component 122, a suggestion component 176, and an analysis component 110. The client device 120 can perform various processes such as substrate classification, predictive data generation, and corrective action execution. The client device 120 can receive data indicating the characteristics of one or more substrates. The client device 120 can receive data from a measuring device 128, a data store 140, etc. The client device 120 can perform processes to generate predictive data 168 and / or to recommend or execute corrective action.

[0036]

[0040] The presentation component 176 of the client device 120 may present a user interface (UI). The UI may include one or more UI elements. The UI may present data associated with the performance and / or characteristics of one or more substrates. The presented substrate data may include numerical data. The presented substrate data may include one or more non-numerical data such as plots, charts, and diagrams.

[0037]

[0041] In some embodiments, sensor data 142, measurement data 160, and / or manufacturing parameters 150 may be processed (e.g., by a client device 120). Processing of sensor data 142, measurement data 160, and / or manufacturing parameters 150 may include generating features. In some embodiments, the features are patterns of sensor data 142, measurement data 160, and / or manufacturing parameters 150 (e.g., gradient, width, length, peak, etc.), or combinations of values ​​from sensor data 142, measurement data 160, and / or manufacturing parameters 150 (e.g., power derived from voltage and current, etc.).

[0038]

[0042] In some embodiments, the analysis component 110 of the client device 120 may generate predictive data 168. The predictive data 168 may indicate corrective actions (for example, which may be performed by the corrective action component 122). The analysis component 110 may generate predictive data 168 based on measurement data provided by the measurement device 128. The analysis component 110 may perform an analysis based on the correlation between substrate production parameters (for example, manufacturing parameters 150) and substrate characteristics (for example, as represented by measurement data 160). The analysis component 110 may perform one or more calculations to generate predictive data 168.

[0039]

[0043] The analysis component 110 can perform calculations on data indicating the in-plane strain of the substrate. The analysis component 110 can perform calculations on data indicating the overlay error of the substrate. The analysis component 110 can predict substrate stress based on the in-plane strain data (e.g., overlay error data). The analysis component 110 can generate a substrate stress map based on a vector field indicating the in-plane strain. The analysis component 110 can perform divergence calculations to determine the stress from the measured values ​​of the in-plane strain. The predicted data 168 may include a stress map calculated based on the overlay error data.

[0040]

[0044] In some embodiments, the analysis component 110 may generate predictive data 168 using statistical models such as regression models, principal component analysis models, and machine learning models. In some embodiments, the analysis component 110 may perform approximations based on substrate production parameters and substrate performance (e.g., measurement data 160).

[0041]

[0045] In some embodiments, the analysis component 110 may generate predictive data 168 using machine learning, such as supervised machine learning (for example, the machine learning model may be configured to generate labels associated with input data, such as measurement predictions or performance predictions). In some embodiments, the analysis component 110 may generate predictive data 168 using unsupervised machine learning (for example, the machine learning model may be trained on unlabeled data, such as a model configured to perform clustering, dimensionality reduction, etc.). In some embodiments, the analysis component 110 may generate predictive data 168 using semi-supervised learning (for example, the machine learning model may be trained on both labeled and unlabeled input datasets).

[0042]

[0046] In some embodiments, network 130 is a public network that provides client device 120 with access to data store 140, components of substrate manufacturing system 170, and other publicly available computing devices. In some embodiments, network 130 is a private network that provides client device 120 with access to manufacturing equipment 124, sensors 126, measuring equipment 128, data store 140, and other privately available computing devices. In some embodiments, one or more functions of client device 120 and / or analysis server 112 may be performed by a virtual machine, for example, using a cloud-based service. Network 130 may provide access to such virtual machine. Network 130 may include one or more wide area networks (WANs), local area networks (LANs), wired networks (e.g., Ethernet networks), wireless networks (e.g., 802.11 networks or Wi-Fi networks), cellular networks (e.g., Long-Term Evolution (LTE) networks), routers, hubs, switches, server computers, cloud computing networks, and / or combinations thereof.

[0043]

[0047] The client device 120 may include computing devices such as personal computers (PCs), laptops, mobile phones, smartphones, tablet computers, notebook computers, network-connected televisions ("smart TVs"), network-connected media players (e.g., Blu-ray players), set-top boxes, over-the-top (OTT) streaming devices, and operator boxes. The client device 120 may include a corrective action component 122. The corrective action component 122 may receive user input for a display associated with the system 100 (e.g., via a graphical user interface (GUI) displayed through the client device 120).

[0044]

[0048] In some embodiments, the corrective action component 122 transmits a notification of a corrective action to the substrate manufacturing system 170 and causes the corrective action to be implemented. Implementing a corrective action may include updating one or more substrate manufacturing processes, such as updating the process recipe or updating the simulation model. The corrective action component 122 may implement one or more corrective actions, such as providing a warning to the user, recommending a modification or update to the process, or recommending and / or scheduling maintenance.

[0045]

[0049] In some embodiments, measurement data 160 corresponds to historical characteristic data of the product. Predictive data 168 may include analysis results, e.g., output of analysis component 110, stress maps generated based on overlay error data and / or in-plane strain data, predicted system failures, corrective actions to be taken, maintenance to be performed, etc. In some embodiments, predictive data 168 is an indication of anomalies (e.g., an abnormal product, an abnormal component, an abnormal manufacturing equipment 124, abnormal energy usage, etc.) and optionally one or more causes of the anomalies. In some embodiments, predictive data 168 is an indication of changes or drift over time in some components, such as manufacturing equipment 124, sensors 126, and measuring devices 128. In some embodiments, predictive data 168 is an indication of the end of life of a component, such as manufacturing equipment 124, sensors 126, and measuring devices 128.

[0046]

[0050] Executing a manufacturing process that results in defective products incurs costs in terms of time, energy, products, parts, and manufacturing equipment 124, as well as costs for identifying defects and discarding defective products. By providing data indicating overlay errors to the processing device, generating substrate stress data, and performing corrective actions based on the stress data, system 100 can reduce the likelihood of manufacturing equipment 124 producing defective products. For example, updating the process recipe or scheduling the replacement of faulty parts can increase the likelihood of manufacturing equipment 124 producing substrates that meet threshold performance criteria. System 100 may have a technical advantage in avoiding the costs of producing, identifying, and discarding defective products.

[0047]

[0051] Manufacturing parameters may be suboptimal in manufacturing products, potentially resulting in costly consequences such as increased consumption of resources (e.g., energy, coolant, gas, etc.), increased time required to manufacture the product, increased component failures, and increased quantities of defective products. By providing data indicating overlay errors to the processing device, generating substrate stress data, and performing corrective actions based on the stress data, system 100 may have the technical advantage of using optimal manufacturing parameters (e.g., hardware parameters, process parameters, optimal design) and / or a sound device to avoid the costly consequences of suboptimal manufacturing parameters.

[0048]

[0052] Executing a manufacturing process that results in component failure of the manufacturing equipment 124 can be costly in terms of downtime, product damage, equipment damage, and excessive component replacement orders. By providing data indicating overlay errors to the processing device, generating substrate stress data, and taking corrective actions based on the stress data, system 100 may have the technical advantage of avoiding one or more costs among unexpected component failures, unscheduled downtime, loss of productivity, unexpected equipment failures, and product scrap. Monitoring the performance of components over time, such as the manufacturing equipment 124, sensors 126, and measuring devices 128, can provide indication of degraded components.

[0049]

[0053] Manufacturing processes can have environmental impacts. Manufacturing parameters may not be optimal for mitigating the environmental impact of the manufacturing process. By providing data indicating overlay errors to the processing device, generating substrate stress data, and performing corrective actions based on the stress data, system 100 may have the technical advantage of adjusting one or more manufacturing processes to reduce the environmental impact of the manufacturing process.

[0050]

[0054] In some embodiments, the corrective action includes providing a warning. The warning may include an alarm to stop or prevent further manufacturing processes on the substrate if the predictive data 168 indicates a predicted anomaly, such as an anomaly in the product, component, or manufacturing equipment 124. In some embodiments, the corrective action includes providing feedback control (e.g., changing manufacturing parameters in response to the predictive data 168 indicating a predicted anomaly). In some embodiments, the execution of the corrective action includes updating one or more manufacturing parameters.

[0051]

[0055] Manufacturing parameters may include hardware parameters. Hardware parameters may include information indicating parts contained within the manufacturing equipment, a display of recently replaced parts, a display of firmware version or updates, etc. Manufacturing parameters may include process parameters. Process parameters may include setpoints such as temperature, pressure, flow rate, current and / or voltage, gas flow, lift speed, etc. In some embodiments, corrective actions include performing preventive operational maintenance. Preventive operational maintenance may include commands to replace, process, clean, etc., the manufacturing equipment 124. In some embodiments, corrective actions include performing design optimization. Design optimization may include updating manufacturing parameters, updating the manufacturing process, updating the manufacturing equipment 124, etc., for an optimized product or process. In some embodiments, corrective actions include updating the recipe. Updating the recipe may include changing the timing of commands to the manufacturing equipment 124 to enter idle mode, sleep mode, warm-up mode, etc., and adjusting setpoints such as temperature, gas flow, plasma generation, etc.

[0052]

[0056] The data store 140 may be memory (e.g., random access memory), a drive (e.g., a hard drive or flash drive), a database system, or another type of component or device capable of storing data. The data store 140 may include multiple storage components (e.g., multiple drives or multiple databases) that span multiple computing devices (e.g., multiple server computers). The data store 140 may store sensor data 142, manufacturing parameters 150, measurement data 160, and prediction data 168. The data store 140 may be a cloud-based data storage device, a virtual data storage device, etc., or may include them.

[0053]

[0057] In some embodiments, the functions of the client device 120 and the substrate manufacturing system 170 may be provided by fewer machines. For example, in some embodiments, the client device 120 and one or more devices included in the substrate manufacturing control unit 129 may be integrated into a single machine.

[0054]

[0058] In general, the functions described in one embodiment, which are performed by the client device 120 or the substrate manufacturing control unit 129, may also be performed by the other of the two components in several other embodiments, where appropriate. In addition, functions belonging to a particular component may be performed by different or multiple components working together. For example, in some embodiments, a device that performs the processes of the substrate manufacturing system 170 may identify corrective actions based on predictive data 168. In another embodiment, the client device 120 may identify predictive data 168 based on output from the substrate manufacturing system 170 or the like.

[0055]

[0059] In addition, the functionality of a particular component may be achieved through the combined operation of different or multiple components. For example, the processes of client device 120 may be accessed as services provided to other systems or devices via an appropriate application programming interface (API).

[0056]

[0060] In several embodiments, “User” may be represented as a single individual. However, several other embodiments of the present disclosure include “User” being an entity controlled by multiple users and / or automated sources. For example, a collection of individual users formed as a group of administrators may be considered “User.”

[0057]

[0061] Figure 2 shows a flow chart 200 for utilizing stress calculations for substrate processing analysis in several embodiments. Substrate production 202 includes a process for producing one or more substrates. Physical substrate production may include providing manufacturing parameters to manufacturing equipment, processing equipment, one or more process tools, etc. Physical substrate production may include performing processing steps on the substrate material to produce the manufactured substrate.

[0058]

[0062] Characteristic data associated with one or more substrates may be generated. In-plane strain data 206 may be generated by a measuring device such as the measuring device 128 in Figure 1. In-plane strain data 206 may show film-induced strain, stress-induced strain, overlay error, etc. In-plane strain data 206 may be related to stress-induced strain of the substrate. For example, when depositing a film on a substrate, some change in the shape of the substrate may be caused by stress associated with the deposited film. The structure, patterns, etc. of various layers of the substrate may be aligned in a manner different from the target alignment. The difference in alignment between these different layers may be measured as overlay error. Overlay error may be represented by a two-dimensional vector field. In this case, each vector of the field indicates the magnitude and direction of the overlay error with respect to the relevant position on the substrate.

[0059]

[0063] Stress data 208 can be generated based on in-plane strain data 206. Stress data 208 can be generated by a processing device. Stress data 208 can be generated by performing one or more operations on in-plane strain data 206. Stress data 208 can be generated by performing a divergence calculation on in-plane strain data 206.

[0060]

[0064] In vector calculus, divergence is a fundamental operation that measures the "spread" or "source / sink" behavior of a vector field. This is typically represented by the symbol ∇·F, where ∇ (del) is the nabla operator and · (dot) is the dot product.

[0061]

[0065] Mathematically, the divergence of a vector field F=(F1, F2, F3) in three-dimensional space is given by the following equation: ∇·F=(∂F1 / ∂x)+(∂F2 / ∂y)+(∂F3 / ∂z), Here, ∂F1 / ∂x, ∂F2 / ∂y, and ∂F3 / ∂z represent the partial derivatives of the components of F with respect to coordinates x, y, and z, respectively. In some embodiments, the vector field may represent the overlay error. In some embodiments, the vector field may be limited to in-plane components, e.g., components in the (x,y) plane. The divergence calculation may further include, for example, the derivative in the (x,y) plane. The divergence calculation transforms the vector field (e.g., a two-dimensional output representing the magnitude and orientation of the overlay error associated with a two-dimensional input space corresponding to the surface of the substrate) into a scalar field (e.g., a single output associated with each point in the two-dimensional input space corresponding to the surface of the substrate).

[0062]

[0066] The stress data 208 is used, for example, to perform corrective action 210 by the corrective action component 122 in Figure 1. The stress data 208 may indicate the root cause of a manufacturing equipment failure. The stress data 208 may indicate appropriate corrective actions to be performed in relation to the manufacturing equipment. Corrective actions may include providing warnings to the user (e.g., via a graphical user interface), updating process recipes, recommending maintenance, performing maintenance, and recommending changes to components of the manufacturing system. For example, the target stress profile can be more closely adapted to different equipment currently in use, such as different gas supply components or different substrate supports.

[0063]

[0067] Figure 3 shows in-plane strain data 302 and corresponding stress maps 304 according to several embodiments. Processing logic (e.g., of the client device 120 in Figure 1) can receive data indicating the in-plane strain of the substrate. This data may be provided by a measuring instrument. This data may indicate the magnitude and orientation of the overlay error at several locations on the substrate. One representation of such in-plane strain data is a vector map illustrated as in-plane strain data 302. Each arrow illustrated in the in-plane strain data 302 represents a vector. These vectors represent the magnitude and orientation of the overlay error at specific locations on the substrate. The in-plane strain data 302 may be a vector map representing the overlay error of the substrate.

[0064]

[0068] The processing logic may generate a stress map 304 based on the in-plane strain data 302. Generating the stress map 304 may include generating a one-dimensional output based on a two-dimensional vector map of the in-plane strain data 302. Generating the stress map 304 may include performing a divergence calculation on the vector map represented in the in-plane strain data 302.

[0065]

[0069] A stress map (e.g., stress map 304) may represent a one-dimensional (e.g., scalar) output based on a two-dimensional input (e.g., two-dimensional coordinates of the substrate position). The stress map 304 may show different regions of relatively high or low substrate stress based on patterning. Other forms of the stress map 304 are also possible, such as contour lines, surface plots, or heatmaps. The stress map 304 may be a visual representation of the results of a divergence calculation performed based on in-plane strain data 302. The stress map 304 (and / or the underlying data constituting the stress map 304) may be used in performing one or more corrective actions in relation to the substrate processing apparatus.

[0066]

[0070] Figure 4 is a flowchart of Method 400 for performing corrective actions based on stress data generated from overlay error data, according to several embodiments. Method 400 can be implemented by processing logic, which may include hardware (e.g., a network, dedicated logic, programmable logic, microcode, processing devices, etc.), software (e.g., instructions executed by a processing device, a general-purpose computer system, or a dedicated machine), firmware, microcode, or a combination thereof. In some embodiments, Method 400 can be implemented at least partially by a client device 120, e.g., a corrective action component 122, an analysis component 110, etc. In some embodiments, a non-temporary machine-readable storage medium stores the instructions. When the instructions are executed by the processing device, the processing device performs the steps of Method 400.

[0067]

[0071] For the sake of simplicity, Method 400 is depicted and described as a series of steps. However, the steps provided herein may be performed in various orders and / or simultaneously, along with other actions not presented or described herein. Furthermore, not all of the illustrated actions may be performed in order to carry out Method 400 relating to the subject matter of the disclosed invention. In addition, those skilled in the art will understand and acknowledge that Method 400 may alternatively be represented as a series of interrelated states via a state transition diagram or events.

[0068]

[0072] In block 402, the processing logic acquires first data indicating a first overlay error of the substrate. The first data may be obtained from a measurement system that measures the characteristics of the substrate, such as an in-plane strain measuring tool. The first data may be a vector map, or may include a vector map that may show the magnitude and orientation of the overlay error at multiple locations on the substrate.

[0069]

[0073] In some embodiments, the first data may relate to the deposition of a first type of film. For example, the first data may relate to in-plane strain of the substrate related to the deposition of the film material on the first substrate. Further data related to a second type of film may also be received. For example, further in-plane strain associated with the film material of a further substrate may be measured by a measuring tool and acquired by processing logic. Further steps of Method 400 may be performed for further data related to further types of films, further sources of overlay errors, and so on.

[0070]

[0074] In block 404, the processing logic generates second data representing a first stress on the substrate based on the first data. Generating the second data may include performing a divergence calculation based on a vector map. The second data may be a map of stress values ​​associated with each of several locations on the substrate. Further divergence calculations may be performed, for example, on further overlay error data for films of different types or materials. In some embodiments, predictive data associated with the substrate stress is generated. For example, the stress on a multi-layer substrate can be predicted by using stress data for one or more types of films and performing a weighted combination of stress map data based, for example, on the film material and thickness. Predictive data representing the predicted substrate stress may be generated based on the film properties of the predicted substrate (e.g., material, thickness, etc.).

[0071]

[0075] In some embodiments, the second data can be decomposed for further analysis. For example, in block 406, the stress map can be decomposed. The stress map can be fitted, for example, by a polynomial approximation. In a further embodiment, the stress map can be decomposed into physically meaningful parts, such as a linear portion, a radial portion, and a residual portion. In one embodiment, the stress map can be decomposed into a first stress map (e.g., a planar portion) that follows a linear change in stress across the substrate, a second stress map (e.g., a radial portion) that includes the change in stress across the substrate as a function of radius, and a third stress map (e.g., a residual portion) that includes stresses not included in the other stress maps.

[0072]

[0076] In block 408, the second overlay error of the substrate is optionally used for further analysis. The processing logic optionally acquires third data indicating the second overlay error of the substrate. The third data may share one or more features with the first data. Based on the third data, the processing logic further generates fourth data indicating the second stress of the substrate. The fourth data may share one or more features with the second data. The first and second data may be associated with a first type of film, a first film material, a first deposition technique, etc. The third and fourth data may be associated with a second type of film, a second film material, a second deposition technique, etc.

[0073]

[0077] In block 410, the processing logic optionally generates prediction data indicating the stress of a predicted substrate, including a first film and a second film, for example, a first film material and a second film material, a first film type and a second film type, a film deposited by a first technique and a film deposited by a second technique. Generating prediction data may include generating weighted combinations of stress maps based on second data, fourth data, and the target thickness of the first and second substrate films (e.g., the cumulative thickness of alternating layers).

[0074]

[0078] In block 412, the processing logic performs corrective actions based on the second data. The corrective actions may include providing warnings to the user, for example, by displaying substrate stress and / or overlay errors in a manner similar to the data presentation in Figure 3. The corrective actions may include updating the process recipe. The corrective actions may include recommending and / or performing maintenance. The corrective actions may include recommending changes to one or more components of the substrate processing system. For example, a different shape or design of a gas supply component or other processing system component may be recommended to adjust the substrate stress profile.

[0075]

[0079] Figure 5 is a block diagram showing computer system 500 in several embodiments. In some embodiments, computer system 500 may be connected to other computer systems (for example, via a network such as a local area network (LAN), intranet, extranet, or internet). Computer system 500 may operate as a server or client computer in a client-server environment, or as a peer computer in a peer-to-peer or distributed network environment. Computer system 500 may be provided by a personal computer (PC), tablet PC, set-top box (STB), personal digital assistant (PDA), mobile phone, web appliance, server, network router, switch or bridge, or any device capable of executing a set of instructions (sequential or otherwise) that specify the actions that the device should perform. Furthermore, the term “computer” includes any collection of computers that individually or collectively execute a set (or set) of instructions in order to perform any one or more of the methods described herein.

[0076]

[0080] In a further embodiment, the computer system 500 may include a processing device 502, a volatile memory 504 (e.g., random access memory (RAM)), a non-volatile memory 506 (e.g., read-only memory (ROM) or electrically erasable programmable ROM (EEPROM)), and a data storage device 518, which can communicate with each other via a bus 508.

[0077]

[0081] The processing device 502 may be provided by one or more processors, such as a general-purpose processor (e.g., a composite instruction set computing (CISK) microprocessor, a reduced instruction set computing (RISK) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of multiple types of instruction sets) or a specialized processor (e.g., an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).

[0078]

[0082] The computer system 500 may further include a network interface device 522 (for example, coupled to network 574). The computer system 500 may also include a video display unit 510 (e.g., an LCD), an alphanumeric input device 512 (e.g., a keyboard), a cursor control device 514 (e.g., a mouse), and a signal generating device 520 (e.g., a speaker).

[0079]

[0083] In some embodiments, the data storage device 518 may include a non-temporary computer-readable storage medium 524 (e.g., a non-temporary machine-readable medium). The non-temporary computer-readable storage medium 524 may store instructions 526 for implementing the methods described herein, including instructions for encoding the components of Figure 1 (e.g., the analysis component 110, the corrective action component 122, etc.). Instructions 526 may encode functions performed by further components, including the presentation component 176, the substrate generation control unit 129, etc.

[0080]

[0084] Instruction 526 may also be entirely or partially present in the volatile memory 504 and / or the processing device 502 during execution by the computer system 500, so that the volatile memory 504 and the processing device 502 may also constitute a machine-readable storage medium.

[0081]

[0085] Although the computer-readable storage medium 524 is shown in the embodiments as a single medium, the term “computer-readable storage medium” includes a single medium or multiple mediums (e.g., a centralized or distributed database, and / or associated caches and servers) that store one or more sets of executable instructions. The term “computer-readable storage medium” also includes any tangible medium capable of storing or encoding a set of instructions for execution by a computer, which causes a computer to perform any one or more of the methods described herein. The term “computer-readable storage medium” includes, but is not limited to, solid-state memory, optical media, and magnetic media.

[0082]

[0086] The methods, components, and features described herein may be implemented by individual hardware components or integrated into the functionality of other hardware components such as ASICS, FPGAs, DSPs, or similar devices. Furthermore, the methods, components, and features may be implemented by firmware modules or functional circuits within a hardware device. Moreover, the methods, components, and features may be implemented in any combination of a hardware device and a computer program component, or in a computer program.

[0083]

[0087] Unless otherwise specified, terms such as “receiving,” “performing,” “providing,” “obtaining,” “causing,” “determining,” “using,” “training,” “generating,” “correcting,” “updating,” and “scheduling” refer to actions and processes performed or implemented by a computer system. Such a computer system manipulates data represented as physical (electronic) quantities in the computer system’s registers and memory, and converts it into other data similarly represented as physical quantities in the computer system’s memory or registers, or in other devices that store, transmit, or display such information. Furthermore, as used herein, terms such as “first,” “second,” “third,” and “fourth” are symbols used to distinguish various elements from one another and do not necessarily have an orderly meaning according to their numerical designations.

[0084]

[0088] The embodiments described herein also relate to apparatus for carrying out the methods described herein. This apparatus may be specifically configured for carrying out the methods described herein, or it may include a general-purpose computer system selectively programmed by a computer program stored in the computer system. Such a computer program may be stored in a computer-readable tangible storage medium.

[0085]

[0089] The methods and exemplary embodiments described herein are not inherently related to any particular computer or other device. Various general-purpose systems may be used in accordance with the teachings described herein, or it may be convenient to construct more specialized devices to perform each of the methods and / or their individual functions, routines, subroutines, or operations described herein. Examples of the construction of such a wide variety of systems are explicitly shown above.

[0086]

[0090] The above description is illustrative and not limiting. While this disclosure has been described with reference to several specific exemplary examples and embodiments, it will be evident that this disclosure is not limited to the examples and embodiments described. The scope of this disclosure should be defined with reference to the following claims, together with the entire scope of equivalents to which such claims are granted.

Claims

1. The processing device acquires first data indicating the first overlay error of the substrate. Based on the first data, second data showing the first stress uniformity of the substrate is generated, and A method comprising taking corrective action based on the second data described above.

2. The method according to claim 1, wherein the first data includes a vector map showing the orientation and magnitude of overlay errors at multiple locations on the substrate.

3. The method according to claim 2, wherein generating second data indicating the stress of the substrate includes performing a divergence calculation based on the vector map.

4. The method according to claim 1, wherein the second data includes a map of stress values ​​associated with each of a plurality of locations on the substrate.

5. The method according to claim 4, further comprising decomposing the stress value map into at least a planar portion and a radial portion, wherein the corrective action is further based on the planar portion or the radial portion.

6. To acquire third data indicating the second overlay error of the substrate, and The method further includes generating a fourth data representing a second stress of the substrate based on the third data, The method according to claim 1, wherein the first data and the second data are associated with a first substrate film material, and the third data and the fourth data are associated with a second substrate film material different from the first substrate film material.

7. The method according to claim 6, further comprising generating prediction data indicating the stress of a predicted substrate comprising the first substrate film material and the second substrate film material, wherein generating the prediction data comprises generating a weighted combination of stress maps based on the second data, the fourth data, and the target thickness of the films of the first substrate film material and the second substrate film material.

8. The aforementioned corrective measures are: To provide warnings to users, Updating the process recipe, or The method according to claim 1, comprising one or more of the following: recommending a change in the components of the substrate processing system.

9. A non-temporary, machine-readable storage medium that stores instructions, wherein, when an instruction is executed, it is sent to a processing device. To obtain first data indicating the first overlay error of the substrate, Based on the first data, second data indicating the first stress of the substrate is generated, and A non-temporary, machine-readable storage medium that causes a process to be performed, which includes taking corrective action based on the second data described above.

10. The non-temporary machine-readable storage medium according to claim 9, wherein the first data includes a vector map showing the orientation and magnitude of overlay errors at multiple locations on the substrate.

11. The non-temporary machine-readable storage medium according to claim 10, wherein generating second data indicating the stress of the substrate includes performing a divergence calculation based on the vector map.

12. The non-temporary machine-readable storage medium according to claim 9, wherein the second data includes a map of stress values ​​associated with each of a plurality of locations on the substrate.

13. The non-temporary machine-readable storage medium according to claim 12, wherein the step further includes decomposing the stress value map into at least a planar portion and a radial portion, and the corrective action is further based on the planar portion or the radial portion.

14. The above process is, To acquire third data indicating the second overlay error of the substrate, and The method further includes generating a fourth data representing a second stress of the substrate based on the third data, The non-temporary machine-readable storage medium according to claim 9, wherein the first data and the second data are associated with a first substrate film material, and the third data and the fourth data are associated with a second substrate film material different from the first substrate film material.

15. The non-transient machine-readable storage medium according to claim 14, wherein the step further comprises generating prediction data indicating the stress of a predicted substrate comprising the first substrate film material and the second substrate film material, the generation of the prediction data comprising generating a weighted combination of stress maps based on the second data, the fourth data, and the target thickness of the films of the first substrate film material and the second substrate film material.

16. The aforementioned corrective measures are: To provide warnings to users, Updating the process recipe, or A non-temporary machine-readable storage medium according to claim 9, comprising one or more of the following: recommending changes to the components of the substrate processing system.

17. A system comprising a memory and a processing device coupled to the memory, wherein the processing device is To obtain first data indicating the first overlay error of the substrate, Based on the first data, second data indicating the first stress of the substrate is generated, and A system configured to perform corrective actions based on the second data described above.

18. The system according to claim 17, wherein the first data includes a vector map showing the orientation and magnitude of overlay errors at multiple locations on the substrate, and generating second data showing stress on the substrate includes performing a divergence calculation based on the vector map.

19. The processing device is To acquire third data indicating the second overlay error of the substrate, and The system is further configured to generate a fourth data representing the second stress of the substrate based on the third data, The system according to claim 17, wherein the first data and the second data are associated with a first substrate film material, and the third data and the fourth data are associated with a second substrate film material different from the first substrate film material.

20. The system according to claim 19, wherein the processing device is further configured to generate prediction data indicating the stress of a predicted substrate comprising the first substrate film material and the second substrate film material, and generating the prediction data comprises generating a weighted combination of stress maps based on the second data, the fourth data, and the target thickness of the films of the first substrate film material and the second substrate film material.

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