Analysis of in-plane distortion

Abstract

A method, system, and non-transitory computer-readable medium are described for generating an assessment map for corrective action. The method includes receiving a first vector map including a first set of vectors. Each of the first set of vectors indicates distortion at a specific one of a plurality of locations on the substrate. The method further includes generating a second vector map including a second set of vectors by rotating the position of each vector in the first set of vectors. The method further includes generating a third vector map including a third set of vectors based on the vectors in the second set of vectors and the corresponding vector in the first set of vectors. The method further includes generating a fourth vector map by subtracting each vector in the third set of vectors from the corresponding vector in the first set of vectors. The fourth vector map indicates a planar component of the first vector map.

Description

Technical Field

[0001]

[0001] This disclosure relates to data integration, and more particularly to the analysis of in-plane distortion.

Background Art

[0002]

[0002] Products may be manufactured by performing one or more manufacturing processes using manufacturing equipment. For example, semiconductor manufacturing equipment may be used to manufacture wafers through semiconductor manufacturing processes. Sensors can be used to identify manufacturing parameters of manufacturing equipment during the manufacturing process. Measuring equipment can be used to identify characteristic data of products manufactured by manufacturing equipment.

Summary of the Invention

[0003]

[0003] What is shown below is a simplified summary of the present disclosure for providing a basic understanding of some aspects of the present disclosure. This summary is not an exhaustive summary of the present disclosure. It is neither for identifying the main points or important elements of the present disclosure, nor for defining any scope of specific embodiments of the present disclosure or any scope of the claims. The sole purpose of this summary is to present some concepts of the present disclosure in a simplified form as an introduction to the more detailed description that follows.

[0004]

[0004] In one aspect of the present disclosure, the method may include receiving a first vector map containing a first set of vectors, each of which represents a distortion at a particular location among a plurality of locations on a substrate. The method further includes generating a second vector map containing a second set of vectors by rotating the position of each vector in the first set of vectors. The method further includes generating a third vector map containing a third set of vectors based on the vectors in the second set of vectors and the corresponding vectors in the first set of vectors. The method further includes generating a fourth vector map by subtracting each vector in the third set of vectors from the corresponding vector in the first set of vectors, the fourth vector map representing the planar component of the first vector map.

[0005]

[0005] In another aspect of the present disclosure, the method may include receiving a first vector map containing a first set of vectors, each of which represents a strain at a particular location among a plurality of locations on the substrate. The method further includes generating a second vector map containing a second set of vectors by rotating the position of each vector in the first set of vectors. The method further includes generating a third vector map containing a third set of vectors based on the vectors in the second set of vectors and the corresponding vectors in the first set of vectors. The method further includes generating a fourth vector map containing a fourth set of vectors by projecting the directional component of each vector in the third set of vectors radially. The method further includes generating a fifth vector map having a fifth set of vectors by grouping the vectors in the fourth set of vectors and specifying the magnitude associated with each group of vectors. The fifth vector map may be a radial map and may represent at least one of the stress or strain exhibited by the substrate.

[0006]

[0006] In another aspect of the present disclosure, the method may generate a sixth vector map containing a sixth set of vectors by subtracting each vector in a fifth set of vectors from the corresponding vector in a first set of vectors. The sixth vector map may be a residual map and may indicate anomalies in the substrate.

[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 is a block diagram showing an exemplary system architecture according to a particular embodiment. [Figure 2]

[0009] This block diagram shows the generation of an evaluation map according to a specific embodiment. [Figure 3]

[0010] This is a flowchart illustrating a method for generating a planar map according to a specific embodiment. [Figure 4]

[0011] This is a flowchart illustrating a method for generating radial maps and residual maps according to a specific embodiment. [Figure 5A]

[0012] Figures 5A to 5E are graphs showing IPD maps and evaluation maps according to specific embodiments. [Figure 5B] Figures 5A to 5E are graphs showing IPD maps and evaluation maps according to specific embodiments. [Figure 5C] Figures 5A to 5E are graphs showing IPD maps and evaluation maps according to specific embodiments. [Figure 5D] Figures 5A to 5E are graphs showing IPD maps and evaluation maps according to specific embodiments. [Figure 5E] Figures 5A to 5E are graphs showing IPD maps and evaluation maps according to specific embodiments. [Figure 6]

[0013] This is a block diagram showing a computer system according to a specific embodiment. [Modes for carrying out the invention]

[0009]

[0014] Techniques for analyzing in-plane distortion are described herein. Vertical NAND (V-NAND) or 3D NAND memory typically stacks memory cells vertically and uses a charge trap flash architecture. To construct it, alternating layers of conductive and insulating films are stacked on a wafer substrate. Stacking multiple layers creates higher storage density. To achieve high throughput and reduce costs, manufacturers typically enable high energy density and gas supply to the reactor. However, as the thickness and number of layers for the stacked film increase, insufficient energy supply and non-uniform gas flow result in a lack of uniformity within the stacked 3D NAND film. Consequently, this lack of uniformity leads to high in-plane distortion (IPD). This can lead to performance degradation problems in 3D NAND memory.

[0010]

[0015] The IPD may be depicted by an overlay vector map on the wafer substrate. For each location on the wafer and / or each logic unit (i.e., die), the IPD may include a vector having x-axis and y-axis components. The vector may indicate the magnitude and direction of strain at a given location on the wafer. Strain can result from improper energy distribution, gas flow non-uniformity, hardware problems, design problems, or other issues that may occur during the manufacturing process. The non-uniformity of the overlay vector distribution may be characterized by its 3-sigma value. 3-sigma is a statistical tool used to calculate probabilities. The 3-sigma value may be used as a criterion for determining whether the IPD distribution meets industry requirements (e.g., follows lithography steps to achieve good yield). However, the 3-sigma value provides only limited data regarding which (one or more) areas need improvement and / or optimization to reduce in-plane strain. Furthermore, the 3-sigma value cannot measure the impact of process or hardware changes on IPD non-uniformity.

[0011]

[0016] The devices, systems, and methods disclosed herein generate evaluation maps from IPD maps associated with a wafer substrate using measurement data. Specifically, the devices, systems, and methods disclosed herein may analyze and decompose various aspects related to the IPD map. In the first embodiment, the system of the disclosure may generate a planar map to identify the planar component of the IPD map. The planar component may represent strain resulting from potential asymmetries in the manufacturing process, either in the hardware configuration of the manufacturing equipment or in the manufacturing process (e.g., the interaction between the plasma and gas flow distribution). The planar map may represent the magnitude of the planar component.

[0012]

[0017] To generate a planar map, the system may first receive a first vector map associated with the manufacturing parameters of the substrate. The first vector map may contain a first set of vectors, each representing a strain at a specific location on the substrate. The first vector map may be an IPD map. The system may then generate a second vector map containing a second set of vectors by rotating the position of each vector in the first set of vectors. Each vector in the second set may represent a change in the direction of the magnitude of the strain at a specific location on the substrate. The system may then generate a third vector map containing a third set of vectors based on the vectors in the second set of vectors and the corresponding vectors in the first set of vectors. The third set of vectors may reflect the reduced noise of the strain across locations on the substrate. The system may then generate a fourth vector map by subtracting each vector in the third set of vectors from the corresponding vector in the first set of vectors. The fourth vector map may represent the planar component of the first vector map.

[0013]

[0018] In another embodiment, the system may generate a radial map to identify the radial component of the IPD map. The radial component may represent the strain resulting from tensile and compressive stresses applied across the wafer. The direction and 3-sigma value of the radial map can be used to measure how the manufacturing process and hardware components of the manufacturing equipment affect the radial IPD of the substrate.

[0014]

[0019] To generate a radial map, the system may receive a first vector map associated with the manufacturing parameters of the substrate. The first vector map may contain a first set of vectors, each representing the strain at a specific location among several locations on the substrate. The system may then generate a second vector map containing a second set of vectors by rotating the position of each vector in the first set of vectors, each representing the change in the direction of the magnitude of the strain at a specific location on the substrate. The system may then further generate a third vector map containing a third set of vectors based on the vectors in the second set of vectors and the corresponding vectors in the first set of vectors, the third set of vectors reflecting the reduced noise of the strain across several locations on the substrate. The system may further generate a fourth vector map containing a fourth set of vectors by projecting the directional component of each vector in the third set of vectors radially. The system may further generate a fifth vector map having a fifth set of vectors by grouping the vectors of the fourth set of vectors and identifying the magnitude associated with each group of vectors. The fifth vector map may include a radial map that shows the stress and / or strain exhibited by the substrate.

[0015]

[0020] In another embodiment, the system may generate a residual map to identify residual components of the IPD map. Residual components may indicate localized defects resulting from hardware failures, process instability, hardware design flaws, etc. To generate a residual map, the system may generate a sixth vector map containing a sixth set of vectors by subtracting each vector in a fifth set of vectors from the corresponding vector in a first set of vectors.

[0016]

[0021] Multiple aspects of this disclosure offer technical advantages such as significant reductions in energy consumption, product defects, performance issues, and processor overhead. In some embodiments, these technical advantages are obtained by generating evaluation maps for each wafer produced by the manufacturing equipment. The evaluation maps may include a planar map detailing the planar component of the IPD map, a radial map detailing the radial component of the IPD map, and a residual map detailing the residual component of the IPD. The evaluation maps enable the user or system to identify manufacturing hardware and process problems that may result in defective or substandard products. Furthermore, the evaluation maps enable the user or system to identify ways to optimize or improve the manufacturing process, which may result in lower energy consumption, fewer defective products, and improved IPD compared to conventional approaches.

[0017]

[0022] Figure 1 is a block diagram showing an exemplary system architecture 100 according to a particular embodiment. The system architecture 100 includes a client device 106, an evaluation map generation system 110, a sensor system 120, a measurement system 130, and a data store 140. The evaluation map generation system 110 may include a planar map generator 112 for generating a planar map 162, a radial map generator 114 for generating a radial map 164, and a residual map generator 116 for generating a residual map 166. The sensor system 120 may include a sensor server 122 (e.g., a field service server (FSS) in a manufacturing facility), manufacturing equipment 124, a sensor 126, and a sensor identifier reader 128 (e.g., a forward-opening unified pod (FOUP) radio frequency identification (RFID) reader for the sensor system 120). The measurement system 130 may include a measurement server 132 (e.g., a measurement database or measurement folder), measurement equipment 134, a measurement identifier reader 136 (e.g., a FOUP RFID reader for the measurement system 130), and an in-plane strain map generator 138.

[0018]

[0023] Sensor 126 may provide sensor values 144 (e.g., manufacturing parameters) associated with manufacturing a corresponding product (e.g., a substrate or wafer) by manufacturing equipment 124. The sensor values 144 may include one or more values such as temperature (e.g., heater temperature), interval (SP), pressure, high frequency radio frequency (HFRF), voltage of an electrostatic chuck (ESC), current, flow, power, voltage, plasma and / or gas flow, energy distribution, etc. The sensor values 144 may be associated with or indicative of manufacturing parameters such as hardware parameters of the manufacturing equipment (e.g., settings or components (e.g., size, type, etc.) of manufacturing equipment 124), or process parameters of the manufacturing equipment (e.g., flow rate). The sensor values 144 may be provided while manufacturing equipment 124 is executing a manufacturing process (e.g., equipment readings when processing a wafer). The sensor values 144 may be different for each product (e.g., each wafer).

[0019]

[0024] Sensor identifier reader 128 (e.g., a FOUP RFID reader for sensor system 120) may provide a sensor carrier identifier (e.g., a FOUP identifier, a wafer carrier identifier, a slot identifier, etc.). Sensor server 122 may generate a sensor data identifier 146 including a sensor carrier identifier and a timestamp (e.g., date, time, etc.). The sensor carrier identifier may be a carrier identifier (e.g., a FOUP identifier, etc.) specified by sensor system 120 (e.g., via sensor identifier reader 128). Sensor server 122 may generate sensor data 142 including sensor value 144 and sensor data identifier 146. In some embodiments, sensor data 142 (e.g., sensor data identifier 146) further includes a product identifier 148. For example, a plurality of products (e.g., 25 wafers) may be associated with the same sensor carrier identifier, and each product identifier 148 may indicate the order of the products (e.g., the first wafer or the second wafer in a wafer carrier, etc.).

[0020]

[0025] The measurement device 134 may provide measurement values 152 (e.g., characteristic data of a wafer) associated with a product (e.g., a wafer) manufactured by the manufacturing device 124. The measurement values 152 may include one or more values such as film characteristic data (e.g., spatial film characteristics of a wafer), dimensions (e.g., thickness, height, etc.), in-plane strain and / or uniformity, dielectric constant, dopant concentration, density, defects, etc. The measurement values 152 may be those of a finished product or a product finished to an intermediate stage. The measurement values 152 may be different for each product (e.g., each wafer).

[0021]

[0026] The measurement identifier reader 136 (e.g., a FOUP RFID reader for the measurement system 130) may provide a measurement carrier identifier (e.g., a FOUP identifier, a wafer carrier identifier, a slot identifier, etc.). The measurement carrier identifier may be a carrier identifier (e.g., a FOUP identifier, etc.) specified by the measurement system 130 (e.g., via the measurement identifier reader 136). The measurement carrier identifier and the sensor carrier identifier corresponding to the same product (e.g., the same wafer) may be the same carrier identifier (e.g., the same FOUP ID) and may correspond to the same carrier (e.g., the same FOUP). The measurement server 132 may generate a measurement data identifier 154 including the measurement carrier identifier and a timestamp (e.g., date, time, etc.). The measurement server 132 may generate measurement data 150 including the measurement values 152 and the measurement data identifier 154. In some embodiments, the measurement data 150 further includes a product identifier 156. For example, a plurality of products (e.g., 25 wafers) may be associated with the same measurement data identifier 154 (e.g., a wafer carrier identifier), and each product identifier 156 may indicate the order of the product (e.g., the first wafer, the second wafer, etc. within the wafer carrier).

[0022]

[0027] In some embodiments, a product carrier (e.g., a FOUP or wafer carrier) may transfer the product from the manufacturing equipment 124 to the measurement equipment 134. The products may maintain the same order (e.g., the same position in the FOUP or wafer carrier) within the sensor system 120 and the measurement system 130. For example, wafers may be loaded into and out of the manufacturing equipment 124 (for wafer processing and to provide sensor data 142 via the sensor server 122) in the same order in which they were loaded into and out of the measurement equipment 134 (to provide measurement data 150 via the measurement system 130). In some embodiments, sensor carrier identifiers (e.g., a FOUP ID associated with the sensor system 120) and (e.g., a FOUP ID associated with the measurement system 130) corresponding to the same product are associated with the same product carrier (e.g., the same FOUP) and / or carrier identifier (e.g., the sensor carrier identifier and the measurement carrier identifier are the same).

[0023]

[0028] The IPD map generator 138 may generate an IPD map 158 from the measured values ​​152. The IPD map 158 may be an overlay vector map that includes strain vectors at each of several locations on the wafer, and the coordinates of the die on the wafer. Each vector on the vector map may include x-axis and y-axis components. Each wafer manufactured by the manufacturing equipment 124 may have an IPD map 158 generated based on its measured values ​​152. Each vector may be associated with one location on the wafer or with a die (e.g., a logic unit) on the wafer. Figure 5A is a graph showing an exemplary IPD map. Specifically, Figure 5A shows an IPD map with vectors at several locations measured in nanometers. Each IPD map 158 may include 3-sigma values ​​for the x-axis and y-axis components.

[0024]

[0029] 3-sigma is a statistical tool used to calculate probabilities. The IPD map generator 138 can determine the 3-sigma value for the x-axis component by first calculating the standard deviation of the x-axis component on the IPD map, and can determine the 3-sigma value for the y-axis component by first calculating the standard deviation of the y-axis component on the IPD map. Each standard deviation may be multiplied by 3, and the product may be subtracted from the mean (mean of the x-axis component and the mean of the y-axis component). The resulting 3-sigma value indicates a high probability (e.g., 99.73%) that other vectors will have a lower value (e.g., magnitude) than the 3-sigma value. As shown in Figure 5A, the 3-sigma value for the x-axis component is 12.3 nanometers, and the 3-sigma value for the y-axis component is 11.9 nanometers. The 3-sigma value may be specified for each type of vector map. For example, the planar map generator 112 may specify the 3-sigma value for the planar map 162, the radial map generator 114 may specify the 3-sigma value for the radial map 164, and the residual map generator 116 may specify the 3-sigma value for the residual map 166. Furthermore, the evaluation map generation system 110 may specify the 3-sigma value for intermediate vector maps generated during the process of generating the planar map 162, the radial map 164, and the residual map 166. A more detailed explanation follows.

[0025]

[0030] Referring back to Figure 1, the client device 106, evaluation map generation system 110, sensor system 120 (e.g., sensor server 122, manufacturing equipment 124, sensor 126, sensor identifier reader 128, etc.), measurement system 130 (e.g., measurement server 132, measurement equipment 134, measurement identifier reader 136, IPD map generator, etc.), and data store 140 may be connected to each other via network 170 to generate a planar map 162, a radial map 164, and a residual map 166 in order to perform corrective actions by the analysis component 108. The corrective actions may be based on data from a correlation database 168. The correlation database 168 may associate one or more types of deformation or defects identified from one or more of the IPD map 158, planar map 162, radial map 164, and / or residual map with one or more causes of the deformation and / or defect.

[0026]

[0031] In some embodiments, network 170 is a public network. The public network provides client device 106 with access to the evaluation map generation system 110, the data store 140, and other publicly available computing devices. In some embodiments, network 170 is a private network. The private network provides evaluation map generation system 110 with access to the sensor system 120, the measurement system 130, the data store 140, and other privately available computing devices, and provides client device 106 with access to the map generation system 110, the data store 140, and other privately available computing devices. Network 170 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.

[0027]

[0032] The client device 106 may include computing devices such as personal computers (PCs), laptops, mobile phones, smartphones, tablet computers, netbooks, 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 106 can acquire measurement data (from data store 140, from measurement system 130, etc.) associated with products manufactured by the manufacturing equipment 124 (e.g., substrates, wafers, dies, etc.), may receive user input requesting one or more evaluation maps 160 generated by the evaluation map generation system 110, may receive the requested evaluation maps, can acquire sensor data (e.g., from data store 140, from sensor system 120, etc.) associated with the manufacturing equipment 124, and can take corrective actions (e.g., adjust the manufacturing parameters of the manufacturing equipment 124) based on the evaluation maps. Each client device 106 may include an operating system that enables the user to generate, view, or edit data (e.g., displays associated with the manufacturing equipment 124 or corrective actions associated with the manufacturing equipment 124). In some embodiments, the measurement data 150 corresponds to historical characteristic data of a product (e.g., manufactured using manufacturing parameters associated with sensor data 142).

[0028]

[0033] Implementing a manufacturing process that results in defective products can lead to increased expenses in terms of time, energy, and manufacturing equipment 124 used to produce the defective products, as well as the costs of identifying defects and discarding the defective products. By inputting current sensor data and / or measurement data, receiving the output of the evaluation map 160, and taking corrective actions based on the evaluation map 160, the system 100 may have the technical advantage of avoiding the expenses of manufacturing, identifying, and discarding defective products.

[0029]

[0034] Manufacturing parameters may be suboptimal in manufacturing products, which can have 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 an increased quantity of defective products. By generating an evaluation map 160 and analyzing the results shown in the evaluation map 160 (e.g., planarity, deformation, stress, strain, anomalies, etc.), and by adjusting the manufacturing parameters of the manufacturing equipment 124, the system 100 may have the technical advantage of using optimal manufacturing parameters (e.g., hardware parameters, process parameters, optimal design) to avoid the costly consequences of suboptimal manufacturing parameters.

[0030]

[0035] Corrective measures may be associated with one or more of the following: computational processing control (CPC), statistical processing control (SPC), automated processing control (APC), preventive operational maintenance, design optimization, updating of manufacturing parameters, feedback control, machine learning modifications, replacement or repair of manufacturing components, etc.

[0031]

[0036] The sensor data 142 may be associated with the manufacturing process of the manufacturing equipment 124, and the measurement data 150 may be associated with the characteristics of the finished product manufactured by the manufacturing process. In another embodiment, the manufacturing equipment may be a film material dispenser, and the manufacturing process may distribute layers of film onto a wafer. The sensor data 142 may show gas flow distribution, flow rate, etc. The measurement data 150 may show film thickness, strain, etc. The measurement data 150 may further show in-plane strain on an IPD map (e.g., IPD map 158).

[0032]

[0037] The evaluation map generation component 110 can generate an evaluation map 160 using the IPD map 158. The evaluation map 160 may evaluate the IPD map and may show the deformation characteristics of the IPD map. For example, the evaluation map generation component 110 may generate one or more planar maps 162, one or more radial maps 164, and one or more residual maps 166. The planar map may show the direction of the planarity of the wafer. Specifically, the planar map may show the direction of the tilt effect caused by the laminated film on the wafer. The radial map may show stress and / or strain (e.g., compressive stress and tensile stress) across the wafer. The residual map may show anomalies on the wafer. Each of the evaluation maps 160 may include 3-sigma values. The 3-sigma values, magnitude, and / or direction of the vectors on each of the evaluation maps 160 may indicate the severity of strain, stress, anomalies, etc. Based on the evaluation map 160, the client device 106 may recommend or execute corrective actions (for example, via the analysis component 108). Either of these actions may be performed via user input or automatically.

[0033]

[0038] Manufacturing parameters may include hardware parameters (e.g., component replacement or use of specific components) and / or process parameters (e.g., temperature, pressure, flow rate). In some embodiments, corrective actions result in preventative operational maintenance (e.g., replacement, processing, cleaning of components of manufacturing equipment 124). In some embodiments, corrective actions result in design optimization (e.g., updating manufacturing parameters, manufacturing processes, manufacturing equipment 124, etc., for the product being optimized). In one embodiment, hardware configuration problems or undesirable interactions between plasma and gas flow distribution may be indicated based on the magnitude, direction, and / or 3-sigma value (when compared to a threshold) of vectors in a planar map. In another embodiment, energy distribution and gas flow problems may be indicated based on the magnitude, direction, and / or 3-sigma value of vectors in a radial map. In yet another embodiment, hardware design defects (e.g., arc discharge spots on electrodes) may be indicated based on the magnitude, direction, and / or 3-sigma value of vectors in a residual map.

[0034]

[0039] The client device 106 may include an analysis component 108. The analysis component 108 may receive user input for an evaluation map request (e.g., via a GUI displayed through the client device 106). In some embodiments, the analysis component 108 sends the request to an evaluation map generation system 110, receives output from the evaluation map generation system 110 (e.g., an evaluation map 160), and displays the evaluation map 160 for analysis. In some embodiments, the analysis component 108 identifies corrective actions based on the output. In some embodiments, the analysis component 108 performs corrective actions automatically or upon receiving user input (e.g., changing manufacturing parameters).

[0035]

[0040] The sensor server 122 and the measurement server 132 may each include one or more computing devices, such as a rack-mount server, router computer, server computer, personal computer, mainframe computer, laptop computer, tablet computer, desktop computer, graphics processing unit (GPU), or accelerator application-specific integrated circuit (ASIC) (e.g., a tensor processing unit (TPU)).

[0036]

[0041] 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, measurement data 150, and evaluation maps 160.

[0037]

[0042] Sensor data 142 may include a sensor value, a sensor data identifier 146, and a product identifier 148. Measurement data 150 may include a measurement value 152, a measurement data identifier 154, a product identifier 156, and an IPD map 158. Each instance (e.g., a set) of sensor data 142 may correspond to a corresponding product carrier (e.g., associated with sensor data identifier 146), a corresponding timestamp (e.g., associated with sensor data identifier 146), and / or a corresponding product (e.g., associated with product identifier 148). Each instance (e.g., a set) of measurement data 150 may correspond to a corresponding product carrier (e.g., associated with measurement data identifier 154), a corresponding timestamp (e.g., associated with measurement data identifier 154), and / or a corresponding product (e.g., associated with product identifier 156).

[0038]

[0043] In some embodiments, the functions of the client device 106, the evaluation map generation system 110, the sensor server 122, and the measurement server 132 may be provided by fewer machines. In some embodiments, the evaluation map generation system 110, the sensor server 122, and the measurement server 132 may be integrated into a single machine.

[0039]

[0044] It should be noted that the functions described in one embodiment, performed by the client device 106, the sensor server 122, and the measurement server 132, may also be performed by the evaluation map generation system 110 in several other embodiments, where appropriate. In addition, functionality attributed to a particular component may be performed by different or multiple components working together. The evaluation map generation system 110 may be accessed as a service provided to other systems or devices via an appropriate application programming interface (API).

[0040]

[0045] In several embodiments, “User” may be represented as a single individual. However, other embodiments of the present disclosure include “User” being an entity controlled by multiple users and / or automated sources. For example, a group of individual users that coalesce as a group of administrators may be considered “User.”

[0041]

[0046] While embodiments of this disclosure are described in terms of generating an evaluation map 160 to perform corrective actions in a manufacturing facility (e.g., a semiconductor manufacturing facility), several embodiments may also apply to aggregating several types of data in general to perform corrective actions. Several embodiments may also apply to integrating different types of data in general. For example, sensor data may be aggregated with failure data of corresponding components to predict the lifespan of components. In another embodiment, images may be aggregated with corresponding image classifications to predict the image classification of multiple images.

[0042]

[0047] Figure 2 is a block diagram illustrating a system 200 for generating an evaluation map (e.g., evaluation map 160 in Figure 1) using an IPD map 258 (e.g., IPD map 158 in Figure 1) according to several specific embodiments. The system in Figure 2 shows a data input 205, an evaluation map generation system 210, and a data output 220.

[0043]

[0048] In some embodiments, the evaluation map generation system 210 may receive one or more data inputs 205 (e.g., one or more IPD maps 258). The one or more data inputs 205 may be sent to the evaluation map generation system 210 automatically or by user input (e.g., request). In some embodiments, a planar map generator 212 (e.g., the planar map generator 112 in Figure 1) may generate one or more planar maps 262. The process for generating planar maps will be described in more detail in Figure 3. In some embodiments, a radial map generator 214 (e.g., the radial map generator 114 in Figure 1) may generate one or more radial maps 264. The process for generating radial maps will be described in more detail in Figure 4. In some embodiments, a residual map generator 216 (e.g., the residual map generator 116 in Figure 1) may generate one or more residual maps 266. The process for generating residual maps will be described in more detail in Figure 4.

[0044]

[0049] Figure 3 is a flowchart of Method 300 for generating a planar map (e.g., planar map 162 in Figure 1) according to several specific embodiments. Method 300 may be executed by processing logic, which may include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, processing devices, etc.), software (e.g., instructions executed by processing devices, general-purpose computer systems, or dedicated machines), firmware, microcode, or a combination thereof. In one embodiment, Method 300 may be partially executed by an evaluation map generation system 110 (e.g., a planar map generator 112). In some embodiments, a non-temporary storage medium stores the instructions. When the instructions are executed by a processing device (e.g., of the evaluation map generation system 110), the processing device executes Method 300.

[0045]

[0050] For the sake of simplicity, Method 300 is depicted and described as a series of operations. However, the operations performed in accordance with this disclosure may be performed in various orders and / or simultaneously, and may be performed in conjunction with other operations not presented or described herein. Furthermore, not all illustrated operations are necessarily performed in order to carry out Method 300 in accordance with the disclosed subject matter. In addition, those skilled in the art will understand and grasp that Method 300 may alternatively be represented as a series of interrelated states via a state diagram or events.

[0046]

[0051] Due to either the hardware configuration of the manufacturing equipment 124 or the manufacturing process (e.g., the interaction between the plasma and the gas flow distribution), the IPD map may have a planar component that can result in high in-plane distortion, due to potential asymmetry in the manufacturing process. The operation of Method 300 generates a planar map that shows the magnitude of the planar component.

[0047]

[0052] Referring to Figure 3, in block 302, the processing logic receives a first vector map associated with the manufacturing parameters of the substrate (e.g., wafer). The first vector map may be an IPD map (e.g., IPD map 158), which will be referred to as the IPD map hereafter. The IPD map may contain a first set of vectors, in which case each vector represents the strain at a specific location on the wafer. Each vector in the IPD map may have x-axis and y-axis components indicating direction and magnitude. The IPD map may be generated using measurement data from the measurement system 130 and stored in a data store (e.g., data store 140 in Figure 1), and the processing logic may retrieve (e.g., read) the IPD map from the data store. In one embodiment, the IPD map may be calculated based on changes in the surface gradient of the substrate.

[0048]

[0053] In block 304, the processing logic generates a second vector map containing a second set of vectors. The processing logic may generate the second vector map by rotating the position of each vector in the first set of vectors. In one embodiment, each vector in the second set of vectors is rotated approximately or exactly by 180 degrees. Each of the second set of vectors may represent a change in the direction of the magnitude of distortion at a specific location on the substrate. The second vector map may be stored in the cache or memory component of the evaluation map generation system 110 or in the data store 140. The second vector map may be stored temporarily or permanently while method 300 is being executed.

[0049]

[0054] In block 306, the processing logic generates a third vector map containing a third set of vectors. The third set of vectors may be based on the vectors in the second set of vectors and the corresponding vectors in the first set of vectors. In one embodiment, the processing logic adds the vectors on the first set of vectors to their respective positions on the second vector map in order to generate the third vector map. The sum at each position can then be divided by 2 to generate the third set of vectors. The third set of vectors may reflect distortion-reduced noise across positions on the substrate. The processing logic may generate 3-sigma values ​​for the x-axis and y-axis components of the third vector map. Figure 5B is a graph showing one embodiment of the third vector map containing the third set of vectors. The third vector map shown in Figure 5B is generated by applying the steps described above of Method 300 to the IPD map shown in Figure 5A. The 3-sigma value for the x-axis of the third vector map in Figure 5B is 5.6 nm, and the 3-sigma value for the y-axis component is 5.1 nm.

[0050]

[0055] Referring back to Figure 3, in block 308, the processing logic generates a fourth vector map containing a fourth set of vectors. The fourth vector map may be a planar map (e.g., planar map 162), which will be referred to as the planar map hereafter. In one embodiment, the processing logic generates the planar map by subtracting each vector from the third set of vectors from the corresponding vector component in the first set of vectors. The planar map shows the planar components of the IPD map. Based on the direction and magnitude of the fourth set of vectors, the processing logic may determine the direction of the planar components. The processing logic may generate 3-sigma values ​​for the x-axis and y-axis components of the planar map. Figure 5C is a graph showing one embodiment of the planar map. In the graph of Figure 5C, the 3-sigma value for the x-axis is 10.7 nm, and the 3-sigma value for the y-axis component is 10.6 nm. The planar direction is indicated by arrow 530. The planar map allows for the quantification of the influence on the planar IPD from hardware components and manufacturing processes (e.g., via magnitude and direction). The analysis component 108 can use a correlation database to associate deformation or defect sections of the planar map with one or more causes (e.g., hardware configuration of the manufacturing equipment, manufacturing process parameters, e.g., interaction between plasma and gas flow distribution). The analysis component 108 may generate recommendations based on the planar map. In one embodiment, the analysis component 108 may recommend design optimization, replacement of manufactured parts, and / or adjustment of the manufacturing process to minimize the planar component. In another embodiment, the analysis component 108 may automatically perform corrective actions based on the planar map, such as adjusting the gas flow distribution and performing leveling of pedestal heaters to correct contributions to planarity.

[0051]

[0056] Figure 4 is a flowchart of Method 400 for generating radial maps (e.g., radial map 164 in Figure 1) and residual maps (e.g., residual map 166 in Figure 1) according to several specific embodiments. Method 400 may be executed by processing logic, which may include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, processing devices, etc.), software (e.g., instructions executed by processing devices, general-purpose computer systems, or dedicated machines), firmware, microcode, or a combination thereof. In one embodiment, Method 400 may be executed in part by an evaluation map generation system 110 (e.g., radial map generator 114 and / or residual map generator 116). In some embodiments, a non-temporary storage medium stores the instructions. When the instructions are executed by a processing device (e.g., of the vector map generation system 110), the processing device executes Method 400.

[0052]

[0057] For the sake of simplicity, Method 400 is depicted and described as a series of operations. However, the operations performed in accordance with this disclosure may be performed in various orders and / or simultaneously, and may be performed in conjunction with other operations not presented or described herein. Furthermore, not all illustrated operations are necessarily performed in order to carry out Method 400 in accordance with the subject matter disclosed. In addition, those skilled in the art will understand and grasp that Method 400 may alternatively be represented as a series of interrelated states via a state diagram or events. Parts of Method 400 may be similar to or identical to the blocks of Method 300 in Figure 3.

[0053]

[0058] Referring to Figure 4, in block 402, the processing logic receives a first vector map associated with the manufacturing parameters of the substrate (e.g., wafer). The first vector map may be an IPD map (e.g., IPD map 158), which will be referred to as the IPD map hereafter. The IPD map may contain a first set of vectors, in which case each vector indicates the strain at a particular location on the wafer. Each vector may contain x-axis and y-axis components. The IPD map may be obtained (e.g., read) from a data store (e.g., data store 140 in Figure 1).

[0054]

[0059] In block 404, the processing logic generates a second vector map containing a second set of vectors. The processing logic may generate the second vector map by rotating the position of each vector in the first set of vectors. In one embodiment, each vector in the second set of vectors is rotated approximately or exactly by 180 degrees. Each of the second set of vectors may represent a change in the direction of the magnitude of distortion at a specific location on the substrate. The second vector map may be stored in the cache or memory component of the vector map generation system 110 or in the data store 140. The second vector map may be stored temporarily or permanently while method 400 is being executed.

[0055]

[0060] In block 406, the processing logic generates a third vector map containing a third set of vectors. The third set of vectors may be based on the vectors in the second set of vectors and the corresponding vectors in the first set of vectors. In one embodiment, the processing logic generates the third vector map by adding the vectors on the first set of vectors to their respective positions on the second vector map. The sum at each position can then be divided by 2 to generate the third set of vectors. The third set of vectors may reflect distortion-reduced noise across positions on the substrate.

[0056]

[0061] In block 408, the processing logic generates a fourth vector map containing a fourth set of vectors. In one embodiment, the processing logic generates the fourth vector map by projecting the directional component of each vector in the third set of vectors radially.

[0057]

[0062] In block 410, the processing logic generates a fifth vector map containing a fifth set of vectors. The fifth vector map may be a radial map (e.g., radial map 164), which will hereafter be referred to as the radial map. In one embodiment, the processing logic may group the fourth set of vectors and identify the average magnitude associated with each group (or range) of vectors. The direction of the vectors may remain unchanged. The radial map may represent the stress and / or strain exhibited by the entire substrate. In particular, in each group within the radial map, all vectors may have the same magnitude (e.g., radial vectors) and may be either toward the center or toward the center. The direction toward the center may indicate that the stress and / or strain in that group is compressive and may indicate a high energy density. The direction toward the center may indicate tensile local stress and / or strain and may indicate a relatively low energy density. In one embodiment, each radial vector may be bidirectional, and the processing logic may assign positive and negative signs to each radial vector based on its direction. For example, a radial vector moving toward the center (indicating compressive stress) may be assigned a positive sign, and a radial vector moving away from the center (indicating tensile stress) may be assigned a negative sign. In another embodiment, each radial vector may be assigned a color based on its direction and magnitude. In yet another embodiment, any combination of sign and color can be used to indicate stress and / or magnitude on the fifth vector map. The processing logic may generate 3-sigma values ​​for the x-axis and y-axis components of the radial map.

[0058]

[0063] Figure 5D is a graph showing an exemplary radial map. The 3-sigma value for the x-axis and the 3-sigma value for the y-axis component of the graph in Figure 5D are 5.3 nm. As can be seen, the radial map may display multiple groups containing radial vectors. Different shades are associated with each group (or range), and these shades indicate whether the group is compressible (and its magnitude) or tensile (and its magnitude).

[0059]

[0064] The direction and 3-sigma value of the radial map allow the user or analysis component 108 to determine how the manufacturing process and hardware components of the manufacturing equipment affect the radial IPD of the substrate. Based on the radial map, the analysis component 108 may generate recommendations. In one embodiment, the analysis component 108 may recommend design optimization, replacement of manufactured parts, and / or adjustment of the manufacturing process to minimize the radial component (e.g., minimizing stress and / or strain on the substrate). In another embodiment, the analysis component 108 may automatically perform corrective actions based on the radial map.

[0060]

[0065] Referring back to Figure 4, in block 412, the processing logic generates a sixth vector map containing a sixth set of vectors. The sixth vector map may be a residual map (e.g., residual map 164), which will be referred to as the residual map hereafter. The processing logic may generate the residual map by subtracting each vector in the fifth set of vectors from the corresponding vector in the first set of vectors. The processing logic may generate 3-sigma values ​​for the x-axis and y-axis components of the residual map. The sixth set of vectors in the residual map may vary in direction and magnitude. If a location in the residual map is large in magnitude, it may indicate a hardware failure, process instability, or any other hardware or manufacturing process that causes a localized defect. For example, a pattern may appear in a particular location, which may indicate a hardware design defect. Accordingly, the analysis component 108 may issue recommendations to change the hardware component or to generate a new design to improve process stability.

[0061]

[0066] Figure 5E is a graph showing an exemplary residual map. The 3-sigma value for the x-axis of the residual map in Figure 5E is 0.7 nm, and the 3-sigma value for the y-axis component is 1.2 nm. As can be seen, the residual map displays small magnitude values ​​and small 3-sigma values, which may indicate the absence of localized anomalies within the IPD map.

[0062]

[0067] The analysis component 108 may generate recommendations based on the residual map using the correction database 168. In one embodiment, the analysis component 108 may recommend design optimization, replacement of manufactured parts, and / or adjustment of the manufacturing process to minimize or eliminate residual components. In another embodiment, the analysis component 108 may automatically perform corrective actions based on the residual map.

[0063]

[0068] Figure 6 is a block diagram showing computer system 600 according to several specific embodiments. In some embodiments, computer system 600 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 600 may operate as a server or client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. Computer system 600 may be 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 machine capable of executing a set of instructions (sequential or distinct) that specify the actions to be performed by that machine. Furthermore, the term “computer” includes any set of computers that individually or collectively execute a set (or more sets) of instructions to perform any one or more of the methods described herein.

[0064]

[0069] In a further embodiment, the computer system 600 may include a processing device 602, a volatile memory 604 (e.g., random access memory (RAM)), a non-volatile memory 606 (e.g., read-only memory (ROM) or electrically erasable programmable ROM (EEPROM)), and a data storage device 616 that can communicate with each other via a bus 608.

[0065]

[0070] The processing device 602 may be provided by one or more processors, such as a general-purpose processor (e.g., a CISC (complex instruction set computing) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing a combination of other 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), a network processor, etc.).

[0066]

[0071] The computer system 600 may further include a network interface device 622. The computer system 600 may also include a video display unit 610 (e.g., an LCD), an alphanumeric input device 612 (e.g., a keyboard), a cursor control device 614 (e.g., a mouse), and a signal generation device 620.

[0067]

[0072] In some embodiments, a non-temporary computer-readable storage medium 624 may include a data storage device 616 that stores instruction commands 626 which encode any one or more of the methods or functions described herein, including instructions for encoding the components of Figure 1 (e.g., the evaluation map generation system 110 and the analysis component 108) and for carrying out the methods described herein.

[0068]

[0073] The instruction 626 may also be entirely or partially present in the volatile memory 604 and / or the processing device 602 during execution by the computer system 600, so that the volatile memory 604 and the processing device 602 may also constitute a machine-readable storage medium.

[0069]

[0074] Although the computer-readable storage medium 624 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.

[0070]

[0075] 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 hardware devices. Moreover, the methods, components, and features may be implemented by any combination of hardware devices and computer program components, or by computer programs.

[0071]

[0076] Unless otherwise specified, terms such as “receive,” “identify,” “generate,” “remember,” “bring forth,” “train,” “interrupt,” “select,” “provide,” and “display” refer to operations and processes performed or implemented by a computer system that manipulate and convert data represented as physical (electronic) quantities in computer system registers and memory into other data similarly represented as physical quantities in computer system memory or registers or other such information storage, transmission, or display devices. Furthermore, terms such as “first,” “second,” “third,” and “fourth” used herein may mean labels used to distinguish between different elements and may not have a sequential meaning in accordance with their numerical designation.

[0072]

[0077] 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.

[0073]

[0078] 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 according to 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 structures for various such systems are described above.

[0074]

[0079] The above description is illustrative and not limiting. While this disclosure has been described with reference to certain exemplary examples and embodiments, it should be recognized that this disclosure is not limited to the described examples and embodiments. Therefore, the scope of this disclosure should be defined with reference to the appended claims and together with the entire scope of equivalents to which such claims are entitled.

Claims

1. Memory, and A system comprising a processing device coupled to the memory, wherein the processing device is Receiving a first vector map associated with the manufacturing parameters of a substrate, wherein the first vector map includes a first set of vectors, each of which represents a distortion at a specific location among a plurality of locations on the substrate. A second vector map is generated, which includes a second set of vectors, by rotating the position of each vector in the first set of vectors, wherein each of the second set of vectors represents a change in the direction of the magnitude of the distortion at a particular location on the substrate. To generate a third vector map including a third set of vectors based on the vectors in the second set of vectors and the corresponding vectors in the first set of vectors, wherein the third set of vectors generates a third vector map that reflects the distortion-reduced noise across the plurality of locations on the substrate, and A system that generates a fourth vector map by subtracting each vector in the third set of vectors from the corresponding vector in the first set of vectors, wherein the fourth vector map represents the planar components of the first vector map.

2. The system according to claim 1, wherein the first vector map includes an in-plane strain map.

3. The processing device further, To generate a 3-sigma value for the x-axis component of the fourth vector map, and The system according to claim 1, which performs the task of generating a 3-sigma value for the y-axis component of the fourth vector map.

4. The processing device further, The system according to claim 1, which recommends corrective measures based on the fourth vector map.

5. The processing device further, The system according to claim 1, which automatically performs corrective measures based on the fourth vector map.

6. Memory, and A system comprising a processing device coupled to the memory, wherein the processing device is Receiving a first vector map associated with the manufacturing parameters of a substrate, wherein the first vector map includes a first set of vectors, each of which represents a distortion at a specific location among a plurality of locations on the substrate. A second vector map is generated, which includes a second set of vectors, by rotating the position of each vector in the first set of vectors, wherein each of the second set of vectors represents a change in the direction of the magnitude of the distortion at a particular location on the substrate. To generate a third vector map including a third set of vectors based on the vectors in the second set of vectors and the corresponding vectors in the first set of vectors, wherein the third set of vectors generates a third vector map that reflects the distortion-reduced noise across the plurality of locations on the substrate. A fourth vector map containing a fourth set of vectors is generated by projecting the directional component of each vector component in the third set of vectors radially, and A system that generates a fifth vector map containing a fifth set of vectors by grouping the vectors from the fourth set of vectors and identifying the magnitude associated with each group of vectors, wherein the fifth vector map represents at least one of the stress or strain exhibited by the substrate.

7. The processing device further, The system according to claim 6, wherein a sixth vector map containing a sixth set of vectors is generated by subtracting each vector in the fifth set of vectors from the corresponding vector in the first set of vectors.

8. The system according to claim 6, wherein the first vector map includes an in-plane strain map.

9. The processing device further, To generate a 3-sigma value for the x-axis component of the fifth vector map, and The system according to claim 6, which performs the task of generating a 3-sigma value for the y-axis component of the fifth vector map.

10. The processing device further, The system according to claim 6, which recommends corrective measures based on the fifth vector map.

11. The processing device further, The system according to claim 6, which automatically performs corrective measures based on the fifth vector map.

12. The processing device further, To generate a 3-sigma value for the x-axis component of the sixth vector map, and The system according to claim 7, which performs the task of generating a 3-sigma value for the y-axis component of the sixth vector map.

13. The processing device further, The system according to claim 7, which recommends corrective measures based on the sixth vector map.

14. The processing device further, The system according to claim 7, which automatically performs corrective measures based on the sixth vector map.

15. The system according to claim 7, wherein the sixth vector map indicates one or more abnormalities on the substrate.

16. A method for causing one or more processors to perform an operation, wherein the operation is Receiving a first vector map associated with the manufacturing parameters of a substrate, wherein the first vector map includes a first set of vectors, each of which represents a distortion at a specific location among a plurality of locations on the substrate. A second vector map is generated, which includes a second set of vectors, by rotating the position of each vector in the first set of vectors, wherein each of the second set of vectors represents a change in the direction of the magnitude of the distortion at a particular location on the substrate. To generate a third vector map including a third set of vectors based on the vectors in the second set of vectors and the corresponding vectors in the first set of vectors, wherein the third set of vectors generates a third vector map that reflects the distortion-reduced noise across the plurality of locations on the substrate, and A method comprising generating a fourth vector map by subtracting each vector in the third set of vectors from the corresponding vector in the first set of vectors, wherein the fourth vector map represents the planar components of the first vector map.

17. The method according to claim 16, wherein the first vector map includes an in-plane strain map.

18. The operation described above is To generate a 3-sigma value for the x-axis component of the fourth vector map, and The method according to claim 16, further comprising generating a 3-sigma value for the y-axis component of the fourth vector map.

19. The method according to claim 16, further comprising recommending corrective measures based on the fourth vector map.

20. The method according to claim 16, further comprising the operation automatically performing a corrective measure based on the fourth vector map.

Images