Substrate processing apparatus, substrate processing method, and storage medium

Abstract

To provide an apparatus effective for easily estimating a film thickness of a film formed on a surface of a substrate with the high reliability.SOLUTION: A substrate processing apparatus comprises: an imaging unit which acquires a surface image of a film formed on a surface of a substrate; an optical property estimation unit which estimates an optical property of the film on the basis of process information acquired during the formation of the film; and a film thickness estimation unit which estimates the film thickness of the film on the basis of the surface image and the estimation result of the optical property.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present disclosure relates to a substrate processing apparatus, a substrate processing method, and a storage medium. [Background technology]

[0002] Patent Document 1 discloses a film thickness measurement device that calculates the film thickness of a film formed on a substrate to be measured based on correlation data between an image of the substrate and the film thickness of the film formed on the substrate, and an image of the substrate to be measured. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2015-215193 Summary of the Invention [Problem to be solved by the invention]

[0004] The present disclosure provides an apparatus that is effective for easily and reliably estimating the thickness of a film formed on the surface of a substrate. [Means for solving the problem]

[0005] A substrate processing apparatus according to one aspect of the present disclosure includes an imaging unit that acquires a surface image of a film formed on the surface of a substrate, an optical property estimation unit that estimates the optical properties of the film based on process information acquired during the formation of the film, and a film thickness estimation unit that estimates the film thickness based on the surface image and the estimated optical properties. [Effects of the Invention]

[0006] According to the present disclosure, it is possible to provide an apparatus that is effective for easily estimating the film thickness of a film formed on the surface of a substrate with high reliability. [Brief explanation of the drawings]

[0007] [Figure 1]FIG. 1 is a schematic diagram illustrating a schematic configuration of a substrate processing apparatus. [Figure 2] FIG. 2 is a schematic diagram illustrating a schematic configuration of an imaging unit. [Figure 3] FIG. 2 is a block diagram illustrating a functional configuration of a control device. [Figure 4] FIG. 2 is a block diagram illustrating a functional configuration of a control device. [Figure 5] 10 is a table illustrating the contents of a learning database. [Figure 6] FIG. 2 is a block diagram illustrating a hardware configuration of a control device. [Figure 7] 1 is a flowchart illustrating a model estimation procedure. [Figure 8] 1 is a flowchart illustrating a substrate processing procedure. [Figure 9] 10 is a flowchart illustrating a film thickness estimation procedure. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, the embodiments will be described in detail with reference to the drawings. In the description, the same elements or elements having the same functions are denoted by the same reference numerals, and redundant description will be omitted.

[0009] [Substrate Processing Apparatus] The substrate processing apparatus 1 shown in Fig. 1 is an apparatus for forming a film on the surface of a substrate. Specific examples of the substrate include a semiconductor wafer, a glass substrate, a mask substrate, and an FPD (Flat Panel Display). The substrate also includes a semiconductor wafer or the like on which a film or the like has been formed in a previous process. The substrate processing apparatus 1 includes a loader module 10, a process module 20, and a control device 100.

[0010] The loader module 10 introduces and removes substrates W into and from the substrate processing apparatus 1. For example, the loader module 10 can support a plurality of carriers 12 (accommodating units) for substrates W, and has a transfer arm 11 built into a housing whose interior is set to atmospheric pressure. The carriers 12 accommodate, for example, a plurality of circular substrates W. The transfer arm 11 removes the substrates W from the carriers 12 and transfers them to a load lock module 25 (described below) of the processing module 20, and receives the substrates W from the load lock module 25 and returns them to the carrier 12. An aligner 13 that calibrates the position of the substrate W may be connected to the loader module 10.

[0011] The processing module 20 performs a process for forming a film on the surface of the substrate W. The process for forming a film may be a process for forming a film, or a process for partially removing the formed film. The process for forming a film may include both a process for forming a film and a process for partially removing the formed film.

[0012] Specific examples of the process for forming a film include a process for growing a film on the surface of the substrate W, or a process for applying a film material to the surface of the substrate W. Specific examples of the process for growing a film on the surface of the substrate W include physical vapor deposition (PVD), chemical vapor deposition (CVD), and atomic layer deposition (ALD). Specific examples of the process for applying a film material to the surface of the substrate W include spin coating, in which a liquid film material is spread over the surface of the substrate W by rotating the substrate W. Specific examples of the process for partially removing a formed film include wet etching, dry etching, polishing, and the like.

[0013] For example, the processing module 20 includes a transfer chamber 24 that can be depressurized, a transfer arm 21 provided in the transfer chamber 24, one or more process units 22 connected to the transfer chamber 24 via a gate valve, one or more imaging units 23, and one or more load lock modules 25. The transfer arm 21 receives and transfers the substrate W from the load lock module 25. The load lock module 25 has an interior that can be switched between atmospheric pressure and vacuum pressure, and is connected to the loader module 10 via a gate valve.

[0014] Each of the one or more process units 22 performs a process for forming a film on the surface of the substrate W transported by the transport arm 21. For example, the process unit 22 performs a process for forming a film on the surface of the substrate W. As an example, the process unit 22 forms a TiN film on the surface of the substrate W by the above-mentioned ALD.

[0015] For example, the process unit 22 performs thermal ALD, which supplies a reactive gas and a purge gas to the surface of the substrate W while heating the substrate W to promote the reaction. Specific examples of the reactive gas include TiCl4 gas and NH3 gas. Specific examples of the purge gas include N2 gas and Ar gas. The process unit 22 may be configured to perform plasma ALD, which promotes the reaction by using plasma.

[0016] The imaging unit 23 acquires a surface image of the film formed by the process unit 22. As shown in Fig. 2, the imaging unit 23 has a housing 31, a holding unit 32, a transport unit 33, an illumination module 34, and a camera 35. The housing 31 accommodates the substrate W to be imaged. The housing 31 has a loading / unloading opening 41 that receives the substrate W, and a gate valve 42 that opens and closes the loading / unloading opening 41.

[0017] The holder 32 holds from below the substrate arranged horizontally inside the housing 31. The transport unit 33 transports the holder 32 along a horizontal transport direction 36 by a power source such as an electric motor. As a result, the substrate W held by the holder 32 is transported along the transport direction 36.

[0018] The lighting module 34 irradiates illumination light from above onto the substrate W being transported along the transport direction 36. For example, the lighting module 34 has a half mirror 43 and a light source 44. The half mirror 43 emits illumination light downward, and irradiates the illumination light along a horizontal irradiation line 37 that is perpendicular to the transport direction 36. The substrate W is transported in the transport direction 36 so as to pass through the irradiation line 37.

[0019] The light source 44 transmits illumination light directed from the half mirror 43 toward the irradiation line 37. On the other hand, the light source 44 reflects light reflected from the irradiation line 37 toward the camera 35. The camera 35 receives the light reflected by the light source 44 and captures an image of the irradiation line 37. The camera 35 repeatedly captures images of the irradiation line 37 while the substrate W passes through the irradiation line 37. The imaging unit 23 compiles multiple images captured by the camera 35 while the substrate W passes through the irradiation line 37, and generates a surface image of the substrate W (a surface image of the film).

[0020] Image data of the surface image of the substrate W (hereinafter referred to as "surface image data") includes a plurality of pixel values mapped in a matrix on the imaging area of the surface image. Each of the plurality of pixel values represents the color of one section within the imaging area. The color includes hue, saturation, and brightness. For example, the pixel value represents the light intensity of each of the three primary colors (R, G, B).

[0021] The control device 100 controls the loader module 10 and the processing module 20 to form a film on the surface of the substrate W. Here, there is a tendency for the surface image and the film thickness to be correlated. For example, the color of the surface image may change depending on the film thickness. Utilizing this property, the control device 100 estimates the film thickness based on the surface image acquired by the imaging unit 23. Since film thickness estimation based on the surface image can be performed in a short time, it is possible to increase the frequency of evaluating the film thickness while suppressing a decrease in throughput.

[0022] However, the relationship between the surface image and the film thickness can vary depending on the optical properties of the film, which can reduce the reliability of the film thickness estimation results.

[0023] In response to this, the control device 100 is configured to estimate the optical properties of the film based on process information acquired during film formation, and estimate the film thickness based on the surface image and the estimated optical properties, thereby reducing the influence of variations in the optical properties and enabling highly reliable estimation of the film thickness.

[0024] 3, the control device 100 has, as functional components, a process control unit 111, an optical physical property model storage unit 112, an optical physical property estimation unit 113, a film thickness model storage unit 114, an error model storage unit 115, and a film thickness estimation unit 116. The process control unit 111 causes the transfer arm 21 to load the substrate W into the process unit 22, and then causes the process module 20 to execute a process for forming a film on the surface of the substrate W. For example, the process control unit 111 controls the transfer arm 11 to remove the substrate W from the carrier 12 and load it into the load lock module 25. Next, the process control unit 111 controls the transfer arm 21 to receive the substrate W loaded into the load lock module 25 by the transfer arm 11 and load it into the process unit 22, and controls the process unit 22 to form a film on the surface of the substrate W. Next, the process control unit 111 controls the transfer arm 21 to receive the substrate W from the process unit 22 and load it into the imaging unit 23, and controls the imaging unit 23 to acquire surface image data. Next, the process control unit 111 controls the transport arm 21 to receive the substrate W from the imaging unit 23 and deliver it to the load lock module 25. Next, the process control unit 111 controls the delivery arm 11 to return the substrate W received from the load lock module 25 to the carrier 12.

[0025] The process unit 22 acquires the above-mentioned process information during processing. The process information includes information about the ambient environment of the substrate W during processing. For example, the process information includes information about the ambient environment of the substrate W when a film is formed on the surface of the substrate W. The ambient environment information includes the operating status of the apparatus, the outside air temperature, humidity, etc.

[0026] For example, when performing the ALD, the process unit 22 acquires, as the ambient environment information, the temperature of a heater that heats the substrate W, the pressure inside the process unit 22, the pressure of the reactive gas, the flow rate of the reactive gas, the pressure of the purge gas, the flow rate of the purge gas, the room temperature of the clean room (the clean room in which the substrate processing apparatus 1 is installed), the processing time, etc. When performing the plasma ALD, the process unit 22 may acquire, as the ambient environment information, the high-frequency power and voltage input for generating plasma.

[0027] When the process unit 22 performs a process of partially removing a film as a process for forming a film, the process information includes information about the ambient environment of the substrate W when the film is partially removed.

[0028] The optical physical property model storage unit 112 stores an optical physical property model. The optical physical property model is generated in advance to represent the relationship between process information acquired during film formation and the optical physical properties of the film. The optical physical property model may be generated to represent the relationship between the process information acquired during film formation and the optical physical properties at multiple locations on the surface of the substrate W. As an example, the optical physical property model is generated to represent the relationship between the process information acquired by the process unit 22 and the optical physical properties at multiple locations corresponding to the above-mentioned multiple pixel values.

[0029] Optical properties represent the effect that a film has on light that enters the film. For example, optical properties include at least one of the film's refractive index and the film's extinction coefficient. The film's refractive index is expressed as the ratio of the propagation speed of light in air to the propagation speed of light in the film. The film's extinction coefficient represents the attenuation of light propagating through the film.

[0030] The optical property model may be any model as long as it represents the relationship between process information and the optical properties of the film. For example, the optical property model may be a trained model obtained by machine learning so as to output the optical properties at the plurality of locations in response to input of process information. Specific examples of machine learning include multiple regression, Gaussian regression, and deep learning. Specific examples of trained models include neural networks. The trained model may be a function or a lookup table.

[0031] The optical physical property estimation unit 113 estimates the optical physical properties of the film based on the process information acquired by the process unit 22. For example, the optical physical property estimation unit 113 estimates the optical physical properties of the film based on the process information acquired by the process unit 22 and the optical physical property model stored in the optical physical property model storage unit 112. For example, the optical physical property estimation unit 113 inputs the process information into the optical physical property model, thereby outputting the optical physical properties corresponding to the process information. The optical physical property estimation unit 113 may estimate the optical physical properties at the above-mentioned multiple locations based on the process information and the optical physical property model.

[0032] The film thickness model storage unit 114 stores a film thickness model. The film thickness model is generated in advance to represent the relationship between the surface image and the film thickness of the film. The film thickness model may be generated in advance to represent the relationship between the surface image and the film thickness at multiple locations on the surface of the substrate W. As an example, the film thickness model is generated to represent the relationship between the surface image data acquired by the imaging unit 23 and the film thickness at multiple locations corresponding to the multiple pixel values.

[0033] The film thickness model may be any model as long as it represents the relationship between the surface image and the film thickness. For example, the film thickness model may be a trained model acquired by machine learning so as to output film thicknesses at the multiple locations in response to input of image data of the surface image (hereinafter referred to as "surface image data"). Specific examples of machine learning include multiple regression, Gaussian regression, and deep learning. Specific examples of trained models include neural networks. The trained model may be a function or a lookup table.

[0034] The error model storage unit 115 stores an error model. The error model is generated in advance to represent the relationship between optical properties and film thickness estimation errors. The film thickness estimation errors are film thickness estimation errors based on the film thickness models stored in the film thickness model storage unit 114.

[0035] The error model may be any model as long as it represents the relationship between optical properties and the film thickness estimation error. For example, the error model may be a trained model obtained by machine learning so as to output the film thickness estimation error in response to the input of optical properties. Specific examples of machine learning include multiple regression, Gaussian regression, and deep learning. Specific examples of trained models include neural networks. The trained model may be a function or a lookup table.

[0036] The film thickness estimation unit 116 estimates the film thickness based on the surface image data acquired by the imaging unit 23 and the estimation result of the optical physical properties by the optical physical property estimation unit 113. For example, the film thickness estimation unit 116 tentatively estimates the film thickness based on the film thickness model stored in the film thickness model storage unit 114 and the surface image data acquired by the imaging unit 23, and estimates the film thickness by correcting the tentative film thickness estimation result based on the optical physical properties estimated by the optical physical property estimation unit 113. The film thickness estimation unit 116 may estimate an error based on the error model stored in the error model storage unit 115 and the optical physical properties estimated by the optical physical property estimation unit 113, and correct the tentative film thickness estimation result based on the estimation result of the error.

[0037] For example, the film thickness estimation unit 116 provisionally estimates the film thickness based on the film thickness model stored in the film thickness model storage unit 114 and the surface image data acquired by the imaging unit 23. For example, the film thickness estimation unit 116 inputs the surface image data into the film thickness model, causing the film thickness model to output a provisionally estimated result of the film thickness corresponding to the surface image data. The film thickness estimation unit 116 may provisionally estimate the film thickness at the above-mentioned multiple locations based on the film thickness model and the surface image data.

[0038] The film thickness estimation unit 116 estimates an estimation error in the film thickness based on the optical physical properties of the film estimated by the optical physical property estimation unit 113 and the error model stored in the error model storage unit 115. For example, the film thickness estimation unit 116 inputs the optical physical properties into the error model, causing the error model to output an estimation error in the film thickness corresponding to the optical physical properties. The film thickness estimation unit 116 corrects the provisional film thickness estimation result based on the film thickness estimation error. For example, the film thickness estimation unit 116 adds or subtracts the film thickness estimation error to or from the provisional film thickness estimation result.

[0039] The film thickness estimation unit 116 may correct the provisional estimates of the film thickness at each of the plurality of locations based on the estimates of the optical physical properties at each of the plurality of locations by the optical physical property estimation unit 113. For example, the film thickness estimation unit 116 adds or subtracts each of the estimates of the optical physical properties at each of the plurality of locations to the provisional estimate of the film thickness at the same location. Note that while the film thickness estimation unit 116 provisionally estimates the film thickness at each of the plurality of locations, the optical physical property estimation unit 113 may estimate one optical physical property for the entire film. For example, the optical physical property estimation unit 113 may estimate an average optical physical property for the entire film. In this case, the film thickness estimation unit 116 corrects the provisional estimates of the film thickness at each of the plurality of locations based on one optical physical property.

[0040] The film thickness estimation unit 116 may display the film thickness estimation result on a display device (for example, the display device 195 described below). The film thickness estimation unit 116 may also add the film thickness estimation result to log data recorded for each substrate W. Furthermore, if the film thickness estimation result is outside a predetermined allowable range, the film thickness estimation unit 116 may notify an error by displaying it on the display device 195 or the like.

[0041] The control device 100 may be configured to generate at least one of the optical physical property model, the error model, and the film thickness model based on the accumulated data. For example, as shown in Fig. 4, the control device 100 further includes a measured data acquisition unit 121, a data accumulation unit 122, a database 123, an optical physical property model generation unit 124, a film thickness model generation unit 125, an error evaluation unit 126, an error model generation unit 127, and a film thickness model correction unit 128.

[0042] The measurement data acquisition unit 121 acquires measurement data of the film by ellipsometry etc. The measurement data includes the measurement values of the film thickness and the measurement values of the optical properties.

[0043] The data storage unit 122 stores in the database 123 records that associate, for each substrate W, the process information acquired by the process unit 22, the surface image data acquired by the imaging unit 23, and the measurement data acquired by the measurement data acquisition unit 121. FIG. 5 is a table illustrating the contents of the database 123. In FIG. 5, each row from the second row onward is one record. Each record includes the process information acquired by the process unit 22, the surface image data acquired by the imaging unit 23 (the address of the storage location of the surface image data), the measured film thickness value, the measured refractive index value (the measured value of the optical property), and the measured extinction coefficient value (the measured value of the optical property).

[0044] The database 123 includes a process database 123a and an image database 123b. The process database 123a is a database that stores process information and actual measurement results of optical properties in association with each other for each substrate W. The image database 123b is a database that stores surface image data and actual measurement results of film thickness in association with each other.

[0045] 4 , the optical physical property model generation unit 124 generates the optical physical property model by machine learning based on the process database 123a, and stores the optical physical property model in the optical physical property model storage unit 112. The optical physical property estimation unit 113 estimates optical physical properties based on the optical physical property model generated by the optical physical property model generation unit 124.

[0046] The film thickness model generating unit 125 generates the film thickness model by machine learning based on the image database 123b, and stores the film thickness model in the film thickness model storage unit 114. The film thickness estimating unit 116 estimates the film thickness based on the film thickness model generated by the film thickness model generating unit 125.

[0047] The error evaluation unit 126 evaluates the film thickness estimation error based on the film thickness model generated by the film thickness model generation unit 125. For example, the error evaluation unit 126 estimates the film thickness based on the surface image data and the film thickness model for each record in the database 123, and compares the estimation result with the actual measured film thickness to calculate the film thickness estimation error. The error evaluation unit 126 adds the film thickness estimation error to the record in the database 123. As a result, the database 123 further includes an error database 123c that associates the actual measurement results of optical physical properties with the film thickness estimation error.

[0048] The error model generating unit 127 generates the error model by machine learning based on the error database 123c, and stores the error model in the error model storage unit 115. The film thickness estimating unit 116 corrects the provisional film thickness estimation result based on the film thickness model, based on the error model generated by the error model generating unit 127.

[0049] After the optical physical property model, the film thickness model, and the error model are generated, the film thickness model correction unit 128 further evaluates the error of the film thickness estimated by the film thickness estimation unit 116 based on the optical physical properties and the surface image data. For example, the film thickness model correction unit 128 causes the film thickness estimation unit 116 to estimate the film thickness based on the optical physical properties and the surface image data for each record in the database 123, and compares the result with the measured film thickness to evaluate the error. The film thickness model correction unit 128 corrects the film thickness model based on the error evaluation result. The film thickness model correction unit 128 may correct the optical physical property model instead of the film thickness model, or may correct both the film thickness model and the optical physical property model. The film thickness model correction unit 128 may correct the error model based on the error evaluation result.

[0050] The data accumulation unit 122 may continue to accumulate records in the database 123 even after the optical physical property model generation unit 124, the film thickness model generation unit 125, and the error model generation unit 127 have generated the optical physical property model, the film thickness model, and the error model. In this case, the optical physical property model generation unit 124, the film thickness model generation unit 125, and the error model generation unit 127 may update the optical physical property model, the film thickness model, and the error model based on the newly accumulated records.

[0051] In the above, a configuration has been exemplified in which a film thickness is tentatively estimated based on the surface image data and the film thickness model, and then the tentative estimation result is corrected based on the estimation result of the optical physical properties, but the configuration of the control device 100 is not limited to this.

[0052] For example, the film thickness estimation unit 116 may be configured to estimate the film thickness based on a film thickness model generated in advance to represent the relationship between the optical properties, the surface image, and the film thickness, the estimated results of the optical properties, and the surface image. The data accumulation unit 122 includes an image database 123d that associates the actual measurement results of the optical properties, the surface image data, and the actual measurement results of the film thickness. The film thickness model generation unit 125d generates a film thickness model that represents the relationship between the optical properties, the surface image, and the film thickness through machine learning.

[0053] According to this configuration, a film thickness model that takes into account the influence of optical properties is generated, so that the error evaluation unit 126 and the error model generation unit 127 can be omitted.

[0054] The film thickness model may be generated in advance to represent the relationship between the optical properties at multiple locations, the surface image, and the film thickness at multiple locations. The film thickness estimation unit 116 may estimate the film thickness at multiple locations based on the film thickness model, the estimation results of the optical properties at multiple locations, and the surface image.

[0055] FIG. 6 is a block diagram illustrating a hardware configuration of the control device 100. As shown in FIG. 6, the control device 100 includes a circuit 190. The circuit 190 includes one or more processors 191, a memory 192, a storage 193, an input / output port 194, a display device 195, and an input device 196. The storage 193 stores a program for causing the control device 100 to execute a substrate processing procedure, including estimating optical properties of a film based on process information acquired during film formation, and estimating a film thickness based on a surface image and the estimated optical properties. For example, the storage 193 stores a program for causing the control device 100 to configure each of the above-described functional components.

[0056] The storage 193 includes a storage medium that stores the above-mentioned programs and a device that reads data from the storage medium. Specific examples of the storage medium include a hard disk, a readable and writable nonvolatile memory, and a read-only memory (ROM).

[0057] The memory 192 is, for example, a RAM (Random Access Memory), and stores a program loaded from the storage 193. The one or more processors 191 execute the program loaded in the memory 192, thereby configuring each of the functional components described above in the control device 100. The one or more processors 191 temporarily store in the memory 192 intermediate calculation results generated during the processing.

[0058] The input / output port 194 outputs commands to the transfer arm 11, the transport arm 21, the process unit 22, and the imaging unit 23 based on requests from one or more processors 191. The input / output port 194 also acquires the above-mentioned process information and the like based on requests from one or more processors 191.

[0059] The display device 195 displays information for the operator based on a request from the one or more processors 191. The input device 196 acquires input from the operator and notifies the one or more processors 191 of the input contents.

[0060] Specific examples of the display device 195 include a liquid crystal monitor or an organic EL (Electro-Luminescence) monitor. Specific examples of the input device 196 include a keypad, a keyboard, a mouse, etc. The input device 196 may be integrated with the display device 195 as a so-called touch panel.

[0061] [Substrate processing procedure] Next, as an example of a substrate processing method, a substrate processing procedure executed by the control device 100 will be illustrated. This procedure includes a model generation procedure and a substrate processing procedure. Each procedure will be described below.

[0062] (Model generation procedure) This procedure is executed when the number of records required for machine learning has been accumulated in the database 123. As shown in Fig. 7, the control device 100 executes steps S01 and S02. In step S01, the optical physical property model generation unit 124 generates the optical physical property model by machine learning based on the process database 123a of the database 123, and stores the optical physical property model in the optical physical property model storage unit 112. In step S02, the film thickness model generation unit 125 generates the film thickness model by machine learning based on the image database 123b of the database 123, and stores the film thickness model in the film thickness model storage unit 114.

[0063] Next, the control device 100 executes step S03. In step S03, the error evaluation unit 126 evaluates the film thickness estimation error based on the film thickness model generated by the film thickness model generation unit 125. For example, the error evaluation unit 126 estimates the film thickness based on the surface image data and the film thickness model for each record in the database 123, and compares the estimation result with the actual measured film thickness to calculate the film thickness estimation error. The error evaluation unit 126 adds the film thickness estimation error to the record in the database 123. As a result, the database 123 further includes the error database 123c.

[0064] Next, the control device 100 executes step S04. In step S04, the error model generating unit 127 generates the error model by machine learning based on the error database 123c, and stores the error model in the error model storage unit 115.

[0065] Next, the control device 100 executes steps S05 and S06. In step S05, the film thickness model correction unit 128 causes the film thickness estimation unit 116 to estimate a film thickness based on the optical physical properties and the surface image data for each record in the database 123. In step S06, the film thickness model correction unit 128 compares the film thickness estimation result by the film thickness estimation unit 116 with the actual film thickness measurement value to evaluate an error, and corrects the film thickness model based on the error evaluation result. This completes the model generation procedure.

[0066] The generation of the film thickness model in step S02 may be performed prior to the generation of the optical physical property model in step S01. Furthermore, the generation of the film thickness model in step S02 and the generation of the optical physical property model in step S01 may be performed at least partially in parallel.

[0067] (Substrate processing procedure) 8, the control device 100 executes steps S11, S12, S13, S14, S15, and S16. In step S11, the process control unit 111 controls the transfer arm 11 to remove the substrate W from the carrier 12 and load it into the load lock module 25. The process control unit 111 also controls the transfer arm 21 to receive the substrate W loaded into the load lock module 25 by the transfer arm 11 and load it into the process unit 22. In step S12, the process control unit 111 controls the process unit 22 to form a film on the surface of the substrate W. In step S13, the process control unit 111 controls the transfer arm 21 to receive the substrate W from the process unit 22 and load it into the imaging unit 23. In step S14, the process control unit 111 controls the imaging unit 23 to acquire surface image data. In step S15, the film thickness estimation unit 116 estimates the film thickness. Specific details of step S15 will be described later. In step S16, the processing control unit 111 controls the transport arm 21 to receive the substrate W from the imaging unit 23 and pass it to the load lock module 25, and controls the transfer arm 11 to return the substrate W received from the load lock module 25 to the carrier 12.

[0068] FIG. 9 is a flowchart illustrating the film thickness estimation procedure in step S16. As shown in FIG. 9, the control device 100 executes steps S21, S22, S23, and S24. In step S21, the optical property estimation unit 113 estimates the optical properties of the film based on the process information acquired by the process unit 22 and the optical property model stored in the data accumulation unit 122. In step S22, the film thickness estimation unit 116 estimates the film thickness estimation error based on the optical properties estimated by the optical property estimation unit 113 and the error model stored in the error model storage unit 115. In step S23, the film thickness estimation unit 116 tentatively estimates the film thickness based on the film thickness model stored in the film thickness model storage unit 114 and the surface image data acquired by the imaging unit 23. In step S24, the film thickness estimation unit 116 corrects the tentative film thickness estimation result based on the film thickness estimation error. This completes the film thickness estimation procedure. The tentative estimation of the film thickness in step S23 may be performed before the estimation of the estimation error in step S22, or before the estimation of the optical properties in step S21. Furthermore, step S23 may be performed at least partially in parallel with at least one of steps S21 and S22.

[0069] [Effects of the embodiment] A substrate processing apparatus 1 according to one aspect of the present disclosure includes an imaging unit 23 that acquires a surface image of a film formed on the surface of a substrate, an optical property estimation unit 113 that estimates the optical properties of the film based on process information acquired during the formation of the film, and a film thickness estimation unit 116 that estimates the film thickness based on the surface image and the estimated optical properties.

[0070] There is a correlation between a surface image and a film thickness. For example, the color of the surface image may change depending on the film thickness. Therefore, it is possible to estimate the film thickness based on the surface image. However, the relationship between the surface image and the film thickness may change depending on the optical properties of the film. In contrast, the present substrate processing apparatus 1 estimates the optical properties based on process information, and estimates the film thickness based on the surface image and the estimated optical properties. Therefore, it is effective in easily estimating the film thickness with high reliability.

[0071] The optical property estimation unit 113 may estimate the optical properties based on the process information and an optical property model that is generated in advance to represent the relationship between the process information and the optical properties, thereby making it possible to easily estimate the optical properties with higher reliability.

[0072] The film thickness estimation unit 116 may tentatively estimate the film thickness based on the surface image and a film thickness model that is generated in advance to represent the relationship between the surface image and the film thickness, and estimate the film thickness by correcting the tentative film thickness estimation result based on the optical physical properties estimated by the optical physical properties estimation unit 113. The estimation result of the optical physical properties can be easily reflected in the estimation result of the film thickness.

[0073] The film thickness estimation unit 116 may estimate the error based on an error model generated in advance to represent the relationship between the optical physical properties and the film thickness error and the optical physical properties estimated by the optical physical property estimation unit 113, and may estimate the film thickness by correcting the tentative film thickness estimation result based on the error estimation result. The estimation result of the optical physical properties can be easily reflected in the film thickness estimation result.

[0074] The film thickness model may be generated in advance to represent the relationship between the surface image and the film thickness at multiple locations on the surface of the substrate, and the film thickness estimation unit 116 may estimate the film thickness at multiple locations based on the film thickness model and the surface image, making it possible to easily estimate the film thickness at each location on the substrate.

[0075] The optical physical property model may be generated in advance to represent the relationship between process information and optical physical properties at multiple locations, the optical physical property estimation unit 113 may estimate the optical physical properties at multiple locations based on the optical physical property model and the process information, and the film thickness estimation unit 116 may correct the provisional film thickness estimation results at multiple locations based on the estimation results of the optical physical properties at the multiple locations. This makes it possible to estimate the film thickness at each location on the substrate with higher reliability.

[0076] The optical physical property model generation unit 124 may generate an optical physical property model by machine learning based on a process database in which process information and actual measurement results of optical physical properties are associated and stored, and a film thickness model generation unit 125 may generate a film thickness model by machine learning based on an image database in which surface images and actual measurement results of film thickness are associated and stored, and the optical physical property estimation unit 113 may estimate the optical physical properties based on the optical physical property model generated by the optical physical property model generation unit 124, and the film thickness estimation unit 116 may estimate the film thickness based on the film thickness model generated by the film thickness model generation unit 125. The optical physical properties can be estimated with higher reliability, and the film thickness can be estimated with higher reliability.

[0077] The film thickness model correction unit 128 may further be provided, which corrects the film thickness model based on a comparison between the film thickness estimation result by the film thickness estimation unit 116 and the film thickness measurement result. This allows the film thickness to be estimated with higher reliability.

[0078] The film thickness estimation unit 116 may estimate the film thickness based on a film thickness model that is generated in advance to represent the relationship between the optical properties, the surface image, and the film thickness, the estimated optical properties, and the surface image. The estimated optical properties can be reflected in the estimated film thickness with higher reliability.

[0079] The optical physical property model may be generated in advance to represent the relationship between process information and the optical physical properties at multiple locations on the surface of the substrate, the optical physical property estimation unit 113 may estimate the optical physical properties at the multiple locations based on the optical physical property model and the process information, the film thickness model may be generated in advance to represent the relationship between the optical physical properties at the multiple locations, the surface image, and the film thickness at the multiple locations, and the film thickness estimation unit 116 may estimate the film thickness at the multiple locations based on the film thickness model, the estimation results of the optical physical properties at the multiple locations, and the surface image. The film thickness for each portion of the substrate can be easily estimated.

[0080] The optical physical property estimation unit 113 may estimate the optical physical properties based on the optical physical property model generated by the optical physical property model generation unit 124, and the film thickness estimation unit 116 may estimate the film thickness based on the film thickness model generated by the film thickness model generation unit 125. The optical physical properties can be estimated with higher reliability, and the film thickness can be estimated with higher reliability.

[0081] Although the embodiments have been described above, the present disclosure is not necessarily limited to the above-described embodiments, and can be modified as appropriate within the scope of the gist thereof. [Explanation of symbols]

[0082] 1...substrate processing apparatus, 22...process unit, 23...imaging section, 113...optical property estimation section, 116...film thickness estimation section, 124...optical property model generation section, 125...film thickness model generation section, 128...film thickness model correction section.

Claims

1. an imaging unit that acquires a surface image of a film formed on the surface of a substrate; an optical property estimation unit that estimates the optical properties based on an optical property model that is generated in advance to represent a relationship between process information acquired during the formation of the film and the optical properties of the film, and the process information; a film thickness estimation unit that estimates a film thickness of the film based on the surface image and the estimation result of the optical physical property, The film thickness estimation unit tentatively estimating the film thickness based on the surface image and a film thickness model that is generated in advance to represent the relationship between the surface image and the film thickness; estimating the error based on an error model previously generated to represent the relationship between the optical physical property and the film thickness error and the optical physical property estimated by the optical physical property estimation unit; The substrate processing apparatus estimates the film thickness by correcting the tentative film thickness estimation result based on the error estimation result.

2. the film thickness model is generated in advance to represent a relationship between the surface image and the film thicknesses at a plurality of locations on the surface of the substrate; The substrate processing apparatus according to claim 1 , wherein the film thickness estimation unit estimates the film thickness at the plurality of locations based on the film thickness model and the surface image.

3. the optical physical property model is generated in advance to represent a relationship between the process information and the optical physical properties at the plurality of locations; the optical physical property estimation unit estimates the optical physical properties at the plurality of locations based on the optical physical property model and the process information; The substrate processing apparatus according to claim 2 , wherein the film thickness estimating unit corrects the provisionally estimated results of the film thickness at the plurality of locations based on the estimated results of the optical physical properties at the plurality of locations.

4. an optical physical property model generation unit that generates the optical physical property model by machine learning based on a process database that stores the process information and the actual measurement results of the optical physical properties in association with each other; a film thickness model generating unit that generates the film thickness model by machine learning based on an image database that stores the surface image and the film thickness measurement results in association with each other, the optical physical property estimation unit estimates the optical physical properties based on the optical physical property model generated by the optical physical property model generation unit; 4. The substrate processing apparatus according to claim 1, wherein the film thickness estimation unit estimates the film thickness based on the film thickness model generated by the film thickness model generation unit.

5. 5. The substrate processing apparatus according to claim 4, further comprising a film thickness model correcting unit that corrects the film thickness model based on a comparison between the film thickness estimation result by the film thickness estimating unit and the film thickness actual measurement result.

6. 6. The substrate processing apparatus according to claim 1, wherein the optical property represents an effect of the film on light that has entered the film.

7. The substrate processing apparatus according to claim 6 , wherein the optical property includes at least one of a refractive index and an extinction coefficient.

8. 8. The substrate processing apparatus according to claim 1, wherein the process information includes information about the surrounding environment of the substrate when the film is formed on the surface of the substrate.

9. 9. The substrate processing apparatus according to claim 1, wherein the process information includes information about the surrounding environment of the substrate when the film formed on the surface of the substrate is partially removed.

10. Obtaining a surface image of a film formed on a surface of a substrate; estimating the optical properties based on an optical property model previously generated to represent a relationship between process information acquired during the formation of the film and the optical properties of the film, and the process information; estimating a film thickness of the film based on the surface image and the estimation result of the optical physical property, estimating the film thickness tentatively estimating the film thickness based on the surface image and a film thickness model that is generated in advance to represent the relationship between the surface image and the film thickness; estimating the error based on an error model previously generated to represent a relationship between the optical properties and the film thickness error and the estimated optical properties; correcting the tentative film thickness estimation result based on the error estimation result to estimate the film thickness; A substrate processing method comprising:

11. A computer-readable storage medium storing a program for causing an apparatus to execute the substrate processing method according to claim 10.

Images