Substrate processing condition setting support method, substrate processing system, storage medium, and learning model

Abstract

To provide a condition setting support method that is effective in simplifying the work of setting a processing condition for substrate processing.SOLUTION: A condition setting support method for substrate processing includes inputting, into a machine learning device, a data set including a processing condition for substrate processing performed by a substrate processing device including supply of processing liquid to a substrate and performance data regarding the quality of the substrate processing, and deriving a recommended processing condition for substrate processing on the basis of a learning model that is generated by the machine learning device through machine learning based on a plurality of sets of data sets and that outputs predictive data regarding the quality of the substrate processing according to the input of the processing condition.SELECTED DRAWING: Figure 8

Description

Technical Field

[0001] The present disclosure relates to a method for supporting setting of substrate processing conditions, a substrate processing system, a storage medium, and a learning model.

Background Art

[0002] Patent Document 1 discloses an apparatus that forms a photosensitive film on the surface of a substrate and performs development processing on the photosensitive film after exposure processing of the photosensitive film.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The present disclosure provides a condition setting support method effective for simplifying the operation of setting processing conditions for substrate processing.

Means for Solving the Problems

[0005] A method for supporting setting of substrate processing conditions according to one aspect of the present disclosure includes inputting a data set including processing conditions of substrate processing executed by a substrate processing apparatus including supply of a processing liquid to a substrate and performance data related to the quality of the substrate processing into a machine learning apparatus, and deriving recommended processing conditions for substrate processing based on a learning model generated by machine learning by the machine learning apparatus based on a plurality of sets of the data sets, the learning model outputting prediction data related to the quality of substrate processing in response to input of the processing conditions.

Effects of the Invention

[0006] According to the present disclosure, it is possible to provide a condition setting support method effective for simplifying the operation of setting processing conditions for substrate processing.

Brief Description of the Drawings

[0007]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Figure 11

Mode for Carrying Out the Invention

[0008] Hereinafter, various exemplary embodiments will be described. In the description, the same reference numerals are given to the same elements or elements having the same function, and redundant descriptions are omitted.

[0009] 〔Substrate Processing System〕 The substrate processing system 1 is a system that forms a photosensitive film on the surface of a substrate and performs a developing process on the photosensitive film after exposure processing. The substrate to be processed is, for example, a semiconductor wafer W. The photosensitive film is, for example, a resist film.

[0010] As illustrated in FIG. 1, the substrate processing system 1 includes a coating / developing apparatus 2 and a control device 100. The coating / developing apparatus 2 includes a carrier block 4, a processing block 5, and an interface block 6.

[0011] The carrier block 4 introduces the wafer W (substrate) into the coating / developing apparatus 2 and discharges the wafer W from the coating / developing apparatus 2. For example, the carrier block 4 can support a plurality of carriers C for the wafer W and incorporates a transfer arm A1. The carrier C accommodates, for example, a plurality of circular wafers W. The transfer arm A1 takes out the unprocessed wafer W from the carrier C and returns the processed wafer W to the carrier C.

[0012] The processing block 5 has a plurality of processing modules 11, 12, 13, 14. The processing modules 11, 12, 13 (processing units) perform a film forming process of applying a film forming liquid (processing liquid for film formation) to the surface Wa of the wafer W to form a film. For example, the processing modules 11, 12, 13 incorporate a coating unit U1, a heat treatment unit U2, and a transfer arm A3 that transfers the wafer W to these units.

[0013] The processing module 11 forms a lower layer film on the surface of the wafer W by the coating unit U1 and the heat treatment unit U2. The coating unit U1 of the processing module 11 applies a processing liquid for forming the lower layer film onto the wafer W. The heat treatment unit U2 of the processing module 11 performs various heat treatments associated with the formation of the lower layer film.

[0014] The processing module 12 forms a resist film on the lower layer film by the coating unit U1 and the heat treatment unit U2. The coating unit U1 of the processing module 12 applies a processing liquid for forming the resist film onto the lower layer film. The heat treatment unit U2 of the processing module 12 performs various heat treatments associated with the formation of the resist film.

[0015] The processing module 13 forms an upper layer film on the resist film by means of the coating unit U1 and the heat treatment unit U2. The coating unit U1 of the processing module 13 applies a liquid for forming the upper layer film onto the resist film. The heat treatment unit U2 of the processing module 13 performs various heat treatments associated with the formation of the upper layer film.

[0016] As illustrated in FIG. 2, the coating unit U1 includes a rotation holding unit 50 and a film-forming liquid supply unit 60. The rotation holding unit 50 holds and rotates the wafer W. For example, the rotation holding unit 50 includes a holding unit 51 and a rotation driving unit 52. The holding unit 51 supports the wafer W disposed horizontally and holds it by, for example, vacuum adsorption or the like. The rotation driving unit 52 rotates the holding unit 51 around a vertical axis using, for example, an electric motor or the like as a power source. Thereby, the wafer W held by the holding unit 51 also rotates.

[0017] The film-forming liquid supply unit 60 supplies a film-forming liquid to the surface Wa of the wafer W held by the holding unit 51. For example, the film-forming liquid supply unit 60 includes a nozzle 61 and a liquid source 62. The nozzle 61 is disposed above the wafer W held by the holding unit 51 and discharges a processing liquid downward. The liquid source 62 pumps the processing liquid to the nozzle 61.

[0018] Returning to FIG. 1, the processing module 14 (processing unit) performs a development process of supplying a processing liquid for development to a resist film (photosensitive film) on which an exposure process has been performed on the surface Wa of the wafer W. For example, the processing module 14 incorporates a development unit U3, a heat treatment unit U4, and a transfer arm A3 that transfers the wafer W to these units. The processing module 14 performs a development process of the resist film after exposure by means of the development unit U3 and the heat treatment unit U4. The development unit U3 applies a developer (processing liquid for development) onto the surface of the exposed wafer W and then rinses it with a rinse liquid (processing liquid for rinsing) to perform a development process of the resist film. The heat treatment unit U4 performs various heat treatments associated with the development process. Specific examples of the heat treatment include a heat treatment (PEB: Post Exposure Bake) before the development process, a heat treatment (PB: Post Bake) after the development process, and the like.

[0019] As illustrated in FIG. 3, the developing unit U3 includes a rotation holding unit 20, a developing solution supply unit 30, and a rinse solution supply unit 40. The rotation holding unit 20 holds and rotates the wafer W. For example, the rotation holding unit 20 includes a holding unit 21 and a rotation driving unit 22. The holding unit 21 supports the wafer W disposed horizontally and holds it by, for example, vacuum suction or the like. The rotation driving unit 22 rotates the holding unit 21 around a vertical axis using, for example, an electric motor or the like as a power source. As a result, the wafer W held by the holding unit 21 also rotates.

[0020] The developing solution supply unit 30 supplies a developing solution to the surface Wa of the wafer W held by the holding unit 21. For example, the developing solution supply unit 30 includes a nozzle 31, a nozzle transfer unit 32, and a liquid source 33. The nozzle 31 is disposed above the wafer W held by the holding unit 21 and discharges the developing solution downward. The nozzle transfer unit 32 moves the nozzle 31 in the horizontal direction using, for example, an electric motor or the like as a power source. The liquid source 33 pumps the developing solution to the nozzle 31.

[0021] The rinse solution supply unit 40 supplies a rinse solution to the surface Wa of the wafer W held by the holding unit 21. For example, the rinse solution supply unit 40 includes a nozzle 41, a nozzle transfer unit 42, and a liquid source 43. The nozzle 41 is disposed above the wafer W held by the holding unit 21 and discharges the rinse solution downward. The nozzle transfer unit 42 moves the nozzle 41 in the horizontal direction using, for example, an electric motor or the like as a power source. The liquid source 43 pumps the rinse solution to the nozzle 41.

[0022] Returning to FIG. 1, the interface block 6 transfers the wafer W to and from an exposure apparatus (not shown) that performs an exposure process on a resist film formed on the wafer W. For example, the interface block 6 incorporates a transfer arm A8 and is connected to the exposure apparatus. The transfer arm A8 transfers the wafer W before the exposure process to the exposure apparatus and receives the wafer W after the exposure process from the exposure apparatus.

[0023] A storage unit U10 is provided between the processing block 5 and the carrier block 4. The storage unit U10 is partitioned into a plurality of cells arranged in the vertical direction, and each cell can accommodate the wafer W. The storage unit U10 is used for the transfer of the wafer W between the carrier block 4 and the processing block 5. A lifting arm A7 is provided near the storage unit U10. The lifting arm A7 raises and lowers the wafer W between the cells of the storage unit U10. A storage unit U11 is provided between the processing block 5 and the interface block 6. The storage unit U11 is also partitioned into a plurality of cells arranged in the vertical direction, and each cell can accommodate the wafer W. The storage unit U11 is used for the transfer of the wafer W between the processing block 5 and the interface block 6.

[0024] The control device 100 controls the coating / developing device 2 to execute the coating / developing process according to the following procedure, for example. First, the control device 100 controls the transfer arm A1 to transfer the wafer W in the carrier C to the storage unit U10, and controls the lifting arm A7 to place the wafer W in the cell for the processing module 11.

[0025] Next, the control device 100 controls the transfer arm A3 to transfer the wafer W in the storage unit U10 to the coating unit U1 and the heat treatment unit U2 in the processing module 11. Also, the control device 100 controls the coating unit U1 and the heat treatment unit U2 to form a lower layer film on the surface of the wafer W. Then, the control device 100 controls the transfer arm A3 to return the wafer W with the lower layer film formed to the storage unit U10, and controls the lifting arm A7 to place the wafer W in the cell for the processing module 12.

[0026] Next, the control device 100 controls the transfer arm A3 to transfer the wafer W in the storage unit U10 to the coating unit U1 and the heat treatment unit U2 in the processing module 12. Further, the control device 100 controls the coating unit U1 and the heat treatment unit U2 to form a resist film on the lower layer film of the wafer W. Thereafter, the control device 100 controls the transfer arm A3 to return the wafer W to the storage unit U10, and controls the lifting arm A7 to place the wafer W in the cell for the processing module 13.

[0027] Next, the control device 100 controls the transfer arm A3 to transfer the wafer W in the storage unit U10 to each unit in the processing module 13. Further, the control device 100 controls the coating unit U1 and the heat treatment unit U2 to form an upper layer film on the resist film of the wafer W. Thereafter, the control device 100 controls the transfer arm A3 to transfer the wafer W to the storage unit U11.

[0028] Next, the control device 100 controls the transfer arm A8 to send out the wafer W in the storage unit U11 to the exposure device 3. Thereafter, the control device 100 controls the transfer arm A8 to receive the wafer W subjected to the exposure process from the exposure device 3 and place it in the cell for the processing module 14 in the storage unit U11.

[0029] Next, the control device 100 controls the transfer arm A3 to transfer the wafer W in the storage unit U11 to each unit in the processing module 14, and controls the developing unit U3 and the heat treatment unit U4 to perform a developing process on the resist film of the wafer W. Thereafter, the control device 100 controls the transfer arm A3 to return the wafer W to the storage unit U10, and controls the lifting arm A7 and the transfer arm A1 to return the wafer W into the carrier C. Thus, the coating and developing process is completed.

[0030] Note that the specific configuration of the substrate processing system is not limited to those exemplified above. The substrate processing system may be any one as long as it includes a processing unit that performs substrate processing including supplying a processing liquid to the substrate and a control device 100 that can control it.

[0031] 〔Condition Setting Support System〕 The substrate processing system 1 further includes a condition setting system 7. The condition setting system 7 has a quality inspection device 70. At least a part of the condition setting system 7 is constituted by the control device 100. That is, the condition setting system 7 has the quality inspection device 70 and the control device 100. The quality inspection device 70 detects information regarding the quality of the substrate processing performed by the coating / development device 2.

[0032] The control device 100 causes the coating / development device 2 (substrate processing device) to execute substrate processing including the supply of a processing liquid to the wafer W according to preset processing conditions, acquires performance data regarding the quality of the substrate processing according to the processing conditions from the quality inspection device 70, inputs a data set including the processing conditions of the substrate processing and the performance data of the substrate processing to the machine learning device 200, and derives recommended processing conditions for the substrate processing based on a learning model generated by the machine learning device 200 through machine learning based on a plurality of sets of data sets so as to output prediction data regarding the quality of the substrate processing in response to the input of the processing conditions. The prediction data is, for example, data for predicting the above performance data. The performance data may be any data as long as it is related to the quality of the substrate processing. The data of the quality of the substrate after the substrate processing is related to the quality of the substrate processing. Also, the supply state of the processing liquid during the substrate processing is related to the quality of the substrate processing.

[0033] The condition setting system 7 may further include a machine learning device 200. The machine learning device 200 is configured to acquire the above dataset, generate, and execute the above learning model by machine learning based on a plurality of sets of datasets. The machine learning device 200 may be housed in the same housing as the control device 100, or may be installed at a position separated from the control device 100. When installed at a position separated from the control device 100, the machine learning device 200 is connected to the control device 100 via, for example, a local area network. The machine learning device 200 may be connected to the control device 100 via a wide area network such as the so-called Internet. Hereinafter, the configuration of each part will be described in detail.

[0034] (Quality data detection device) The quality inspection device 70 has, for example, a post-processing inspection unit 80 shown in FIG. 4. The post-processing inspection unit 80 detects information regarding the quality of the substrate after substrate processing. As an example, the post-processing inspection unit 80 detects information regarding the line width of the resist pattern formed on the surface of the wafer W after development processing. For example, the post-processing inspection unit 80 detects image information capable of recognizing the difference in the line width of the resist pattern as a difference in at least one of hue, lightness, and chroma.

[0035] Specifically, the post-processing inspection unit 80 includes a holding unit 83, a linear drive unit 84, an imaging unit 81, and a light projection / reflection unit 82. The holding unit 83 holds the wafer W horizontally. The linear drive unit 84 uses, for example, an electric motor as a power source and moves the holding unit 83 along a horizontal linear path. The imaging unit 81 acquires image data of the surface of the wafer W. The imaging unit 81 is provided at one end side within the post-processing inspection unit 80 in the moving direction of the holding unit 83 and is directed toward the other end side in the moving direction.

[0036] The light projection and reflection unit 82 projects light onto the imaging range and guides the reflected light from the imaging range toward the imaging unit 81. For example, the light projection and reflection unit 82 includes a half mirror 86 and a light source 87. The half mirror 86 is provided at an intermediate position within the movement range of the holding unit 83 at a position higher than the holding unit 83, and reflects light from below toward the imaging unit 81. The light source 87 is provided above the half mirror 86 and irradiates illumination light downward through the half mirror 86.

[0037] The post - processing inspection unit 80 operates as follows to acquire image data of the surface of the wafer W. First, the linear drive unit 84 moves the holding unit 83. As a result, the wafer W passes under the half mirror 86. During this passing process, the reflected light from each part of the surface of the wafer W is sequentially sent to the imaging unit 81. The imaging unit 81 forms an image of the reflected light from each part of the surface of the wafer W and acquires image data of the surface of the wafer W. Thereby, the image information of the resist pattern is detected.

[0038] The post - processing inspection unit 80 may detect information regarding the film thickness of the film formed on the surface of the wafer W after the film formation process. For example, the post - processing inspection unit 80 detects image information that can recognize the difference in the film thickness of the film as at least one of the differences in hue, lightness, and chroma. The said image information can also be detected by the configuration illustrated in FIG. 4.

[0039] The quality inspection apparatus 70 may further include a in - process inspection unit 90 shown in FIG. 5. The in - process inspection unit 90 detects information regarding the supply state of the processing liquid during the substrate processing. As an example, the in - process inspection unit 90 detects information regarding the supply state of the developer during the development process. For example, the in - process inspection unit 90 includes a liquid splash detection unit 91, a liquid accumulation detection unit 92, and a liquid drip detection unit 93.

[0040] The liquid splash detection unit 91 detects information regarding the occurrence state of liquid splash during the supply of the developing liquid. For example, the liquid splash detection unit 91 includes an irradiation unit 94 and an imaging unit 95. The irradiation unit 94 is fixed to, for example, the nozzle 31 or the like, and irradiates laser light in the horizontal direction above the wafer W. The installation height of the irradiation unit 94 is set to a height reachable by liquid droplets splashed from the surface Wa. The imaging unit 95 acquires image data of the irradiation range of the laser light from the irradiation unit 94. When liquid splash occurs, scattering of the laser light occurs due to the splashed liquid droplets, and the image data acquired by the imaging unit 95 changes. Therefore, the image data acquired by the imaging unit 95 includes information regarding the occurrence state of the liquid droplets.

[0041] The liquid accumulation detection unit 92 detects information regarding the formation state of the liquid film of the developing liquid on the surface Wa. For example, the liquid accumulation detection unit 92 includes an imaging unit 96. The imaging unit 96 acquires image data of the surface Wa of the wafer W held by the holding unit 21. The image data acquired by the imaging unit 96 includes information regarding the formation state of the liquid film.

[0042] The liquid drip detection unit 93 detects information regarding the occurrence state of liquid dripping of the developing liquid from the nozzle 31. Liquid dripping means a phenomenon in which the developing liquid drops from the nozzle 31 outside a preset supply period of the developing liquid. For example, the liquid drip detection unit 93 includes an imaging unit 97. The imaging unit 97 acquires image data of the nozzle 31 and the area below it. The image data acquired by the imaging unit 97 includes information regarding the occurrence state of the liquid dripping.

[0043] The in - process inspection unit 90 may detect information regarding the supply state of the film - forming liquid during the film - forming process. Also in this case, it is possible to detect information regarding the supply state of the film - forming liquid in the coating unit U1 by a configuration similar to that of the liquid splash detection unit 91, the liquid accumulation detection unit 92, the liquid drip detection unit 93, etc. described above.

[0044] (Control Device and Machine Learning Device) As shown in FIG. 6, the control device 100 includes, as functional components (hereinafter referred to as "function modules"), a processing condition holding unit 111, a processing control unit 112, a data acquisition unit 113, a data input unit 114, and a recommended condition derivation unit 115.

[0045] The processing condition holding unit 111 stores preset processing conditions. For example, the processing condition holding unit 111 stores the development processing conditions by the processing module 14. The development processing conditions include the heat treatment conditions by the heat treatment unit U4 and the liquid treatment conditions by the development unit U3. The liquid treatment conditions by the development unit U3 include sequences such as the supply of the developer, the supply of the rinse liquid, and drying (spin drying by rotation). Further, the liquid treatment conditions by the development unit U3 include the rotation speed of the wafer W, the supply amount of the developer, the supply time of the developer, the supply amount of the rinse liquid, the discharge time of the rinse liquid, and the spin drying time, etc. in each sequence. When supplying the developer while moving the nozzle 31 by the nozzle transfer unit 32, the liquid treatment conditions by the development unit U3 may further include the movement start position, the movement speed, the movement end position, etc. of the nozzle 31 during the supply of the developer.

[0046]

[0047] The processing condition holding unit 111 may store the film formation processing conditions by the processing modules 11, 12, 13. The film formation processing conditions include the liquid treatment conditions by the coating unit U1 and the heat treatment conditions by the heat treatment unit U2. The liquid treatment conditions by the coating unit U1 include sequences such as the supply of the film forming liquid. Further, the liquid treatment conditions by the coating unit U1 include the rotation speed of the wafer W, the supply amount of the film forming liquid, the supply time of the film forming liquid, etc. in each sequence.The processing control unit 112 causes the processing unit to execute substrate processing according to the processing conditions stored in the processing condition holding unit 111. For example, the processing control unit 112 causes the processing module 14 to execute development processing according to the development processing conditions stored in the processing condition holding unit 111. As an example, the processing control unit 112 controls the heat treatment unit U4 to perform heat treatment (for example, the above-mentioned PEB) on the wafer W after exposure processing according to preset heat treatment conditions. Thereafter, the processing control unit 112 controls the development unit U3 to perform development processing on the wafer W according to preset liquid processing conditions. Thereafter, the processing control unit 112 controls the heat treatment unit U4 to perform heat treatment (for example, the above-mentioned PB) on the wafer W according to preset heat treatment conditions.

[0048] The processing control unit 112 may cause the processing modules 11, 12, and 13 to execute film formation processing according to the film formation processing conditions stored in the processing condition holding unit 111. As an example, the processing control unit 112 controls the coating unit U1 to apply a film forming liquid to the surface Wa of the wafer W according to preset liquid processing conditions. Thereafter, the processing control unit 112 controls the heat treatment unit U2 to perform heat treatment on the wafer W according to preset heat treatment conditions.

[0049] The data acquisition unit 113 acquires performance data regarding the quality of substrate processing according to the processing conditions. The data acquisition unit 113 may acquire performance data including performance values of a plurality of items. The performance values of the plurality of items may include performance values of post-processing items indicating the quality of the wafer W after substrate processing and in-processing items indicating the supply state of the processing liquid during substrate processing. The performance values of the plurality of items may acquire performance data including a plurality of performance values of the same type. The plurality of performance values of the same type means a plurality of performance values that should ideally be the same value. Specific examples of the plurality of performance values of the same type include a plurality of performance values acquired at a plurality of locations.

[0050] For example, as an example of a post - processing item, the data acquisition unit 113 acquires an actual value indicating the actual line width of a resist pattern formed on the surface Wa of the wafer W by development processing (hereinafter referred to as "actual line width value"). Specifically, the data acquisition unit 113 acquires the actual line width value based on the information detected by the post - processing inspection unit 80. The data acquisition unit 113 may acquire the actual line width values at a plurality of locations on the surface Wa based on the information detected by the post - processing inspection unit 80.

[0051] As an example of a processing - in - progress item, the data acquisition unit 113 acquires an actual value indicating the supply state of the developing solution during the development process. Specifically, the data acquisition unit 113 acquires the actual values of splashing of the developing solution, defective formation of the liquid film, and presence or absence of dripping based on the information detected by the in - processing inspection unit 90.

[0052] As an example of a post - processing item, the data acquisition unit 113 may acquire an actual value indicating the actual film thickness of a film formed on the surface Wa of the wafer W by film - forming processing (hereinafter referred to as "actual film thickness value"). Specifically, the data acquisition unit 113 may acquire the actual film thickness value based on the information detected by the post - processing inspection unit 80. The data acquisition unit 113 may acquire the actual film thickness values at a plurality of locations on the surface Wa based on the information detected by the post - processing inspection unit 80.

[0053] As an example of a processing - in - progress item, the data acquisition unit 113 may acquire an actual value indicating the supply state of the film - forming solution during the film - forming process. Specifically, the data acquisition unit 113 may acquire the actual values of splashing of the film - forming solution, defective formation of the liquid film, and presence or absence of dripping based on the information detected by the in - processing inspection unit 90.

[0054] The data input unit 114 inputs a data set including processing conditions and performance data corresponding to the processing conditions into a model generation unit 214 (described later) of the machine learning device 200. The data input unit 114 may select a data set to be input to the model generation unit 214 based on the performance values of the above-described processing items. For example, the data input unit 114 may exclude a data set in which the supply state of the processing liquid is poor from the input targets to the model generation unit 214. Specific examples of the supply state of the processing liquid being poor include at least one of the above-described liquid splashing, poor formation of the liquid film, and dripping.

[0055] The recommended condition derivation unit 115 derives recommended processing conditions for substrate processing based on a learning model generated by the model generation unit 214 through machine learning based on a plurality of sets of data sets. As described later, the learning model is generated to output prediction data regarding the quality of substrate processing in response to the input of processing conditions. The recommended processing conditions are processing conditions determined to be recommended for adoption based on the learning model and predetermined evaluation conditions for the prediction data.

[0056] For example, the recommended condition derivation unit 115 includes an evaluation condition input unit 121 and a search result acquisition unit 122 as more refined functional modules. The evaluation condition input unit 121 inputs the evaluation conditions for the prediction data into a condition search unit 216 (described later) of the machine learning device 200. The evaluation conditions are conditions for determining whether the prediction data is at an acceptable level.

[0057] The evaluation condition input unit 121 may input evaluation conditions for evaluating prediction values of a plurality of items into the condition search unit 216. The evaluation condition input unit 121 may input evaluation conditions including conditions regarding the variation in prediction values in at least a part of the plurality of items into the condition search unit 216. For example, the evaluation conditions include a method for deriving an evaluation score of the prediction data and an acceptable level of the evaluation score.

[0058] As an example, the evaluation condition input unit 121 inputs evaluation conditions for evaluating the predicted values of the line widths (hereinafter referred to as "line width predicted values") at a plurality of locations on the surface Wa to the condition search unit 216. As an example of the method for deriving the evaluation score, the evaluation condition includes a calculation formula for the variation of the line width predicted values (for example, a calculation formula for the standard deviation) at at least a part (for example, all) of the plurality of locations. As the allowable level of the evaluation score, the evaluation condition includes an allowable upper limit value of the variation calculated by the calculation formula.

[0059] The evaluation condition input unit 121 may input evaluation conditions for evaluating the predicted values of the film thickness at a plurality of locations on the surface Wa to the condition search unit 216. As an example of the method for deriving the evaluation score, the evaluation condition includes a calculation formula for the variation of the film thickness predicted values (for example, a calculation formula for the standard deviation) at at least a part (for example, all) of the plurality of locations. As the allowable level of the evaluation score, the evaluation condition includes an allowable upper limit value of the variation calculated by the calculation formula.

[0060] The search result acquisition unit 122 acquires the recommended processing conditions derived by the condition search unit 216 and stores them in the processing condition holding unit 111. As will be described later, the recommended processing conditions are derived based on a plurality of sets of data sets, a learning model, and the evaluation conditions input by the evaluation condition input unit 121.

[0061] Here, the processing control unit 112 may further cause the processing unit to execute substrate processing according to the recommended processing conditions. The data acquisition unit 113 may further acquire additional performance data regarding the quality of the substrate processing according to the recommended processing conditions. The data input unit 114 may further input an additional data set including the recommended processing conditions and the additional performance data to the model generation unit 214. The recommended condition derivation unit 115 may update the recommended processing conditions based on the learning model updated by the model generation unit 214 based on the additional data set. Updating the learning model means generating a new learning model based on a plurality of sets of data sets including the additional data set. Updating the recommended processing conditions means deriving new recommended processing conditions based on the learning model updated by the model generation unit 214.

[0062] In this case, the control device 100 may further include a condition evaluation unit 116 and an iteration management unit 117. The condition evaluation unit 116 evaluates whether the recommended processing conditions can be adopted. The iteration management unit 117 repeats at least the following until the evaluation result by the condition evaluation unit 116 becomes adoptable. i) The processing control unit 112 causes the processing unit to further execute the substrate processing according to the recommended processing conditions. ii) The data acquisition unit 113 further acquires additional performance data. iii) The data input unit 114 further inputs an additional data set to the model generation unit 214. iv) Based on the updated learning model generated by the model generation unit 214 based on the additional data set, the recommended condition derivation unit 115 updates the recommended processing conditions.

[0063] There are no particular restrictions on the method for evaluating the recommended processing conditions by the condition evaluation unit 116. For example, the condition evaluation unit 116 evaluates whether the recommended processing conditions can be adopted based on the evaluation result of the additional performance data based on a predetermined evaluation condition. The evaluation condition may be the same as the evaluation condition for the prediction data described above. For example, the evaluation condition includes a method for calculating the evaluation score of the additional performance data and an allowable level of the evaluation score.

[0064] As an example, the condition evaluation unit 116 evaluates the line width performance values at a plurality of locations on the surface Wa based on a predetermined evaluation condition. The evaluation condition includes, as an example of the method for calculating the evaluation score, a calculation formula for the variation of the line width performance values at at least a part (e.g., all) of the plurality of locations (e.g., a calculation formula for the standard deviation). The evaluation condition includes, as the allowable level of the evaluation score, an allowable upper limit value of the variation calculated by the calculation formula.

[0065] The evaluation condition input unit 121 may evaluate the film thickness actual values at a plurality of locations on the surface Wa based on a predetermined evaluation condition. As an example of a method for deriving the evaluation score, the evaluation condition includes a calculation formula for the variation in the film thickness actual values at at least a part (for example, all locations) of the plurality of locations (for example, a calculation formula for the standard deviation). As the allowable level of the evaluation score, the evaluation condition includes an allowable upper limit value of the variation calculated by the calculation formula.

[0066] The condition evaluation unit 116 may evaluate whether or not to adopt the latest recommended processing condition based on whether or not the difference between the latest recommended processing condition and the past recommended processing condition (for example, the previous recommended processing condition) is within the allowable level. It is assumed that the recommended processing condition gradually converges to one condition by the iterative processing by the iteration management unit 117. By reducing the difference between the latest recommended processing condition and the past recommended processing condition to the allowable level, it becomes possible to adopt a recommended processing condition close to the convergence result.

[0067] The condition evaluation unit 116 may evaluate whether or not to adopt the latest recommended processing condition based on whether or not the difference between the latest additional actual data and the past additional actual data is within the allowable level. The condition evaluation unit 116 may evaluate whether or not to adopt the latest recommended processing condition based on whether or not the difference between the evaluation score of the latest additional actual data and the evaluation score of the past additional actual data is within the allowable level.

[0068] The control device 100 may further include an actual data correction unit 118. Before the data input unit 114 inputs the data set to the model generation unit 214, the actual data correction unit 118 excludes components resulting from factors other than the substrate processing by the processing unit of the coating / developing apparatus 2 from the actual data of the data set. For example, the actual data correction unit 118 excludes the variation component caused by the exposure process from the line width actual values at the plurality of locations. Specifically, the actual data correction unit 118 excludes the variation pattern specific to the exposure process, which has been investigated in advance, from the line width actual values at the plurality of locations.

[0069] The machine learning device 200 includes, as functional modules, a search operation unit 211, a data acquisition unit 212, a data storage unit 213, a model generation unit 214, a model storage unit 215, and a condition search unit 216. The search operation unit 211 is the engine for machine learning in the machine learning device 200. For example, the search operation unit 211 performs a search for a solution by a genetic algorithm based on preset learning conditions. The learning conditions include the individuals of the first generation, the method for deriving the evaluation score of the individuals, and the allowable level of the evaluation score.

[0070] The search operation unit 211 acquires a plurality of individuals of the first generation and calculates the evaluation score of each individual. Thereafter, the search operation unit 211 evolves the plurality of individuals into a plurality of individuals of the next generation by operations such as crossover, inversion, and mutation while eliminating the individuals whose evaluation scores are far from the allowable level. Thereafter, the search operation unit 211 repeats the derivation of the evaluation score of the individuals, the elimination of the individuals, and the evolution of the individuals to derive an individual whose evaluation score is at the allowable level.

[0071] The data acquisition unit 212 acquires the above data set and additional data set from the data input unit 114. The data storage unit 213 stores the data set acquired by the data acquisition unit 212 as a learning database.

[0072] The model generation unit 214 generates the above learning model by machine learning based on a plurality of sets of data sets stored in the data storage unit 213. The model generation unit 214 may generate a learning model by machine learning including an operation process for searching the above learning model by a genetic program. For example, the model generation unit 214 generates a learning model including a plurality of model formulas that respectively output predicted values of a plurality of items in response to the input of processing conditions. In generating each model formula, the model generation unit 214 sets the above learning conditions for deriving the model formula and requests the search operation unit 211 to derive a model formula according to the learning conditions.

[0073] For example, the model generation unit 214 generates a plurality of temporary model expressions that generate predicted values in response to the input of processing conditions, and uses these as the plurality of individuals in the first generation. The temporary model expression represents a mathematical expression in a tree structure with various operators and random numerical values as elements. The model generation unit 214 sets the divergence score indicating the divergence between the predicted value based on the temporary model expression and the actual value as the evaluation score in the learning conditions, and determines the derivation method thereof. For example, the model generation unit 214 determines a derivation method including at least the following steps. a1) Inputting the processing conditions of a plurality of sets of data sets into the temporary model expression to derive a plurality of predicted values. a2) Deriving a divergence score indicating the divergence between the plurality of predicted values and the actual values of the plurality of sets of data sets.

[0074] The divergence score may be any value as long as it indicates the divergence between the plurality of predicted values and the actual values of the plurality of sets of data sets. Specific examples of the divergence score include the sum of squares of the differences between the predicted value and the actual value, or the square root of the sum of squares. The model generation unit 214 sets the upper limit value preset for the divergence score as the allowable level of the evaluation score in the learning conditions.

[0075] The search operation unit 211 repeats the derivation of the divergence score of the temporary model expression, the elimination of the temporary model expression, and the evolution of the temporary model expression to derive a model expression whose divergence score is equal to or less than the upper limit value. The model generation unit 214 acquires the model expression derived by the search operation unit 211 and stores it in the model holding unit 215. Through the above procedure, by the model generation unit 214 storing each model expression in the model holding unit 215, a learning model including a plurality of model expressions is generated in the model holding unit 215.

[0076] The condition exploration unit 216 derives recommended processing conditions based on a plurality of sets of data sets stored in the data storage unit 213, a learning model stored in the model storage unit 215, and evaluation conditions input by the evaluation condition input unit 121. The condition exploration unit 216 may derive the recommended processing conditions through a search process including an arithmetic process of searching for the recommended processing conditions by a genetic algorithm. For example, the condition exploration unit 216 sets the learning conditions for deriving the recommended processing conditions and requests the search arithmetic unit 211 to search for the derivation of the recommended processing conditions according to the learning conditions.

[0077] For example, the condition exploration unit 216 uses the processing conditions of a plurality of sets of data sets stored in the data storage unit 213 as a plurality of individuals in the first generation. Each processing condition represents conditions of a plurality of items in a tree structure.

[0078] The condition exploration unit 216 determines a method for deriving an evaluation score in the learning conditions so as to include at least the following steps. b1) Inputting the processing conditions of a plurality of sets of data sets into the learning model stored in the model storage unit 215 to derive prediction data. b2) Deriving an evaluation score of the prediction data according to the derivation method in the evaluation conditions input by the evaluation condition input unit 121.

[0079] The condition exploration unit 216 sets the allowable level in the evaluation conditions input by the evaluation condition input unit 121 as the allowable level of the evaluation score in the learning conditions.

[0080] The search arithmetic unit 211 repeats the derivation of the evaluation score of the processing conditions, the elimination of the processing conditions, and the evolution of the processing conditions to derive recommended processing conditions whose evaluation score is at the allowable level. The condition exploration unit 216 acquires the recommended processing conditions derived by the search arithmetic unit 211 and outputs them to the search result acquisition unit 122.

[0081] FIG. 7 is a block diagram illustrating the hardware configurations of the control device 100 and the machine learning device 200. The control device 100 includes a circuit 190. The circuit 190 includes at least one processor 191, a memory 192, a storage 193, a display device 194, an input device 195, an input / output port 196, and a communication port 197. The storage 193 is a computer-readable non-volatile storage medium (e.g., flash memory). For example, the storage 193 stores a program for causing the control device 100 to execute causing the coating / development device 2 to perform substrate processing according to preset processing conditions, acquiring performance data regarding the quality of the substrate processing according to the processing conditions from the quality inspection device 70, inputting a data set including the processing conditions of the substrate processing and the performance data of the substrate processing to the machine learning device 200, and deriving recommended processing conditions for the substrate processing based on the learning model generated by the machine learning device 200 based on a plurality of sets of data sets. For example, the storage 193 includes a storage area for storing a program for configuring the above functional modules and a storage area assigned to the processing condition holding unit 111.

[0082] The display device 194 is used for displaying recommended processing conditions and the like. The display device 194 and the input device 195 function as a user interface of the control device 100. The display device 194 includes, for example, a liquid crystal monitor or the like and is used for displaying information to the user. The input device 195 is, for example, a keyboard or the like and acquires input information from the user. The display device 194 and the input device 195 may be integrated as a so-called touch panel. The input device 195 is used for inputting processing conditions, evaluation conditions, and the like.

[0083] The memory 192 temporarily stores the program loaded from the storage 193, the calculation results by the processor 191, etc. The processor 191 executes the control of the coating / development device 2 by executing the above program in cooperation with the memory 192. The input / output port 196 inputs and outputs electrical signals to and from the display device 194 and the input device 195 according to commands from the processor 191. The communication port 197 performs network communication with the machine learning device 200 according to commands from the processor 191.

[0084] The machine learning device 200 includes a circuit 290. The circuit 290 includes a processor 291, a memory 292, a storage 293, and a communication port 294. The storage 293 is a computer-readable non-volatile storage medium (e.g., flash memory). For example, the storage 293 stores a program for causing the machine learning device 200 to acquire the above dataset and generate the above learning model by machine learning based on a plurality of sets of datasets. For example, the storage 293 includes a storage area for storing a program for configuring the above functional module, and a storage area assigned to the data holding unit 213 and the model holding unit 215.

[0085] The memory 292 temporarily stores the program loaded from the storage 293, the calculation results by the processor 291, etc. The processor 291 executes the generation of the above learning model by executing the above program in cooperation with the memory 292. The communication port 294 performs network communication with the control device 100 according to commands from the processor 291.

[0086] 〔Condition Setting Support Procedure〕 Next, as an example of the condition setting support method, the condition setting support procedures executed by the control device 100 and the machine learning device 200 will be described. The condition setting support procedure executed by the control device 100 includes a procedure for deriving recommended processing conditions and a procedure for improving the recommended processing conditions. The condition setting support procedure executed by the machine learning device 200 includes a procedure for generating a learning model and a procedure for searching for recommended processing conditions. Hereinafter, each procedure will be specifically exemplified.

[0087] (Procedure for Deriving Recommended Processing Conditions) The procedure for deriving recommended processing conditions by the control device 100 includes causing the coating / developing device 2 to execute substrate processing including supplying a processing liquid to the wafer W according to preset processing conditions, obtaining performance data regarding the quality of the substrate processing according to the processing conditions, inputting a data set including the processing conditions of the substrate processing and the performance data of the substrate processing to the machine learning device 200, and deriving recommended processing conditions based on the learning model generated by the machine learning device 200 based on a plurality of sets of data sets. Deriving the recommended processing conditions may include inputting evaluation conditions for prediction data to the machine learning device 200 and obtaining the recommended processing conditions derived by the machine learning device 200 based on a plurality of sets of data sets, the learning model, and the evaluation conditions.

[0088] As illustrated in FIG. 8, the control device 100 first executes steps S01, S02, and S03. In step S01, the processing control unit 112 causes the coating / developing device 2 to start substrate processing according to the processing conditions stored in the processing condition holding unit 111. In step S02, the data acquisition unit 113 acquires the actual values of the in-process items. The data acquisition unit 113 may acquire the actual values of a plurality of in-process items. For example, the data acquisition unit 113 acquires the actual values of liquid splashing of the developing solution, formation failure of the liquid film, and presence or absence of liquid dripping based on the information detected by the in-process inspection unit 90. The data acquisition unit 113 may acquire the actual values of liquid splashing of the film-forming solution, formation failure of the liquid film, and presence or absence of liquid dripping based on the information detected by the in-process inspection unit 90. In step S03, the processing control unit 112 checks whether the substrate processing according to the processing conditions is completed.

[0089] If it is determined in step S03 that the substrate processing is not completed, the control device 100 returns the process to step S02. Thereafter, the acquisition of the actual values of the in-process items continues until the substrate processing is completed. If it is determined in step S03 that the substrate processing is completed, the control device 100 executes step S04. In step S04, the data input unit 114 checks whether there is no defect in the supply state of the processing liquid based on the actual values of the in-process items.

[0090] If it is determined in step S04 that there is no defect in the supply state of the processing liquid, the control device 100 executes steps S05, S06, and S07. In step S05, the data acquisition unit 113 acquires the actual values of the post-process items. The data acquisition unit 113 may acquire the actual values of a plurality of post-process items. For example, the data acquisition unit 113 acquires the actual line width values at a plurality of locations on the surface Wa based on the information detected by the post-process inspection unit 80. The data acquisition unit 113 may also acquire the actual film thickness values at a plurality of locations on the surface Wa based on the information detected by the post-process inspection unit 80. In step S06, the actual data correction unit 118 excludes components resulting from factors other than the substrate processing from the actual values of the plurality of post-process items. In step S07, the data input unit 114 inputs a data set including the processing conditions and the actual data (the actual values of the plurality of post-process items) corresponding to the processing conditions to the machine learning device 200.

[0091] Next, the control device 100 executes step S08. If it is determined in step S04 that there is a defect in the supply state of the processing liquid, the control device 100 executes step S08 without executing steps S05, S06, and S07. In step S08, the data input unit 114 checks whether the input of the required number of data sets for machine learning in the machine learning device 200 is completed.

[0092] If it is determined in step S08 that the input of the number of data sets required for machine learning has not been completed, the control device 100 executes step S09. In step S09, the processing control unit 112 changes the processing conditions. For example, the processing control unit 112 changes the processing conditions based on the user input to the input device 195 or the like. Thereafter, the control device 100 returns the process to step S01. Thereafter, until the input of the number of data sets required for machine learning is completed, the change of the processing conditions, the execution of the substrate processing, and the input of the data sets are repeated.

[0093] If it is determined in step S08 that the input of the number of data sets required for machine learning has been completed, the control device 100 executes steps S11, S12, S13, and S14. In step S11, the evaluation condition input unit 121 waits for a learning completion notification from the machine learning device 200. In step S12, the evaluation condition input unit 121 sets the evaluation conditions for the prediction data. For example, the evaluation condition input unit 121 sets the evaluation conditions for the prediction data based on the user input to the input device 195 or the like. In step S13, the evaluation condition input unit 121 inputs the evaluation conditions set in step S12 to the machine learning device 200. In step S14, the search result acquisition unit 122 acquires the recommended processing conditions derived by the machine learning device 200 based on the plurality of sets of data sets, the learning model, and the evaluation conditions input by the evaluation condition input unit 121, and stores them in the processing module 11. Thus, the procedure for deriving the recommended processing conditions is completed.

[0094] (Procedure for improving the recommended processing conditions) The procedure for improving the recommended processing conditions by the control device 100 includes causing the coating / developing device 2 to further execute substrate processing according to the recommended processing conditions, further obtaining additional performance data regarding the quality of the substrate processing according to the recommended processing conditions, further inputting an additional data set including the recommended processing conditions and the additional performance data to the machine learning device 200, and updating the recommended processing conditions based on the learning model updated by the machine learning device 200 based on the additional data set. This improvement procedure may further include evaluating the recommended processing conditions, and may repeat causing the coating / developing device 2 to further execute substrate processing according to the recommended processing conditions, further obtaining additional performance data, further inputting the additional data set to the machine learning device 200, and updating the recommended processing conditions based on the learning model updated by the machine learning device 200 based on the additional data set until the evaluation result of the recommended processing conditions reaches a predetermined level.

[0095] As illustrated in FIG. 9, the control device 100 first executes steps S21, S22, S23, S24, and S25. In step S21, the processing control unit 112 causes the coating / developing device 2 to execute substrate processing according to the recommended processing conditions stored in the processing condition holding unit 111. In step S22, the data acquisition unit 113 acquires the additional performance values of the post-processing items. The data acquisition unit 113 may acquire the additional performance values of a plurality of post-processing items. In step S23, the performance data correction unit 118 excludes components resulting from factors other than the substrate processing from the additional performance values of the plurality of post-processing items. In step S24, the condition evaluation unit 116 evaluates the recommended processing conditions. In step S25, the iteration management unit 117 confirms whether the recommended processing conditions can be adopted based on the evaluation result in step S24.

[0096] When it is determined in step S25 that the recommended processing conditions cannot be adopted, the control device 100 executes steps S26, S27, and S28. In step S26, the data input unit 114 inputs an additional data set including the processing conditions and additional performance data (additional performance values of a plurality of processed items) corresponding to the processing conditions to the machine learning device 200. In step S27, the search result acquisition unit 122 waits for a learning model update completion notification from the machine learning device 200. In step S28, the search result acquisition unit 122 acquires the recommended processing conditions updated by the machine learning device 200 based on the additional data set and stores them in the processing module 11. Thereafter, the control device 100 returns the process to step S21. Thereafter, acquisition of additional performance data and update of the recommended processing conditions are repeated until the recommended processing conditions become adoptable.

[0097] When it is determined in step S25 that the recommended processing conditions can be adopted, the control device 100 completes the process. Thus, the procedure for improving the recommended processing conditions is completed.

[0098] (Procedure for generating the learning model) The procedure for generating the learning model by the machine learning device 200 includes acquiring the above data set and generating a learning model by machine learning based on a plurality of sets of data sets. Generating a learning model by machine learning may include an arithmetic process of searching for a learning model by a genetic program. A learning model including a plurality of model formulas that respectively output predicted values of a plurality of items in response to the input of processing conditions may be generated.

[0099] As illustrated in FIG. 10, the machine learning device 200 first executes steps S31, S32, and S33. In step S31, the data acquisition unit 212 waits for the input of the data set from the data input unit 114. In step S32, the data acquisition unit 212 accumulates the input data set in the data holding unit 213. In step S33, the data holding unit 213 checks whether the number of data sets accumulated in the data holding unit 213 has reached the number required for machine learning.

[0100] In step S33, when it is determined that the number of accumulated data sets has not reached the number required for machine learning, the control device 100 returns the process to step S31. Thereafter, acquisition of data sets is repeated until the number of data sets required for machine learning is accumulated.

[0101] In step S33, when it is determined that the number of accumulated data sets has reached the number required for machine learning, the control device 100 executes steps S34, S35, and S36. In step S34, the model generation unit 214 sets the above learning conditions for deriving a model formula corresponding to any predicted value, and requests the search operation unit 211 to derive a model formula according to the learning conditions. For example, the model generation unit 214 generates a plurality of temporary model formulas that generate predicted values according to the input of processing conditions, and uses these as the plurality of individuals in the first generation. Further, the model generation unit 214 determines the derivation method using the divergence score as an evaluation score, and sets the upper limit value of the divergence score as the allowable level of the evaluation score. In step S35, the search operation unit 211 calculates the divergence score of each temporary model formula according to the above learning conditions. In step S36, the search operation unit 211 checks whether there is a temporary model formula whose divergence score is less than or equal to the upper limit value according to the above learning conditions.

[0102] In step S36, when it is determined that there is no temporary model formula whose divergence score is less than or equal to the upper limit value, the machine learning device 200 executes step S37. In step S37, the search operation unit 211 evolves a plurality of temporary model formulas into a plurality of next-generation temporary model formulas by operations such as crossover, inversion, and mutation while eliminating temporary model formulas with a large excess of the divergence score over the upper limit value. Thereafter, the machine learning device 200 returns the process to step S35. Thereafter, derivation of the divergence score of the temporary model formula, elimination of the temporary model formula, and evolution of the temporary model formula are repeated until a temporary model formula whose divergence score is less than or equal to the upper limit value is derived.

[0103] In step S36, if it is determined that there is a temporary model formula whose divergence score is less than or equal to the upper limit value, the machine learning device 200 executes steps S38 and S39. In step S38, the search operation unit 211 selects the temporary model formula with the best (smallest) divergence score and stores it in the model holding unit 215 as one of the model formulas of the learning model. In step S39, the model generation unit 214 checks whether the derivation of all the model formulas required to configure the learning model (that is, all the model formulas required to derive the predicted values of multiple items) has been completed.

[0104] In step S39, if it is determined that the derivation of all the model formulas has not been completed, the machine learning device 200 executes step S41. In step S41, the model generation unit 214 changes the model formula to be derived. In other words, the model generation unit 214 changes the item to be predicted by the model formula. Thereafter, the machine learning device 200 returns the process to step S34. Thereafter, until the derivation of all the model formulas is completed, the setting of the learning conditions and the derivation of the model formulas based thereon are repeated.

[0105] In step S39, if it is determined that the derivation of all the model formulas has been completed, the machine learning device 200 completes the generation of the learning model. Thus, the generation procedure of the learning model is completed.

[0106] (Search Procedure for Recommended Processing Conditions) The search procedure for the recommended processing conditions by the machine learning device 200 includes deriving the recommended processing conditions for the substrate processing based on a plurality of sets of data sets, a learning model, and the evaluation conditions for the prediction data. Deriving the recommended processing conditions may include an arithmetic process of searching for the recommended processing conditions by a genetic algorithm. The recommended processing conditions may be derived based on a plurality of sets of data sets, a plurality of model formulas, and the evaluation conditions for evaluating the predicted values of multiple items. For example, the recommended processing conditions may be derived based on the evaluation conditions including the conditions regarding the variation of the predicted values of multiple items.

[0107] As illustrated in FIG. 11, the machine learning device 200 first executes steps S51 and S52. In step S51, the condition search unit 216 waits for the input of the evaluation conditions from the evaluation condition input unit 121. In step S52, the condition search unit 216 sets the learning conditions for deriving the recommended processing conditions, and requests the search operation unit 211 to search for and derive the recommended processing conditions according to the learning conditions. For example, the condition search unit 216 uses the processing conditions of a plurality of sets of data sets stored in the data holding unit 213 as a plurality of individuals in the first generation. Further, the condition search unit 216 determines the method for deriving the evaluation score and the allowable level of the evaluation score based on the evaluation conditions input by the evaluation condition input unit 121.

[0108] Subsequently, the machine learning device 200 executes steps S53, S54, and S55. In step S53, the search operation unit 211 inputs each processing condition to the learning model stored in the model holding unit 215 to derive prediction data. In step S54, the search operation unit 211 derives the evaluation score of the prediction data. In step S55, the search operation unit 211 checks whether there is a processing condition whose evaluation score is at the allowable level.

[0109] If it is determined in step S55 that there is no processing condition whose evaluation score is at the allowable level, the machine learning device 200 executes step S56. In step S56, the search operation unit 211 evolves a plurality of processing conditions into a plurality of processing conditions of the next generation by operations such as crossover, inversion, and mutation while eliminating the processing conditions whose evaluation scores are far from the allowable level. Thereafter, the machine learning device 200 returns the process to step S53. Thereafter, until a processing condition whose evaluation score reaches the allowable level is derived, the derivation of the evaluation score of the processing condition, the elimination of the processing condition, and the evolution of the processing condition are repeated.

[0110] In step S55, when it is determined that there is a processing condition where the evaluation score is at the acceptable level, the machine learning device 200 executes steps S57 and S58. In step S57, the search operation unit 211 sets the processing condition with the best evaluation score as the recommended processing condition. In step S58, the condition search unit 216 acquires the recommended processing condition derived by the search operation unit 211 and outputs it to the search result acquisition unit 122. Thus, the search procedure for the recommended processing condition is completed.

[0111] Deriving the recommended processing condition is not limited to the arithmetic process of searching for the recommended processing condition by the genetic algorithm described above. For example, in step S55, it is also possible to derive the recommended processing condition by an arithmetic process that repeatedly changes the processing condition and derives the evaluation score until the evaluation score reaches the acceptable level.

[0112] 〔Specific Example〕 As an example, the procedure for assisting in setting the processing conditions for the development process in the development unit U3 will be specifically illustrated. The processing conditions for the development process in the development unit U3 include, for example, the rotation speed of the wafer W, the supply amount of the developer, the supply time of the developer, the supply amount of the rinse liquid, the discharge time of the rinse liquid, the spin-drying time, the starting position of the movement of the nozzle 31, the movement speed of the nozzle 31, and the ending position of the movement of the nozzle 31. Among these, the items for which the recommended processing conditions are required are, for example, the rotation speed of the wafer W during the supply of the developer and the movement speed of the nozzle 31. In this case, in the above steps S01 to S09, it is repeatedly performed to input a data set to the machine learning device 200 while changing the rotation speed of the wafer W and the movement speed of the nozzle 31.

[0113] For example, in steps S01 to S09, with the rotation speed of the wafer W set to 200 rpm, the moving speeds of the nozzle 31 are set to 15 mm / s, 20 mm / s, and 25 mm / s. Next, with the rotation speed of the wafer W set to 250 rpm, the moving speeds of the nozzle 31 are set to 15 mm / s, 20 mm / s, and 25 mm / s. Then, with the rotation speed of the wafer W set to 300 rpm, the moving speeds of the nozzle 31 are set to 15 mm / s, 20 mm / s, and 25 mm / s. In step S04 executed under any of these processing conditions, if it is determined that there is a defect in the supply state of the processing liquid, the data set corresponding to the processing condition is excluded from the input target to the machine learning device 200. In this case, in order to obtain the required number of data sets for machine learning, further changes to the processing conditions are made in step S09. For example, if it is determined that liquid splashing of the developing solution occurs under the processing conditions of a rotation speed of 300 rpm and a moving speed of 25 mm / s, the rotation speed is changed to 290 rpm, and again, the performance data under the conditions of a rotation speed of 290 rpm and a moving speed of 25 mm / s is acquired.

[0114] In step S05, for example, the average value of the line widths in each of the divided regions of the wafer W divided into n locations is obtained as n line width performance values. The data sets in this case are exemplified below. Processing conditions: Rotation speed of wafer W = 200 rpm, Moving speed of nozzle = 15 mm / s Performance data: W1 = 23 nm, W2 = 28 nm, W3 = 31 nm, ··· Wn = 24 nm (Wi: Average line width value in divided region i)

[0115] Based on this dataset, the learning model generated in the machine learning device 200 outputs predicted values of the line width average values in n divided regions in response to inputs such as the rotation speed of the wafer W and the moving speed of the nozzle. In step S12, as the calculation formula for the evaluation score, for example, a calculation formula for the standard deviation of n line width predicted values is set, and as the allowable level, an allowable value of the standard deviation is set. Based on the evaluation conditions set in this way, in the machine learning device 200, recommended values for the rotation speed of the wafer W and the moving speed of the nozzle 31 (for example, rotation speed of the wafer W = 234 rpm, moving speed of the nozzle 31 = 22 rpm) are derived as the above recommended processing conditions.

[0116] 〔Effects of this Embodiment〕 As described above, the method for assisting in setting the processing conditions of the substrate according to this embodiment includes inputting a dataset including the processing conditions of the substrate processing executed by the coating / developing device 2 including the supply of the processing liquid to the wafer W and the performance data regarding the quality of the substrate processing into the machine learning device 200, and a model generated by the machine learning device 200 by machine learning based on a plurality of sets of datasets, and deriving recommended processing conditions for the substrate processing based on the learning model that outputs prediction data regarding the quality of the substrate processing in response to the input of the processing conditions.

[0117] According to this method for assisting in setting conditions, since the recommended processing conditions are derived based on the learning model generated by machine learning, appropriate processing conditions can be efficiently searched for. Therefore, it is effective in simplifying the work of setting the processing conditions of the substrate processing.

[0118] The method for supporting the setting of substrate processing conditions may include causing the coating / development apparatus 2 to further perform substrate processing according to the recommended processing conditions, further obtaining additional performance data regarding the quality of substrate processing according to the recommended processing conditions, further inputting an additional data set including the recommended processing conditions and the additional performance data into the machine learning apparatus 200, and updating the recommended processing conditions based on the learning model updated by the machine learning apparatus 200 based on the additional data set. In this case, the recommended processing conditions are updated by the feedback of the recommended processing conditions and the additional performance data. Therefore, more appropriate processing conditions can be efficiently searched for.

[0119] The method for supporting the setting of substrate processing conditions may further include evaluating the recommended processing conditions, causing the coating / development apparatus 2 to further perform substrate processing according to the recommended processing conditions, further obtaining additional performance data, further inputting the additional data set into the machine learning apparatus 200, and updating the recommended processing conditions based on the learning model updated by the machine learning apparatus 200 based on the additional data set, and repeating this until the evaluation result of the recommended processing conditions reaches a predetermined level. In this case, more appropriate processing conditions can be efficiently searched for by the repeated processing.

[0120] Deriving the recommended processing conditions may include inputting the evaluation conditions of the prediction data into the machine learning apparatus 200 and obtaining the recommended processing conditions derived by the machine learning apparatus 200 based on a plurality of sets of data sets, a learning model, and the evaluation conditions. In this case, since the search for the recommended processing conditions is also performed by the machine learning apparatus 200, more appropriate processing conditions can be searched for more efficiently.

[0121] In the method for supporting the setting of substrate processing conditions, a data set may be input into the machine learning apparatus 200 that generates a learning model including a plurality of model formulas that output respective predicted values of a plurality of items in response to the input of processing conditions, and evaluation conditions for evaluating the predicted values of the plurality of items may be input into the machine learning apparatus 200. In this case, by expanding the evaluation conditions to a plurality of items, the quality of the processing can be more appropriately evaluated, and more appropriate processing conditions can be searched for.

[0122] In the substrate processing condition setting support method, evaluation conditions including conditions related to the variation in predicted values for at least a part of a plurality of items may be input to the machine learning device 200. In this case, since a plurality of items can be efficiently evaluated, more appropriate processing conditions can be efficiently searched for.

[0123] In the substrate processing condition setting support method, performance data including performance values of post-processing items indicating the quality of the wafer W after substrate processing and in-process items indicating the supply state of the processing liquid during substrate processing is acquired, and a data set to be input to the machine learning device 200 may be selected based on the performance values of the in-process items. In this case, by directly capturing the abnormality during processing as data during processing, the search range of the recommended processing conditions based on the quality after processing can be narrowed down. Therefore, appropriate processing conditions can be searched for more efficiently.

[0124] The substrate processing condition setting support method may further include excluding components caused by factors other than substrate processing from the performance data of the data set before inputting the data set to the machine learning device 200. In this case, more appropriate processing conditions can be searched for.

[0125] The substrate processing may include a development process of supplying a developer to a photosensitive film exposed on the surface Wa of the wafer W, and performance data including the performance value of the line width of the pattern formed on the surface Wa of the wafer W by the development process may be acquired. When the substrate processing includes a development process, a great deal of effort is required to derive suitable processing conditions. For this reason, according to the above condition setting support method, appropriate processing conditions can be efficiently searched for, and the effectiveness is remarkable.

[0126] The substrate processing may include a film-forming process of applying a film-forming liquid to the surface Wa of the wafer W to form a film, and performance data including the actual value of the film thickness of the film formed on the surface Wa of the wafer W may be obtained by the film-forming process. Even when the substrate processing includes a film-forming process, since the quality of the substrate processing is very sensitive to the processing conditions, a great deal of effort tends to be required to derive suitable processing conditions. Therefore, according to the above-described condition setting support method, appropriate processing conditions can be efficiently searched, and the effectiveness is remarkable.

[0127] As described above, the embodiments have been described. However, the present disclosure is not necessarily limited to the above-described embodiments, and various modifications are possible without departing from the gist thereof. For example, the substrate to be processed is not limited to a semiconductor wafer, and may be, for example, a glass substrate, a mask substrate, an FPD (Flat Panel Display), or the like.

Description of Reference Numerals

[0128] 2... Coating / development device (substrate processing device), 11, 12, 13, 14... Processing modules (processing units), 112... Processing control unit, 113... Data acquisition unit, 114... Data input unit, 115... Recommended condition derivation unit, 121... Evaluation condition input unit, 122... Search result acquisition unit, 214... Model generation unit, W... Wafer, Wa... Surface.

Claims

In a substrate processing system including a processing unit that performs a development process of supplying a developer to a photosensitive film exposed on the surface of a substrate, and a processing control unit that causes the processing unit to execute the development process according to preset processing conditions, a model generation device that generates a learning model for generating recommended processing conditions for the development process, wherein during the development process, a data acquisition unit that acquires in-process performance data indicating the supply state of the developer and post-processing performance data including an actual value of the line width of a pattern formed on the surface of the substrate by the development process; a data input unit that determines whether or not the supply state is defective based on the in-process performance data, and when it is determined that the supply state is not defective, accumulates a data set including the processing conditions and the post-processing performance data in a database; a model generation unit that generates the learning model so as to output prediction data of the line width in response to an input of the processing conditions by machine learning based on a plurality of sets of the data sets accumulated in the database; and the fact that the supply state of the developer is defective includes liquid splashing of the developer on the substrate; the data acquisition unit acquires the in-process performance data based on a captured image of the substrate and the developer by an imaging unit, a model generation device that acquires the post-processing performance data based on a detection result by a sensor different from the imaging unit.

2. The model generation device according to claim 1, wherein the data acquisition unit acquires the in-process performance data based on the captured image including a height reachable by droplets splashed from the substrate in the liquid splashing of the developer.

3. The model generation device according to claim 1 or 2, wherein the fact that the supply state of the developer is defective further includes at least one of poor formation of a liquid film of the developer and dripping of the developer. In a substrate processing system including a processing unit that performs a film formation process of applying a film forming liquid to the surface of a substrate to form a film, and a processing control unit that causes the processing unit to execute the film formation process according to preset processing conditions, a model generation device that generates a learning model for generating recommended processing conditions for the film formation process, wherein During the film formation process, a data acquisition unit acquires in-process performance data indicating the supply state of the film formation liquid, and acquires post-process performance data including the actual value of the film thickness of the film formed on the surface of the substrate by the film formation process; Based on the in-process performance data, it is determined whether the supply state is defective. When it is determined that the supply state is not defective, a data input unit accumulates a data set including the processing conditions and the post-process performance data in a database; A model generation unit that generates a learning model so as to output prediction data of the film thickness in response to an input of the processing conditions by machine learning based on a plurality of sets of the data sets accumulated in the database; The fact that the supply state of the film formation liquid is defective includes liquid splashing of the film formation liquid on the substrate; The data acquisition unit acquires the in-process performance data based on the captured images of the substrate and the film formation liquid by the imaging unit, A model generation device that acquires the post-process performance data based on the detection results by a sensor different from the imaging unit.

5. The data acquisition unit acquires the in-process performance data based on the captured image including the height that the droplets splashed up from the substrate can reach during the liquid splashing of the film formation liquid. The model generation device according to claim 4.

6. The fact that the supply state of the film formation liquid is defective further includes at least one of a defective formation of the liquid film of the film formation liquid and dripping of the film formation liquid. The model generation device according to claim 4 or 5.

7. In a substrate processing system including a processing unit that performs a development process of supplying a developer to a photosensitive film subjected to an exposure process on the surface of a substrate, and a processing control unit that causes the processing unit to execute the development process according to preset processing conditions, a model generation method for generating a learning model for generating recommended processing conditions for the development process, During the development process, acquiring in-process performance data indicating the supply state of the developer, and acquiring post-process performance data including the actual value of the line width of the pattern formed on the surface of the substrate by the development process; Based on the in-process performance data, it is determined whether the supply state is defective. When it is determined that the supply state is not defective, a data set including the processing conditions and the post-process performance data is accumulated in a database; Generating the learning model to output prediction data of the line width in response to an input of the processing conditions by machine learning based on a plurality of sets of the data sets stored in the database, The fact that the supply state of the developing solution is in a defective state includes liquid splashing of the developing solution on the substrate, Obtaining the in-process performance data based on the captured images of the substrate and the developing solution by the imaging unit, A model generation method for obtaining the post-processing performance data based on the detection result by a sensor different from the imaging unit.

8. The model generation method according to claim 7, wherein in the liquid splashing of the developing solution, the in-process performance data is obtained based on the captured image including the height reachable by the droplets splashed from the substrate.

9. The fact that the supply state of the developing solution is in a defective state further includes at least one of a defective formation of the liquid film of the developing solution and dripping of the developing solution, according to the model generation method of claim 7 or 8.

10. In a substrate processing system including a processing unit that performs a film formation process of applying a film forming solution to the surface of a substrate to form a film, and a processing control unit that causes the processing unit to execute the film formation process according to preset processing conditions, a model generation method for generating a learning model for generating recommended processing conditions for the film formation process, During the film formation process, obtaining in-process performance data indicating the supply state of the film forming solution, and obtaining post-processing performance data including the actual value of the film thickness of the film formed on the surface of the substrate by the film formation process, Determining whether or not the supply state is defective based on the in-process performance data, and when it is determined that the supply state is not in a defective state, accumulating a data set including the processing conditions and the post-processing performance data in a database, Generating the learning model to output prediction data of the film thickness in response to an input of the processing conditions by machine learning based on a plurality of sets of the data sets stored in the database, The fact that the supply state of the film forming solution is in a defective state includes liquid splashing of the film forming solution on the substrate, Obtaining the in-process performance data based on the captured images of the substrate and the film forming solution by the imaging unit, A model generation method for obtaining the post-processing performance data based on the detection result by a sensor different from the imaging unit.

11. The method for generating a model according to claim 10, wherein in the splash of the film-forming liquid, the in-process performance data is acquired based on the captured image including the height that can be reached by the droplets splashed from the substrate.

12. The method for generating a model according to claim 10 or 11, wherein the state in which the supply state of the film-forming liquid is defective further includes at least one of a defective formation of the liquid film of the film-forming liquid and dripping of the film-forming liquid.

13. A program for causing a device to execute the method for generating a model according to any one of claims 7 to 12.

Images