Learning device, information processing device, substrate processing device, substrate processing system, learning method, recipe determination method, and learning program

Abstract

To generate a learning model for inferring a film thickness characteristic in processing for removing a sold film or a liquid film formed on a substrate from the substrate to dry the substrate.SOLUTION: A learning device includes an experiment data acquisition part 251 for acquiring a solid film or a liquid film formed on a substrate acquired by driving a substrate processing device for removing the sold film or the liquid film from the substrate to dry the substrate after executing a series of processes for supplying liquid to the substrate and forming the sold film or the liquid film on the substrate under a first condition, and a first learning part 253 for making the learning model learn first teacher data including the film thickness characteristic showing the characteristic of solid film thickness or liquid film thickness acquired by the experiment data acquisition part 251 and the first condition.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a learning device that trains a learning model, an information processing device that causes a trained learning model to make inferences, a substrate processing device that includes the information processing device, a substrate processing system that includes the information processing device and the substrate processing device, a learning method that trains a learning model, a recipe determination method that causes a trained learning model to make inferences, and a learning program that causes a computer to execute the learning method. [Background technology]

[0002] In recent years, the miniaturization of patterns formed on substrates has tended to weaken the pattern strength. As a result, when a liquid applied to a substrate is dried, the pattern may collapse due to the surface tension acting between the liquid and the surface of the pattern formed on the substrate. To address this issue, there is a technology known as sublimation drying, which converts the liquid applied to the patterned surface of the substrate into a solid and dries it by changing the phase from solid to gas.

[0003] In processes using this sublimation drying technique, it is desirable that the thickness of the solid film before sublimation (hereinafter referred to as the "solidified film") be uniform across the entire substrate. If the solidified film thickness is not uniform and there are thin and thick areas, the pattern formed on the substrate may collapse. Therefore, in order to make the solidified film thickness uniform, it is desirable to optimize the parameters that affect the solidified film thickness. However, there are many types of parameters that affect the thickness of the liquid or solid film in the sublimation drying process, making it difficult to determine the optimal values.

[0004] One way to determine appropriate parameters is to use a simulator that simulates the solidified film thickness, but this requires a huge amount of time, making it impossible to determine the parameters in real time during the manufacturing process.

[0005] International Publication WO2020 / 049974 describes a learning device that acquires simulator parameters for image data, inputs the image data and parameters into a learning model, and trains the learning model so that the output of the learning model approaches the result of the simulator for the image data. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Laid-Open No. 2013-016699 [Patent Document 2] Japanese Patent Application Laid-Open No. 2012-243869 [Patent Document 3] Japanese Patent Application Laid-Open No. 2013-258272 [Patent Document 4] Japanese Patent Application Laid-Open No. 2015-142069 [Patent Document 5] International Publication No. WO2020 / 049974 Summary of the Invention [Problem to be solved by the invention]

[0007] However, in the learning device described in International Publication WO2020 / 049974, the same number of image data to be input into the simulator as the teacher data to be provided to the learning model must be prepared, and it takes a huge amount of time to obtain a significant number of teacher data for modeling using machine learning.

[0008] One of the objects of the present invention is to provide a learning device, a learning method, and a learning program that enable the generation of a learning model for inferring film thickness characteristics that indicate the characteristics of a solid film or a liquid film formed on a substrate in a process of removing the solid film or the liquid film formed on the substrate from the substrate and drying the substrate.

[0009] Another object of the present invention is to provide a learning device and a learning method that facilitates the generation of a learning model.

[0010] It is still another object of the present invention to provide an information processing apparatus and a recipe determination method that can easily determine a recipe for operating a substrate processing apparatus using a learning model. [Means for solving the problem]

[0011] (1) According to one aspect of the present invention, a learning device includes an experimental data acquisition unit that acquires the thickness of a solid film or a liquid film formed on a substrate by operating a substrate processing device under first conditions, the substrate processing device performing a series of processes to supply a liquid to a substrate and form a solid film or a liquid film on the substrate, and then removing the solid film or liquid film from the substrate and drying the substrate; and a learning unit that trains a learning model to learn first teacher data including the first conditions and film thickness characteristics that indicate the characteristics of the solid film or liquid film acquired by the experimental data acquisition unit.

[0012] According to this aspect, in a process of removing a solid film or liquid film formed on a substrate from the substrate and drying the substrate, it is possible to generate a learning model for inferring film thickness characteristics that indicate the characteristics of the solid film or liquid film formed on the substrate.

[0013] (2) The learning device further includes a first intermediate data acquisition unit that acquires an intermediate processing state before the series of processes by the substrate processing apparatus is completed, and the learning unit causes the learning model to learn first teacher data that further includes the intermediate processing state acquired by the first intermediate data acquisition unit, thereby improving the accuracy of inference by the learning model.

[0014] (3) The learning device further includes a reinforcement data acquisition unit that acquires film thickness characteristics that indicate the characteristics of the film thickness of a solid or liquid film formed on a substrate, the film thickness characteristics being obtained by operating a simulator that simulates a solid film or a liquid film formed in the substrate processing apparatus under second conditions different from the first conditions, and a reinforcement learning unit that causes the learning model to further learn second teacher data including the second conditions and the film thickness characteristics acquired by the reinforcement data acquisition unit. This makes it easy to generate the second teacher data, thereby facilitating the generation of the learning model.

[0015] (4) The reinforcement data acquisition unit operates the simulator under second conditions different from the first conditions to further acquire an intermediate processing state before the series of processes by the simulator is completed, and the reinforcement learning unit causes the learning model to learn second teacher data further including the intermediate processing state acquired by the reinforcement data acquisition unit. Therefore, the learning model is reinforced learning using the second teacher data including the intermediate processing state, making it easier to generate a learning model with improved inference accuracy.

[0016] (5) The learning device further includes an adjustment unit that adjusts a simulator that simulates a solid film or a liquid film formed in the substrate processing apparatus using the first condition, the intermediate processing state acquired by the first intermediate data acquisition unit, and either a solid film thickness or a liquid film thickness acquired by the experimental data acquisition unit, thereby improving the accuracy of the simulator.

[0017] (6) The learning device further includes a second intermediate data acquisition unit that drives a simulator that simulates a solid film or liquid film formed in the substrate processing apparatus under first conditions and acquires an intermediate processing state at a point before a series of processes by the simulator is completed, and the learning unit causes the learning model to learn first teacher data that further includes the intermediate processing state acquired by the second intermediate data acquisition unit.

[0018] According to this aspect, since the intermediate processing state is generated by the simulator, it is possible to have the simulator calculate a state that cannot be measured by experiment. Therefore, by including the intermediate processing state in the first teacher data, it is possible to improve the accuracy of inference by the learning model.

[0019] (7) The learning device further includes a reinforcement data acquisition unit that operates the simulator under second conditions different from the first conditions and acquires an intermediate processing state at a point before a series of processes by the simulator is completed and a film thickness characteristic that indicates the characteristics of the film thickness of a solid film or a liquid film formed on a substrate, and a reinforcement learning unit that causes the learning model to further learn second teacher data including the second conditions and the intermediate processing state and the film thickness characteristic acquired by the reinforcement data acquisition unit. Therefore, the learning model is reinforced learning using the second teacher data including the intermediate processing state, which facilitates reinforcement learning of a learning model with improved inference accuracy.

[0020] (8) The simulator is constructed based on a physical model.

[0021] (9) The first training data is classified into multiple channels according to the surface condition of the substrate, the shape of the pattern formed on the substrate, and the physical properties of the liquid. This allows a learning model to be generated for each of the multiple channels, thereby improving the accuracy of inference from the learning model.

[0022] (10) The information processing apparatus includes an operating condition determination unit that determines a recipe using the learning model generated by the learning apparatus, thereby making it possible to easily determine a recipe for operating the substrate processing apparatus.

[0023] (11) According to another aspect of the present invention, an information processing apparatus includes a recipe determination unit that determines a recipe using a learning model that infers film thickness characteristics that indicate the characteristics of the solid film thickness or liquid film thickness formed on the substrate at the time when the series of processes by the substrate processing apparatus is completed from the driving conditions when driving a substrate processing apparatus that supplies a liquid to a substrate and performs a series of processes to form a solid film or liquid film on the substrate, and then removes the solid film or liquid film from the substrate and dries the substrate, and the driving conditions have values ​​set for each of a plurality of items, and the recipe determination unit provides the learning model with hypothetical conditions as driving conditions in which some of the plurality of items are set to specified values ​​and other items are set to arbitrary values, and determines the recipe based on the hypothetical conditions if the film thickness characteristics inferred by the learning model satisfy the allowable conditions.

[0024] According to this aspect, when the film thickness characteristics inferred by the learning model to which the tentative conditions are given as operating conditions satisfy the allowable conditions, a recipe is determined based on the tentative conditions. Therefore, a recipe for operating the substrate processing apparatus can be easily obtained using the learning model.

[0025] (12) The operating conditions further include an intermediate processing state at a point in time before a series of processes is completed by the substrate processing apparatus, and further include an intermediate processing state acquisition unit that acquires the intermediate processing state at a point in time before a series of processes executed by the substrate processing apparatus according to the recipe is completed, and a correction unit that provides the intermediate processing state acquired by the intermediate processing state acquisition unit and hypothetical conditions in which some of the multiple items are set to specified values ​​and the other items are set to arbitrary values ​​to the learning model as operating conditions, and determines a new recipe based on the hypothetical conditions if the film thickness characteristics inferred by the learning model satisfy the allowable conditions.

[0026] According to this aspect, a new recipe is determined based on an intermediate processing state before a series of processes executed by the substrate processing apparatus according to the recipe is completed, so that when the operating conditions of the substrate processing apparatus change while the substrate processing apparatus is executing the series of processes, the film thickness characteristics of the solid film or liquid film formed by the substrate processing apparatus can be kept within an allowable range.

[0027] (13) According to yet another aspect of the present invention, an information processing apparatus includes an intermediate processing state acquisition unit that acquires an intermediate processing state at a point before the series of processes is completed by a substrate processing apparatus that supplies a liquid to a substrate and performs a series of processes to form a solid film or liquid film on the substrate, and then removes the solid film or liquid film from the substrate and dries the substrate; and a correction unit that determines a recipe using a learning model that infers film thickness characteristics that indicate the characteristics of the solid film thickness or liquid film thickness formed on the substrate at the point when the series of processes by the substrate processing apparatus is completed, from the driving conditions when driving the substrate processing apparatus and the intermediate processing state at a point before the series of processes are completed by the substrate processing apparatus operated under the driving conditions, wherein values ​​are set for each of a plurality of items, and the correction unit provides the learning model with the intermediate processing state acquired by the intermediate processing state acquisition unit and hypothetical conditions in which some of the plurality of items are set to specified values ​​and other items are set to arbitrary values ​​as driving conditions, and determines a new recipe based on the hypothetical conditions if the film thickness characteristics inferred by the learning model satisfy the allowable conditions.

[0028] According to this aspect, a new recipe is determined based on an intermediate processing state before a series of processes is completed by the substrate processing apparatus, so that when the operating conditions change while the substrate processing apparatus is performing the series of processes, the film thickness characteristics of the solid film or liquid film formed by the substrate processing apparatus can be kept within an allowable range.

[0029] (14) According to yet another aspect of the present invention, an information processing apparatus includes: a recipe determination unit that determines a recipe using a first learning model that infers film thickness characteristics that indicate the characteristics of a solid film thickness or liquid film thickness formed on a substrate at the time when a series of processes by the substrate processing apparatus is completed from driving conditions when driving a substrate processing apparatus that supplies a liquid to a substrate and performs a series of processes to form a solid film or liquid film on the substrate, and then removes the solid film or liquid film from the substrate and dries the substrate; an intermediate processing state acquisition unit that acquires an intermediate processing state at a time before the series of processes performed by the substrate processing apparatus according to the recipe are completed from the driving conditions and the intermediate processing state at a time before the series of processes performed by the substrate processing apparatus according to the recipe are completed; and a correction unit that determines a new recipe by applying the intermediate processing state acquired by the intermediate processing state acquisition unit to a second learning model that infers film thickness characteristics that indicate the characteristics of a solid film thickness or liquid film thickness formed on a substrate at the time when a series of processes by the substrate processing apparatus is completed from the driving conditions and the intermediate processing state at a time before the series of processes by the substrate processing apparatus according to the recipe are completed.

[0030] According to this aspect, a recipe before the substrate processing apparatus starts a series of processes is determined using the first learning model, and a new recipe before the substrate processing apparatus completes a series of processes is determined using the second learning model. Therefore, different learning models are used before the substrate processing apparatus starts a series of processes and during the series of processes, so that an appropriate recipe can be determined at each timing.

[0031] (15) The learning device further includes a distillation unit that causes a new learning model to learn training data including the recipe determined by the information processing device and the inferred film thickness characteristics.

[0032] (16) A substrate processing apparatus includes the above information processing apparatus.

[0033] (17) The substrate processing apparatus further includes the learning device.

[0034] (18) A substrate processing system includes the information processing apparatus and a substrate processing apparatus.

[0035] (19) According to yet another aspect of the present invention, a learning method includes an experimental data acquisition step of acquiring the thickness of a solid film or a liquid film formed on a substrate, the thickness being obtained by operating a substrate processing apparatus under first conditions, which performs a series of processes to supply a liquid to a substrate and form a solid film or a liquid film on the substrate, and then removes the solid film or the liquid film from the substrate and dries the substrate; and a learning step of training a learning model to learn first teacher data including the film thickness characteristics indicating the characteristics of the film thickness acquired in the experimental data acquisition step and the first conditions.

[0036] (20) The learning method further causes the computer to execute a first intermediate data acquisition step of acquiring an intermediate processing state at a point before a series of processes by the substrate processing apparatus is completed, and the learning step causes the learning model to learn first teacher data that further includes the intermediate processing state acquired by the first intermediate data acquisition step.

[0037] (21) The learning method is twist The computer is further made to execute a reinforcement data acquisition step of acquiring film thickness characteristics that indicate the characteristics of the film thickness of a solid or liquid film formed on a substrate, which are obtained by operating a simulator that simulates the formed solid film or liquid film under second conditions different from the first conditions, and a reinforcement learning step of further making the learning model learn second teacher data that includes the second conditions and the film thickness characteristics acquired in the reinforcement data acquisition step.

[0038] (22) The reinforcement data acquisition step operates the simulator under second conditions different from the first conditions to further acquire an intermediate processing state at a point before the series of processes by the simulator is completed, and the reinforcement learning step causes the learning model to learn second teacher data that further includes the intermediate processing state acquired in the reinforcement data acquisition step.

[0039] (23) The learning method further causes the computer to execute a second intermediate data acquisition step of operating a simulator that simulates a solid film or liquid film formed in a substrate processing apparatus under first conditions and acquiring an intermediate processing state at a point before a series of processes by the simulator is completed, and the learning step causes the learning model to learn first teacher data that further includes the intermediate processing state acquired in the second intermediate data acquisition step.

[0040] (24) A recipe determination method includes a computer executing a recipe determination step in which a recipe is determined using a learning model that infers film thickness characteristics that indicate the characteristics of the solid film thickness or liquid film thickness formed on a substrate when a series of processes by a substrate processing apparatus is completed, from the driving conditions when the substrate processing apparatus is driven to supply a liquid to a substrate and perform a series of processes to form a solid film or liquid film on the substrate, and then remove the solid film or liquid film from the substrate and dry the substrate, wherein the driving conditions have values ​​set for each of a plurality of items, and the recipe determination step includes a step of providing the learning model with tentative conditions as driving conditions, in which some of the plurality of items are set to specified values ​​and other items are set to arbitrary values, and determining a recipe based on the tentative conditions if the film thickness characteristics inferred by the learning model satisfy the allowable conditions.

[0041] (25) The operating conditions further include an intermediate processing state at a point in time before a series of processes is completed by the substrate processing apparatus, and the computer is further caused to execute an intermediate processing state acquisition step of acquiring the intermediate processing state at a point in time before a series of processes executed by the substrate processing apparatus according to the recipe is completed, and a correction step of providing the learning model with the intermediate processing state acquired in the intermediate processing state acquisition step and hypothetical conditions in which some of the multiple items are set to specified values ​​and the other items are set to arbitrary values ​​as operating conditions, and determining a new recipe based on the hypothetical conditions if the film thickness characteristics inferred by the learning model satisfy the allowable conditions.

[0042] (26) A recipe determination method includes a computer that executes the following steps: an intermediate processing state acquisition step for acquiring an intermediate processing state at a point before the series of processes is completed by a substrate processing apparatus that supplies a liquid to a substrate and performs a series of processes to form a solid film or liquid film on the substrate, and then removes the solid film or liquid film from the substrate and dries the substrate; and a correction step for determining a recipe using a learning model that infers film thickness characteristics that indicate the characteristics of the solid film thickness or liquid film formed on the substrate at the point when the series of processes by the substrate processing apparatus is completed, from the driving conditions when driving the substrate processing apparatus and the intermediate processing state at a point before the series of processes are completed by the substrate processing apparatus operated under the driving conditions.The driving conditions have values ​​set for each of a plurality of items, and the correction step includes a step of providing the learning model with the intermediate processing state acquired in the intermediate processing state acquisition step and tentative conditions in which some of the plurality of items are set to specified values ​​and other items are set to arbitrary values ​​as driving conditions, and determining a new recipe based on the tentative conditions if the film thickness characteristics inferred by the learning model satisfy the allowable conditions.

[0043] (27) According to yet another aspect of the present invention, the learning program causes a computer to execute an experimental data acquisition step of acquiring the thickness of a solid film or a liquid film formed on a substrate, the thickness being obtained by operating a substrate processing apparatus under first conditions, which performs a series of processes of supplying a liquid to a substrate and forming a solid film or a liquid film on the substrate, and then removing the solid film or the liquid film from the substrate and drying the substrate; and a learning step of training a learning model to learn first teacher data including the film thickness characteristics indicating the characteristics of the film thickness acquired in the experimental data acquisition step and the first conditions. [Effects of the Invention]

[0044] In a process of removing a solid or liquid film formed on a substrate from the substrate and drying the substrate, it is possible to generate a learning model for inferring film thickness characteristics that indicate the characteristics of the solid or liquid film thickness formed on the substrate.

[0045] It also makes it easier to generate learning models.

[0046] Furthermore, it is possible to easily obtain a recipe for operating the substrate processing apparatus using the learning model. [Brief explanation of the drawings]

[0047] [Figure 1] 1 is a diagram illustrating an example of a configuration of a processing system according to a first embodiment of the present invention. [Figure 2] FIG. 1 illustrates an example of a configuration of an information processing device. [Figure 3] FIG. 2 illustrates an example of a functional configuration of a processing system according to a first embodiment. [Figure 4] FIG. 10 is a diagram showing an example of a film thickness obtained in an experiment. [Figure 5] 10 is a flowchart illustrating an example of the flow of a learning model generation process. [Figure 6] 10 is a flowchart illustrating an example of the flow of a recipe determination process. [Figure 7] 1 is a horizontal schematic view of the interior of a processing unit provided in a substrate processing apparatus. FIG. [Figure 8] 1A to 1C are process diagrams for explaining an example of substrate processing by the substrate processing apparatus. [Figure 9] FIG. 10 is a diagram illustrating an example of a functional configuration of a processing system according to a second embodiment. [Figure 10] 10 is a flowchart showing an example of the flow of a learning model generation process in the second embodiment. [Figure 11] 10 is a flowchart showing an example of the flow of a recipe determination process in the second embodiment. [Figure 12] 10 is a flowchart showing an example of the flow of a correction process in the second embodiment. [Figure 13] FIG. 10 is a diagram illustrating an example of a functional configuration of a processing system according to a third embodiment. [Figure 14] FIG. 10 is a diagram illustrating an example of a functional configuration of a processing system according to a fourth embodiment. [Figure 15]13 is a flowchart showing an example of the flow of a recipe determination process in the fourth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0048] [First embodiment] (1) Processing system Hereinafter, a processing system according to an embodiment of the present invention will be described with reference to the drawings. Fig. 1 is a diagram showing an example of the configuration of a processing system according to a first embodiment of the present invention. As shown in Fig. 1, the processing system 300 includes a substrate processing system 301, a learning device 200, and a simulator 250. The substrate processing system 301 includes an information processing device 100 and a substrate processing apparatus 1.

[0049] The learning device 200, simulator 250, and information processing device 100 are general-purpose computers. Therefore, they can basically be configured with the same hardware. Here, an information processing device will be used as an example. The learning device 200, simulator 250, and information processing device 100 have different programs installed. Therefore, the learning device 200, simulator 250, and information processing device 100 execute different processes.

[0050] The substrate processing apparatus 1 processes substrates such as semiconductor substrates (semiconductor wafers), FPD (Flat Panel Display) substrates such as liquid crystal display devices or organic EL (Electro Luminescence) display devices, optical disk substrates, magnetic disk substrates, magneto-optical disk substrates, photomask substrates, ceramic substrates, or solar cell substrates.

[0051] The information processing apparatus 100, the substrate processing apparatus 1, the learning apparatus 200, and the simulator 250 are each connected to a network 303, and are capable of transmitting and receiving data to and from each other. The network 303 may be, for example, a local area network (LAN) or a wide area network (WAN). The network 303 may also be the Internet. The information processing apparatus 100 and the substrate processing apparatus 1 may also be connected by a dedicated network instead of the network 303. The connection form of the network 303 may be a wired connection or a wireless connection.

[0052] Note that learning device 200 and simulator 250 do not necessarily need to be connected to network 303. In this case, the teacher data generated by simulator 250 may be transferred to learning device 200 via a recording medium. Also, the learning model generated by learning device 200 may be transferred to information processing device 100 via a recording medium.

[0053] The substrate processing apparatus 1 supplies a liquid to a substrate, performs a series of processes to form a solid or liquid film on the substrate, and then removes the solid or liquid film from the substrate and dries the substrate. Specifically, the substrate processing apparatus 1 applies a pre-drying treatment liquid, which is a mixture of a sublimable substance dissolved in a solvent at a predetermined concentration, to the surface of the substrate on which a pattern has been formed, and then performs a series of processes to deposit the sublimable substance on the substrate by rotating the substrate and evaporating the solvent. The substrate processing apparatus 1 then sprays an inert gas onto the substrate to dry it by sublimation.

[0054] Fig. 2 is a diagram showing an example of the configuration of an information processing device. Referring to Fig. 2, information processing device 100 is configured with CPU (Central Processing Unit) 101, RAM (Random Access Memory) 102, ROM (Read Only Memory) 103, storage device 104, operation unit 105, display device 106, and input / output I / F (Interface) 107. CPU 101, RAM 102, ROM 103, storage device 104, operation unit 105, display device 106, and input / output I / F 107 are connected to bus 108.

[0055] The RAM 102 is used as a working area for the CPU 101. The ROM 103 stores a system program. The storage device 104 includes a storage medium such as a hard disk or semiconductor memory, and stores the program. The program may be stored in the ROM 103 or another external storage device.

[0056] The operation unit 105 is an input device such as a keyboard, a mouse, or a touch panel. A user can give predetermined instructions to the information processing device 100 by operating the operation unit 105. The display device 106 is a display device such as a liquid crystal display device, and displays a GUI ( Graphical User Interface The input / output I / F 107 is connected to a network 303 .

[0057] (2) Functional configuration of the processing system Fig. 3 is a diagram showing an example of the functional configuration of the processing system according to the first embodiment. Referring to Fig. 3, the simulator 250 is realized by a CPU included in the simulator 250 executing a simulation program. The simulator 250 simulates the movement of a mixture of a sublimable substance dissolved in a solvent on a substrate in the substrate processing apparatus 1. The simulator 250 is configured by a physical model. Therefore, the simulator 250 is a physical model that reproduces the movement of the mixture during a series of processes executed by the substrate processing apparatus 1. The physical model is realized using, for example, a flow analysis program that analyzes the flow of the mixture on the substrate.

[0058] The functions of the learning device 200 are realized by executing a learning program by a CPU included in the learning device 200. The learning device 200 includes an experimental data acquisition unit 251, a first learning unit 253, a first reinforcement data acquisition unit 255, a first reinforcement learning unit 257, and a first adjustment unit 259.

[0059] The experimental data acquisition unit 251 acquires experimental data and outputs the acquired experimental data to the first learning unit 253 and the first adjustment unit 259. The experimental data is obtained by having the substrate processing apparatus 1 perform a series of processes and measuring the thickness of a liquid film or solid film formed on a substrate. The experimental data includes first conditions and the film thickness, which is the experimental result. The first conditions are operating conditions for driving the substrate processing apparatus 1. The operating conditions include a recipe set to have the substrate processing apparatus 1 perform a series of processes and a state condition indicating the state at which the substrate processing apparatus 1 starts the series of processes. The recipe is information that defines values ​​to be set for controlling the substrate processing apparatus 1, and is information that defines setting values ​​corresponding to each setting item. The state condition includes information that affects the movement of the mixed liquid applied to the substrate but is not defined by the recipe. Specifically, the state condition includes environmental conditions that indicate the environment in which the substrate to be processed is placed and characteristic information of the mixed liquid. The environmental conditions include, for example, the temperature and pressure of the processing chamber in which the substrate is placed. The property information of the mixed liquid includes information indicating the property of the mixed liquid in which a sublimable substance is dissolved in a solvent.

[0060] FIG. 4 is a diagram showing an example of film thickness obtained in an experiment. Referring to FIG. 4, the horizontal axis indicates the radial position of the substrate, and the vertical axis indicates the film thickness. The origin of the horizontal axis indicates the center of the substrate. When a series of processes is performed by the substrate processing apparatus 1, the film thickness of the liquid film or solid film is not necessarily uniform over the entire substrate. The film thickness characteristic is a value that indicates the film thickness characteristic of the liquid film or solid film over the entire substrate.

[0061] Returning to FIG. 3 , the first learning unit 253 generates first training data based on experimental data and trains the first training data into a first learning model. The first training data includes a first condition and a film thickness characteristic. The film thickness characteristic is information indicating the characteristics of the film thickness of a liquid film or solid film formed on a substrate. The film thickness characteristic includes a value indicating that the film thickness of the liquid film or solid film formed on the substrate is within an acceptable range and a value indicating that the film thickness of the liquid film or solid film formed on the substrate is not within the acceptable range.

[0062] For example, if the maximum and minimum values ​​of the film thickness of a liquid film or solid film formed on a substrate are within a predetermined tolerance range, the film thickness characteristic is set to a value indicating that the film thickness is within the tolerance range, and if the film thickness range is not within the tolerance range, the film thickness characteristic is set to a value indicating that the film thickness is not within the tolerance range. The tolerance range is defined by a maximum value and a minimum value.

[0063] Furthermore, the value indicating whether the thickness of a liquid film or solid film formed on a substrate is within the acceptable range may have multiple levels. For example, the levels may be determined based on the percentage of the acceptable range that the range of film thickness (the difference between the maximum and minimum values) occupies. If the percentage of the acceptable range that the range of film thickness occupies is 50% or less, a rank A is assigned to the film thickness characteristic, and if the percentage exceeds 50%, a rank B is assigned to the film thickness characteristic.

[0064] The first training data is preferably classified into a plurality of channels according to the surface condition of the substrate, the shape of the pattern formed on the substrate, and the physical properties of the mixed liquid applied to the substrate. By generating a first learning model for each of the plurality of channels, the accuracy of inference by the first learning model can be improved.

[0065] The first adjustment unit 259 adjusts the simulator 250 based on experimental data. The experimental data includes a first condition and a film thickness when the substrate processing apparatus 1 executes a series of processes. The first adjustment unit 259 adjusts the simulator 250 so that the result of having the simulator 250 execute the movement of the mixed liquid on the substrate while the substrate processing apparatus 1 executes a series of processes under the first condition included in the experimental data approaches the film thickness included in the experimental data.

[0066] The first reinforcement data acquisition unit 255 acquires film thickness characteristics indicating the characteristics of a liquid film thickness or a solid film thickness obtained by operating the simulator 250 under the second condition. The second condition has the same condition items as the first condition included in the experimental data but different values. The first reinforcement data acquisition unit 255 generates the second condition according to a predetermined rule. The second condition may also be generated randomly. The first reinforcement data acquisition unit 255 determines the film thickness characteristics by comparing the film thickness of the liquid film or the solid film output by the simulator 250 with a predetermined allowable range. The first reinforcement data acquisition unit 255 outputs second teacher data including the second condition and the film thickness characteristics as first reinforcement data to the first reinforcement learning unit 257.

[0067] The first reinforcement learning unit 257 trains the trained first learning model trained by the first learning unit 253 using the second teacher data, which is the first reinforcement data. Because the trained first learning model trained by the first learning unit 253 is further trained using the second teacher data, there is no need to prepare the number of first teacher data required to train the first learning model by experimentation, making it easier to generate the first learning model. In other words, the number of teacher data required to train the first learning model can be provided by the second teacher data generated by the simulator 250. The first reinforcement learning unit 257 outputs the trained first learning model, which has been reinforced learned using the second teacher data, which is the first reinforcement data, to the information processing device 100.

[0068] The functions of the information processing device 100 are realized by executing a recipe determination program by a CPU 101 included in the information processing device 100. The information processing device includes a first inference unit 151 and a recipe determination unit 153.

[0069] The recipe determination unit 153 includes a first tentative condition determination unit 155. The first tentative condition determination unit 155 determines first tentative conditions. The first tentative conditions are operating conditions to be input to the first learning model. The first tentative condition determination unit 155 determines state conditions based on information set for operating the substrate processing apparatus 1 and sensor values ​​detected by various sensors provided in the substrate processing apparatus 1. The first tentative condition determination unit 155 acquires characteristic information indicating the characteristics of the mixed liquid supplied to the substrate processing apparatus 1. The characteristic information may be acquired from the substrate processing apparatus 1 or may be acquired as a predetermined value.

[0070] The first tentative condition determination unit 155 also sets random values ​​as values ​​for each setting item of the recipe. The first tentative condition determination unit 155 determines a first tentative condition including a recipe in which random values ​​are set for each setting item and a state condition. The first tentative condition determination unit 155 outputs the first tentative condition to the first inference unit 151.

[0071] The first inference unit 151 infers film thickness characteristics using a trained first learning model generated by the learning device 200. The first inference unit 151 provides a first tentative condition to the first learning model and outputs the film thickness characteristics inferred by the first learning model to the recipe determination unit 153.

[0072] If the film thickness characteristics input from the first inference unit 151 indicate a value within an allowable range, the recipe determination unit 153 determines a recipe based on the first tentative conditions output to the first inference unit 151. The recipe set in the first tentative conditions is determined as the recipe for operating the substrate processing apparatus 1. The first tentative condition determination unit 155 repeatedly determines new first tentative conditions until a recipe is determined by the recipe determination unit 153. The recipe determination unit 153 provides the determined recipe to the substrate processing apparatus 1 and operates the substrate processing apparatus 1 according to the recipe.

[0073] (3) Processing flow 5 is a flowchart showing an example of the flow of a learning model generation process. The learning model generation process is a process executed by the CPU of the learning device 200 as the CPU executes a learning program. Referring to FIG. 5, the CPU of the learning device 200 acquires experimental data (step S01) and proceeds to step S02. If experimental data is input from the substrate processing apparatus 1, the CPU acquires the experimental data. If a recording medium such as a CD-ROM on which the experimental data is recorded is loaded, the CPU reads the experimental data from the CD-ROM. The experimental data includes a first condition and a film thickness, which is an experimental result.

[0074] In step S02, first training data is determined, and the process proceeds to step S03. A film thickness specification is determined based on the liquid film thickness or solid film thickness included in the experimental data, and the first training data including the first condition and film thickness characteristics included in the experimental data is determined. For example, the film thickness characteristics are determined by comparing the liquid film thickness or solid film thickness included in the experimental data with a predetermined allowable range.

[0075] In step S03, the CPU included in learning device 200 trains the first learning model, and the process proceeds to step S04. The first training data is provided to the first learning model, and a trained first learning model is generated.

[0076] In step S04, a second condition is determined, and the process proceeds to step S05. The second condition has the same condition items as the first condition included in the experimental data but different values. Here, a plurality of second conditions are determined.

[0077] In step S05, the simulator is caused to simulate the thickness of the liquid or solid film of the mixed liquid, and the process proceeds to step S06. The second conditions generated in step S04 are input to the simulator, and the thickness of the liquid or solid film is calculated. As a result, the thickness of the liquid or solid film is calculated for each of the plurality of second conditions.

[0078] In step S06, first reinforcement data is determined, and the process proceeds to step S07. In step S05, film thickness characteristics are determined for each of the plurality of second conditions based on the liquid film thickness or solid film thickness calculated for each of the plurality of second conditions. The film thickness characteristics are determined by comparing the liquid film thickness or solid film thickness with a predetermined allowable range. Then, first reinforcement data (second teacher data) including the second conditions and the film thickness characteristics is determined. Therefore, the same number of first reinforcement data as the number of second conditions generated in step S04 is determined.

[0079] In step S07, the CPU included in learning device 200 performs reinforcement learning on the first learning model and ends the process. The first reinforcement data is provided to the trained first learning model trained in step S03, and the trained first learning model is reinforced.

[0080] 6 is a flowchart showing an example of the flow of a recipe determination process. The recipe determination process is a process executed by CPU 101 included in information processing apparatus 100 as CPU 101 executes a recipe determination program. Referring to FIG. 6, CPU 101 included in information processing apparatus 100 acquires state conditions (step S11) and proceeds to step S12. The state conditions include environmental conditions and property information of the mixed liquid. The environmental conditions are determined based on values ​​detected by sensors included in substrate processing apparatus 1. The property information of the mixed liquid is determined based on values ​​set in substrate processing apparatus 1 or values ​​detected by sensors included in substrate processing apparatus 1.

[0081] In step S12, a temporary recipe with arbitrary values ​​is determined, and the process proceeds to step S 13. A temporary recipe is determined in which random values ​​are set as the values ​​of each setting item of the recipe.

[0082] In step S13, first tentative conditions are determined, and the process proceeds to step S14. The first tentative conditions are determined, including the state conditions acquired in step S11 and the tentative recipe determined in step S12.

[0083] In step S14, the film thickness characteristics are inferred using the first learning model. The first hypothetical condition is given to the first learning model, and the film thickness characteristics inferred by the first learning model are obtained.

[0084] In step S15, it is determined whether the film thickness characteristic is within an acceptable range. If step S14 indicates that the film thickness characteristic is within an acceptable range, the process proceeds to step S16; otherwise, the process returns to step S12.

[0085] In step S16, the tentative recipe included in the first tentative conditions determined in step S13 is determined as the recipe for operating the substrate processing apparatus 1, and the process proceeds to step S17. In step S17, the substrate processing apparatus 1 is operated according to the determined recipe, and the process ends.

[0086] (4) Substrate processing equipment 7 is a horizontal schematic diagram of the interior of a processing unit provided in a substrate processing apparatus. The processing unit 2 is controlled by a control device 3. The control device 3 is capable of communicating with an information processing apparatus 100. The processing unit 2 includes a box-shaped chamber 4, a spin chuck 10 that holds a single substrate W horizontally within the chamber 4 and rotates the substrate W about a vertical rotation axis A1 that passes through the center of the substrate W, and a cylindrical processing cup 21 that surrounds the spin chuck 10 about the rotation axis A1.

[0087] The spin chuck 10 includes a disk-shaped spin base 12 held in a horizontal position, a plurality of chuck pins 11 that hold the substrate W in a horizontal position above the spin base 12, a spin shaft 13 that extends downward from the center of the spin base 12, and a spin motor 14 that rotates the spin shaft 13 to rotate the spin base 12 and the plurality of chuck pins 11.

[0088] The processing cup 21 includes a plurality of guards 24 that receive the processing liquid discharged outward from the substrate W, a plurality of cups 23 that receive the processing liquid guided downward by the plurality of guards 24, and a cylindrical outer wall member 22 that surrounds the plurality of guards 24 and the plurality of cups 23. The plurality of guards 24 can be individually raised and lowered by a guard lifting unit 27. The processing unit 2 includes a chemical liquid nozzle 31 that discharges a chemical liquid, a rinse liquid nozzle 35 that discharges a rinse liquid, a pre-drying processing liquid nozzle 39 that discharges a pre-drying processing liquid, and a substitute liquid nozzle 43 that discharges a substitute liquid. The chemical liquid nozzle 31, the rinse liquid nozzle 35, the pre-drying processing liquid nozzle 39, and the substitute liquid nozzle 43 can be independently moved horizontally within the chamber 4 by nozzle moving units 34, 38, 42, and 46 provided corresponding to the respective nozzles.

[0089] The pre-drying treatment liquid nozzle 39 is connected to a pre-drying treatment liquid pipe 40 that guides the treatment liquid to the pre-drying treatment liquid nozzle 39. When a pre-drying treatment liquid valve 41 disposed in the pre-drying treatment liquid pipe 40 is opened, the pre-drying treatment liquid is continuously discharged downward from the discharge port of the pre-drying treatment liquid nozzle 39. The pre-drying treatment liquid is a mixed liquid containing a sublimable substance corresponding to a solute and a solvent that dissolves in the sublimable substance.

[0090] The pre-drying treatment liquid nozzle 39 is connected to a nozzle moving unit 42. The nozzle moving unit 42 moves the pre-drying treatment liquid nozzle 39 in at least one of the vertical and horizontal directions. The nozzle moving unit 42 moves the pre-drying treatment liquid nozzle 39 horizontally between a processing position where the pre-drying treatment liquid discharged from the pre-drying treatment liquid nozzle 39 is supplied to the upper surface of the substrate W and a standby position where the pre-drying treatment liquid nozzle 39 is positioned around the processing cup 21 in a plan view.

[0091] The processing unit 2 includes a disk-shaped blocking member 51 arranged above the spin chuck 10. The blocking member 51 includes a disk portion 52 arranged horizontally above the spin chuck 10. The blocking member 51 is supported horizontally by a cylindrical support shaft 53 extending upward from the center of the disk portion 52. The center line of the disk portion 52 is arranged on the rotation axis A1 of the substrate W. The lower surface of the disk portion 52 corresponds to the lower surface 51L of the blocking member 51. The lower surface 51L of the blocking member 51 is parallel to the upper surface of the substrate W and has an outer diameter equal to or greater than the diameter of the substrate W.

[0092] The blocking member 51 is connected to a blocking member lifting unit 54 that vertically raises and lowers the blocking member 51. The blocking member lifting unit 54 moves the blocking member 51 to any position between an upper position (the position shown in FIG. 7) and a lower position.

[0093] A center nozzle 55 is disposed in a through-hole that passes vertically through the center of the blocking member 51. The center nozzle 55 moves up and down together with the blocking member 51. The center nozzle 55 is connected to an upper gas pipe 56 that guides inert gas to the center nozzle 55. The substrate processing apparatus 1 is equipped with an upper temperature regulator 59 that heats or cools the inert gas discharged from the center nozzle 55. When an upper gas valve 57 installed in the upper gas pipe 56 is opened, the inert gas is continuously discharged downward from the discharge port of the center nozzle 55 at a flow rate that corresponds to the opening of a flow rate adjustment valve 58 that changes the flow rate of the inert gas. The inert gas discharged from the center nozzle 55 is nitrogen gas.

[0094] 8 is a process diagram illustrating an example of substrate processing by the substrate processing apparatus 1. When a substrate W is processed by the substrate processing apparatus 1, the substrate W is loaded into the chamber 4 and held by the spin chuck 10. Thereafter, the guard lifting unit 27 raises at least one guard 24 from the lower position to the upper position. The surface of the substrate W is a surface on which devices such as transistors and capacitors are formed, and a pattern is formed.

[0095] In step S51, the spin motor 14 is driven to start rotating the substrate W. In the next step S52, a chemical supplying step is performed in which a chemical liquid is supplied to the upper surface of the substrate W to form a liquid film of the chemical liquid covering the entire upper surface of the substrate W. Specifically, the chemical liquid nozzle 31 is moved from the standby position to the processing position, the chemical liquid nozzle 31 discharges the chemical liquid for a predetermined period of time, and then moves back to the standby position. The chemical liquid discharged from the chemical liquid nozzle 31 collides with the upper surface of the rotating substrate W, and then flows outward along the upper surface of the substrate W due to centrifugal force.

[0096] In step S53, a rinse liquid supply process is performed in which pure water, an example of a rinse liquid, is supplied to the upper surface of the substrate W to rinse away the chemical liquid on the substrate W. Specifically, the rinse liquid nozzle 35 moves from the standby position to the processing position, the rinse liquid nozzle 35 discharges the rinse liquid for a predetermined period of time, and then moves back to the standby position. The pure water discharged from the rinse liquid nozzle 35 collides with the upper surface of the rotating substrate W, and then flows outward along the upper surface of the substrate W due to centrifugal force.

[0097] In step S54, a substitute liquid supply step is performed in which a substitute liquid that is miscible with both the rinse liquid and the pre-drying treatment liquid is supplied to the upper surface of the substrate W to replace the pure water on the substrate W with the substitute liquid. Specifically, the substitute liquid nozzle 43 moves from the standby position to the processing position, discharges the substitute liquid for a predetermined period of time, and then returns to the standby position. The substitute liquid discharged from the substitute liquid nozzle 43 collides with the upper surface of the substrate W and then flows outward along the upper surface of the substrate W due to centrifugal force. The pure water on the substrate W is replaced with the substitute liquid discharged from the substitute liquid nozzle 43. As a result, a liquid film of the substitute liquid is formed that covers the entire upper surface of the substrate W.

[0098] In step S55, a pre-drying treatment liquid supply step is performed in which a pre-drying treatment liquid is supplied to the upper surface of the substrate W to form a liquid film of the pre-drying treatment liquid on the substrate W. Specifically, the nozzle moving unit 42 moves the pre-drying treatment liquid nozzle 39 from the standby position to the treatment position. Thereafter, the pre-drying treatment liquid valve 41 is opened, and the pre-drying treatment liquid nozzle 39 starts to discharge the pre-drying treatment liquid. Before the start of the discharge of the pre-drying treatment liquid, the guard lifting unit 27 may vertically move at least one guard 24 to switch the guard 24 that receives the liquid discharged from the substrate W. When a predetermined time has elapsed since the pre-drying treatment liquid valve 41 was opened, the pre-drying treatment liquid valve 41 is closed, and the discharge of the pre-drying treatment liquid is stopped. Thereafter, the nozzle moving unit 42 moves the pre-drying treatment liquid nozzle 39 to the standby position.

[0099] The pre-drying treatment liquid discharged from the pre-drying treatment liquid nozzle 39 collides with the upper surface of the substrate W rotating at the liquid supply speed, and then flows outward along the upper surface of the substrate W due to centrifugal force. The replacement liquid on the substrate W is replaced with the pre-drying treatment liquid discharged from the pre-drying treatment liquid nozzle 39. As a result, a liquid film of the pre-drying treatment liquid is formed covering the entire upper surface of the substrate W. While the pre-drying treatment liquid nozzle 39 is discharging the pre-drying treatment liquid, the nozzle moving unit 42 moves the landing position of the pre-drying treatment liquid on the upper surface of the substrate W so that the landing position passes through the center and the outer periphery.

[0100] In step S56, a film thickness reducing step is performed in which part of the pre-drying treatment liquid on the substrate W is removed to reduce the film thickness (liquid film thickness) of the pre-drying treatment liquid on the substrate W while maintaining a state in which the entire upper surface of the substrate W is covered with the liquid film of the pre-drying treatment liquid. Specifically, the spin motor 14 rotates the substrate W with the blocking member 51 in the lower position. The pre-drying treatment liquid on the substrate W is discharged outward from the substrate W by centrifugal force even after the discharge of the pre-drying treatment liquid has stopped. Therefore, the thickness of the liquid film of the pre-drying treatment liquid on the substrate W decreases. When a certain amount of the pre-drying treatment liquid on the substrate W has been discharged, the amount of the pre-drying treatment liquid discharged from the substrate W per unit time decreases to zero or approximately zero.

[0101] In step S57, a solidification forming process is performed in which the pre-drying treatment liquid on the substrate W is solidified to form a solidification containing a sublimable substance on the substrate W. Specifically, with the blocking member 51 in the lower position, the spin motor 14 rotates the substrate W. Furthermore, the upper gas valve 57 is opened to start discharging nitrogen gas from the center nozzle 55. In the solidification forming process, evaporation of the pre-drying treatment liquid is promoted, and part of the pre-drying treatment liquid on the substrate W evaporates. Therefore, the concentration of the sublimable substance gradually increases, and the film thickness of the pre-drying treatment liquid gradually decreases.

[0102] In step S58, a sublimation process is performed in which the solidified material on the substrate W is sublimated and removed from the upper surface of the substrate W. Specifically, with the blocking member 51 in the lower position, the spin motor 14 rotates the substrate W. Furthermore, the upper gas valve 57 is opened to start discharging nitrogen gas from the center nozzle 55. When the sublimation process is completed, the spin motor 14 stops, and the rotation of the substrate W is stopped (step S59).

[0103] (5) Specific examples of driving conditions The simulator 250 simulates the movement of the pre-drying treatment liquid discharged onto the substrate W during a series of processes executed in the substrate processing apparatus 1. This series of processes includes the substitution liquid supplying step (step S54), the pre-drying treatment liquid supplying step (step S55), the film thickness reducing step (step S56), and the coagulation forming step (step S57) shown in Fig. 8. Therefore, the film thickness of the liquid film indicates the film thickness of the substitution liquid or the pre-drying treatment liquid, and the film thickness of the solid film indicates the film thickness of the coagulation containing a sublimable substance precipitated by evaporating the solvent from the pre-drying treatment liquid.

[0104] The operating conditions for adjusting the simulator 250 and the operating conditions for learning the first learning model include a recipe set to cause the substrate processing apparatus 1 to perform a series of processes, and a state condition indicating the state at the time the substrate processing apparatus 1 starts the series of processes.

[0105] <Recipe> The recipe includes the chemical flow rate, nozzle discharge position, nozzle discharge diameter, nozzle scan speed, nozzle scan position information (time change), substrate rotation speed, processing time, dry gas (N2) flow rate, and dry gas (N2) discharge position.

[0106] The chemical liquid flow rate is the amount of the substitute liquid discharged from the substitute liquid nozzle 43 per unit time. The chemical liquid flow rate is the amount of the pre-drying treatment liquid discharged from the pre-drying treatment liquid nozzle 39 per unit time, and is represented here by the opening of the pre-drying treatment liquid valve 41. The nozzle discharge position for the substitute liquid is the height position of the substitute liquid nozzle 43 relative to the substrate W, and is determined by the nozzle moving unit 46. The nozzle discharge position for the pre-drying treatment liquid is the height position of the pre-drying treatment liquid nozzle 39 relative to the substrate W, and is determined by the nozzle moving unit 42. The nozzle discharge diameter for the substitute liquid is the inner diameter of the discharge opening of the substitute liquid nozzle 43, and is determined by the substitute liquid nozzle 43. The nozzle discharge diameter for the pre-drying treatment liquid is the inner diameter of the discharge opening of the pre-drying treatment liquid nozzle 39, and is determined by the pre-drying treatment liquid nozzle 39.

[0107] The nozzle scan speed for the substitute liquid is the speed at which the substitute liquid nozzle 43 scans, and is determined by the nozzle movement unit 46. The nozzle scan speed for the pre-drying treatment liquid is the speed at which the pre-drying treatment liquid nozzle 39 scans, and is determined by the nozzle movement unit 42. The nozzle scan position information (time change) for the substitute liquid is the change over time in the position of the substitute liquid nozzle 43 relative to the substrate W, and is determined by the nozzle movement unit 46. The nozzle scan position information (time change) for the pre-drying treatment liquid is the change over time in the position of the pre-drying treatment liquid nozzle 39 relative to the substrate W, and is determined by the nozzle movement unit 42.

[0108] The substrate rotation speed is the rotation speed of the spin motor 14. The processing time includes the chemical solution discharge time, the chemical solution shake-off time, and the solvent drying time. The chemical solution discharge time is the time during which the substitution liquid is discharged from the substitution liquid nozzle 43 in the substitution liquid supply step and the time during which the pre-drying treatment liquid is discharged from the pre-drying treatment liquid nozzle 39 in the pre-drying treatment liquid supply step. The chemical solution shake-off time is the time during which the spin motor 14 rotates the substrate W with the blocking member 51 in the lower position in the film thickness reducing step. The solvent drying time is the time during which the spin motor 14 rotates the substrate W with the blocking member 51 in the lower position and nitrogen gas is discharged from the center nozzle 55 in the solidification forming step.

[0109] The dry gas (N2) flow rate is the amount of dry gas (N2) discharged from the central nozzle 55 per unit time, and is indicated here by the opening of the flow rate control valve 58. The dry gas (N2) discharge position is the position of the central nozzle 55 relative to the substrate W, which is above the center of the substrate W in this embodiment.

[0110] <Status Condition> The state conditions include chemical liquid conditions, substrate conditions, apparatus configuration, and environmental conditions. The chemical liquid conditions are information about the replacement liquid and the pre-drying treatment liquid, including viscosity, surface tension, temperature, concentration, contact angle with respect to the substrate, vapor pressure, heat of solidification, saturation concentration, and diffusion coefficient. The substrate conditions are information about the substrate W, including the surface state (water-repellent, hydrophilic) and contact angle with respect to the pre-drying treatment liquid. The apparatus configuration is information about the substrate processing apparatus 1, including the position and shape of the treatment cup 21 with respect to the substrate W, the position and shape of the spin chuck 10, the position and shape of the spin base 12, and the position and shape of the pre-drying treatment liquid nozzle 39. The environmental conditions are information about the environment in which the substrate W is processed, including the internal pressure of the chamber 4 and the temperature within the chamber 4.

[0111] (6) Effects In processing system 300 according to the first embodiment, learning device 200 acquires the thickness of a solid film or a liquid film formed on a substrate, which is obtained by operating substrate processing apparatus 1 under first conditions, and causes a learning model to learn first teacher data including film thickness characteristics indicative of the characteristics of the solid film or the liquid film, and the first conditions. This makes it possible to generate a learning model for inferring film thickness characteristics indicative of the characteristics of the solid film or the liquid film formed on a substrate in processing using sublimation drying technology, which has a relatively large number of parameters.

[0112] Furthermore, the learning device 200 acquires film thickness characteristics that indicate the characteristics of the film thickness of a solid or liquid formed on a substrate, which are obtained by operating the simulator 250 under second conditions different from the first conditions, and further trains the learning model with second training data that includes the second conditions and the film thickness characteristics. This makes it easy to generate the second training data, which in turn makes it easy to generate the learning model.

[0113] The first training data is classified into multiple channels according to the surface condition of the substrate, the shape of the pattern formed on the substrate, and the physical properties of the liquid. Therefore, a learning model is generated for each of the multiple channels, improving the accuracy of inference of the learning model.

[0114] The information processing apparatus 100 determines a recipe using the learning model generated by the learning apparatus 200. A recipe for driving the substrate processing apparatus 1 can be easily determined.

[0115] Furthermore, values ​​are set for each of a plurality of items in the operating conditions, and the information processing apparatus 100 provides the learning model with hypothetical conditions as operating conditions, in which some of the items are set to specified values ​​and other items are set to arbitrary values, and determines a recipe based on the hypothetical conditions when the film thickness characteristics inferred by the learning model satisfy the allowable conditions. Therefore, when the film thickness characteristics inferred by the learning model to which the hypothetical conditions are provided as operating conditions satisfy the allowable conditions, a recipe is determined based on the hypothetical conditions. This makes it possible to easily obtain a recipe for operating the substrate processing apparatus using the learning model.

[0116] [Second embodiment] The processing system 300 in the second embodiment is obtained by adding to the processing system 300 in the first embodiment a function of correcting a recipe based on intermediate processing states detected by a sensor while the substrate processing apparatus 1 is executing a series of processes. Below, differences from the processing system 300 in the first embodiment will be mainly described.

[0117] In the processing system 300 according to the second embodiment, the intermediate processing state will be described by taking as an example the pressure and temperature inside the chamber 4. The pressure is detected by a pressure sensor disposed inside the chamber 4, and the temperature is detected by a temperature sensor disposed inside the chamber 4.

[0118] FIG. 9 is a diagram illustrating an example of a functional configuration of a processing system according to the second embodiment. Referring to FIG. 9, the functions differ from those illustrated in FIG. 3 in that, in learning device 200, first adjustment unit 259 is changed to second adjustment unit 259A, first reinforcement data acquisition unit 255 is changed to second reinforcement data acquisition unit 255A, first learning unit 253 is changed to second learning unit 253A, and first reinforcement learning unit 257 is changed to second reinforcement learning unit 257A, and in that a first intermediate state acquisition unit 261 is added. Also, in information processing device 100, first inference unit 151 is changed to second inference unit 151A, recipe determination unit 153 is changed to recipe determination unit 153A, and correction unit 157 is added. The other functions are the same as those illustrated in FIG. 3, and therefore will not be described again here.

[0119] The first intermediate state acquisition unit 261 included in the learning device 200 in the second embodiment acquires an intermediate processing state while the substrate processing apparatus 1 is performing a series of processes through an experiment. The first intermediate state acquisition unit 261 acquires the intermediate processing state at a timing when a predetermined time has elapsed since the substrate processing apparatus 1 started the series of processes. Specifically, the first intermediate state acquisition unit 261 acquires, as the intermediate processing state, the temperature inside the chamber 4 measured by a temperature sensor and the pressure inside the chamber 4 measured by a pressure sensor. The intermediate processing state is paired with a first condition included in the experimental data. The first intermediate state acquisition unit 261 outputs the intermediate processing state to the second adjustment unit 259A and the second learning unit 253A.

[0120] The second learning unit 253A generates first training data based on the experimental data and the intermediate processing state, and trains the second learning model using the first training data. In the second embodiment, the first training data includes a first condition, an intermediate processing state, and a film thickness characteristic. Hereinafter, the first training data including the first condition, the intermediate processing state, and the film thickness characteristic is referred to as "first training data including the intermediate processing state." The film thickness characteristic is determined by comparing the liquid film thickness or solid film thickness included in the experimental data with an allowable range.

[0121] The first training data including the intermediate processing state is preferably classified into a plurality of channels according to the surface state of the substrate, the shape of the pattern formed on the substrate, and the physical properties of the mixed liquid applied to the substrate. By generating a second learning model for each of the plurality of channels, the accuracy of inference by the second learning model can be improved.

[0122] The second adjustment unit 259A adjusts the simulator 250 based on the experimental data and the intermediate processing state. The second adjustment unit 259A causes the simulator 250 to simulate the movement of the mixed liquid on the substrate while the substrate processing apparatus 1 performs a series of processes under the first condition included in the experimental data, and adjusts the simulator 250 so that the temperature and pressure in the chamber during the series of processes approach the intermediate processing state and so that the result of performing the series of processes approaches the film thickness of the liquid film or solid film included in the experimental data.

[0123] The second reinforced data acquisition unit 255A acquires an intermediate processing state and a film thickness characteristic that indicates the characteristics of a liquid film thickness or a solid film thickness obtained by driving the simulator 250 under the second condition. The second condition is the same as that used in the experimental data. First condition The second reinforcement data acquisition unit 255A generates the second condition according to a predetermined rule. The second condition may be generated randomly. The second reinforcement data acquisition unit 255A acquires an intermediate processing state calculated by the simulator 250 before the series of processes is completed by operating the simulator 250 under the second condition. The timing at which the intermediate processing state is acquired is the same as the timing for the intermediate processing state acquired by the first intermediate state acquisition unit 261. That is, it is when a predetermined time has elapsed since the series of processes was started by the substrate processing apparatus 1. The second reinforcement data acquisition unit 255A determines the film thickness characteristics by comparing the liquid film thickness or solid film thickness output by the simulator 250 with a predetermined allowable range. The second reinforcement data acquisition unit 255A outputs second reinforcement data (second teacher data) including the second condition, the intermediate processing state, and the film thickness characteristics to the second reinforcement learning unit 257A.

[0124] The second reinforcement learning unit 257A trains the second learning model trained by the second learning unit 253A using the second reinforcement data. Since the second learning model trained by the second learning unit 253A is further trained using the second reinforcement data, there is no need to prepare, through experimentation, first training data including the number of intermediate processing states required to train the second learning model, making it easier to generate the second learning model. In other words, the number of training data required to train the second learning model can be provided by the second training data including the intermediate processing states generated by the simulator 250. The second reinforcement learning unit 257A outputs the trained second learning model to the information processing device 100.

[0125] The recipe determination unit 153A includes an intermediate state generation unit 156 and a first tentative condition determination unit 155A. The first tentative condition determination unit 155A determines second tentative conditions. The second tentative conditions include operating conditions and intermediate processing states. The first tentative condition determination unit 155A determines operating conditions. The first tentative condition determination unit 155A determines state conditions based on information set for operating the substrate processing apparatus 1 and sensor values ​​detected by various sensors provided in the substrate processing apparatus 1. The first tentative condition determination unit 155A acquires characteristic information indicating the characteristics of the mixed liquid supplied to the substrate processing apparatus 1. The first tentative condition determination unit 155A also sets random values ​​as values ​​for each setting item of the recipe. The first tentative condition determination unit 155A determines operating conditions including the recipe in which random values ​​are set for each setting item and the state conditions. The first tentative condition determination unit 155A outputs the operating conditions to the intermediate state generation unit 156 and acquires the intermediate processing states from the intermediate state generation unit 156.

[0126] The intermediate state generation unit 156 drives the simulator 250 under the driving conditions input from the first tentative condition determination unit 155A and acquires the intermediate processing state output by the simulator 250. The intermediate state generation unit 156 outputs the acquired intermediate processing state to the first tentative condition determination unit 155A. The timing at which the simulator 250 outputs the intermediate processing state is preferably the same as the timing at which the intermediate processing state is acquired from the experimental data. Because the intermediate processing state is information acquired before the substrate processing apparatus 1 completes a series of processes, the time it takes for the simulator 250 to calculate the intermediate processing state is shorter than the time it takes for the simulator 250 to calculate the film thickness at the time the series of processes executed in the substrate processing apparatus 1 is completed. The first tentative condition determination unit 155A outputs second tentative conditions including the driving conditions and the intermediate processing state generated by the intermediate state generation unit 156 to the second inference unit 151A.

[0127] The second inference unit 151A infers film thickness characteristics using the trained second learning model generated by the learning device 200. The second inference unit 151A provides the second tentative conditions to the second learning model, and outputs the film thickness characteristics inferred by the second learning model to the recipe determination unit 153A.

[0128] If the film thickness characteristics input from the second inference unit 151A indicate a value within the allowable range, the recipe determination unit 153A determines a recipe based on the second tentative conditions output to the second inference unit 151A. The recipe set in the second tentative conditions is determined as the recipe for operating the substrate processing apparatus 1. The first tentative condition determination unit 155A repeatedly determines new second tentative conditions until a recipe is determined by the recipe determination unit 153A. The recipe determination unit 153A provides the determined recipe to the substrate processing apparatus 1 and operates the substrate processing apparatus 1 according to the recipe.

[0129] The correction unit 157 includes a second tentative condition determination unit 159. The second tentative condition determination unit 159 acquires an intermediate processing state from the substrate processing apparatus 1, which is executing a series of processes according to the recipe determined by the recipe determination unit 153A. Specifically, the second tentative condition determination unit 159 acquires, as the intermediate processing state, the temperature in the chamber 4 measured by a temperature sensor provided in the substrate processing apparatus 1 and the pressure in the chamber 4 measured by a pressure sensor provided in the substrate processing apparatus 1. The second tentative condition determination unit 159 also determines operating conditions. The second tentative condition determination unit 159 determines state conditions based on information set for operating the substrate processing apparatus 1 and sensor values ​​detected by various sensors provided in the substrate processing apparatus 1. The second tentative condition determination unit 159 also acquires characteristic information indicating the characteristics of the mixed liquid supplied to the substrate processing apparatus 1. The second tentative condition determination unit 159 also sets random values ​​as the values ​​of each setting item of the recipe. The second tentative condition determination unit 159 determines operating conditions including the recipe in which random values ​​are set for each setting item and the state conditions. The second tentative condition determination unit 159 outputs the second tentative conditions, including the intermediate processing state acquired from the substrate processing apparatus 1 and the driving conditions, to the second inference unit 151A.

[0130] The second inference unit 151A infers film thickness characteristics using the trained second learning model generated by the learning device 200. The second inference unit 151A provides the second tentative conditions to the second learning model, and outputs the film thickness characteristics inferred by the second learning model to the recipe determination unit 153A.

[0131] If the film thickness characteristics input from the second inference unit 151A indicate a value within the allowable range, the correction unit 157 determines a new recipe based on the second tentative conditions output to the second inference unit 151A. The recipe set in the second tentative conditions is determined as the new recipe for operating the substrate processing apparatus 1. The second tentative condition determination unit 159 repeats the determination of new second tentative conditions until a new recipe is determined by the correction unit 157. The correction unit 157 updates the recipe determined by the recipe determination unit 153A with the new recipe, provides the updated recipe to the substrate processing apparatus 1, and operates the substrate processing apparatus 1 according to the recipe.

[0132] 10 is a flowchart showing an example of the flow of a learning model generation process in the second embodiment. Referring to FIG. 10, the CPU included in the learning device 200 acquires experimental data (step S01A) and proceeds to step S02A. The acquired experimental data includes intermediate processing states. When experimental data is input from the substrate processing device 1, the experimental data is acquired. When a recording medium such as a CD-ROM on which the experimental data is recorded is loaded, the experimental data is read from the CD-ROM.

[0133] In step S02A, first training data including an intermediate processing state is determined, and the process proceeds to step S03A. Film thickness characteristics are determined based on the liquid film thickness or solid film thickness included in the experimental data, and the first training data including the intermediate processing state is determined. In the second embodiment, the first training data including the intermediate processing state includes the first condition, the film thickness characteristics, and the intermediate processing state. The film thickness characteristics are determined by comparing the liquid film thickness or solid film thickness included in the experimental data with a predetermined allowable range. The first training data including the intermediate processing state is determined from the first condition and the intermediate processing state included in the experimental data and the film thickness characteristics determined from the experimental data.

[0134] In step S03A, the CPU included in learning device 200 trains the second learning model, and the process proceeds to step S04A. The first training data including the intermediate processing state is provided to the second learning model, and a trained second learning model is generated.

[0135] In step S04A, a second condition is determined, and the process proceeds to step S05A. The second condition has the same condition items as the first condition included in the experimental data but different values. Here, multiple second conditions are determined.

[0136] In step S05A, the simulator is caused to simulate the intermediate processing state and film thickness, and the process proceeds to step S06A. The second conditions generated in step S04 are input into the simulator, and the intermediate processing state and the liquid film thickness or the solid film thickness are calculated. As a result, the intermediate processing state and the liquid film thickness or the solid film thickness are calculated for each of the multiple second conditions.

[0137] In step S06A, second enhancement data is determined, and processing proceeds to step S07A. In step S05A, film thickness characteristics are determined for each of the plurality of second conditions based on the liquid film thickness or solid film thickness calculated for each of the plurality of second conditions. The film thickness characteristics are determined by comparing the liquid film thickness or solid film thickness with a predetermined allowable range. Then, second enhancement data including the second conditions, intermediate processing states, and film thickness characteristics is determined. Therefore, the same number of second enhancement data as the number of driving conditions included in the second conditions generated in step S04A is determined.

[0138] In step S07A, the CPU included in learning device 200 performs reinforcement learning on the second learning model and ends the process. In step S03A, second reinforcement data is provided to the trained second learning model, and the trained second learning model is reinforced.

[0139] Fig. 11 is a flowchart showing an example of the flow of a recipe determination process in the second embodiment. Referring to Fig. 11, differences from the process shown in Fig. 6 are that steps S13 and S14 are changed to steps S13A and S14A, step S12A is added between step S12 and step S13A, and step S18 is added after step S17. The other processes are the same as those shown in Fig. 6, and therefore description thereof will not be repeated here.

[0140] After setting the tentative recipe in step S12, the CPU 101 included in the information processing device 100 acquires an intermediate processing state (step S12A). The simulator 250 is driven under the driving conditions, and the intermediate processing state output by the simulator 250 is acquired. The timing at which the intermediate processing state is acquired from the simulator 250 is preferably the same as the timing at which the intermediate processing state is acquired in the experimental data. The driving conditions include the state conditions acquired in step S11 and the tentative recipe set in step S12. In the next step S13A, second tentative conditions are determined, and the process proceeds to step S14A. The second tentative conditions are determined, including the state conditions acquired in step S11, the tentative recipe determined in step S12, and the intermediate processing state acquired in step S12A.

[0141] In step S14A, the film thickness characteristics are inferred using the second learning model. A second hypothetical condition is given to the second learning model, and the film thickness characteristics inferred by the second learning model are obtained.

[0142] In step S15, it is determined whether the film thickness characteristic is within an acceptable range. If step S14A indicates that the film thickness characteristic is within an acceptable range, the process proceeds to step S16; otherwise, the process returns to step S12.

[0143] In step S16, the tentative recipe included in the second tentative conditions determined in step S13A is determined as the recipe for operating the substrate processing apparatus 1, and the process proceeds to step S17. In step S17, the substrate processing apparatus 1 is operated according to the determined recipe, and the process proceeds to step S18. In step S18, a correction process is performed, and the process ends.

[0144] Fig. 12 is a flowchart showing an example of the flow of the correction process in the second embodiment. Referring to Fig. 12, in the correction process, CPU 101 included in information processing device 100 determines whether it is the correction timing (step S21). The process waits until the current time becomes the correction timing (NO in step S21), and when the current time becomes the correction timing, the process proceeds to step S22. The correction timing is a predetermined timing. It is preferable that the correction timing be the same timing as the timing when the intermediate processing state is measured in the experimental data.

[0145] In step S22, a state condition is acquired, and the process proceeds to step S23. The state condition is determined based on the output value of a sensor provided in the substrate processing apparatus 1. In step S23, an intermediate processing state is acquired, and the process proceeds to step S24. The intermediate processing state is acquired based on the output value of a sensor provided in the substrate processing apparatus 1. The intermediate processing state includes the temperature in the chamber 4, which is determined based on the output value of a temperature sensor, and the pressure in the chamber 4, which is determined based on the output value of a pressure sensor.

[0146] In step S24, a temporary recipe with arbitrary values ​​is determined, and the process proceeds to step S25. A temporary recipe is determined in which random values ​​are set as the values ​​of each setting item of the recipe.

[0147] In step S25, second tentative conditions are determined, and the process proceeds to step S26. The second tentative conditions are determined, including the state conditions acquired in step S22, the tentative recipe determined in step S24, and the intermediate processing state acquired in step S23.

[0148] In step S26, the film thickness characteristics are inferred using the second learning model. The second hypothetical conditions are given to the second learning model, and the film thickness characteristics inferred by the second learning model are obtained.

[0149] In step S27, it is determined whether the film thickness characteristic is within an acceptable range. If step S26 indicates that the film thickness characteristic is within an acceptable range, the process proceeds to step S28; otherwise, the process returns to step S24.

[0150] In step S28, the tentative recipe included in the second tentative conditions determined in step S25 is determined as the new recipe for driving the substrate processing apparatus 1, and the process proceeds to step S29. In step S29, the tentative recipe set in step S24 is determined as the new recipe for driving the substrate processing apparatus 1. In step S29, the CPU 101 provides the new recipe to the substrate processing apparatus 1, drives the substrate processing apparatus 1 according to the new recipe, and returns the process to the recipe determination process.

[0151] The information processing device 100 in the second embodiment may have the functions of the simulator 250. In this case, the simulator 250 is not necessary.

[0152] The processing system 300 in the second embodiment has the following advantages in addition to the advantages of the processing system 300 in the first embodiment.

[0153] The learning device 200 acquires an intermediate processing state before a series of processes by the substrate processing apparatus is completed, and causes the learning model to learn the first training data including the intermediate processing state, thereby improving the accuracy of inference by the learning model.

[0154] Furthermore, the learning device 200 operates the simulator under second conditions different from the first conditions to further acquire an intermediate processing state before the series of processes by the simulator is completed, and causes the learning model to learn second teacher data including the intermediate processing state. This makes it easy to generate the second teacher data, facilitating the generation of the learning model. Furthermore, since the learning model is reinforced learning using the second teacher data including the intermediate processing state, it is easy to generate a learning model with improved inference accuracy.

[0155] The learning device also adjusts the simulator using the first condition, the intermediate processing state, and either the solid film thickness or the liquid film thickness obtained through an experiment, thereby improving the accuracy of the simulator.

[0156] Furthermore, in the information processing apparatus 100, the operating conditions further include an intermediate processing state before the series of processes is completed by the substrate processing apparatus 1. The information processing apparatus 100 acquires the intermediate processing state before the series of processes executed by the substrate processing apparatus 1 according to the recipe are completed, and provides the intermediate processing state and hypothetical conditions, in which some of the multiple items are set to specified values ​​and the other items are set to arbitrary values, to the learning model as operating conditions. If the film thickness characteristics inferred by the learning model satisfy the allowable conditions, a new recipe is determined based on the hypothetical conditions. Therefore, a new recipe is determined based on the intermediate processing state before the series of processes executed by the substrate processing apparatus 1 according to the recipe are completed. Therefore, even if the operating conditions change while the substrate processing apparatus 1 is executing the series of processes, the film thickness characteristics of the film thickness of the solid film or liquid film formed by the substrate processing apparatus 1 can be kept within the allowable range.

[0157] [Third embodiment] In the second embodiment, the experimental data includes the intermediate processing state, but in the processing system 300 of the third embodiment, the experimental data does not include the intermediate processing state.

[0158] Fig. 13 is a diagram showing an example of the functional configuration of a processing system according to the third embodiment. Referring to Fig. 13, the functions differ from those shown in Fig. 9 in that the first intermediate state acquisition unit 261 is changed to a second intermediate state acquisition unit 261A. The other functions are the same as those shown in Fig. 9, and therefore description thereof will not be repeated here.

[0159] The experimental data acquired by the experimental data acquisition unit 251 does not include an intermediate processing state. Therefore, the second intermediate state acquisition unit 261A provides the driving conditions, which are the first conditions included in the experimental data, to the simulator 250, and causes the simulator 250 to perform a simulation. Then, the second intermediate state acquisition unit 261A acquires the intermediate processing state calculated by the simulator 250. The second intermediate state acquisition unit 261A outputs the intermediate processing state to the second learning unit 253A and the second adjustment unit 259A.

[0160] In the processing system 300 in the second embodiment, there is no need to collect intermediate processing states in the experimental data, and therefore the experiment for generating the first teacher data including the intermediate processing states can be simplified.

[0161] The processing system 300 in the third embodiment has the following advantages in addition to the advantages of the processing system 300 in the second embodiment.

[0162] In the processing system 300 according to the third embodiment, the learning device 200 operates the simulator under a first condition, acquires an intermediate processing state before the series of processes by the simulator is completed, and causes the learning model to learn first teacher data that further includes the intermediate processing state. Because the intermediate processing state is generated by the simulator, the simulator can calculate states that cannot be measured experimentally. Therefore, by including the intermediate processing state in the first teacher data, the accuracy of inference by the learning model can be improved.

[0163] Furthermore, the learning device 200 operates the simulator under second conditions different from the first conditions, acquires an intermediate processing state before the series of processes by the simulator is completed, and a film thickness characteristic indicating the characteristics of the film thickness of a solid or liquid film formed on the substrate, and further trains the learning model with second training data including the second conditions, the intermediate processing state, and the film thickness characteristic. This facilitates the generation of the learning model, as the second training data including the intermediate processing state is easily generated. Furthermore, reinforcement learning of the learning model with improved inference accuracy is facilitated because the learning model is reinforced with the second training data including the intermediate processing state.

[0164] [Fourth embodiment] In the processing system 300 of the fourth embodiment, the information processing device 100 determines a recipe using a first learning model generated by the learning device 200 before the substrate processing device 1 performs a series of processes, and corrects the recipe using a second learning model generated by the learning device 200 while the substrate processing device 1 is performing the series of processes.

[0165] Fig. 14 is a diagram showing an example of the functional configuration of a processing system according to the fourth embodiment. Referring to Fig. 14, the functions differ from those shown in Fig. 3 in that in learning device 200, first adjustment unit 259 is changed to second adjustment unit 259A, and second learning unit 253A, second reinforcement data acquisition unit 255A, second reinforcement learning unit 257A, and first intermediate state acquisition unit 261 are added. Also, in information processing device 100, second inference unit 151A and correction unit 157 are added. The other functions are the same as those shown in Fig. 3, and therefore description thereof will not be repeated here.

[0166] In addition, the second adjustment unit 259A, the second learning unit 253A, the second reinforcement data acquisition unit 255A, the second reinforcement learning unit 257A and the first intermediate state acquisition unit 261 in the learning device 200, and the second inference unit 151A and the correction unit 157 in the information processing device 100 have the same functions as those denoted with the same symbols in Figure 9.

[0167] In learning device 200, experimental data acquisition section 251 acquires experimental data and outputs the acquired experimental data to first learning section 253 and second adjustment section 259A.

[0168] First learning unit 253 generates first training data based on the experimental data acquired by experimental data acquisition unit 251, and causes the first training data to be trained by the first learning model, thereby generating a trained first learning model.

[0169] Furthermore, the first reinforcement learning unit 257 causes the learned first learning model to further learn the first reinforcement data acquired by the first reinforcement data acquisition unit 255. As a result, the first learning model is further trained using the first reinforcement data.

[0170] Furthermore, the second adjustment unit 259A adjusts the simulator 250 based on the experimental data acquired by the experimental data acquisition unit 251 and the intermediate processing state acquired by the first intermediate state acquisition unit 261.

[0171] The second learning unit 253A generates first training data including the intermediate processing state based on the experimental data acquired by the experimental data acquisition unit and the intermediate processing state acquired by the first intermediate state acquisition unit 261, and causes the second learning model to learn the first training data including the intermediate processing state. First condition , intermediate processing states, and film thickness characteristics.

[0172] The second reinforcement learning unit 257A causes the second learning model, which has been learned by the second learning unit 253A, to further learn the second reinforcement data acquired by the second reinforcement data acquisition unit 255A. As a result, the second learning model is further trained using the second reinforcement data.

[0173] In the information processing device 100 according to the fourth embodiment, the recipe determination unit 153 determines first tentative conditions including a recipe set to a random value, and outputs the first tentative conditions to the first inference unit 151. The first inference unit 151 provides the first tentative conditions to a first learning model, and outputs film thickness characteristics inferred by the first learning model to the recipe determination unit 153.

[0174] If the film thickness characteristic input from the first inference unit 151 is a value indicating that it is within the allowable range, the recipe determination unit 153 determines a recipe based on the first tentative condition.

[0175] The correction unit 157 also acquires an intermediate processing state from the substrate processing apparatus 1 that is executing a series of processes according to the recipe determined by the recipe determination unit 153. The correction unit 157 also determines second tentative conditions including a recipe set to a random value, and outputs the second tentative conditions to the second inference unit 151A. The second tentative conditions include the recipe, the intermediate processing state acquired from the substrate processing apparatus 1, and a state condition. The second inference unit 151A provides the second tentative conditions to a second learning model, and outputs film thickness characteristics inferred by the second learning model to the correction unit 157.

[0176] If the film thickness characteristics input from the second inference unit 151A indicate a value within the allowable range, the correction unit 157 determines a new recipe based on the second tentative conditions output to the second inference unit 151A. The recipe set in the second tentative conditions is determined as the new recipe for driving the substrate processing apparatus 1.

[0177] The learning device 200 in the fourth embodiment executes the learning model generation processes shown in Fig. 5 and Fig. 10. As a result, a first learning model and a second learning model are generated.

[0178] Fig. 15 is a flowchart showing an example of the flow of a recipe determination process in the fourth embodiment. Referring to Fig. 15, what differs from the process shown in Fig. 6 is that step S18 is added. The other processes are the same as the processes shown in Fig. 6, so description thereof will not be repeated here. In step S18, the correction process shown in Fig. 12 is executed.

[0179] The processing system 300 in the fourth embodiment has the following advantages in addition to the advantages of the processing system 300 in the third embodiment.

[0180] In the processing system 300 of the fourth embodiment, the information processing device 100 determines a recipe using a first learning model, obtains an intermediate processing state before the series of processes executed by the substrate processing device 1 according to the recipe is completed, and provides the intermediate processing state to a second learning model to determine a new recipe. Therefore, different learning models are used before the substrate processing device starts a series of processes and during the series of processes, so that an appropriate recipe can be determined at each timing.

[0181] [5] Other embodiments The learning device 200 may generate a distillation model by training a new learning model on training data including the recipe determined by the information processing device 100 and the inferred film thickness characteristics.

[0182] The control device 3 included in the substrate processing apparatus 1 may have the functions of the information processing apparatus 100. The control device 3 included in the substrate processing apparatus 1 may also have the functions of the information processing apparatus 100 and the learning apparatus 200. Furthermore, the control device 3 included in the substrate processing apparatus 1 may also have the functions of the simulator 250, the learning apparatus 200, and the information processing apparatus 100.

[0183] The intermediate processing state is not limited to the pressure and temperature inside the chamber 4, and in addition to or separately from these, at least one of the thickness, flow (speed, direction), concentration, and temperature of the liquid film, and the flow (speed, direction), partial pressure, and temperature of the airflow above the substrate W may be used. If these intermediate processing states are detected by a sensor or the like, the value detected by the sensor is used, and if they cannot be detected by a sensor or the like, they are found by the simulator 250.

[0184] [6] Correspondence between each element of the claims and each part of the embodiment Below, examples of correspondence between each element of the claims and each element of the embodiments will be described, but the present invention is not limited to the following examples. Various other elements having the configuration or function described in the claims can also be used as each element of the claims. In the above embodiment, the simulator 250 is an example of a simulator, the learning device 200 is an example of a learning device, the information processing device 100 is an example of an information processing device, and the substrate processing device 1 is an example of a substrate processing device.

[0185] The experimental data acquisition unit 251 is an example of an experimental data acquisition unit, the first learning unit 253 and the second learning unit 253A are examples of a learning unit, the first intermediate state acquisition unit 261 is an example of a first intermediate data acquisition unit, the second intermediate state acquisition unit 261A is an example of a second intermediate data acquisition unit, the first reinforcement data acquisition unit 255 and the second reinforcement data acquisition unit 255A are examples of a reinforcement data acquisition unit, the first reinforcement learning unit 257 and the second reinforcement learning unit 257A are examples of a reinforcement learning unit, and the first adjustment unit 259 and the second adjustment unit 259A are examples of an adjustment unit.

[0186] The recipe determination unit 153 and the recipe determination unit 153A are examples of a recipe determination unit, the intermediate state generation unit 156 and the correction unit 157 are examples of an intermediate processing state acquisition unit, the correction unit 157 is an example of a correction unit, the recipe determination unit 153 is an example of a recipe determination unit 153 that determines a recipe using a first learning model, and the recipe determination unit 153A is an example of a recipe determination unit that determines a recipe using a second learning model. [Explanation of symbols]

[0187] 300...processing system, 301...substrate processing system, 303...network, 100...information processing device, 200...learning device, 250...simulator, 1...substrate processing device, 2...processing unit, 3...control device, 4...chamber, 10...spin chuck, 14...spin motor, 21...processing cup, 23...cup, 31...chemical solution nozzle, 35...rinse liquid nozzle, 39...pre-drying treatment liquid nozzle, 40...pre-drying treatment liquid piping, 41...pre-drying treatment liquid valve, 42...nozzle moving unit, 43...substitution liquid nozzle, 51...blocking member, 54...blocking member lifting / lowering unit, 55...center nozzle, 58...flow rate adjustment valve, 61...film thickness sensor, 62...sensor moving part, 100...information processing device, 101 ...CPU, 102...RAM, 103...ROM, 104...storage device, 105...operation unit, 106...display device, 107...input / output I / F, 151...first inference unit, 151A...second inference unit, 153, 153A...recipe determination unit, 155, 155A...first tentative condition determination unit, 156...intermediate state generation unit, 157...correction unit, 159...second tentative condition determination unit, 200...learning device, 251...experimental data acquisition unit, 253...first learning unit, 253A...second learning unit, 255...first reinforcement data acquisition unit, 255A...second reinforcement data acquisition unit, 257...first reinforcement learning unit, 257A...second reinforcement learning unit, 259...first adjustment unit, 259A...second adjustment unit, 261...first intermediate state acquisition unit, 261A...second intermediate state acquisition unit.

Claims

1. an experimental data acquisition unit that acquires a thickness of a solid film or a liquid film formed on a substrate, the thickness being obtained by driving a substrate processing apparatus under first conditions, the substrate processing apparatus removing the solid film or the liquid film from the substrate and drying the substrate after a series of processes of supplying a liquid onto the substrate and forming a solid film or a liquid film on the substrate; a learning unit that causes a learning model to learn first teacher data including the first condition and a film thickness characteristic indicating a film thickness characteristic of a solid or a liquid, acquired by the experimental data acquisition unit; an enhanced data acquisition unit that acquires film thickness characteristics that indicate the characteristics of a film thickness of a solid or liquid formed on the substrate, the film thickness characteristics being obtained by operating a simulator that simulates a film of a solid or liquid formed in the substrate processing apparatus under second conditions different from the first conditions; and a reinforcement learning unit that causes the learning model to further learn second teacher data including the second condition and the film thickness characteristic acquired by the reinforcement data acquisition unit.

2. a first intermediate data acquiring unit that acquires an intermediate processing state at a time before a series of processing steps by the substrate processing apparatus is completed; the learning unit causes the learning model to learn the first teacher data, which further includes an intermediate processing state acquired by the first intermediate data acquisition unit; the reinforcement data acquisition unit operates the simulator under a second condition different from the first condition, and further acquires an intermediate processing state at a point in time before the series of processes by the simulator is completed; The learning device according to claim 1 , wherein the reinforcement learning unit causes the learning model to learn second teacher data that further includes an intermediate processing state acquired by the reinforcement data acquisition unit.

3. The learning device of claim 1 further comprises an adjustment unit that adjusts a simulator that simulates a solid film or a liquid film formed in the substrate processing apparatus using the first condition and either a solid film thickness or a liquid film thickness acquired by the experimental data acquisition unit.

4. an experimental data acquisition unit that acquires a thickness of a solid film or a liquid film formed on a substrate, the thickness being obtained by driving a substrate processing apparatus under first conditions, the substrate processing apparatus removing the solid film or the liquid film from the substrate and drying the substrate after a series of processes of supplying a liquid onto the substrate and forming a solid film or a liquid film on the substrate; a learning unit that causes a learning model to learn first teacher data including the first condition and a film thickness characteristic indicating a film thickness characteristic of a solid or a liquid, acquired by the experimental data acquisition unit; a second intermediate data acquisition unit that drives a simulator that simulates a solid film or a liquid film formed in the substrate processing apparatus under the first condition and acquires an intermediate processing state at a time point before the series of processes by the simulator is completed, The learning unit is a learning device that causes the learning model to learn the first teacher data, which further includes an intermediate processing state acquired by the second intermediate data acquisition unit.

5. an enhanced data acquisition unit that operates the simulator under second conditions different from the first conditions and acquires an intermediate processing state at a point before the series of processes by the simulator is completed and a film thickness characteristic that indicates a film thickness characteristic of a solid film or a liquid film formed on the substrate; The learning device according to claim 4, further comprising a reinforcement learning unit that causes the learning model to further learn second teacher data including the second condition and the intermediate processing state and film thickness characteristics acquired by the reinforcement data acquisition unit.

6. 6. The learning device according to claim 1, wherein the simulator is configured based on a physical model.

7. an experimental data acquisition unit that acquires a thickness of a solid film or a liquid film formed on a substrate, the thickness being obtained by driving a substrate processing apparatus under first conditions, the substrate processing apparatus removing the solid film or the liquid film from the substrate and drying the substrate after a series of processes of supplying a liquid onto the substrate and forming a solid film or a liquid film on the substrate; a learning unit that causes a learning model to learn first teacher data including the film thickness characteristics indicating the film thickness characteristics of a solid or a liquid, and the first condition, acquired by the experimental data acquisition unit; A learning device in which the first teaching data is classified into a plurality of channels according to the surface condition of the substrate, the shape of the pattern formed on the substrate, and the physical properties of the liquid.

8. 8. An information processing device comprising: a drive condition determination unit that determines a recipe using the learning model generated by the learning device according to claim 1.

9. a recipe determination unit that determines a recipe using a learning model that infers film thickness characteristics that indicate the characteristics of a solid film thickness or a liquid film thickness formed on a substrate at the time when a series of processes by a substrate processing apparatus is completed, from driving conditions when the substrate processing apparatus is driven to supply a liquid to a substrate and perform a series of processes to form a solid film or a liquid film on the substrate, and then remove the solid film or the liquid film from the substrate and dry the substrate, The drive conditions have values ​​set for each of a plurality of items, The recipe determination unit is an information processing device that provides the learning model with hypothetical conditions as the driving conditions, in which some of the multiple items are set to specified values ​​and other items are set to arbitrary values, and determines the recipe based on the hypothetical conditions if the film thickness characteristics inferred by the learning model satisfy the allowable conditions.

10. the driving conditions further include an intermediate processing state before the series of processes is completed by the substrate processing apparatus; an intermediate processing state acquiring unit that acquires an intermediate processing state at a time before the series of processes executed by the substrate processing apparatus according to the recipe is completed; The information processing apparatus of claim 9, further comprising: a correction unit that provides the learning model with the intermediate processing state acquired by the intermediate processing state acquisition unit and provisional conditions in which some of the plurality of items are set to specified values ​​and other items are set to arbitrary values ​​as the driving conditions, and determines a new recipe based on the provisional conditions if the film thickness characteristics inferred by the learning model satisfy the allowable conditions.

11. an intermediate processing state acquisition unit that acquires an intermediate processing state at a time before a series of processes, which includes supplying a liquid onto a substrate and forming a solid or liquid film on the substrate, is completed by a substrate processing apparatus that removes the solid or liquid film from the substrate and dries the substrate; a correction unit that determines a recipe using a learning model that infers film thickness characteristics that indicate the characteristics of a solid film thickness or a liquid film thickness formed on the substrate at the time when the series of processes by the substrate processing apparatus is completed, from driving conditions when the substrate processing apparatus is driven and an intermediate processing state at a time before the series of processes by the substrate processing apparatus driven under the driving conditions is completed, The drive conditions have values ​​set for each of a plurality of items, The correction unit provides the learning model with the intermediate processing state acquired by the intermediate processing state acquisition unit and provisional conditions in which some of the multiple items are set to specified values ​​and other items are set to arbitrary values ​​as the driving conditions, and determines a new recipe based on the provisional conditions if the film thickness characteristics inferred by the learning model satisfy the allowable conditions.

12. a recipe determination unit that determines a recipe using a first learning model that infers film thickness characteristics that indicate the characteristics of a solid film thickness or a liquid film thickness formed on a substrate when a series of processes by a substrate processing apparatus is completed, from driving conditions when the substrate processing apparatus is driven to supply a liquid to a substrate and perform a series of processes to form a solid film or a liquid film on the substrate, and then remove the solid film or the liquid film from the substrate and dry the substrate; an intermediate processing state acquiring unit that acquires an intermediate processing state at a time before the series of processes executed by the substrate processing apparatus according to the recipe is completed; and a correction unit that determines a new recipe by applying the intermediate processing state acquired by the intermediate processing state acquisition unit to a second learning model that infers film thickness characteristics that indicate the characteristics of a solid film thickness or a liquid film thickness formed on the substrate at the time when the series of processes performed by the substrate processing apparatus according to the recipe is completed, from the driving conditions and the intermediate processing state at a time before the series of processes performed by the substrate processing apparatus according to the recipe is completed.

13. The learning device according to any one of claims 1 to 7, further comprising a distillation unit that trains a new learning model using teacher data including a recipe determined by the information processing device according to any one of claims 8 to 12 and an inferred film thickness characteristic.

14. A substrate processing apparatus comprising the information processing apparatus according to any one of claims 8 to 12.

15. The substrate processing apparatus according to claim 14, further comprising the learning device according to any one of claims 1 to 7 and 13.

16. A substrate processing system comprising the information processing apparatus according to any one of claims 8 to 12 and the substrate processing apparatus.

17. an experimental data acquisition step of acquiring a thickness of a solid film or a liquid film formed on the substrate, the thickness being obtained by operating a substrate processing apparatus under first conditions, the substrate processing apparatus removing the solid film or the liquid film from the substrate and drying the substrate after a series of processes of supplying a liquid onto the substrate and forming a solid film or a liquid film on the substrate; a learning step of causing a learning model to learn first teacher data including the film thickness characteristics indicating the film thickness characteristics acquired in the experimental data acquisition step and the first condition; an enhanced data acquisition step of acquiring film thickness characteristics that indicate the characteristics of the film thickness of a solid film or a liquid film formed on the substrate, the film thickness characteristics being obtained by operating a simulator that simulates a solid film or a liquid film formed in the substrate processing apparatus under second conditions different from the first conditions; a reinforcement learning step of causing the learning model to further learn second teacher data including the second condition and the film thickness characteristics acquired in the reinforcement data acquisition step,

18. a first intermediate data acquisition step of acquiring an intermediate processing state at a time point before a series of processing steps by the substrate processing apparatus is completed; the learning step causes the learning model to learn the first teacher data further including the intermediate processing state acquired in the first intermediate data acquisition step; the reinforcement data acquisition step further acquires an intermediate processing state at a point in time before the series of processes by the simulator is completed by operating the simulator under a second condition different from the first condition; The learning method according to claim 17 , wherein the reinforcement learning step causes the learning model to learn second teacher data that further includes an intermediate processing state acquired in the reinforcement data acquisition step.

19. The learning method described in claim 17, further comprising causing the computer to execute an adjustment step of adjusting a simulator that simulates a solid film or a liquid film formed in the substrate processing apparatus using the first condition and either the solid film thickness or the liquid film thickness acquired in the experimental data acquisition step.

20. an experimental data acquisition step of acquiring a thickness of a solid film or a liquid film formed on the substrate, the thickness being obtained by operating a substrate processing apparatus under first conditions, the substrate processing apparatus removing the solid film or the liquid film from the substrate and drying the substrate after a series of processes of supplying a liquid onto the substrate and forming a solid film or a liquid film on the substrate; a learning step of causing a learning model to learn first teacher data including the film thickness characteristics indicating the film thickness characteristics acquired in the experimental data acquisition step and the first condition; a second intermediate data acquisition step of operating a simulator that simulates a solid film or a liquid film formed in the substrate processing apparatus under the first condition, and acquiring an intermediate processing state at a time point before the series of processes by the simulator is completed; The learning step is a learning method in which the learning model learns the first teacher data, which further includes the intermediate processing state acquired in the second intermediate data acquisition step.

21. a recipe determination step in which a recipe is determined using a learning model that infers film thickness characteristics that indicate the characteristics of a solid film thickness or a liquid film thickness formed on a substrate at the time when a series of processes by a substrate processing apparatus is completed, from driving conditions when the substrate processing apparatus is driven to supply a liquid to a substrate and form a solid film or a liquid film on the substrate, and then remove the solid film or the liquid film from the substrate and dry the substrate; The drive conditions have values ​​set for each of a plurality of items, The recipe determination step includes a step of providing the learning model with tentative conditions as the driving conditions, in which some of the plurality of items are set to specified values ​​and other items are set to arbitrary values, and determining the recipe based on the tentative conditions if the film thickness characteristics inferred by the learning model satisfy the allowable conditions.

22. the driving conditions further include an intermediate processing state before the series of processes is completed by the substrate processing apparatus; an intermediate processing state acquiring step of acquiring an intermediate processing state at a time point before the series of processes executed by the substrate processing apparatus according to the recipe is completed; 22. The recipe determination method according to claim 21, further comprising the step of causing the computer to execute a correction step of providing the learning model with the intermediate processing state acquired in the intermediate processing state acquisition step and provisional conditions in which some of the plurality of items are set to specified values ​​and other items are set to arbitrary values ​​as the driving conditions, and determining a new recipe based on the provisional conditions if the film thickness characteristics inferred by the learning model satisfy an allowable condition.

23. an intermediate processing state acquiring step of acquiring an intermediate processing state at a time point before a series of processes of supplying a liquid to a substrate and forming a solid film or a liquid film on the substrate is completed by a substrate processing apparatus that removes the solid film or the liquid film from the substrate and dries the substrate; a correction step of determining a recipe using a learning model that infers film thickness characteristics that indicate the characteristics of a solid film thickness or a liquid film thickness formed on the substrate at the time when the series of processes by the substrate processing apparatus is completed, from driving conditions when the substrate processing apparatus is operated and an intermediate processing state at a time before the series of processes by the substrate processing apparatus operated under the driving conditions is completed; The drive conditions have values ​​set for each of a plurality of items, The recipe determination method includes a step in which the correction step provides the learning model with the intermediate processing state acquired in the intermediate processing state acquisition step and hypothetical conditions in which some of the multiple items are set to specified values ​​and other items are set to arbitrary values ​​as the driving conditions, and determines a new recipe based on the hypothetical conditions if the film thickness characteristics inferred by the learning model satisfy the allowable conditions.

24. an experimental data acquisition step of acquiring a thickness of a solid film or a liquid film formed on the substrate, the thickness being obtained by operating a substrate processing apparatus under first conditions, the substrate processing apparatus removing the solid film or the liquid film from the substrate and drying the substrate after a series of processes of supplying a liquid onto the substrate and forming a solid film or a liquid film on the substrate; a learning step of causing a learning model to learn first teacher data including the film thickness characteristics indicating the film thickness characteristics acquired in the experimental data acquisition step and the first condition; an enhanced data acquisition step of acquiring film thickness characteristics that indicate the characteristics of the film thickness of a solid film or a liquid film formed on the substrate, the film thickness characteristics being obtained by operating a simulator that simulates a solid film or a liquid film formed in the substrate processing apparatus under second conditions different from the first conditions; a reinforcement learning step of causing the learning model to further learn second teacher data including the second condition and the film thickness characteristics acquired in the reinforcement data acquisition step.

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