Parameter optimization device, parameter optimization method, and computer program product

Abstract

The invention provides a parameter optimization device, a parameter optimization method and a computer program product, which can effectively perform optimization of control parameters. A parameter specifying unit (913) specifies a control parameter to be evaluated next using the plurality of pieces of acquired film thickness information. A film thickness information acquisition unit (914) acquires film thickness information (TH11) based on a measurement result of a film thickness of a coating film formed by controlling the coating device using the control parameter (P1) determined by the parameter determination unit. A film thickness information estimation unit (915) estimates film thickness information corresponding to the control parameter (P2) identified by the parameter identification unit using an estimation model (Y) that outputs film thickness information using the control parameter and the process information as inputs. The loop control unit (918) performs control so as to alternately execute first processing including processing by the parameter determination unit and processing by the film thickness information acquisition unit, and second processing including processing by the parameter determination unit and processing by the film thickness information estimation unit.

Description

Technical Field

[0001] The subject matter disclosed in this specification relates to a parameter optimization device, a parameter optimization method, and a computer program. Background Art

[0002] In the manufacturing process of flat-panel displays, a device called a coater is used. A coater is a substrate processing device that uses a pump to spray a treatment liquid from a slit nozzle and apply the treatment liquid to the entire substrate being transported. With the recent improvement in product quality, such coaters are required to apply the treatment liquid in a manner that achieves a uniform film thickness across the entire substrate. For example, Patent Document 1 describes optimization by repeatedly measuring the discharge characteristics of the treatment liquid during discharge and adjusting the parameters used to control the pump.

[0003] Patent Document 1: Japanese Patent Application Laid-Open No. 2020-040046

[0004] Patent Document 2: International Publication No. 2019 / 244474

[0005] Non-Patent Literature 1: K. Swersky et al., "Multi-Task Bayesian Optimization," [Online], [Retrieved June 1, 2018], NIPS, 2013, URL: <https: / / papers.nips.cc / paper / 5086-multi-task-bayesian-optimization.pdf>

[0006] However, in the case of Patent Document 1, since the control process and the measurement of the discharge characteristics are repeated multiple times, the time required for optimizing the control parameters may be prolonged. Summary of the Invention

[0007] An object of the present invention is to provide a technology that can effectively optimize control parameters.

[0008] In order to solve the above-mentioned problems, a first method is provided. A parameter optimization device is provided for optimizing control parameters for controlling a coating device, wherein the coating device sprays a processing liquid onto a substrate to form a coating film, wherein the parameter optimization device comprises: a parameter determination unit, which uses a plurality of film thickness information respectively associated with specific control parameters to determine the control parameters to be evaluated next; a film thickness information acquisition unit, which acquires film thickness information based on a measurement result of the film thickness of a coating film, wherein the coating film is formed by controlling the coating device using the control parameters determined by the parameter determination unit; a film thickness information estimating unit, which uses an estimation model that takes control parameters and process information as input and outputs film thickness information to estimate film thickness information corresponding to the control parameters determined by the parameter determination unit; and a cycle control unit, which controls the parameter determination unit, the film thickness information acquisition unit, and the film thickness information estimating unit to alternately perform a first process and a second process, wherein the first process includes the process of the parameter determination unit and the process of the film thickness information acquisition unit, and the second process includes the process of the parameter determination unit and the process of the film thickness information estimating unit.

[0009] A second aspect is the parameter optimization device according to the first aspect, further comprising a learning unit configured to learn the estimation model using the film thickness information acquired by the film thickness information acquisition unit.

[0010] A third method is to use a parameter optimization device of the first method or the second method, wherein the second processing is a processing that repeatedly performs the processing of the parameter determination unit and the processing of the film thickness information estimation unit multiple times, and when the number of times the film thickness information is estimated by the film thickness information estimation unit in the second processing has reached a specified number, the loop control unit executes the first processing.

[0011] According to a fourth aspect, in the parameter optimization device of any one of the first to third aspects, the parameter determination unit calculates an evaluation value based on the film thickness information, and determines a control parameter to be evaluated next based on the evaluation value.

[0012] According to a fifth aspect, in the parameter optimization device of the fourth aspect, the parameter determination unit determines the control parameter to be evaluated next using a multi-task Bayesian optimization algorithm.

[0013] A sixth aspect is a parameter optimization method for optimizing parameters for controlling a coating device that sprays a processing liquid onto a substrate to form a coating film, wherein the parameter optimization method includes: a) a parameter determination step of determining a control parameter to be evaluated next using a plurality of film thickness information corresponding to specific parameters; b) a film thickness information acquisition step of acquiring film thickness information based on a measurement result of the film thickness of a coating film, wherein the coating film is formed by controlling the coating device using the control parameters determined in the parameter determination step; and c) a film thickness information estimation step of estimating film thickness information corresponding to the control parameters determined by the parameter determination step using an estimation model that takes control parameters and process information as input and outputs film thickness information, wherein a first process and a second process are alternately performed in the parameter optimization method, the first process including the parameter determination step and the film thickness information acquisition step, and the second process including the parameter determination step and the film thickness information estimation step.

[0014] A seventh aspect is a computer program product, comprising a computer program, wherein the computer program implements the steps of the parameter optimization method of the sixth aspect when executed by a processor.

[0015] According to the first to seventh methods, by alternating between searching for control parameters using the estimation model and searching for control parameters based on the time-consuming film thickness measurement results, it is possible to both reduce film thickness measurements and efficiently optimize control parameters. Furthermore, since the estimation model input includes process information, film thickness information obtained from different processes can be used as training data. Therefore, by inputting process information, the output accuracy of the estimation model can be improved, enabling high-precision optimization of control parameters.

[0016] According to the parameter optimization device of the second aspect, the accuracy of the estimation model can be improved by performing learning using the film thickness information of the coating film measured during the optimization process.

[0017] According to the parameter optimization device of the third aspect, the parameter optimization time can be further shortened.

[0018] According to the parameter optimization device of the fifth aspect, even if there is a difference between the distribution of evaluation values ​​of film thickness information based on measurement results and the distribution of evaluation values ​​of film thickness information via the estimation model, Bayesian optimization search can be performed more efficiently. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a diagram schematically showing the overall configuration of a coating device according to an embodiment.

[0020] Figure 2 Yes Figure 1FIG. 1 is a diagram showing the structure of a processing liquid supply mechanism included in the coating device shown.

[0021] Figure 3 It is a block diagram showing the structure of the control unit.

[0022] Figure 4 This diagram shows the structure of the control unit together with the flow of data.

[0023] Figure 5 This is a diagram showing an example of a flow of optimization processing of control parameters performed by the control unit.

[0024] Description of reference numerals:

[0025] 1: Coating device

[0026] 9: Control unit (parameter optimization device)

[0027] 913: Parameter determination unit

[0028] 914: Film thickness information acquisition unit

[0029] 915: Film thickness information estimation department

[0030] 916: Learning Department

[0031] 918: Circulation Control Department

[0032] 931: Computer Programs

[0033] Y: Estimated model DETAILED DESCRIPTION

[0034] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. The components described in the embodiments are merely illustrative, and the scope of the present invention is not limited thereto. In the accompanying drawings, the dimensions or quantities of various components may be exaggerated or simplified as necessary for ease of understanding.

[0035] <1. Implementation Method>

[0036] Figure 1 This figure schematically illustrates the overall structure of a coating apparatus 1 according to an embodiment. The coating apparatus 1 is a substrate processing apparatus that forms a coating film on the substrate S by spraying a processing liquid onto the upper surface Sf of the substrate S. As described later, the coating apparatus 1 functions as a parameter optimization device that optimizes the parameters used to control the spraying of the processing liquid.

[0037] The substrate S is, for example, a glass substrate for a liquid crystal display device. Furthermore, the substrate S may be a variety of substrates to be processed for electronic devices, such as semiconductor wafers, glass substrates for photomasks, glass substrates for plasma displays, glass or ceramic substrates for magneto-optical disks, glass substrates for organic EL (electroluminescence), glass substrates or silicon substrates for solar cells, other flexible substrates, and printed circuit boards. The coating device 1 is, for example, a slit coater.

[0038] exist Figure 1 In order to illustrate the configuration relationship of each element of the coating device 1, an XYZ coordinate system is defined. The conveying direction of the substrate S is the "X direction". The direction in which the substrate S moves in the X direction (the direction toward the downstream of the conveying direction) is the +X direction, and the opposite direction (the direction toward the upstream of the conveying direction) is the -X direction. In addition, the direction orthogonal to the X direction is the Y direction, and the direction orthogonal to the X and Y directions is the Z direction. In the following description, the Z direction is set as the vertical direction, and the X and Y directions are set as the horizontal directions. In the Z direction, the +Z direction is the upper direction, and the -Z direction is the lower direction. It should be noted that these directions are not intended to limit the configuration of the coating device.

[0039] The coating apparatus 1 includes, in order from the +X direction, an input conveyor 100, an input transfer unit 2, a floating stage unit 3, an output transfer unit 4, and an output conveyor 110. The input conveyor 100, the input transfer unit 2, the floating stage unit 3, the output transfer unit 4, and the output conveyor 110 form a transport path for the substrate S. The coating apparatus 1 also includes a substrate transport unit 5, a coating mechanism 7, a processing liquid supply mechanism 8, and a control unit 9.

[0040] The substrate S is conveyed from an upstream device of the coating device 1 to the input conveyor 100. The input conveyor 100 includes a roller conveyor 101 and a rotation drive mechanism 102. The rotation drive mechanism 102 rotates the rollers of the roller conveyor 101. The rotation of the rollers of the roller conveyor 101 conveys the substrate S in a horizontal position downstream (in the +X direction). The "horizontal position" refers to a state in which the main surface (the surface with the largest area) of the substrate S is parallel to the horizontal plane (XY plane).

[0041] The input transfer unit 2 includes a roller conveyor 21 and a rotation and lifting drive mechanism 22. The rotation and lifting drive mechanism 22 rotates the rollers of the roller conveyor 21 and raises and lowers the roller conveyor 21. The rotation of the roller conveyor 21 transports the substrate S in a horizontal position downstream (in the +X direction). Furthermore, the raising and lowering of the roller conveyor 21 changes the position of the substrate S in the Z direction. The substrate S is transferred from the input conveyor 100 to the floating stage 3 via the input transfer unit 2.

[0042] like Figure 1 As shown, the floating workbench portion 3 is roughly flat. The floating workbench portion 3 is divided into three parts along the X direction. The floating workbench portion 3 has an inlet floating workbench 31, a coating workbench 32 and an outlet floating workbench 33 in sequence toward the +X direction. The upper surface of the inlet floating workbench 31, the upper surface of the coating workbench 32 and the upper surface of the outlet floating workbench 33 are located in the same plane. The floating workbench portion 3 also has a lifting pin drive mechanism 34, a floating control mechanism 35 and a lifting drive mechanism 36. The lifting pin drive mechanism 34 lifts and lowers a plurality of lifting pins arranged on the inlet floating workbench 31. The floating control mechanism 35 supplies compressed air for floating the substrate S to the inlet floating workbench 31, the coating workbench 32 and the outlet floating workbench 33. The lifting drive mechanism 36 lifts and lowers the outlet floating workbench 33.

[0043] A plurality of ejection holes are arranged in a matrix on the upper surfaces of the inlet flotation stage 31 and the outlet flotation stage 33. These ejection holes eject compressed air supplied from the flotation control mechanism 35. When compressed air is ejected from each ejection hole, the substrate S floats upward relative to the flotation stage 3. As a result, the lower surface Sb of the substrate S separates from the upper surface of the flotation stage 3, and the substrate S is supported in a horizontal position. The distance (floatation amount) between the lower surface Sb of the substrate S and the upper surface of the flotation stage 3 when the substrate S is in the floating state is, for example, not less than 10 μm and not more than 500 μm.

[0044] The upper surface of the coating workbench 32 is provided with a spray hole for spraying compressed air supplied from the floating control mechanism 35 and a suction hole for sucking gas. The spray hole and the suction hole are alternately arranged in the X direction and the Y direction. The floating control mechanism 35 controls the amount of compressed air sprayed from the spray hole and the amount of air sucked from the suction hole. Thus, the floating amount of the substrate S relative to the coating workbench 32 is precisely controlled so that the position in the Z direction of the upper surface Sf of the substrate S passing above the coating workbench 32 becomes a specified value. In addition, based on the detection results of the sensor 61 or the sensor 62 described later, the floating amount of the substrate S relative to the coating workbench 32 is calculated by the control unit 9. In addition, preferably, the floating amount of the substrate S relative to the coating workbench 32 can be adjusted with high precision by airflow control.

[0045] The substrate S, which has been loaded into the floatation stage 3, is propelled in the +X direction by the roller conveyor 21 and is transported to the entrance floatation stage 31. The entrance floatation stage 31, the coating stage 32, and the exit floatation stage 33 support the substrate S in a floating state. For example, the structure described in Japanese Patent No. 5346643 can be used as the floatation stage 3.

[0046] The substrate transport unit 5 is positioned below the floating stage 3. It includes a chuck mechanism 51 and a suction and travel control mechanism 52. The chuck mechanism 51 includes a suction pad (not shown) attached to a suction member. The chuck mechanism 51 brings the suction pad into contact with the peripheral edge of the lower surface Sb of the substrate S, thereby supporting the substrate S from below. The suction and travel control mechanism 52 applies negative pressure to the suction pad, thereby sucking the substrate S onto the suction pad. Furthermore, the suction and travel control mechanism 52 reciprocates the substrate transport unit 5 in the X-direction.

[0047] The chuck mechanism 51 holds the substrate S with its lower surface Sb positioned higher than the upper surface of the float stage 3 . The substrate S is held at its periphery by the chuck mechanism 51 and maintained in a horizontal position by the buoyancy applied by the float stage 3 .

[0048] like Figure 1 As shown, the coating apparatus 1 includes a sensor 61 for measuring the plate thickness. The sensor 61 is disposed near the roller conveyor 21. The sensor 61 detects the position in the Z direction of the upper surface Sf of the substrate S held by the chuck mechanism 51. Furthermore, by positioning the chuck (not shown) without holding the substrate S directly below the sensor 61, the sensor 61 can detect the position in the vertical direction Z of the suction surface, which serves as the upper surface of the suction member.

[0049] The chuck mechanism 51 holds the substrate S loaded into the float stage 3 and moves the substrate S in the +X direction. This allows the substrate S to be transported from above the entrance float stage 31, through above the coating stage 32, to above the exit float stage 33. The substrate S is then moved from the exit float stage 33 to the output transfer unit 4.

[0050] The output transfer unit 4 moves the substrate S from a position above the exit float stage 33 to the output conveyor 110. The output transfer unit 4 includes a roller conveyor 41 and a rotation and elevation drive mechanism 42. The rotation and elevation drive mechanism 42 rotates and drives the roller conveyor 41, raising and lowering it in the Z direction. Rotating the rollers of the roller conveyor 41 moves the substrate S in the +X direction. Furthermore, raising and lowering the roller conveyor 41 displaces the substrate S in the Z direction.

[0051] The output conveyor 110 includes a roller conveyor 111 and a rotation drive mechanism 112. The output conveyor 110 transports the substrate S in the +X direction by rotating the rollers of the roller conveyor 111, thereby unloading the substrate S from the coating apparatus 1. The input conveyor 100 and the output conveyor 110 are part of the coating apparatus 1. However, the input conveyor 100 and the output conveyor 110 may also be incorporated into a device separate from the coating apparatus 1.

[0052] The coating mechanism 7 applies the coating liquid to the upper surface Sf of the substrate S. The coating mechanism 7 is arranged above the conveying path of the substrate S. The coating mechanism 7 has a nozzle 71. The nozzle 71 is a slit nozzle having a slit-shaped nozzle on the lower surface. The nozzle 71 is connected to a positioning mechanism (not shown). The positioning mechanism positions the nozzle 71 at a coating position ( Figure 1 The processing liquid supply mechanism 8 is connected to the nozzle 71. The processing liquid supply mechanism 8 supplies the processing liquid to the nozzle 71, so that the processing liquid is ejected from the ejection port arranged on the lower surface of the nozzle 71.

[0053] Figure 2 Yes Figure 1 The figure shows the structure of the treatment liquid supply mechanism 8 of the coating device 1 shown. The treatment liquid supply mechanism 8 includes a pump 81, a pipe 82, a treatment liquid replenishing unit 83, a pipe 84, an opening and closing valve 85, a pressure sensor 86 and a driving unit 87. The pump 81 is a delivery source for delivering the treatment liquid to the nozzle 71, and delivers the treatment liquid by volume change. The pump 81 can be, for example, a bellows type pump described in Japanese Patent Application Publication No. 10-61558. Figure 2 As shown, the pump 81 includes a flexible tube 811 that is elastically expandable and contractible in the radial direction. One end of the flexible tube 811 is connected to the processing liquid replenishing unit 83 via a pipe 82 , and the other end of the flexible tube 811 is connected to the nozzle 71 via a pipe 84 .

[0054] Pump 81 includes a bellows 812 that is elastically deformable in the axial direction. Bellows 812 includes a small bellows portion 813, a large bellows portion 814, a pump chamber 815, and a working disc portion 816. Pump chamber 815 is located between flexible tube 811 and bellows 812. An incompressible medium is enclosed in pump chamber 815. Working disc portion 816 is connected to drive unit 87.

[0055] The processing liquid replenishing unit 83 includes a storage tank 831 for storing processing liquid. The storage tank 831 is connected to the pump 81 via a pipe 82. An on-off valve 833 is attached to the pipe 82. The on-off valve 833 opens and closes in response to commands from the control unit 9. When the on-off valve 833 is open, the processing liquid can be replenished from the storage tank 831 to the flexible tube 811 of the pump 81. When the on-off valve 833 is closed, the replenishment of the processing liquid from the storage tank 831 to the flexible tube 811 of the pump 81 can be restricted.

[0056] Pipe 84 is connected to the output side of pump 81. An on-off valve 85 is provided on pipe 84. Valve 85 opens and closes pipe 84 in response to commands from control unit 9. Opening and closing valve 85 switches between supplying and stopping the treatment liquid to nozzle 71. A pressure sensor 86 is provided on pipe 84. Pressure sensor 86 detects the pressure (discharge pressure) applied to the treatment liquid supplied to nozzle 71 and outputs a signal indicating the detected pressure value to control unit 9.

[0057] like Figure 1 and Figure 2 As shown, a sensor 62 is provided at the nozzle 71 to which the treatment liquid is supplied from the treatment liquid supply mechanism 8. The sensor 62 detects the height of the substrate S in the Z direction in a non-contact manner. The sensor 62 is electrically connected to the control unit 9. Based on the detection result of the sensor 62, the control unit 9 measures the distance (separation distance) between the floating substrate S and the upper surface of the coating workbench 32. Then, the control unit 9 adjusts the coating position of the nozzle 71 using the positioning mechanism according to the measured separation distance. In addition, as the sensor 62, for example, an optical sensor or an ultrasonic sensor can be used.

[0058] In addition, the substrate S carried out from the output conveyor 110 is dried by a drying device or the like, thereby forming a coating film. Figure 1 As shown, the substrate S with the coating film formed thereon is conveyed to a film thickness measuring device AP1 as needed to measure the film thickness of the coating film. For example, a spectroscopic ellipsometer, an X-ray reflectivity measuring device, or the like can be used as the film thickness measuring device AP1.

[0059] The coating mechanism 7 includes a nozzle cleaning standby unit 72. This unit performs prescribed maintenance on the nozzle 71, which is located at the maintenance position. The unit includes a roller 721, a cleaning portion 722, and a roller groove 723. The unit cleans the nozzle 71 and creates a liquid reservoir, thereby adjusting the nozzle's discharge port to a state suitable for coating.

[0060] Figure 3This is a block diagram showing the structure of the control unit 9. The control unit 9 controls the operation of each component in the coating device 1. The control unit 9 can use a computer. The control unit 9 includes a processor 91 and a memory 93. The processor includes, for example, a CPU (Central Processing Unit). The memory 93 includes a temporary storage device such as a RAM (Random Access Memory). In addition, the memory 93 may also include a non-temporary storage device such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive). The memory 93 is connected to the processor 91 via bus wiring.

[0061] The control unit 9 includes a display device 95 for displaying various information and an input device 97 for receiving user input commands. The display device 95 and the input device 97 are connected to the processor 91 via bus wiring. The display device 95 is, for example, a liquid crystal display. The input device 97 is, for example, a mouse or a keyboard. Furthermore, the display device 95 may include a touch panel, thereby allowing the display device 95 to function as an input device.

[0062] The memory 93 stores a computer program 931. The computer program 931 is provided to the control unit 9 via a recording medium M. Specifically, the computer program 931 is recorded on the recording medium M in a manner readable by the computer, i.e., the control unit 9. Specifically, the recording medium M is a USB (Universal Serial Bus) memory, an optical disk such as a DVD (Digital Versatile Disc), or a magnetic disk.

[0063] The processor 91 executes the computer program 931 to function as a discharge control unit 910 , a parameter determination unit 913 , a film thickness information acquisition unit 914 , a film thickness information estimation unit 915 , a learning unit 916 , and a circulation control unit 918 .

[0064] Figure 4 This diagram illustrates the structure of the control unit along with the flow of data. The discharge control unit 910 controls the operation (delivery operation) of the pump 81 that delivers the process liquid to the nozzle 71 based on pre-set control parameters. In the coating apparatus 1, the process liquid discharged from the nozzle 71 is applied to the upper surface Sf of the substrate S with a uniform film thickness. Therefore, before the start of substrate S production (or batch production), the control parameters closely related to the discharge pressure waveform can be optimized in advance to achieve an ideal discharge pressure waveform.

[0065] The control parameters are set values ​​for pump control, and are, for example, various parameters that define the movement of the work plate 816 (e.g., acceleration time, constant speed, time to maintain constant speed, deceleration time, etc.). The control parameters are optimized by the parameter determination unit 913, the film thickness information acquisition unit 914, the film thickness information estimation unit 915, the learning unit 916, and the circulation control unit 918. In other words, the parameter determination unit 913, the film thickness information acquisition unit 914, the film thickness information estimation unit 915, the learning unit 916, and the circulation control unit 918 constitute a parameter optimization device that optimizes the control parameters.

[0066] The parameter determination unit 913 uses the acquired plurality of film thickness information corresponding to specific control parameters to determine the control parameters to be evaluated next. Figure 4 As shown, the acquired film thickness information used by the parameter determination unit 913 is stored in the database DB1 in a state corresponding to a specific control parameter. The database DB1 is a function implemented by the memory 93. The film thickness information is information related to the film thickness of the coating film formed on the substrate S, and is the film thickness distribution (film thickness profile) in one direction. In addition, the film thickness information is not limited to the film thickness distribution. As the film thickness information, for example, an indicator representing the uniformity of the film thickness (such as the average value and variance) can also be used.

[0067] The film thickness information acquisition unit 914 acquires film thickness information TH1, which is the measurement result of the coating film formed on the substrate S by controlling the coating device 1 using the control parameter P1 determined by the parameter determination unit 913. Specifically, the discharge control unit 910 performs a discharge process based on the control parameter P1 to form a coating film on the substrate S. The film thickness meter AP1 then measures the thickness of the formed coating film. The film thickness information acquisition unit 914 uses the measured film thickness to acquire the film thickness information TH1. The film thickness information acquisition unit 914 stores the acquired film thickness information TH1 in a database DB1 in a state associated with the control parameter P1 (i.e., the corresponding control parameter P1), which is the control condition when the film thickness information TH1 was obtained.

[0068] The film thickness information estimation unit 915 uses the estimation model Y to estimate the film thickness information TH2 corresponding to the control parameter P2 determined by the parameter determination unit 913. The estimation model Y is a learned model that takes the control parameter and process information as input and outputs the film thickness information. Process information is information associated with the process conditions for forming the coating film, and is information different from the control parameter. As process information, for example, the model name of the coating device, the size of the substrate forming the coating film or the type of substrate (surface material, etc.), and information related to the processing liquid (physical property information, etc.) can be used. In addition, the process information used as input can be one type of information or multiple types of information. The film thickness information estimation unit 915 stores the estimated film thickness information TH2 in the database DB1 in a state corresponding to the corresponding control parameter P2.

[0069] The parameter determination unit 913 determines the control parameter to be evaluated next using the film thickness information TH1 stored in the database DB1 by the film thickness information acquisition unit 914 or the film thickness information TH2 stored in the database DB1 by the film thickness information estimation unit 915 .

[0070] The learning unit 916 learns the estimation model Y through machine learning using teacher data that takes the acquired control parameters and process information as input and outputs film thickness information. For example, using an RNN (Recurrent Neural Network) as the estimation model Y allows estimation of film thickness information from one-dimensional data, i.e., film thickness distribution. Furthermore, when using film thickness information as an indicator of uniformity, a random forest regression model can be used as the estimation model Y, for example.

[0071] like Figure 4 As shown, the teacher data (learning data set) used by the learning unit 916 for machine learning is stored in the database DB2. The database DB2 is a function implemented by the memory 93. The film thickness information is stored in the database DB2 in a state associated with the corresponding control parameters and the corresponding process information. The film thickness information acquisition unit 914 stores the acquired film thickness information TH1 in a state associated with the corresponding control parameters and process information in the database DB2. In addition, as shown in FIG. Figure 4 As shown, the database DB2 also stores film thickness information provided from a database DB3 different from the coating apparatus 1 via a network (not shown) or the like.

[0072] The loop control unit 918 controls the parameter determination unit 913, the film thickness information acquisition unit 914, and the film thickness information estimation unit 915 to alternately perform the first process and the second process. Here, the first process is a process including the determination process of the control parameters performed by the parameter determination unit 913 and the acquisition process of the film thickness information acquisition unit 914. The second process is a process including the determination process of the control parameters performed by the parameter determination unit 913 and the estimation process of the film thickness information performed by the film thickness information estimation unit 915. As described later, in the second process, the loop control unit 918 controls to repeat the process performed by the parameter determination unit 913 and the process performed by the film thickness information estimation unit 915 multiple times. For details, please refer to Figure 5 Provide explanation.

[0073] <Parameter optimization processing>

[0074] Figure 5 : is a diagram showing an example of the flow of parameter optimization processing performed by the control unit 9. Figure 5 In the flow, parameter determination step S10 corresponds to the control parameter determination process performed by parameter determination unit 913. Furthermore, estimation step S3 corresponds to the film thickness information estimation process performed by film thickness information estimation unit 915, and acquisition step S5 corresponds to the film thickness information estimation process performed by film thickness information acquisition unit 914. That is, the first process corresponds to parameter determination step S10 and acquisition step S5, and the second process corresponds to parameter determination step S10 and estimation step S3.

[0075] In addition, Figure 5 In the process, the estimated upper limit number N is pre-set. est . Estimated upper limit number N est The number of times the estimation step S3 is repeated in one second process. In addition, the loop control unit 918 counts the number of times the estimation step S3 is executed, and the estimation execution count C is calculated. est Use as a variable.

[0076] In addition, Figure 5 In the process, the search limit N is pre-set. all .Search upper limit N all is the number of times the control parameter is searched in one optimization process (ie, the number of times the parameter determination unit 913 determines the control parameter). The loop control unit 918 counts the number of times the search is performed, thereby calculating the number of search execution times C. ser Use as a variable.

[0077] When the optimization process starts, the parameter determination unit 913 first determines the initial control parameters (initial parameter determination step S1). The initial control parameters can be values ​​already used in the coating device. Alternatively, the initial control parameters can be randomly determined values.

[0078] Next, the loop control unit 918 determines the estimated execution count C est Has the estimated upper limit N been reached? est (Determination step S2) In the determination step S2, if it is determined that the estimated number of executions C est The estimated upper limit N has not been reached est In the case of , the loop control unit 918 causes the film thickness information estimation unit 915 to perform the estimation process. That is, the film thickness information estimation unit 915 inputs the control parameters determined in the initial parameter determination step S1 (or the parameter determination step S10 described later) into the estimation model Y, thereby estimating the film thickness information (estimation step S3). In addition, the loop control unit 918 est 1 is added (increment step S4). When the increment step S4 is completed, the control unit 9 proceeds to the evaluation value calculation step S9.

[0079] In the determination step S2, if it is determined that the estimated number of executions C est Reaching the estimated upper limit N est In the case of , the film thickness information acquisition unit 914 performs acquisition processing (acquisition process S5). Specifically, the ejection control unit 910 uses the control parameter P1 determined in the initial parameter determination process S1 (or the parameter determination process S10 described later) to control the pump 81, thereby forming a coating film on the substrate S. Then, the film thickness of the coating film is measured using the film thickness meter AP1. The film thickness information acquisition unit 914 acquires the film thickness measurement result, that is, the film thickness information. After the acquisition process S5, the loop control unit 918 estimates the number of executions C est Set to 0 (reset step S6).

[0080] After resetting step S6, the loop control unit 918 determines whether to update the estimated model Y (determination step S7). Regarding whether to update the estimated model Y, for example, if the film thickness information TH1 obtained by the acquisition step S5, that is, the film thickness information TH1 that has not been used for re-learning of the estimated model Y, is more than the specified number, the estimated model Y can be updated. If it is determined in the determination step S7 that the estimated model Y is to be updated, the loop control unit 918 causes the learning unit 916 to update the estimated model Y (model update step S8). That is, the film thickness information added to the database DB1 by the acquisition step S5 is added and used to re-learn the estimated model Y. After the model update step S8 is completed, the control unit 9 enters the evaluation value calculation step S9. In addition, if it is determined in the determination step S7 that it is not to be updated, the control unit 9 skips the model update step S8 and enters the evaluation value calculation step S9.

[0081] In the evaluation value calculation step S9, the parameter determination unit 913 calculates an evaluation value for the film thickness information TH2 estimated in the estimation step S3 or the film thickness information TH1 obtained in the acquisition step S5. For example, an indicator representing the magnitude of the deviation in the film thickness distribution can be used as an evaluation value for achieving a uniform film thickness. In this case, the error relative to a predetermined target film thickness or the deviation of the error can also be used as the evaluation value.

[0082] Next, the parameter determination unit 913 determines the control parameters to be evaluated next based on the evaluation values ​​obtained in the evaluation value calculation step S9 (parameter determination step S10). Specifically, methods such as reinforcement learning (RL), Bayesian optimization, or particle swarm optimization can be used. These methods can update the control parameters during each trial to optimize the evaluation values.

[0083] In addition, as a parameter search method, the multi-task Bayesian optimization (Multi-Task Bayesian Optimization) described in non-patent document 1 or patent document 2 (International Publication No. 2019 / 244474) can also be used. The film thickness information TH1 obtained by the acquisition process S5 may be affected by noise components generated by actual measurement, etc. Therefore, there may be a difference in the distribution of evaluation values ​​between the film thickness information TH2 estimated by the estimation process S3 and the film thickness information TH1 acquired by the acquisition process S5. Therefore, by using multi-task Bayesian optimization as the object observation data and the other as the reference observation data, even if there is a difference in the distribution of evaluation values ​​between the two, the Bayesian optimization search can be performed more effectively.

[0084] After the parameter determination step S10, the loop control unit 918 sets the search execution count C serThen, the loop control unit 918 determines the number of search executions C. ser Has the search limit N been reached? all (Determination step S12) If it is determined in the determination step S12 that the search execution count C ser The search limit N has not been reached all , the control unit 9 returns to the determination step S2 to continue processing. On the other hand, if it is determined in the determination step S12 that the search execution number C ser Reached the search limit N all , the control unit 9 ends the process. As described above, the optimized control parameters are stored in the memory 93 and used in the subsequent coating process of the coating device 1.

[0085] As described above, according to the control unit 9 of this embodiment, the search for control parameters via the estimation model Y and the search for control parameters based on the film thickness measurement that requires time are alternately performed. As a result, the formation of the coating film and the measurement of the film thickness can be reduced, thereby shortening the time required for adjusting the control parameters. In addition, since the number of times the coating film is formed can be reduced, the consumption of the substrate S and the processing liquid can be reduced, thereby reducing the environmental load. In addition, the input of the estimation model includes process information, so that the film thickness information obtained by different processes can be used as teacher data. Therefore, by inputting process information, the output accuracy of the estimation model can be improved. Therefore, since the search for control parameters can be performed with high precision, optimization can be performed efficiently.

[0086] Furthermore, by relearning the estimation model Y using the film thickness information measured during the optimization of the control parameters, the output accuracy of the estimation model Y can be improved by optimization.

[0087] In the second process, the next control parameter determination process by the parameter determination unit and the estimation of the film thickness information TH2 by the film thickness information estimation unit 915 are performed multiple times until the estimation upper limit number N is reached. est This can further shorten the time required for parameter optimization.

[0088] <2. Modifications>

[0089] Although the embodiments have been described above, the present invention is not limited to the above contents and various modifications are possible.

[0090] For example, in Figure 5 In the flow shown, in the second process, the parameter determination step S10 and the estimation step S3 are repeated for the estimation upper limit number N. estIn contrast, in the first process, the parameter determination step S10 and the acquisition step S5 are executed only once. However, in the first process, the loop control unit 918 may control so that the parameter determination step S10 and the acquisition step S5 are repeatedly executed two or more times.

[0091] Alternatively, the film thickness information obtained in the estimation step S3 and the acquisition step S5 may be set to be the same as the evaluation value obtained in the evaluation value calculation step S9. In this case, the estimation model Y may be learned so that the estimation model Y outputs an evaluation value based on the control parameters. Alternatively, the film thickness information acquisition unit 914 may calculate the evaluation value based on the film thickness distribution.

[0092] In addition, Figure 5 In the flow shown, in the determination step S12, the control parameter generation is repeated until the search execution count C is reached. ser Reached the search limit N all Alternatively, the loop control unit 918 may perform control so as to repeatedly generate the control parameter until the evaluation value calculated in the evaluation value calculation step S9 exceeds a predetermined reference value.

[0093] Furthermore, in the above-described embodiment, the parameter optimization device is implemented by the control unit 9 included in the coating device 1 , but may be configured as a device separate from the coating device 1 .

[0094] While the present invention has been described in detail, the above description is in all respects illustrative and the present invention is not limited thereto. It will be appreciated that numerous modifications not shown in the examples above are conceivable without departing from the scope of the present invention. The various structures described in the above embodiments and modifications may be appropriately combined or omitted as long as they do not conflict with each other.

Claims

1. A parameter optimization device for optimizing control parameters for controlling a coating device that sprays a processing liquid onto a substrate to form a coating film, wherein: The parameter optimization device has: a parameter determination unit that determines a control parameter to be evaluated next using a plurality of film thickness information items that are respectively associated with specific control parameters; a film thickness information acquisition unit that acquires film thickness information based on a measurement result of a film thickness of a coating film formed by controlling a coating device using the control parameter determined by the parameter determination unit; a film thickness information estimating unit that estimates film thickness information corresponding to the control parameter determined by the parameter determining unit using an estimation model that takes the control parameter and process information as input and outputs the film thickness information; as well as A circulation control unit controls the parameter determination unit, the film thickness information acquisition unit and the film thickness information estimation unit to alternately perform a first process and a second process, wherein the first process includes the process of the parameter determination unit and the process of the film thickness information acquisition unit, and the second process includes the process of the parameter determination unit and the process of the film thickness information estimation unit.

2. The parameter optimization device according to claim 1, wherein: The device further includes a learning unit configured to learn the estimation model using the film thickness information acquired by the film thickness information acquisition unit.

3. The parameter optimization device according to claim 1 or 2, wherein: The second process is a process of repeatedly performing the process of the parameter determination unit and the process of the film thickness information estimation unit a plurality of times. The loop control unit executes the first process when the number of times the film thickness information estimating unit estimates the film thickness information in the second process reaches a predetermined number of times.

4. The parameter optimization device according to any one of claims 1 to 3, wherein: The parameter determination unit calculates an evaluation value based on the film thickness information and determines a control parameter to be evaluated next based on the evaluation value.

5. The parameter optimization device according to claim 4, wherein: The parameter determination unit determines the control parameter to be evaluated next using a multi-task Bayesian optimization algorithm.

6. A parameter optimization method for optimizing parameters for controlling a coating device that sprays a processing liquid onto a substrate to form a coating film, wherein: The parameter optimization method comprises: a) parameter determination step, using a plurality of film thickness information associated with specific parameters to determine the control parameters to be evaluated next; b) a film thickness information acquisition step of acquiring film thickness information based on a measurement result of the film thickness of a coating film formed by controlling a coating device using the control parameters determined in the parameter determination step; and c) a film thickness information estimating step of estimating film thickness information corresponding to the control parameters determined in the parameter determining step using an estimation model that takes control parameters and process information as input and outputs film thickness information, In the parameter optimization method, a first process and a second process are alternately performed. The first process includes the parameter determination step and the film thickness information acquisition step. The second process includes the parameter determination step and the film thickness information estimation step.

7. A computer program product comprising a computer program, wherein When the computer program is executed by a processor, the steps of the parameter optimization method according to claim 6 are implemented.

Images