Parameter correction device, parameter correction method, and computer program

Abstract

To provide a technique capable of quickly correcting discharge characteristics while reducing an environmental load.SOLUTION: A regression model construction unit 919 constructs a regression model Y, by utilizing machine learning that uses training data which uses as input a feature difference dF, which is a difference between a reference feature F0 of a reference waveform Wst measured with a reference parameter P0 and the peripheral feature F1 to Fm of the peripheral waveforms W1 to Wm measured with peripheral parameters P1 to Pm obtained by changing some of the values of the reference parameter P0, and which uses as output the parameter difference dP, which is the difference between the reference parameter P0 and the peripheral parameters P1 to Pm. A correction unit 917 inputs a feature difference F0'-F0, which is the difference between the reference feature F0 of the reference waveform Wst and a target feature F0' of a target waveform Wst' measured with the reference parameter P0 after the reference waveform Wst was measured, into the regression model Y, and corrects the reference parameter P0 according to the correction amount output from the regression model Y.SELECTED DRAWING: Figure 10

Description

Technical Field

[0001] The subject matter disclosed herein relates to a parameter correction device, a parameter correction 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 discharges a processing liquid from a slit nozzle by driving a pump and applies the processing liquid to the entire conveyed substrate. In such a coater, in recent years, with the improvement of product quality, it has been required to apply the processing liquid so that the film thickness of the processing liquid is uniform over the entire substrate. For example, in Patent Document 1, by repeatedly measuring the discharge characteristics when discharging the processing liquid, parameters for controlling the pump are adjusted and optimized.

[0003] That is, the optimization process of Patent Document 1 includes a pseudo-discharge process of discharging the processing liquid other than the substrate, a discharge characteristic measurement process of measuring the discharge characteristics of the processing liquid in the pseudo-discharge process, a state quantity derivation process of deriving a state quantity of the deviation from the target characteristics of the measured discharge characteristics, and a learning process of constructing a learning model by machine learning the change in the state quantity accompanying the change in the parameter. Then, while the state quantity exceeds a predetermined allowable range, the parameter is changed based on the learning model, and then the pseudo-discharge process, the discharge characteristic measurement process, the state quantity derivation process, and the learning process are repeatedly executed. When the state quantity falls within the allowable range, the last changed parameter is set as the parameter when discharging the processing liquid in the processing liquid supply process.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, even when discharging is performed using optimized control parameters, disturbances may occur in the discharge characteristics due to factors such as wear and deterioration of parts. In this case, if the control parameters are readjusted as in Patent Document 1, it takes time to resume the process, and the yield significantly decreases. Also, by consuming a large amount of processing liquid, there is a risk of increasing the environmental load.

[0006] An object of the present invention is to provide a technique capable of quickly correcting discharge characteristics while reducing the environmental load.

Means for Solving the Problems

[0007] To solve the above problems, a first aspect is a parameter correction device that corrects parameters for controlling a coating device so that discharge characteristics measured when a processing liquid is discharged from a nozzle become target discharge characteristics. The parameter correction device includes a reference parameter Discharge using a feature quantity of the measured reference discharge characteristics, and peripheral parameters obtained by changing some values of the reference parameters Discharge using A regression model construction unit that constructs a regression model that outputs a correction amount for correcting the reference parameter from the feature quantity difference, which is the difference between the feature quantity of the measured discharge characteristics and the input feature quantity difference, using teacher data having the difference between the reference parameter and the peripheral parameter as an output; and the feature quantity of the reference discharge characteristics, and the reference parameter after the time when the reference discharge characteristics were measured Discharge using a correction unit that inputs the difference between the feature quantity of the measured discharge characteristics into the regression model and corrects the reference parameter according to the correction amount output from the regression model.

[0008] A second aspect is the parameter correction device according to the first aspect, further including an abnormality determination unit that determines whether or not the discharge characteristics are abnormal. The correction unit corrects the reference parameter when the determination unit determines that the discharge characteristics are abnormal.

[0009] A third embodiment is a parameter correction device according to the first or second embodiment, wherein the discharge characteristics are the discharge pressure applied to the processing liquid.

[0010] The fourth aspect is a parameter correction method for correcting parameters for controlling a coating apparatus so that the discharge characteristics measured when a processing liquid is discharged become the target discharge characteristics, wherein the reference parameter Discharge using Measured characteristic quantities of the reference discharge characteristics and peripheral parameters obtained by changing some of the values ​​of the reference parameters. Discharge using The process involves constructing a regression model that outputs a correction amount for correcting the reference parameter from the feature difference, which is the difference between the measured ejection characteristic features and the reference parameter, by machine learning using training data that takes the feature difference as input and the parameter difference, which is the difference between the reference parameter and the peripheral parameter as output, and the feature difference of the reference ejection characteristic and the reference parameter after the time the reference ejection characteristic was measured. Discharge using The process includes inputting the difference between the measured discharge characteristics and the characteristic quantities into the regression model, and correcting the reference parameters according to the correction amount output from the regression model.

[0011] The fifth aspect is a computer program that causes the computer to execute the parameter correction method of the fourth aspect. [Effects of the Invention]

[0012] According to the first to fifth aspects, the reference parameter Discharge using Even if disturbances occur in the measured discharge characteristics, these disturbances can be quickly corrected by correcting the reference parameters using the parameter difference calculated using a regression model as a correction amount. Therefore, a decrease in the yield of the coating device can be avoided.

[0013] According to the parameter correction device of the second embodiment, if an abnormality occurs in the discharge characteristics of the reference parameter, the reference parameter can be appropriately corrected.

[0014] According to the parameter correction device of the third embodiment, the reference parameters can be corrected based on the discharge pressure. [Brief explanation of the drawing]

[0015] [Figure 1] This diagram schematically shows the overall configuration of the coating apparatus according to the embodiment. [Figure 2] This figure shows the configuration of the processing liquid supply mechanism included in the coating apparatus shown in Figure 1. [Figure 3] Figure 2 is a graph showing an example of the movement pattern of the operating disk in the pump. [Figure 4] This is a block diagram showing an example of the configuration of a control unit. [Figure 5] This figure shows an example of a discharge pressure waveform. [Figure 6] This is a diagram illustrating an example of a feature. [Figure 7] This is a diagram to illustrate other examples of features. [Figure 8] This diagram shows the flow of the regression model construction process by the control unit. [Figure 9] This diagram shows the flow of the correction process for reference parameters performed by the control unit. [Figure 10] This is a block diagram that conceptually illustrates the functions of the control unit. [Modes for carrying out the invention]

[0016] Embodiments of the present invention will be described below with reference to the attached drawings. Note that the components described in these embodiments are merely illustrative and are not intended to limit the scope of the present invention to them alone. In the drawings, for ease of understanding, the dimensions and number of parts may be exaggerated or simplified as needed.

[0017] <1. Embodiment> Figure 1 is a schematic diagram showing the overall configuration of a coating apparatus 1 according to an embodiment. The coating apparatus 1 is a substrate processing apparatus that coats a processing liquid onto the upper surface Sf of a substrate S. The coating apparatus 1 also functions as a discharge characteristic monitoring device that monitors the discharge characteristics of the processing liquid. Specifically, the discharge characteristics are physical quantities related to discharge, such as discharge pressure and discharge flow rate. In the following description, the case where the discharge characteristic is discharge pressure will be explained.

[0018] The substrate S is, for example, a glass substrate for a liquid crystal display device. The substrate S may also be various types of substrates for electronic devices, such as semiconductor wafers, photomask glass substrates, plasma display glass substrates, magnetic / optical disk glass or ceramic substrates, organic EL glass substrates, solar cell glass or silicon substrates, and other flexible substrates and printed circuit boards. The coating apparatus 1 is, for example, a slit coater.

[0019] In Figure 1, an XYZ coordinate system is defined to explain the arrangement of each element of the coating apparatus 1. The transport direction of the substrate S is the "X direction". In the X direction, the direction in which the substrate S moves (downstream in the transport direction) is the +X direction, and the opposite direction (upstream in the transport direction) is the -X direction. Also, the direction perpendicular to the X direction is the Y direction, and the direction perpendicular to both the X and Y directions is the Z direction. In the following explanation, the Z direction will be considered the vertical direction, and the X and Y directions will be considered the horizontal directions. In the Z direction, the +Z direction will be considered the upward direction, and the -Z direction will be considered the downward direction. Note that these directions are not intended to limit the arrangement of the coating apparatus.

[0020] The coating apparatus 1 comprises, in order in 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 through which the substrate S passes. The coating apparatus 1 further comprises a substrate transport unit 5, a coating mechanism 7, a processing liquid supply mechanism 8, and a control unit 9.

[0021] The substrate S is transported to the input conveyor 100 from equipment upstream of the coating apparatus 1. The input conveyor 100 is equipped with a roller conveyor 101 and a rotary drive mechanism 102. The rotary drive mechanism 102 rotates each roller of the roller conveyor 101. The rotation of each roller of the roller conveyor 101 transports the substrate S downstream (+X direction) in a horizontal position. "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).

[0022] The input transfer unit 2 includes a roller conveyor 21 and a rotation / lifting drive mechanism 22. The rotation / lifting drive mechanism 22 rotates each roller of the roller conveyor 21 and also raises and lowers the roller conveyor 21. The rotation of the roller conveyor 21 transports the substrate S downstream (+X direction) in a horizontal position. 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 levitation stage unit 3 via the input transfer unit 2.

[0023] As shown in Figure 1, the levitation stage section 3 is substantially flat. The levitation stage section 3 is divided into three sections along the X direction. The levitation stage section 3 comprises, in order toward the +X direction, an inlet levitation stage 31, a coating stage 32, and an outlet levitation stage 33. The upper surfaces of the inlet levitation stage 31, the coating stage 32, and the outlet levitation stage 33 are on the same plane. The levitation stage section 3 further comprises a lift pin drive mechanism 34, a levitation control mechanism 35, and a lifting drive mechanism 36. The lift pin drive mechanism 34 raises and lowers a plurality of lift pins located on the inlet levitation stage 31. The levitation control mechanism 35 supplies compressed air for levitating the substrate S to the inlet levitation stage 31, the coating stage 32, and the outlet levitation stage 33. The lifting drive mechanism 36 raises and lowers the outlet levitation stage 33.

[0024] Numerous ejection holes for ejecting compressed air supplied from the levitation control mechanism 35 are arranged in a matrix on the upper surfaces of the inlet levitation stage 31 and the outlet levitation stage 33. When compressed air is ejected from each ejection hole, the substrate S levitates upward relative to the levitation stage 3. As a result, the lower surface Sb of the substrate S separates from the upper surface of the levitation stage 3, and the substrate S is supported in a horizontal position. When the substrate S is levitated, the distance between the lower surface Sb of the substrate S and the upper surface of the levitation stage 3 (levitation amount) is, for example, 10 μm to 500 μm.

[0025] The upper surface of the coating stage 32 is provided with ejection holes for ejecting compressed air supplied from the levitation control mechanism 35 and suction holes for drawing in gas. The ejection holes and suction holes are arranged alternately in the X and Y directions. The levitation control mechanism 35 controls the amount of compressed air ejected from the ejection holes and the amount of air drawn in from the suction holes. This precisely controls the amount of levitation of the substrate S relative to the coating stage 32 so that the position of the upper surface Sf of the substrate S passing above the coating stage 32 in the Z direction is a specified value. The amount of levitation of the substrate S relative to the coating stage 32 is calculated by the control unit 9 based on the detection results of the sensor 61 or sensor 62, which will be described later. Furthermore, the amount of levitation of the substrate S relative to the coating stage 32 is preferably adjustable with high precision by airflow control.

[0026] The substrate S, which has been brought into the levitation stage section 3, is propelled in the +X direction by the roller conveyor 21 and transported onto the inlet levitation stage 31. The inlet levitation stage 31, coating stage 32, and outlet levitation stage 33 support the substrate S in a levitated state. For example, the configuration described in Japanese Patent No. 5346643 may be used as the levitation stage section 3.

[0027] The substrate transport unit 5 is located below the levitation stage unit 3. The substrate transport unit 5 comprises a chuck mechanism 51 and a suction / travel control mechanism 52. The chuck mechanism 51 is equipped with a suction pad (not shown) provided on a suction member. The chuck mechanism 51 supports the substrate S from below by bringing the suction pad into contact with the peripheral edge of the lower surface Sb of the substrate S. The suction / travel control mechanism 52 applies negative pressure to the suction pad to attract the substrate S to the suction pad. The suction / travel control mechanism 52 also causes the substrate transport unit 5 to reciprocate in the X direction.

[0028] The chuck mechanism 51 holds the substrate S such that its lower surface Sb is positioned higher than the upper surface of the levitation stage 3. With its peripheral edge held by the chuck mechanism 51, the substrate S maintains a horizontal position due to the buoyancy provided by the levitation stage 3.

[0029] As shown in Figure 1, the coating apparatus 1 is equipped with a sensor 61 for measuring the plate thickness. The sensor 61 is positioned 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 a chuck (not shown) that is not holding the substrate S directly below the sensor 61, the sensor 61 can detect the position in the vertical Z direction of the suction surface, which is the upper surface of the suction member.

[0030] The chuck mechanism 51 moves in the +X direction while holding the substrate S that has been brought into the levitation stage section 3. As a result, the substrate S is transported from above the inlet levitation stage 31, through above the coating stage 32, to above the outlet levitation stage 33. Then, the substrate S is moved from the outlet levitation stage 33 to the output transfer section 4.

[0031] The output transfer unit 4 moves the substrate S from a position above the exit floating stage 33 to the output conveyor 110. The output transfer unit 4 includes a roller conveyor 41 and a rotation / lifting drive mechanism 42. The rotation / lifting drive mechanism 42 rotates the roller conveyor 41 and also moves the roller conveyor 41 up and down along the Z direction. As each roller of the roller conveyor 41 rotates, the substrate S moves in the +X direction. Also, as the roller conveyor 41 moves up and down, the substrate S is displaced in the Z direction.

[0032] The output conveyor 110 comprises a roller conveyor 111 and a rotary drive mechanism 112. The output conveyor 110 transports the substrate S in the +X direction by the rotation of each roller of the roller conveyor 111, and discharges the substrate S outside 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 be incorporated into a separate apparatus from the coating apparatus 1.

[0033] The coating mechanism 7 coats the upper surface Sf of the substrate S with processing liquid. The coating mechanism 7 is positioned above the transport path of the substrate S. The coating mechanism 7 has a nozzle 71. The nozzle 71 is a slit nozzle with a slit-shaped discharge port on its lower surface. The nozzle 71 is connected to a positioning mechanism (not shown). The positioning mechanism moves the nozzle 71 between a coating position above the coating stage 32 (indicated by a solid line in Figure 1) and a maintenance position, which will be described later. The processing liquid supply mechanism 8 is connected to the nozzle 71. The processing liquid supply mechanism 8 supplies processing liquid to the nozzle 71, causing the processing liquid to be discharged from a discharge port located on the lower surface of the nozzle 71.

[0034] Figure 2 shows the configuration of the processing liquid supply mechanism 8 provided in the coating apparatus 1 shown in Figure 1. The processing liquid supply mechanism 8 comprises a pump 81, piping 82, a processing liquid replenishment unit 83, piping 84, an on-off valve 85, a pressure sensor 86, and a drive unit 87. The pump 81 is a supply source for supplying the processing liquid to the nozzle 71, and supplies the processing liquid by volume change. As the pump 81, for example, a bellows-type pump described in Japanese Patent Application Publication No. 10-61558 can be used. As shown in Figure 2, the pump 81 has a flexible tube 811 that can elastically expand and contract in the radial direction. One end of the flexible tube 811 is connected to the processing liquid replenishment unit 83 via piping 82. The other end of the flexible tube 811 is connected to the nozzle 71 via piping 84.

[0035] Pump 81 has a bellows 812 that is elastically deformable in the axial direction. The bellows 812 has a small bellows section 813, a large bellows section 814, a pump chamber 815, and an operating disk section 816. The pump chamber 815 is located between the flexible tube 811 and the bellows 812. An incompressible medium is sealed inside the pump chamber 815. The operating disk section 816 is connected to the drive unit 87.

[0036] The processing liquid replenishment unit 83 has a storage tank 831 for storing processing liquid. The storage tank 831 is connected to the pump 81 via piping 82. An on-off valve 833 is inserted in the piping 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, processing liquid can be supplied from the storage tank 831 to the flexible tube 811 of the pump 81. When the on-off valve 833 is closed, the supply of processing liquid from the storage tank 831 to the flexible tube 811 of the pump 81 is restricted.

[0037] The piping 84 is connected to the output side of the pump 81. The on-off valve 85 is located on the piping 84. The on-off valve 85 opens and closes the piping 84 in response to a command from the control unit 9. By opening and closing the on-off valve 85, the supply of processing liquid to the nozzle 71 is switched between and the cessation of liquid supply. The pressure sensor 86 is located on the piping 84. The pressure sensor 86 detects the pressure (discharge pressure) applied to the processing liquid supplied to the nozzle 71 and outputs a signal indicating the detected pressure value to the control unit 9.

[0038] Figure 3 is a graph showing an example of the movement pattern of the operating disk 816 in the pump 81 shown in Figure 2. In Figure 3, the horizontal axis represents time, and the vertical axis represents the movement speed of the operating disk 816. The drive unit 87 displaces the operating disk 816 axially in the movement pattern shown in Figure 3 (a pattern showing the change in the speed of the operating disk 816 over time) in response to a command from the control unit 9. The displacement of the operating disk 816 changes the volume inside the bellows 812. As a result, the flexible tube 813 expands and contracts radially, performing the pump operation, and the processing liquid supplied from the processing liquid replenishment unit 83 is delivered to the nozzle 71. The movement pattern of the operating disk 816 is closely related to the discharge characteristics of the processing liquid discharged from the nozzle 71. For this reason, a discharge pressure waveform (see Figure 5) showing the change in discharge pressure over time according to the movement pattern of the operating disk 816 is measured. The discharge volume (the amount of processed liquid discharged from the nozzle 71) also increases or decreases in accordance with the increase or decrease in discharge pressure. The discharge pressure waveform is an example of the discharge characteristics when the nozzle 71 discharges the processed liquid.

[0039] In this embodiment, optimization processing is appropriately performed to match or approximate the discharge characteristics of the processed liquid discharged from the nozzle 71 (specifically, the time change of discharge speed (discharge pressure)) to desired target characteristics by adjusting various parameters (acceleration time, steady speed, steady speed time, deceleration time, etc.) that define the movement of the operating disk 816.

[0040] As shown in Figures 1 and 2, a sensor 62 is positioned on the nozzle 71 to which the processing liquid is supplied from the processing liquid supply mechanism 8. The sensor 62 non-contactually detects the height of the substrate S in the Z direction. 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 stage 32. Then, based on the measured separation distance, the control unit 9 adjusts the coating position of the nozzle 71 by the positioning mechanism. Note that an optical sensor or an ultrasonic sensor can be used as the sensor 62.

[0041] The coating mechanism 7 includes a nozzle cleaning standby unit 72. The nozzle cleaning standby unit 72 performs predetermined maintenance on the nozzle 71, which is positioned in the maintenance position. The nozzle cleaning standby unit 72 has a roller 721, a cleaning section 722, and a roller butt 723. The nozzle cleaning standby unit 72 prepares the nozzle 71's discharge port for coating by cleaning and forming a liquid reservoir on the nozzle 71. In addition, in the coating apparatus 1, in order to evaluate the discharge pressure applied to the processing liquid, a simulated discharge is performed by discharging the processing liquid from the nozzle 71 while the nozzle 71 is in the maintenance position.

[0042] Figure 4 is a block diagram showing an example configuration of the control unit 9. The control unit 9 controls the operation of each component in the coating apparatus 1. A computer can be used as the control unit 9. The control unit 9 comprises a processor 91 and a memory 93. The processor is, for example, a CPU (Central Processing Unit). The memory 93 has a transient storage device such as RAM (Random Access Memory). The memory 93 may also have a non-transient storage device such as an HDD (Hard Disk Drive) or SSD (Solid State Drive). The memory 93 is connected to the processor 91 via bus wiring.

[0043] The control unit 9 includes a display device 95 for displaying various information and an input device 97 for receiving user commands. The display device 95 and the input device 97 are connected to the processor 91 via a wiring bus. The display device 95 is, for example, a liquid crystal display. The input device 97 is, for example, a mouse or keyboard. The display device 95 may also function as an input device if it has a touch panel.

[0044] The memory 93 stores the computer program 931. The computer program 931 is provided to the control unit 9 via the recording medium M. That is, the computer program 931 is recorded on the recording medium M in a way that it can be read by the control unit 9, which is a computer. The recording medium M can be a USB (Universal Serial Bus) memory, an optical disc such as a DVD (Digital Versatile Disc), or a magnetic disc.

[0045] The processor 91 functions as a discharge control unit 910, a discharge pressure measurement unit 911, a feature calculation unit 913, an anomaly determination unit 915, a correction unit 917, and a regression model construction unit 919 by executing the computer program 931.

[0046] The discharge control unit 910 controls the operation (supply operation) of the pump 81 that supplies the processing liquid to the nozzle 71 based on preset control parameters. In the coating apparatus 1, in order to coat the processing liquid discharged from the nozzle 71 onto the upper surface Sf of the substrate S with a uniform film thickness, control parameters closely related to the discharge pressure waveform are optimized in advance so that the discharge pressure waveform has an ideal shape before production (or mass production) of the substrate S is started.

[0047] The control parameters are, for example, setting values ​​that define the movement of the operating disk unit 816, and the multiple pump control setting values ​​shown in Figure 3 and below can be used as the control parameters to be optimized. • Steady-state speed V1 • Acceleration time T1: Time taken to accelerate from a standstill to a steady speed V1. • Steady-state speed time T2: The time required to maintain a steady-state speed V1. • Steady-state speed V2 • Acceleration time T3: Time taken to decelerate from steady speed V1 to steady speed V2. • Steady-state speed time T4: The time required to maintain a steady-state speed V2. • Steady-state speed V3 • Acceleration time T5: The time it takes to accelerate from steady speed V2 to steady speed V3. • Steady-state speed time T6: The time required to maintain a steady-state speed V3. • Steady-state speed V4 • Acceleration time T7: Time taken to decelerate from steady speed V3 to steady speed V4. • Steady-state speed time T8: The time required to maintain a steady-state speed V4. • Steady speed V5 • Acceleration time T9: Time required to accelerate from steady speed V4 to steady speed V5 • Steady-state speed time T10: The time required to maintain a steady-state speed V5. • Deceleration time T11: Time taken to decelerate from steady speed V5 to a stop.

[0048] Returning to Figure 4, the discharge pressure measurement unit 911 measures the discharge pressure. Specifically, the discharge pressure measurement unit 911 acquires the discharge pressure measured by the pressure sensor 86 at a predetermined sampling period. The discharge pressure measurement unit 911 acquires a discharge pressure waveform that shows the time change of the discharge pressure applied to the processing liquid during the period in which the processing liquid is discharged from the nozzle 71, and appropriately stores the discharge pressure data showing the acquired discharge pressure waveform in the memory 93. The discharge pressure data is time-series data showing the pressure measured at each time point.

[0049] Figure 5 shows an example of a discharge pressure waveform W. In Figure 5, the horizontal axis represents time, and the vertical axis represents discharge pressure. As shown in Figure 5, the discharge pressure is measured for a predetermined period from before the start of discharge of the processed liquid from the nozzle 71 until the end of discharge of the processed liquid from the nozzle 71.

[0050] In the example shown in Figure 5, the discharge pressure at time ta, when the discharge of the processed liquid from the nozzle 71 begins, and the discharge pressure at time te, when the discharge of the processed liquid from the nozzle 71 ends, are the initial pressure Pi. Note that the pressure at the start of discharge and the pressure at the end of discharge do not always coincide with the initial pressure Pi. The discharge period from time ta to time te is divided into the rise period Tab, the transition period Tbc, the steady-state period Tcd, and the fall period Tde.

[0051] The rise-up period Tab is the period from time ta, when the processing liquid supply mechanism 8 starts discharging the processing liquid from the nozzle 71, to time tb, when the discharge pressure reaches the target pressure Pt. Time ta is the time when the processing liquid supply mechanism 8 starts moving the operating disk 816. In other words, when the discharge of the processing liquid from the nozzle 71 starts at time ta, the discharge pressure increases from the initial pressure Pi to the target pressure Pt between time ta and time tb.

[0052] The transition period Tbc is the period from time tb to time tc, which is the time after a predetermined vibration damping period has elapsed. This vibration damping period is the time required for the time variation of the discharge pressure to stabilize.

[0053] The steady-state period Tcd is the period from time tc to time td, when the processing fluid supply mechanism 8 begins to decrease the discharge pressure. Time td is the time when the processing fluid supply mechanism 8 begins to decelerate the operating disk unit 816 from its target speed. The processing fluid supply mechanism 8 moves the operating disk unit 816 at a constant speed from time tc to time td, and begins to decelerate the operating disk unit 816 at time td. During the steady-state period Tcd, the discharge pressure basically stabilizes at the target pressure Pt. However, even during the steady-state period Tcd, the time change in discharge pressure includes small oscillations, and the discharge pressure may become greater than or less than the target pressure Pt.

[0054] The fall-off period Tde is the period from time td to time te, when the processing liquid supply mechanism 8 stops discharging the processing liquid from the nozzle 71. Time te is the time when the processing liquid supply mechanism 8 stops the operating disk unit 816. In other words, the discharge pressure decreases to the initial pressure Pi between time td and time te, and at time te, the discharge of the processing liquid from the nozzle 71 stops.

[0055] In the coating apparatus 1, before production (or mass production) of the substrate S begins, the control parameters are adjusted in advance so that the discharge pressure waveform W becomes the target reference waveform Wst (for example, the waveform shown by the dashed line in Figure 5). Specifically, the control parameters are adjusted by repeating a series of processes including simulated discharge using the control parameters, measurement of the discharge pressure waveform during simulated discharge, evaluation of the measured discharge pressure waveform, and updating the control parameters based on the evaluation. For updating the control parameters, for example, the method described in Patent Document 1 can be used. Hereinafter, the control parameters adjusted so that the discharge pressure waveform becomes the reference waveform Wst will be referred to as "reference parameters P0".

[0056] When producing substrate S, the coating apparatus 1 performs the coating process using a reference parameter P0. That is, the discharge control unit 910 controls the pump using the adjusted reference parameter P0 so that the processing liquid is discharged onto the substrate S according to the reference waveform Wst. However, even if coating is performed according to the reference parameter P0, the discharge pressure waveform may become distorted (abnormal) due to various reasons such as wear and deterioration of parts such as the pump 81 over time, or the influence of external disturbances. In other words, the discharge pressure waveform may have a partially different shape from the reference waveform Wst. When the discharge pressure waveform becomes distorted, it becomes difficult to perform normal coating. Therefore, the control unit 9 measures the discharge pressure waveform, for example by performing simulated discharge periodically, and corrects the reference parameter P0 so that the measured discharge pressure waveform approaches the original reference waveform Wst. In this way, the control unit 9 functions as a "parameter correction device".

[0057] Returning to Figure 4, the feature calculation unit 913 calculates features from the discharge pressure data measured by the discharge pressure measurement unit 911. Figure 6 is a diagram illustrating an example of features. In this example, the degree of overshoot that occurs at the rising edge of the discharge pressure waveform W is calculated as a feature. Specifically, the feature calculation unit 913 determines the sign (positive or negative) of the second derivative value Dif2 of the discharge pressure at time t11 when the discharge pressure reaches its maximum value Pmax. Then, the feature calculation unit 913 calculates the time t12 at which the sign of the second derivative value Dif2 changes twice from the sign at time t11. Furthermore, the feature calculation unit 913 determines the features of the time change of the discharge pressure during the initial oscillation period T2_s from time t11 to t12.

[0058] For example, the feature calculation unit 913 selects the smaller of the minimum discharge pressure Pmin during the initial oscillation period T2_s and the steady-state pressure Pm (the average value of the discharge pressure during the steady-state period T3) as the target pressure Pg. The feature calculation unit 913 may then calculate the difference between the maximum value Pmax and the target pressure Pg (=Pmax-Pg) as a feature. This feature becomes relatively larger as the overshoot of the time change in discharge pressure increases due to the momentum of the rise in discharge pressure.

[0059] Figure 7 illustrates another example of a feature. In this example, the stability of the time change of the discharge pressure during the transition period Tbc is calculated as a feature. Specifically, the feature calculation unit 913 calculates the root mean square error RMSE(P_measure,Pm) between the discharge pressure during the transition period Tbc and the steady-state pressure Pm, which is the average value of the discharge pressure during the steady-state period Tcd, as a feature. Such a feature becomes relatively larger as the ringing (waveform oscillation) of the discharge pressure increases during the transition period Tbc.

[0060] The features calculated by the feature calculation unit 913 are not limited to those described in Figures 6 and 7. For example, several features described in Japanese Patent Publication No. 2022-138109 can be used as features.

[0061] Returning to Figure 4, the abnormality determination unit 915 determines whether the discharge pressure waveform W measured by the discharge pressure measurement unit 911 is abnormal. As described above, the control unit 9 determines whether or not an abnormality has occurred in the discharge pressure waveform (hereinafter also referred to as "target waveform Wst'") measured by simulated discharge using the reference parameter P0, in order to correct the reference parameter P0.

[0062] For example, the anomaly detection unit 915 calculates the anomaly score of the target waveform Wst' and performs an anomaly determination based on this score. The anomaly score is a value that indicates the degree of deviation from the distribution of feature quantities of a normal discharge pressure waveform. A trained model constructed to take the feature quantities of the discharge pressure waveform as input and output the anomaly score can be used to calculate the anomaly score. Such a trained model is constructed by performing machine learning based on the k-nearest neighbors method using a set of feature quantities of multiple discharge pressure waveforms considered to be normal as training data. The anomaly score is preferably the Mahalanobis distance from the k-nearest neighbors, but it may also be the Euclidean distance or the Manhattan distance.

[0063] Furthermore, the abnormality detection unit 915 may determine whether or not there is an abnormality in the target waveform Wst' based on the probability that it corresponds to an abnormality. The target waveform Wst' can be classified into a normal state (i.e., a state close to the reference waveform Wst) and one or more abnormal states that are predetermined. Therefore, the abnormality detection unit 915 may calculate the probability (hereinafter also referred to as "abnormality probability") for each of the assumed abnormalities.

[0064] To calculate the anomaly probability, a pre-trained model can be used that is constructed to output anomaly probabilities using features as input. When constructing such a pre-trained model, first, several normal pressure discharge waveform features (hereinafter referred to as "normal features") and a considerable number of discharge pressure waveform features for each type of anomaly are prepared. Then, in order to distinguish between normal and one or more anomaly states, target values ​​are set for each state, and the model is trained to output target values ​​corresponding to the input data. A linear regression model can be used as the base model. In the case of a pre-trained model obtained in this way, the closer the output value is to a specific target value, the higher the probability that it corresponds to the anomaly associated with that specific target value.

[0065] The abnormality detection unit 915 may calculate both the degree of abnormality and the probability of abnormality, and determine whether or not an abnormality exists based on these results.

[0066] The correction unit 917 corrects the reference parameter P0. In the coating apparatus 1, the abnormality determination unit 915 determines whether or not an abnormality (disturbance) has occurred in the discharge pressure waveform W. If the abnormality determination unit 915 determines that an abnormality has occurred, the correction unit 917 corrects the reference parameter P0. However, the abnormality determination unit 915 may be omitted. In this case, the correction unit 917 may correct the reference parameter P0 based on the measured discharge pressure waveform W regardless of whether or not an abnormality has occurred.

[0067] The correction unit 917 calculates the feature difference F0'-F0, which is the difference between the reference feature F0, which is the feature of the reference waveform Wst, and the feature F0', which is the feature of the target waveform Wst', as described later. Then, the correction unit 917 inputs the feature difference F0'-F0 into the regression model Y to calculate the parameter difference ΔP, which is the correction amount, and adds the obtained parameter difference ΔP to the reference parameter P0, thereby obtaining the corrected parameter P C Obtain it.

[0068] The regression model construction unit 919 constructs a regression model Y. The method for constructing the regression model Y will be described while referring to FIG. 8. FIG. 8 is a diagram showing the flow of the process for constructing the regression model Y by the control unit 9.

[0069] First, the regression model construction unit 919 creates a plurality of peripheral parameters P1, P2, ···, P m (where m is the number of peripheral parameters) (step S11). The peripheral parameters P1 to P m are parameters obtained by changing at least one or more values among some of the values included in the reference parameter P0 by a small amount. For example, if the reference parameter P0 consists of n values (p 10 , p 20 , ···, p<x n0 )(where n is the number of parameter values), by changing some or all of these values by a small amount, m different peripheral parameters P1 to P m are created.

[0070] Subsequently, the coating device 1 performs pseudo ejection respectively using the peripheral parameters P1 to P m and measures the ejection pressure waveform respectively (step S12). As a result, the ejection pressure data of the plurality of peripheral waveforms W1, W2, ···, Wm corresponding to the plurality of peripheral parameters P1, P2, ···, P<x m are respectively obtained.

[0071] Next, the feature quantity calculation unit 913 calculates the reference feature quantity F0 of the reference waveform Wst and the peripheral feature quantities F1, F2, ···, F m of the peripheral waveforms W1 to Wm respectively (step S13). Then, the regression model construction unit 919 creates difference data sets D1, D2, ···, D<x m for each of the peripheral parameters P1 to P m (step S14). Each of the difference data sets D1 to D m is composed of a pair of parameter difference d P and feature quantity difference d F .

[0072] Parameter difference d P This is the reference parameter P0 and each peripheral parameter P1~P m This is the difference from (p 10 ,p 20 ,···p n0 (n is the number of parameters), surrounding parameter P1 is (p 11 ,p 21 ,···,p n1 ) If so, the parameter difference d P1 This is calculated by subtracting each value of the peripheral parameter P1 from each value of the reference parameter P0, as shown in the following equation.

[0073] d P1 =(p 10 -p 11 ,p 20 -p 21 ,···,p n0 -p n1 )

[0074] Also, feature difference d F This is the reference feature F0 of the reference waveform Wst and the peripheral feature F1 to F of the surrounding waveforms W1 to Wm. m This is the difference between the two, specifically the surrounding features F1~F m It is calculated by subtracting the baseline feature F0 from (f 10 ,f 20 ,···,f k0 (k is the number of features), and the surrounding features F1 are (f 11 ,f 21 ,···,f k1 ) If so, the feature difference d F1 It is calculated by subtracting each feature of the reference feature F0 from each feature of the peripheral feature F1, as shown in the following equation.

[0075] d F1 =( f 11 -f 10 ,f 21 -f 20 ,···,f k1 -f k0 )

[0076] Next, the regression model construction unit 919 generates multiple difference datasets D1~D m A regression model Y is constructed using machine learning with the difference dataset D1~D as training data. That is, the regression model construction unit 919 uses the difference dataset D1~D m Feature difference d in F1 ~d Fm The input is the difference dataset D1~D m Parameter difference d in P1 ~d Pm Supervised learning is performed with the output being [output]. As the regression model Y, for example, a random forest or a neural network can be used. The trained regression model Y can predict the correction amount (parameter difference) of the reference parameter P0 necessary to eliminate the feature difference from the feature difference. The parameters of the regression model Y obtained by machine learning (trained parameters) are appropriately stored in memory 93.

[0077] <Correction of reference parameters> Next, we will describe the process of correcting the reference parameter P0 using the constructed regression model Y. Figure 9 shows the flow of the correction process performed by the control unit 9. The correction process shown in Figure 9 is performed, for example, periodically or based on operator command input, during the stage of performing the coating process using the adjusted reference parameter P0.

[0078] First, the control unit 9 performs a simulated discharge using the reference parameter P0 and obtains the target waveform Wst' by measuring the discharge pressure (step S21). The target waveform Wst' is an example of a discharge characteristic measured after the reference waveform Wst, which is the reference discharge characteristic, is measured.

[0079] Next, the abnormality determination unit 915 of the control unit 9 determines whether or not the target waveform Wst' is abnormal (step S22). If it is determined in step S22 that the target waveform Wst' is not abnormal, the control unit 9 terminates the correction process. On the other hand, if it is determined in step S22 that the target waveform Wst' is abnormal, the feature calculation unit 913 calculates the target feature F0', which is a feature of the target waveform Wst' (step S23).

[0080] Next, the correction unit 917 of the control unit 9 corrects the reference parameter P0 using the target feature quantity F0' calculated in step S23 (step S24). Specifically, the correction unit 917 calculates the feature difference F0'-F0, which is the difference between the target feature quantity F0' and the reference feature quantity F0. The correction unit 917 inputs the calculated feature difference F0'-F0 into the regression model Y to obtain the parameter difference ΔP, which is the correction amount. The correction unit 917 adds the calculated parameter difference ΔP to the reference parameter P0 to obtain the corrected parameter P C Calculate.

[0081] Correction parameter P C Once calculated, the correction unit 917 calculates the correction parameter P C The value is saved in memory 93. Thereafter, the discharge control unit 910 adjusts the correction parameter P C The coating process is performed on the substrate S using this method.

[0082] Figure 10 is a block diagram conceptually showing the functions of the control unit 9. As explained in Figure 8, in the stage of constructing the regression model Y, the reference feature F0 of the reference waveform Wst and the surrounding waveforms W1~W are used. m The surrounding features F1~F m However, the feature quantities are calculated by the feature calculation unit 913 and passed to the regression model construction unit 919. The regression model construction unit 919 then calculates the feature difference d from the passed features. F and parameter difference d P The difference datasets D1-D are combinations of the above. mBy performing machine learning using this as training data, we construct a regression model Y.

[0083] Furthermore, during the operation phase of the regression model construction unit 919, as explained in Figure 9, the target feature F0' of the target waveform Wst' measured with the reference parameter P0 is calculated by the feature calculation unit 913 and passed to the correction unit 917. The correction unit 917 then inputs the feature difference F0'-F0 between the target feature F0' and the reference feature F0 to the regression model Y. The correction unit 917 then corrects the reference parameter P0 using the parameter difference ΔP, which is the correction amount output by the regression model Y, thereby correcting the parameter P C This is generated.

[0084] <Effects> According to the control unit 9, even if the target waveform Wst′ measured with reference parameter P0 becomes distorted, the distortion of the target waveform Wst′ can be corrected by correcting the reference parameter P0 using the parameter difference ΔP calculated using the regression model Y as a correction amount. In other words, since it is not necessary to readjust the reference parameter P0 by repeatedly performing simulated discharge, the distortion of the target waveform Wst′ can be corrected while reducing the environmental burden. This prevents a decrease in the yield of the coating apparatus 1.

[0085] <2. Variant Example> Although embodiments have been described above, the present invention is not limited to those described above, and various modifications are possible.

[0086] For example, in the above embodiment, the parameter correction device is configured as a control unit 9 provided in the coating device 1. However, the parameter correction device may be configured as a separate computer device from the coating device 1.

[0087] Furthermore, in the above embodiment, the discharge pressure is measured as a discharge characteristic, but the discharge characteristic is not limited to the discharge pressure. For example, the discharge flow rate of the processed liquid discharged from the nozzle 71 may be measured as a discharge characteristic. In this case, the discharge flow rate may be measured by, for example, providing a flow meter in the piping 84.

[0088] Although this invention has been described in detail, the above description is illustrative in all respects, and the invention is not limited thereto. It is understood that countless variations not illustrated can be conceived without falling outside the scope of this invention. The components described in each of the above embodiments and variations can be combined or omitted as appropriate, as long as they do not contradict each other. [Explanation of Symbols]

[0089] 1: Coating device 9: Control unit (parameter correction device) 913: Feature calculation unit 915: Abnormality determination section 917: Correction section 919: Regression Model Construction Department 931: Computer program W: Discharge pressure waveform (discharge characteristics) Wst: Standard waveform (standard discharge characteristics) Y: Regression model

Claims

1. A parameter correction device that corrects parameters for controlling a coating apparatus so that the discharge characteristics measured when a processing liquid is discharged from a nozzle become the target discharge characteristics, A regression model construction unit constructs a regression model that uses training data, which takes as input the feature difference, which is the difference between the feature quantity of the reference discharge characteristic measured by discharge using a reference parameter and the feature quantity of the discharge characteristic measured by discharge using peripheral parameters in which some values ​​of the reference parameter are changed, and outputs the parameter difference, which is the difference between the reference parameter and the peripheral parameter, to output a correction amount for correcting the reference parameter from the feature difference. A correction unit inputs the difference between the feature quantity of the reference discharge characteristic and the feature quantity of the discharge characteristic measured by discharge using the reference parameter after the time the reference discharge characteristic was measured into the regression model, and corrects the reference parameter according to the correction amount output from the regression model. A parameter correction device equipped with the following features.

2. A parameter correction device according to claim 1, An abnormality determination unit that determines whether or not the discharge characteristics are abnormal. Furthermore, The correction unit is a parameter correction device that corrects the reference parameters when the determination unit determines that the discharge characteristics are abnormal.

3. A parameter correction device according to claim 1 or claim 2, The discharge characteristics are the discharge pressure applied to the processing liquid, and this is a parameter correction device.

4. A parameter correction method for correcting parameters for controlling a coating apparatus so that the discharge characteristics measured when the processing liquid is discharged become the target discharge characteristics, The process involves constructing a regression model that outputs a correction amount for correcting the reference parameter from the feature difference, using training data that takes as input the feature difference, which is the difference between the feature quantity of the reference discharge characteristic measured by discharge using a reference parameter and the feature quantity of the discharge characteristic measured by discharge using peripheral parameters in which some values ​​of the reference parameter are changed, and outputs the parameter difference, which is the difference between the reference parameter and the peripheral parameter. The process involves inputting the difference between the feature quantities of the reference discharge characteristics and the feature quantities of the discharge characteristics measured by discharge using the reference parameters after the time the reference discharge characteristics were measured into the regression model, and correcting the reference parameters according to the correction amount output from the regression model. A parameter correction method, including the following.

5. It is a computer program, A computer program that causes the computer to execute the parameter correction method described in claim 4.

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