Computer program, information processing method, and information processing device

Abstract

To provide a computer program, an information processing method, and an information processing device that are expected to determine the state of discharge of liquid such as chemical or cleaning liquid in substrate processing.SOLUTION: A computer program acquires moving images of a discharge portion of a substrate processing device that discharges liquid onto a substrate to be processed, inputs frame images contained in the acquired moving images into a learning model that has been machine-learned to accept images of the discharge portion as input and output information related to the discharge status of liquid from the discharge portion, acquires the information related to the discharge status output by the learning model, and causes a computer to execute a process to determine whether the liquid discharge is correct or not on the basis of the acquired information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a computer program, an information processing method, and an information processing device. [Background technology]

[0002] Patent Document 1 proposes a substrate processing method including a holding step of transporting and holding a substrate inside a chamber, a supply step of supplying a fluid to the substrate inside the chamber, an imaging step of using a camera to sequentially capture images of the inside of the chamber to obtain image data, a condition setting step of identifying a monitoring target from a plurality of candidate monitoring targets inside the chamber and changing image conditions based on the monitoring target, and a monitoring step of performing monitoring processing on the monitoring target based on image data having image conditions according to the monitoring target. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-190511 Summary of the Invention [Problem to be solved by the invention]

[0004] The present disclosure provides a computer program, an information processing method, and an information processing device that are expected to determine the state related to the discharge of a liquid such as a chemical liquid or a cleaning liquid in substrate processing. [Means for solving the problem]

[0005] A computer program according to one embodiment acquires video images of a discharge section of a substrate processing apparatus discharging liquid onto a substrate to be processed, inputs frame images contained in the acquired video images into a machine-learned learning model that accepts images of the discharge section as input and outputs information relating to the discharge status of liquid from the discharge section, acquires the information relating to the discharge status output by the learning model, and causes a computer to execute a process to determine whether the liquid was discharged correctly based on the acquired information. [Effects of the Invention]

[0006] According to the present disclosure, it is expected that the state regarding the discharge of liquid such as chemical liquid or cleaning liquid in substrate processing can be determined. [Brief explanation of the drawings]

[0007] [Figure 1] 1 is a schematic diagram for explaining a configuration example of a substrate processing apparatus according to an embodiment of the present invention; [Figure 2] 1 is a schematic diagram illustrating an overview of an information processing system according to an embodiment of the present invention. [Figure 3] 1 is a block diagram showing an example of a configuration of an information processing device according to an embodiment of the present invention; [Figure 4] 1 is a schematic diagram for explaining an example of the configuration of a learning model used by the information processing device according to the present embodiment. FIG. [Figure 5] FIG. 10 is a schematic diagram illustrating an example of a discharge state. [Figure 6] 10 is a flowchart showing an example of the procedure of a discharge state determination process performed by the information processing device according to the present embodiment. [Figure 7] 10 is a flowchart illustrating an example of a procedure of an abnormality determination process performed by the information processing device according to the present embodiment. [Figure 8] 10 is a flowchart illustrating an example of a procedure of an abnormality determination process performed by the information processing device according to the present embodiment. [Figure 9] 10 is a schematic diagram showing an example of a warning screen displayed by the information processing device according to the present embodiment; FIG. [Figure 10]FIG. 2 is a schematic diagram showing an example of information display by the information processing device according to the present embodiment. [Figure 11] 10 is a flowchart showing an example of a procedure of an abnormality determination process performed by an information processing device according to Modification 1. [Figure 12] FIG. 11 is a schematic diagram showing an example of a warning screen displayed by an information processing device according to Modification 2. DETAILED DESCRIPTION OF THE INVENTION

[0008] Specific examples of information processing systems according to embodiments of the present disclosure will be described below with reference to the drawings. Note that the present disclosure is not limited to these examples, but is defined by the claims, and is intended to include all modifications within the meaning and scope of the claims.

[0009] <System configuration> 1 is a schematic diagram illustrating an example of the configuration of a substrate processing apparatus 1 according to this embodiment. The substrate processing apparatus 1 according to this embodiment is an apparatus that performs substrate processing, known as wet etching, in which a substrate to be processed (e.g., a wafer having an oxide film, nitride film, or the like formed thereon) is rotated while a chemical solution that dissolves the film is supplied onto the film to process the substrate into a desired shape. The substrate processing apparatus 1 according to this embodiment is configured to include a chamber 11, a substrate holding mechanism 12, a discharge unit 13, and a collection cup 14.

[0010] The chamber 11 is a sealed reaction vessel, and houses therein a substrate holding mechanism 12, a discharge unit 13, a collection cup 14, etc. An FFU (Fan Filter Unit) 15 is provided on the ceiling of the chamber 11. The FFU 15 forms a downflow within the chamber 11.

[0011] The substrate holding mechanism 12 has a holder 12a, a support 12b, and a drive unit 12c. The holder 12a is, for example, disk-shaped and holds a substrate (wafer) to be processed horizontally on the disk. The support 12b is a cylindrical member connected to the center of the underside of the holder 12a and extending vertically (up and down in FIG. 1 ), supporting the holder 12a horizontally. The lower end of the support 12b is connected to the drive unit 12c and is rotatably supported by the drive unit 12c. The drive unit 12c has a prime mover such as a motor and rotates the support 12b around its axis. As a result, the substrate holding mechanism 12 rotates the support 12b using the drive unit 12c, thereby rotating the holder 12a supported by the support 12b and rotating the substrate held by the holder 12a.

[0012] The discharge unit 13 discharges a liquid such as a chemical solution or a cleaning solution onto the substrate held by the substrate holding mechanism 12. For example, dilute hydrofluoric acid is used as the chemical solution, and pure water is used as the cleaning solution, but the liquids discharged by the discharge unit 13 are not limited to these. The discharge unit 13 is connected to a liquid supply source 16 provided outside the chamber 11 via, for example, a tubular liquid supply path, and discharges the liquid supplied from this supply source 16 onto the substrate. The discharge unit 13 is also connected to a drive mechanism (not shown) and can move horizontally between the center and peripheral edges of the substrate. By combining the rotation of the substrate by the substrate holding mechanism 12 and the horizontal movement of the discharge unit 13 by the drive mechanism, the substrate processing apparatus 1 can discharge the liquid from the discharge unit 13 to an appropriate position on the substrate to be processed.

[0013] Recovery cup 14 is disposed so as to surround holding portion 12a of substrate holding mechanism 12, and collects liquid scattered from the substrate due to the rotation of holding portion 12a. A drainage port 14a is provided at the bottom of recovery cup 14, and liquid collected by recovery cup 14 is discharged from drainage port 14a to the outside of chamber 11. In addition, an exhaust port 14b is provided at the bottom of recovery cup 14, and gas supplied from FFU 15 is discharged from exhaust port 14b to the outside of chamber 11.

[0014] 1 is configured to include one discharge unit 13 that discharges a liquid. The substrate processing apparatus 1 can selectively discharge either a chemical liquid for dissolving a substrate or a cleaning liquid for cleaning the substrate by switching between the chemical liquid for dissolving the substrate and the cleaning liquid using a supply source 16. However, the substrate processing apparatus 1 may be configured to include multiple discharge units 13. For example, the substrate processing apparatus 1 may be configured to include a separate discharge unit 13 for discharging a chemical liquid and a separate discharge unit 13 for discharging a cleaning liquid.

[0015] FIG. 2 is a schematic diagram for explaining an overview of an information processing system according to this embodiment. The information processing system according to this embodiment is configured to include the above-described substrate processing apparatus 1, an information processing apparatus 3, and a camera 5. The camera 5 has an imaging element such as a CCD (Charge Coupled Device) or a CMOS (Complementary Metal Oxide Semiconductor), and can capture so-called moving images by capturing images several tens of times per second. The camera 5 is provided, for example, in the chamber 11 of the substrate processing apparatus 1, and captures images of the discharge unit 13 during substrate processing. The camera 5 transmits data of the moving images obtained by capturing the images to the information processing apparatus 3. The moving image data is, for example, data in which a plurality of still images (frame images) are arranged in time series. The camera 5 may be, for example, a device provided in the substrate processing apparatus 1, or may be, for example, a device separate from the substrate processing apparatus 1.

[0016] The information processing device 3 is a device that controls and monitors the substrate processing by the substrate processing device 1. In this embodiment, the information processing device 3 is provided as a device separate from the substrate processing device 1, but this is not limited thereto and the information processing device 3 may be a device integrated with the substrate processing device 1. The information processing device 3 is connected to the substrate processing device 1 and the camera 5 via, for example, a communication cable, and can transmit and receive data between the substrate processing device 1 and the camera 5. The information processing device 3 receives moving image data of the discharge unit 13 transmitted by the camera 5 and determines the state of liquid discharge by the discharge unit 13 based on the received moving image data. The information processing device 3 controls the operation of the substrate processing device 1 according to the determined state of liquid discharge. The information processing device 3 also determines whether the substrate processing is normal or abnormal based on the determined state of liquid discharge, and if an abnormality is detected, notifies the user of the abnormality by outputting a message, voice, or the like.

[0017] 3 is a block diagram showing an example of the configuration of an information processing device 3 according to this embodiment. The information processing device 3 according to this embodiment can be realized by installing a predetermined application program or the like in a general-purpose information processing device such as a personal computer or a server computer. The information processing device 3 according to this embodiment is configured to include a processing unit 31, a storage unit 32, a communication unit 33, a display unit 34, and an operation unit 35. Note that, in this embodiment, the processing will be described as being performed by one information processing device 3, but the processing of the information processing device 3 may be distributed among a plurality of devices.

[0018] The processing unit 31 is configured using an arithmetic processing device such as a CPU (Central Processing Unit), an MPU (Micro-Processing Unit), a GPU (Graphics Processing Unit) or a quantum processor, a ROM (Read Only Memory), a RAM (Random Access Memory), etc. The processing unit 31 reads and executes a program 32a stored in the storage unit 32 to perform various processes, such as determining the discharge state of the discharge unit 13 of the substrate processing apparatus 1 based on the moving image acquired from the camera 5, controlling the operation of the substrate processing apparatus 1 based on the determined discharge state, and notifying of an abnormality related to the substrate processing based on the determined discharge state.

[0019] The storage unit 32 is configured using a large-capacity storage device such as a hard disk or an SSD (Solid State Drive). The storage unit 32 stores various programs executed by the processing unit 31 and various data required for the processing of the processing unit 31. In this embodiment, the storage unit 32 stores a program 32a executed by the processing unit 31. The storage unit 32 also includes a model information storage unit 32b that stores information related to a machine-learned learning model used by the information processing device 3, and a log information storage unit 32c that stores log information related to substrate processing by the substrate processing apparatus 1.

[0020] In this embodiment, the program (computer program, program product) 22a is provided in a form recorded on a recording medium 99 such as a memory card or an optical disc, and the information processing device 3 reads the program 32a from the recording medium 99 and stores it in the storage unit 32. However, the program 32a may also be written to the storage unit 32, for example, during the manufacturing stage of the information processing device 3. Furthermore, for example, the program 32a may be distributed by a remote server device or the like and acquired by the information processing device 3 via communication. For example, the program 32a may be recorded on the recording medium 99 and read by a writing device and written to the storage unit 32 of the information processing device 3. The program 32a may be provided in a form distributed via a network, or may be provided in a form recorded on the recording medium 99.

[0021] The model information storage unit 32b stores information about a learning model that has undergone machine learning. The information about the learning model may include, for example, information indicating the configuration of the learning model and information such as the values ​​of internal parameters determined by machine learning.

[0022] 4 is a schematic diagram for explaining an example of the configuration of a learning model used by the information processing device 3 according to this embodiment. The learning model according to this embodiment is a learning model that has been machine-learned in advance to receive an image (still image) of the discharge unit 13 of the substrate processing device 1 as an input and output information about the state of liquid discharged by the discharge unit 13. The learning model according to this embodiment may be configured, for example, as a CNN (Convolutional Neural Network) or a DNN (Deep Neural Network), but is not limited to these and may be a learning model with any configuration.

[0023] In this embodiment, the liquid ejection state by the ejection unit 13 is classified into four states: "liquid column present," "liquid column broken and falling," "droplets present," and "no liquid." The learning model outputs four values ​​corresponding to these four states, and the information processing device 3 can determine the state corresponding to the largest value of the four values ​​output by the learning model as the ejection state at that time. Note that the ejection states are not limited to the above four states, and states other than the above four may be used, or three or fewer states or five or more states may be used.

[0024] In order to perform machine learning to generate a learning model, images of the discharge unit 13 of the substrate processing apparatus 1 discharging liquid taken by the camera 5 are collected in advance, and a designer or the like performs a task of labeling the discharge state of the discharge unit 13 captured in the collected images as corresponding to one of the four states (annotation task). The information processing apparatus 3 can generate a learning model by performing so-called supervised machine learning processing based on learning data in which the images of the discharge unit 13 are associated with labels of the discharge states. The information processing apparatus 3 stores information about the learning model generated by machine learning in the model information storage unit 32b.

[0025] In this embodiment, information about the learning model is stored in the information processing device 3, and processing using the learning model is performed by the information processing device 3, but this is not limited to this. Information about the learning model may be stored in a device different from the information processing device 3, and this device may perform processing using the learning model, and the information processing device 3 may acquire the processing results from this device. Furthermore, machine learning processing of the learning model may be performed by the information processing device 3, or may be performed by a device different from the information processing device 3.

[0026] The log information storage unit 32c stores various information obtained in association with the substrate processing performed by the substrate processing apparatus 1, for example, in association with the date and time, identification information of the substrate processing, etc. The information stored in the log information storage unit 32c may include, for example, information such as moving images captured by the camera 5, frame images extracted from the moving images, a discharge state determined by a learning model based on the frame images, whether or not there is an abnormality in the substrate processing based on the discharge state, or control details regarding the substrate processing performed based on the discharge state.

[0027] The communication unit 33 transmits and receives data between the substrate processing apparatus 1 and the camera 5 via, for example, a wired or wireless network N. In this embodiment, the communication unit 33 receives image data transmitted from the camera 5 and transmits the image data to the processing unit 31. The communication unit 33 also receives control information from the processing unit 31, including operation settings and instructions related to substrate processing, and transmits the received control information to the substrate processing apparatus 1, thereby controlling the operation of the substrate processing apparatus 1.

[0028] The display unit 34 is configured using a liquid crystal display or the like, and displays various images, characters, etc. based on the processing of the processing unit 31. The display unit 34 displays various information, such as images (moving images or still images) captured by the camera 5, information on the discharge state determined by the learning model, or notifications on abnormalities in substrate processing.

[0029] The operation unit 35 accepts user operations and notifies the processing unit 31 of the accepted operations. For example, the operation unit 35 accepts user operations using an input device such as a mechanical button or a touch panel provided on the surface of the display unit 34. Furthermore, for example, the operation unit 35 may be an input device such as a mouse and a keyboard, and these input devices may be configured to be detachable from the information processing device 3.

[0030] The storage unit 32 may be an external storage device connected to the information processing device 3. The information processing device 3 may be a multi-computer including multiple computers, or may be a virtual machine virtually constructed by software. The information processing device 3 is not limited to the above configuration, and may include, for example, a reading unit that reads information stored in a portable storage medium, and may not include, for example, the display unit 34 and the operation unit 35.

[0031] In the information processing device 3 according to this embodiment, the processing unit 31 reads and executes the program 32a stored in the storage unit 32, whereby the image acquisition unit 31a, the discharge state determination unit 31b, the abnormality determination unit 31c, the display processing unit 31d, the control processing unit 31e, etc. are realized as software functional units in the processing unit 31. Note that in this drawing, functional units that perform processing related to the discharge state of the discharge unit 13 of the substrate processing apparatus 1 are shown as functional units of the processing unit 31, and functional units related to processing other than these are not shown.

[0032] The image acquisition unit 31a communicates with the camera 5 via the communication unit 33, thereby acquiring image data of the discharge unit 13 of the substrate processing apparatus 1 captured by the camera 5. In this embodiment, the camera 5 is a camera that captures moving images by capturing images several tens of times per second. The image data acquired by the image acquisition unit 31a may be in the form of moving images or still images (frame images) included in the moving images. The image acquisition unit 31a repeatedly acquires images from the camera 5, and stores the images acquired from the camera 5 in the log information storage unit 32c with information such as the date and time of image capture and identification information of the substrate being processed. The image acquisition unit 31a also repeatedly acquires images from the camera 5 while the substrate is being processed, thereby obtaining time-series still images of the discharge unit 13.

[0033] The discharge state determination unit 31b performs a process of determining the discharge state of the discharge unit 13 of the substrate processing apparatus 1 based on the images (frame images included in the moving image) acquired by the image acquisition unit 31a. As described above, in this embodiment, the discharge state determination unit 31b determines the discharge state using the learning model stored in the model information storage unit 32b. The discharge state determination unit 31b inputs the image of the discharge unit 13 acquired by the image acquisition unit 31a to the learning model and acquires four values ​​output by the learning model. By comparing the four values ​​acquired from the learning model, the discharge state determination unit 31b determines whether the discharge state of the discharge unit 13 is "liquid column present," "liquid column broken and falling," "liquid droplets present," or "no liquid." The discharge state determination unit 31b stores information related to the determined discharge state in the log information storage unit 32c in association with the original image. In addition, the ejection state determination unit 31b inputs multiple images acquired by the image acquisition unit 31a in chronological order from the camera 5 into a learning model in chronological order to determine the ejection state, and can obtain a determination result of the ejection state in chronological order.

[0034] Based on the discharge state determination result by the discharge state determination unit 31b, the abnormality determination unit 31c determines whether the substrate processing performed by the substrate processing apparatus 1 is normal or abnormal. In this embodiment, the abnormality determination unit 31c determines the timing at which the discharge state changed based on a plurality of time-series discharge states determined by the discharge state determination unit 31b based on a plurality of images acquired in time series by the image acquisition unit 31a, and determines whether the substrate processing is normal or abnormal based on whether this timing is appropriate.

[0035] In this embodiment, the information processing device 3 controls the substrate processing by the substrate processing apparatus 1. For example, the information processing device 3 controls the start and stop of liquid discharge from the discharge unit 13 by transmitting to the substrate processing apparatus 1 a command to open or close a valve provided in a liquid supply path from the liquid supply source 16 to the discharge unit 13. The abnormality determination unit 31c calculates the time from when a command to close the valve is given to the substrate processing apparatus 1 to when the liquid column discharged from the discharge unit 13 is cut off based on multiple chronological determination results by the discharge state determination unit 31b. In this case, the abnormality determination unit 31c identifies, for example, the timing at which the discharge state changes from "liquid column present" to "liquid column cut off and falling," and calculates the time from when the command to close the valve was given to this timing. If the calculated time exceeds a predetermined threshold (e.g., 0.6 to 0.8 seconds), the abnormality determination unit 31c determines that an abnormality has occurred in the liquid discharge during substrate processing.

[0036] The abnormality determination unit 31c also calculates the time from when the discharge unit 13 finishes discharging the liquid to when the last droplet falls. In this case, the abnormality determination unit 31c identifies, for example, a first timing when the discharge state changes from "liquid column present" to "liquid column broken and falling" and a second timing when the discharge state changes from "liquid column broken and falling" to "no liquid," and calculates the time from the identified first timing to the second timing. However, if the state changes from "liquid column broken and falling" to "no liquid" and then changes again to "liquid droplets present" within a predetermined time, the abnormality determination unit 31c determines that the second timing is the timing when this droplet falls and the state changes back to "no liquid." If the calculated time exceeds a predetermined threshold (for example, several seconds to several tens of seconds), the abnormality determination unit 31c determines that an abnormality has occurred in the discharge of liquid during substrate processing.

[0037] The abnormality determination unit 31c may also perform the abnormality determination using methods other than these. For example, the abnormality determination unit 31c may simply determine that an abnormality exists when a "droplet present" ejection state occurs. For example, the abnormality determination unit 31c may calculate the size of the droplets shown in a frame image determined to be in a "droplet present" ejection state, and determine that an abnormality exists when the calculated droplet size exceeds a threshold value. For example, the abnormality determination unit 31c may also determine that an abnormality exists when the number of times droplets are generated exceeds a threshold value.

[0038] The display processing unit 31d performs processing to display various characters, images, and the like on the display unit 34. In the present embodiment, when the abnormality determination unit 31c determines that an abnormality has occurred, the display processing unit 31d displays a warning screen for the user on the display unit 34 to notify the user of the occurrence of the abnormality. The display processing unit 31d displays, for example, an image (moving image or frame image) that caused the determination that an abnormality has occurred, information related to the abnormality that has occurred, and identification information of the substrate to be processed on the warning screen. Note that the display processing unit 31d may display various information other than the above on the display unit 34.

[0039] The control processing unit 31e controls the discharge of liquid performed in substrate processing by the substrate processing apparatus 1 based on the discharge state determination result by the discharge state determination unit 31b and the abnormality determination result by the abnormality determination unit 31c. In this embodiment, the substrate processing apparatus 1 is provided with a valve in a liquid supply path from the liquid supply source 16 to the discharge unit 13, and the control processing unit 31e can control the discharge of liquid by the discharge unit 13 by issuing an opening / closing command for this valve to the substrate processing apparatus 1. The control of the valve may be, for example, a simple control of opening and closing, or may be a control of adjusting the opening / closing amount or opening / closing speed. Furthermore, if a single discharge unit 13 is configured to be able to selectively discharge either a chemical liquid or a cleaning liquid, the control processing unit 31e may perform switching control to determine which liquid is to be discharged.

[0040] For example, when the abnormality determination unit 31c determines that an abnormality has occurred, the control processing unit 31e performs control to close the valve, thereby stopping the discharge of liquid by the discharge unit 13. In this case, the control processing unit 31e may, for example, discharge a cleaning liquid from the discharge unit 13. Furthermore, for example, when the time from when the control to close the valve is performed until the liquid column discharged from the discharge unit 13 is cut off is long and exceeds a threshold, the control processing unit 31e can perform control to increase the speed at which the valve is closed.

[0041] Furthermore, the determination of the discharge state from the discharge unit 13 may be used not during actual substrate processing, but during a preparation stage such as startup of the substrate processing apparatus 1. In this case, the control processing unit 31e can perform control to search for the valve opening at which droplets are generated, for example, by gradually opening and closing the valve while adjusting the opening degree. Furthermore, the control processing unit 31e can perform control to search for the speed at which droplets are generated or the speed at which the time until the liquid column breaks exceeds a threshold, for example, by repeatedly opening and closing the valve while adjusting the opening and closing speed. Based on information obtained through such searches, it is expected that the user can determine appropriate setting values, etc. for the substrate processing apparatus 1 and perform substrate processing.

[0042] <Discharge status determination and abnormality determination> The information processing device 3 according to this embodiment determines the discharge state of a liquid, such as a chemical liquid or a cleaning liquid, by the discharge unit 13 based on video images of the discharge unit 13 of the substrate processing device 1 captured by the camera 5. In this embodiment, there are four types of discharge states: "liquid column present," "liquid column broken and falling," "droplets present," and "no liquid." FIG. 5 is a schematic diagram showing an example of a discharge state. FIG. 5 shows an example of an image of the discharge unit 13 captured by the camera 5, with the top diagram corresponding to the discharge state "liquid column present," the middle diagram corresponding to the discharge state "liquid column broken and falling," and the bottom diagram corresponding to the discharge state "droplets present." The "no liquid" discharge state is not shown.

[0043] In this example, the discharge unit 13 is cylindrical and is disposed a predetermined distance above the substrate to be processed. A liquid such as a chemical solution or a cleaning solution is discharged from an opening at the bottom of the discharge unit 13, and the discharged liquid falls onto the top surface of the substrate to be processed. The imaging range of the camera 5 is defined to include the area from the bottom of the discharge unit 13 to the top surface of the substrate. The discharge unit 13 is movable horizontally by a drive mechanism (not shown). The camera 5 moves in conjunction with the movement of the discharge unit 13, or captures images of the entire range of movement of the discharge unit 13, allowing the camera 5 to capture the liquid being discharged regardless of the position of the discharge unit 13.

[0044] The discharge state "with liquid column" is a state in which liquid is continuously discharged from the discharge unit 13, and a columnar liquid (liquid column) connects the lower end of the discharge unit 13 and the upper surface of the substrate. The discharge state "liquid column broken and falling" is a state immediately after discharge of liquid from the discharge unit 13 has stopped, and a space exists between the lower end of the discharge unit 13 and the upper end of the liquid column, and a liquid column stands on the upper surface of the substrate. The discharge state "with droplets" is a state in which no liquid column exists between the lower end of the discharge unit 13 and the upper surface of the substrate, and one or more spherical liquids (droplets) exist. The discharge state "no liquid" is a state in which neither a liquid column nor droplets exist between the lower end of the discharge unit 13 and the upper surface of the substrate.

[0045] The information processing device 3 according to this embodiment acquires moving images captured by the camera 5, extracts frame images included in the moving images, inputs these into the learning model shown in Fig. 4, and acquires information relating to the ejection state output by the learning model. Based on the information acquired from the learning model, the information processing device 3 determines whether the ejection state of the ejector 13 shown in the frame image is "liquid column present," "liquid column broken and falling," "liquid droplets present," or "no liquid."

[0046] Furthermore, the video captured by the camera 5 contains, for example, tens of frames per second, and the information processing device 3 repeatedly determines the ejection state using the learning model for multiple frame images included in the video in chronological order. This allows the information processing device 3 to obtain, for example, several dozen ejection state determination results per second. Based on the multiple chronological ejection state determination results, the information processing device 3 can determine, for example, the timing when the ejection state changes from "liquid column present" to "liquid column broken and falling" and the timing when the ejection state changes from "liquid column broken and falling" to "no liquid." Furthermore, the information processing device 3 can calculate the time during which one ejection state is maintained based on the timing at which the ejection state changes. For example, the information processing device 3 can calculate the time during which the ejection state "liquid column broken and falling" is maintained based on the number of frames of the video that exist between the timing at which the ejection state changes from "liquid column present" to "liquid column broken and falling" and the timing at which the ejection state changes from "liquid column broken and falling" to "no liquid."

[0047] Furthermore, the information processing device 3 according to this embodiment controls the start and stop of liquid discharge from the discharge unit 13 of the substrate processing device 1 by issuing to the substrate processing device 1 commands to open and close a valve provided in a liquid supply path from the liquid supply source 16 to the discharge unit 13. For example, the information processing device 3 can calculate the elapsed time between the timing at which a command to close a valve is issued to the substrate processing device 1 and the timing that can be determined from the image captured by the camera 5 as described above.

[0048] The information processing device 3 determines whether or not there is an abnormality in the liquid discharge during substrate processing performed by the substrate processing device 1, depending on whether or not the time calculated from the moving images captured by the camera 5 in this manner exceeds a threshold. The information processing device 3 according to this embodiment determines whether or not there is an abnormality by making a judgment based on two conditions: whether or not the time from issuing a command to close the valve until the liquid column breaks exceeds a threshold, and whether or not the time from the break of the liquid column until the discharge of the liquid ends exceeds a threshold.

[0049] 6 is a flowchart showing an example of the procedure of the discharge state determination process performed by the information processing device 3 according to this embodiment. The discharge state determination unit 31b of the processing unit 31 of the information processing device 3 according to this embodiment acquires one chronologically oldest frame image for which the discharge state has not been determined from among a plurality of frame images included in a moving image captured by the camera 5 (step S1). The discharge state determination unit 31b inputs the frame image acquired in step S1 to a machine-learned learning model (learning model shown in FIG. 4) stored in advance in the model information storage unit 32b (step S2). The discharge state determination unit 31b acquires a discharge state determination result output by the learning model in accordance with the image input in step S2 (step S3).

[0050] The discharge state determination unit 31b stores the discharge state acquired in step S3 in the log information storage unit 32c together with various information such as the frame image acquired in step S1, the date and time the frame image was acquired, or identification information of the substrate to be processed (step S4). The discharge state determination unit 31b determines whether or not substrate processing by the substrate processing apparatus 1 has finished (step S5). If substrate processing has not finished (S5: NO), the discharge state determination unit 31b returns to step S1 and performs the same process on the next frame image in chronological order. If substrate processing has finished (S5: YES), the discharge state determination unit 31b ends the discharge state determination process.

[0051] 7 and 8 are flowcharts showing an example of the procedure of the abnormality determination process performed by the information processing device 3 according to this embodiment. The process shown in this flowchart starts when the substrate processing device 1 is discharging a liquid, such as a chemical or cleaning liquid, from the discharge unit 13. The information processing device 3 controls the substrate processing of the substrate processing device 1 according to a predetermined procedure, and stops discharging the liquid by issuing a command to the substrate processing device 1 to stop discharging the liquid after discharging the liquid for a time or amount set as the procedure. The abnormality determination unit 31c of the processing unit 31 of the information processing device 3 according to this embodiment determines whether it is time to stop discharging the liquid from the discharge unit 13 of the substrate processing device 1 (step S11). If it is not time to stop discharging the liquid (S11: NO), the abnormality determination unit 31c waits until it is time to stop discharging the liquid.

[0052] When it is time to stop discharging the liquid (S11: YES), the control processing unit 31e of the processing unit 31 performs valve closing control by issuing a command to the substrate processing apparatus 1 to close a valve provided in a liquid supply path from the liquid supply source 16 to the discharge unit 13 (step S12). The abnormality determination unit 31c determines the discharge state of the liquid from the discharge unit 13 based on moving images captured by the camera 5 (step S13). In step S13, the process of the flowchart shown in FIG. 6 is performed. Based on the determination result of step S13, the abnormality determination unit 31c determines whether the discharge state of the discharge unit 13 is "liquid column broken and falling" (step S14). When the discharge state is not "liquid column broken and falling" (S14: NO), the abnormality determination unit 31c returns the process to step S13 and repeats the discharge state determination.

[0053] If the discharge state is "dropping due to liquid column breakdown" (S14: YES), the abnormality determination unit 31c calculates the time (liquid column breakdown time) from the timing at which the command to close the valve was given in step S12 to the timing at which the discharge state is determined to be "dropping due to liquid column breakdown" in step S14 (step S15). The abnormality determination unit 31c determines whether the liquid column breakdown time calculated in step S15 exceeds a predetermined threshold (step S16). If the liquid column breakdown time exceeds the threshold (S16: YES), the display processing unit 31d of the processing unit 31 notifies the user of the abnormality by displaying a warning screen on the display unit 34 (step S17), and ends the processing.

[0054] If the liquid out-of-state time does not exceed the threshold value (S16: NO), the abnormality determination unit 31c determines the discharge state of the liquid from the discharge unit 13 based on the video image captured by the camera 5 (step S18). Based on the determination result of step S18, the abnormality determination unit 31c determines whether the discharge state of the discharge unit 13 is "no liquid" (step S19). If the discharge state is not "no liquid" (S19: NO), the abnormality determination unit 31c returns to step S18 and repeats the discharge state determination. If the discharge state is "no liquid" (S19: YES), the abnormality determination unit 31c calculates the time (end time) from the time when the discharge state was determined to be "liquid column broken and falling" in step S14 to the time when the discharge state was determined to be "no liquid" in step S19 (step S20).

[0055] Next, the abnormality determination unit 31c determines the discharge state of the liquid from the discharge unit 13 based on the moving image captured by the camera 5 (step S21). Based on the determination result of step S21, the abnormality determination unit 31c determines whether the discharge state of the discharge unit 13 is "droplets present" (step S22). If the discharge state is "droplets present" (S22: YES), the abnormality determination unit 31c returns the process to step S18 and repeats the discharge state determination. If the discharge state is not "droplets present" (S22: NO), the abnormality determination unit 31c determines whether a predetermined time has elapsed since the timing at which the discharge state was determined to be "no droplets present" in step S19 (step S23). If the predetermined time has not elapsed (S23: NO), the abnormality determination unit 31c returns the process to step S21 and repeats the discharge state determination.

[0056] If the predetermined time has elapsed (S23: YES), the abnormality determination unit 31c determines whether the end time calculated in step S20 exceeds a predetermined threshold (step S24). If the end time exceeds the threshold (S24: YES), the display processing unit 31d notifies the user of the abnormality by displaying a warning screen on the display unit 34 (step S25), and ends the processing. If the end time does not exceed the threshold (S24: NO), the abnormality determination unit 31c ends the processing without notifying the user of the abnormality by displaying a warning screen.

[0057] <Display processing> 9 is a schematic diagram showing an example of a warning screen displayed by the information processing device 3 according to the present embodiment. The information processing device 3 according to the present embodiment displays the warning screen on the display unit 34, for example, in steps S17 and S25 of the above-described flowchart. The warning screen shown in FIG. 9 is an example of a warning screen displayed in step S25 when it is determined that droplets have occurred and the end time has exceeded a threshold. In this warning screen, the information processing device 3 acquires identification information such as the lot number and slot number assigned to the substrate to be processed, and displays a warning message such as "Droplets have occurred at LotX, slotY!" at the top of the screen. Note that the identification information of the substrate to be processed can be input by a user to the information processing device 3 or the substrate processing device 1, for example, before the start of substrate processing.

[0058] Furthermore, the information processing device 3 displays one of the frame images when it is determined that "droplets are present" or a moving image including multiple frame images when it is determined that "droplets are present" below this warning message on the warning screen. The information processing device 3 also displays information such as "Droplet fall time: xx seconds" based on the end time calculated in step S20 of the above-mentioned flowchart. Furthermore, the information processing device 3 may count the number of droplets generated and display information such as "Number of droplets: y", or may display information such as "Droplet volume: zz mL" based on the size of the droplets generated. Note that the calculation process for the number and volume of droplets is omitted from the flowcharts of FIGS. 6 to 8.

[0059] 10 is a schematic diagram showing an example of information display by the information processing device 3 according to this embodiment. The information processing device 3 according to this embodiment may display information regarding the occurrence of droplets separately from (or together with) the warning screen described above. The information processing device 3 stores, for example, the droplet fall time when droplets occur (the end time of step S20) in the log information storage unit 32c. The information processing device 3 reads out multiple past droplet fall time values ​​stored in the log information storage unit 32c and displays, for example, a histogram on the display unit 34, with the droplet fall time on the horizontal axis and the number of data on the vertical axis.

[0060] The information processing device 3 indicates which part of the histogram contains the latest droplet fall time, for example, by highlighting the corresponding part using a different color. The information processing device 3 also displays a distribution curve, for example, assuming that the distribution of droplet fall times follows a normal distribution, overlaid on the histogram of droplet fall times. The information processing device 3 also calculates a predetermined confidence interval for the distribution of droplet fall times and displays a range of droplet fall times corresponding to the confidence interval on the histogram. The information processing device 3 can determine the threshold used for determination in step S24 of the above-mentioned flowchart, for example, based on the upper limit of the confidence interval for the distribution of past droplet fall times. The information processing device 3 may periodically perform a process of calculating the predetermined confidence interval based on information on multiple past droplet fall times stored in the log information storage unit 32c, and periodically update the threshold used for determination.

[0061] 9 and 10 are merely examples and are not limiting, and the information processing device 3 may notify of an abnormality that has occurred in the substrate processing in any display mode. Furthermore, the information processing device 3 may display this information not only when an abnormality has occurred, but also when no abnormality has occurred.

[0062] <Modification> (Variation 1) In the information processing system according to the first modification, in addition to the camera 5 that photographs the discharge unit 13, a second camera that photographs the surface of the substrate to be processed is provided in the chamber 11 of the substrate processing apparatus 1. The information processing apparatus 3 acquires data of the moving images captured by the second camera, and determines the dryness of the surface of the substrate (whether it is dry or wet) based on frame images included in the acquired moving images.

[0063] The degree of dryness of the substrate surface may be determined using, for example, a learning model previously generated by machine learning. The learning model for determining the degree of dryness may be generated by performing so-called supervised machine learning using, for example, learning data (teacher data) that associates a photographed image of the substrate surface with a flag indicating whether the substrate surface shown in the image is dry or wet. However, the degree of dryness of the substrate surface may also be determined using a method that does not use a learning model, such as a method that makes a determination based on a comparison between pixel values ​​of a photographed image of the substrate surface and a threshold value.

[0064] The information processing device 3 according to the first modification determines both the discharge state based on frame images included in a moving image of the discharge unit 13, and the dryness state based on frame images included in a moving image of the substrate surface. For example, when the information processing device 3 determines that the discharge state is "droplets present" and that the substrate surface is dry, it determines that an abnormality has occurred in the discharge of liquid during substrate processing, and can notify the user.

[0065] The above conditions for determining an abnormality are merely examples and are not limiting. The information processing device 3 may perform any notification, control, etc. for any combination of the determination result of the liquid discharge state and the determination result of the dryness of the substrate surface. Furthermore, instead of providing a camera that photographs the substrate surface separately from the camera 5 that photographs the discharge unit 13, the camera 5 may photograph both the discharge unit 13 and the substrate surface.

[0066] (Variation 2) In the above-described embodiment, the substrate processing apparatus 1 determines whether or not there is an abnormality when it stops discharging the liquid from the discharging unit 13, but this is not limited to this. The information processing apparatus 3 according to Modification 2 determines whether or not there is an abnormality when the substrate processing apparatus 1 starts discharging the liquid from the discharging unit 13. For example, after issuing a command to open a valve to discharge the liquid from the discharging unit 13, the information processing apparatus 3 according to Modification 2 determines that an abnormality has occurred if droplets are discharged from the discharging unit 13 before a columnar liquid (liquid column) is discharged from the discharging unit 13, and displays a warning screen or performs control processing according to the abnormality.

[0067] 11 is a flowchart showing an example of the procedure of an abnormality determination process performed by the information processing device 3 according to Modification 1. The control processing unit 31e of the processing unit 31 of the information processing device 3 according to Modification 2 performs control to open a valve provided in a liquid supply path from the liquid supply source 16 to the discharge unit 13 by communicating with the substrate processing device 1 via the communication unit 33 (step S41).

[0068] Thereafter, the abnormality determination unit 31c of the processing unit 31 determines the discharge state of the liquid from the discharge unit 13 based on the moving image captured by the camera 5 (step S42). The abnormality determination unit 31c determines whether the discharge state of the discharge unit 13 is "liquid droplets present" or not based on the determination result of step S42 (step S43). If the discharge state is "liquid droplets present" (S43: YES), the abnormality determination unit 31c notifies the user of the abnormality by displaying a warning screen on the display unit 34 (step S44), and ends the processing.

[0069] If the ejection state is not "liquid droplets present" (S43: NO), the abnormality determination unit 31c determines whether the ejection state of the ejection unit 13 is "liquid column present" based on the determination result of step S42 (step S45). If the ejection state is not "liquid column present" (S45: NO), the abnormality determination unit 31c returns the process to step S42 and repeats the ejection state determination. If the ejection state is "liquid column present" (S45: YES), the abnormality determination unit 31c ends the abnormality determination process at the start of ejection.

[0070] FIG. 12 is a schematic diagram showing an example of a warning screen displayed by the information processing device 3 according to Modification 2. The information processing device 3 according to Modification 2 displays the warning screen on the display unit 34, for example, in step S44 of the above-described flowchart. In the warning screen shown in FIG. 12, the information processing device 3 acquires identification information such as the lot number and slot number assigned to the substrate to be processed, and displays a warning message, for example, "Droplets occurred at the start of discharge on LotX, slotY!" at the top of the screen. In addition, the information processing device 3 displays one of the frame images when it is determined that "droplets are present" or a moving image including multiple frame images when it is determined that "droplets are present" below this warning message on the warning screen. In addition, the information processing device 3 may count the number of droplets generated and display the information "Number of droplets: y" or may display the information "Droplet volume: zz mL" based on the size of the droplets generated.

[0071] <Summary> In the information processing system according to the present embodiment having the above configuration, the information processing device 3 acquires video images of the discharge unit 13 of the substrate processing device 1 discharging liquid onto a substrate to be processed. The information processing device 3 inputs frame images included in the acquired video images into a learning model that has undergone machine learning in advance so as to accept images of the discharge unit 13 as input and output information related to the state of liquid being discharged from the discharge unit 13. The information processing device 3 acquires the information output by the learning model and determines whether or not the liquid is being discharged properly (normal / abnormal) during substrate processing based on the acquired information. As a result, the information processing system according to the present embodiment is expected to enable the information processing device 3 to automatically determine whether or not the liquid is being discharged properly based on the video images captured by the camera 5.

[0072] Furthermore, in the information processing system according to this embodiment, the discharge states determined by the information processing device 3 include a first state "liquid column present" in which liquid is being discharged in a columnar form from the discharge unit 13 onto the substrate to be processed, a second state "liquid column broken and falling" in which the liquid discharged from the discharge unit 13 has stopped and a columnar liquid is falling onto the substrate, a third state "droplets present" in which droplets are falling from the discharge unit 13 onto the substrate, and a fourth state "no liquid" in which no liquid is being discharged from the discharge unit 13. As a result, the information processing system according to this embodiment is expected to make accurate determinations regarding the discharge of liquid by utilizing a learning model that has learned these discharge states.

[0073] Furthermore, in the information processing system according to this embodiment, the information processing device 3 calculates the time from the point at which the discharge unit 13 stops discharging the liquid to the point at which the liquid discharge state changes from "liquid column present" to "liquid column broken and falling" based on information acquired from the learning model, and determines whether the discharge of the liquid for substrate processing is normal or abnormal based on the calculated time. The information processing device 3 stops discharging the liquid, for example, by controlling to close a valve provided in the flow path from the liquid supply source 16 to the discharge unit 13. As a result, the information processing system according to this embodiment can be expected to determine an abnormality, for example, when discharging does not stop even after a predetermined time has elapsed since the liquid discharging stopped.

[0074] Furthermore, in the information processing system according to this embodiment, the information processing device 3 determines whether the ejection of liquid for substrate processing is normal or abnormal based on whether the information acquired from the learning model indicates "the presence of droplets." As a result, the information processing system according to this embodiment is expected to determine whether the substrate processing is normal or abnormal based on whether droplets are generated. Furthermore, the information processing device 3 may further calculate the size of the droplets and determine whether the substrate processing is normal or abnormal based on the calculated size.

[0075] Furthermore, in the information processing system according to this embodiment, the information processing device 3 calculates the time it takes for the information acquired from the learning model to change from "liquid column broken and falling" to "no liquid," and determines whether the discharge of liquid for substrate processing is normal or abnormal based on the calculated time. As a result, the information processing system according to this embodiment is expected to accurately determine abnormalities based on the time it takes for the liquid discharge to be completely stopped.

[0076] Furthermore, in the information processing system according to this embodiment, the information processing device 3 controls the opening and closing of a valve provided in a flow path from the liquid supply source 16 to the discharge unit 13 based on information acquired from the learning model, thereby controlling the discharge of liquid from the discharge unit 13. For example, when the information processing device 3 determines that there is an abnormality based on information acquired from the learning model, it can perform control such as stopping the discharge of liquid from the discharge unit 13. As a result, the information processing system according to this embodiment can be expected to perform accurate control of the discharge of liquid in substrate processing.

[0077] Furthermore, in the information processing system according to this embodiment, if the information processing device 3 determines that the ejection of droplets during substrate processing is incorrect, it notifies the user of the abnormality, for example, by displaying a warning screen on the display unit 34. At this time, the information processing device 3 may notify the user of, for example, the presence or absence of droplets, the number of droplets, the amount of droplets, or the fall time of the droplets, or may also notify the user of, for example, identification information assigned to the substrate to be processed. In this way, the information processing system according to this embodiment is expected to notify the user of the presence or absence of an abnormality in the ejection of liquid during substrate processing, and, if an abnormality occurs, related information, and prompt the user to take action to address the abnormality.

[0078] Furthermore, in the information processing system according to this embodiment, the information processing device 3 stores, in the log information storage unit 32c, at least the frame image when it is determined that the liquid ejection is incorrect and information acquired from the learning model. Note that the information processing device 3 may or may not store, in the log information storage unit 32c, information when it is determined that the liquid ejection is correct. This makes it possible for the information processing system according to this embodiment to enable the user to verify the cause of an abnormality, etc., based on the information stored in the log information storage unit 32c.

[0079] In the information processing system according to this embodiment, the information processing device 3 acquires video images of the substrate to be processed, determines the surface condition of the substrate based on frame images included in the acquired video, and determines whether the discharge of the liquid for substrate processing is normal or abnormal based on information about the discharge condition acquired from the learning model and the determined surface condition of the substrate. The surface condition of the substrate includes, for example, a state in which the substrate surface is dry and a state in which the substrate surface is wet. As a result, the information processing system according to this embodiment can be expected to accurately determine whether the substrate is normal or abnormal, taking into account the surface condition of the substrate to be processed.

[0080] Furthermore, in the information processing system according to this embodiment, the information processing device 3 determines whether the discharge of liquid for substrate processing is normal or abnormal based on whether a "droplet present" state occurs during the period from when the information processing device 3 controls the start of discharge of liquid by the discharge unit 13 until the liquid discharge state becomes "liquid column present." As a result, the information processing system according to this embodiment can be expected to determine whether the discharge of liquid is normal or abnormal not only when the discharge of liquid from the discharge unit 13 is stopped, but also when the discharge of liquid is started.

[0081] The embodiments disclosed herein are to be considered in all respects as illustrative and not restrictive. The scope of the present disclosure is defined by the claims, not by the above meaning, and is intended to include all modifications within the meaning and scope of the claims.

[0082] The matters described in each embodiment can be combined with each other. In addition, the independent claims and dependent claims described in the claims can be combined with each other in any combination, regardless of the reference format. Furthermore, although the claims use a format in which a claim references two or more other claims (multiple claim format), this is not limited to this format. A multiple claim (multi-multi claim) that references at least one other multiple claim may also be used. [Explanation of symbols]

[0083] 1. Substrate processing equipment 3. Information processing equipment (computers) 5. Camera 11 Chamber 12 Board holding mechanism 12a Holding part 12b Support section 12c Drive unit 13 Discharge part 14 Collection cup 14a Drainage port 14b Exhaust port 15 FFU 16 Source 31 Processing section 31a Image acquisition section 31b Discharge state determination unit 31c Abnormality determination section 31d Display processing section 31e Control processing section 32 Storage section 32a Program (computer program) 32b Model information storage section 32c Log information storage unit 33 Communications Department 34 Display section 25 Control section N Network

Claims

1. acquiring a moving image of a discharge unit of the substrate processing apparatus that discharges a liquid onto a substrate to be processed; inputting frame images included in the acquired moving image into a learning model that has undergone machine learning so as to receive an image of the ejection unit as an input and output information relating to the ejection state of liquid from the ejection unit; Acquire information relating to the discharge state output by the learning model; Based on the acquired information, it is determined whether the liquid is discharged correctly or not. A computer program that causes a computer to perform a process.

2. The ejection state includes: a first state in which the liquid is discharged in a columnar shape from the discharge unit onto the substrate to be processed; a second state in which the liquid discharged from the discharge unit stops and a column of liquid falls onto the substrate; a third state in which droplets are falling from the discharge portion onto the substrate; and A fourth state in which liquid is not being ejected from the ejection portion.

2. The computer program of claim 1, comprising:

3. calculating a time from a point in time when control is performed to stop the discharge of the liquid by the discharge unit to a point in time when the discharge state of the liquid changes from the first state to the second state based on the information acquired from the learning model; determining whether the liquid is discharged correctly or not based on the calculated time; 3. A computer program according to claim 2.

4. Controlling the opening and closing of a valve provided in a flow path of the liquid; The stop control is performed by controlling to close the valve.

4. A computer program according to claim 3.

5. determining whether the liquid ejection is correct or not based on whether the liquid ejection state is the third state based on the information acquired from the learning model; 3. A computer program according to claim 2.

6. When the liquid ejection state is the third state, the size of the liquid droplet is calculated; determining whether the liquid is ejected correctly or not based on the calculated size; 6. A computer program according to claim 5.

7. calculating a time required for the liquid ejection state to change from the second state to the fourth state based on the information acquired from the learning model; determining whether the liquid is discharged correctly or not based on the calculated time; 3. A computer program according to claim 2.

8. controlling opening and closing of a valve provided in a flow path of the liquid based on the information acquired from the learning model; 2. The computer program of claim 1.

9. If it is determined that the liquid is not being dispensed correctly, a notification is sent.

2. The computer program of claim 1.

10. Notifying the presence or absence of droplets, the number of droplets, the amount of droplets, or the falling time of droplets; 10. A computer program according to claim 9.

11. notifying the identification information assigned to the substrate to be processed; 10. A computer program according to claim 9.

12. storing the frame image and information acquired from the learning model when it is determined that the liquid ejection is incorrect in a storage unit; 2. The computer program of claim 1.

13. Acquire a moving image of the substrate to be processed; determining a surface state of the substrate based on frame images included in the acquired moving image; determining whether the liquid is discharged correctly or not based on the information relating to the discharge state output by the learning model and the determined surface state of the substrate; 2. The computer program of claim 1.

14. The surface state includes a state in which the surface of the substrate is dry and a state in which the surface of the substrate is wet.

14. A computer program according to claim 13.

15. determining whether the ejection of the liquid is successful or not based on whether the third state occurs during the period from when the ejection unit starts ejecting the liquid until the ejection state of the liquid becomes the first state after the ejection unit has performed start control of the ejection of the liquid; 3. A computer program according to claim 2.

16. The information processing device acquiring a moving image of a discharge unit of the substrate processing apparatus that discharges a liquid onto a substrate to be processed; inputting frame images included in the acquired moving image into a learning model that has undergone machine learning so as to receive an image of the ejection unit as an input and output information relating to the ejection state of liquid from the ejection unit; Acquire information relating to the discharge state output by the learning model; determining whether the liquid is ejected correctly or not based on the acquired information; Information processing methods.

17. a processing unit; The processing unit acquiring a moving image of a discharge unit of the substrate processing apparatus that discharges a liquid onto a substrate to be processed; inputting frame images included in the acquired moving image into a learning model that has undergone machine learning so as to receive an image of the ejection unit as an input and output information relating to the ejection state of liquid from the ejection unit; Acquire information relating to the discharge state output by the learning model; determining whether the liquid is ejected correctly or not based on the acquired information; Information processing device.

Images