SUBSTRATE PROCESSING APPARATUS AND SUBSTRATE PROCESSING METHOD

Abstract

A substrate processing apparatus and a substrate processing method are provided that are capable of maintaining a resin member at an appropriate timing. The substrate processing apparatus includes a processing section (110), a sensor (60), and a weight acquisition section. The processing section (110) includes a resin member that comes into contact with a processing liquid, and is configured to be able to perform substrate processing using the processing liquid. The sensor (60) measures the vibration frequency of the resin member in the processing section (110). The weight acquisition section acquires the weight of the resin member based on the vibration frequency of the resin member measured by the sensor (60).

Description

[Technical Field]

[0001] The present invention relates to a substrate processing apparatus and a substrate processing method. [Background technology]

[0002] Substrate processing apparatuses are used to process substrates such as semiconductor wafers, glass substrates for liquid crystal displays, glass substrates for photomasks, or glass substrates for optical disks using processing liquids such as developing liquid, cleaning liquid, rinsing liquid, or photoresist liquid. For example, in the cleaning apparatus described in Patent Document 1, the substrate is held horizontally by a spin chuck. A cup is disposed so as to surround the sides and below the substrate W held by the spin chuck. A cleaning liquid supply nozzle is disposed above the substrate held by the spin chuck.

[0003] While the substrate is held and rotated by the spin chuck, a cleaning liquid is supplied from a cleaning liquid supply nozzle to the center of the substrate surface. In this case, the resist liquid at the center of the surface is spread over the entire surface by centrifugal force caused by the rotation of the substrate. This cleans the surface of the substrate. Furthermore, the cleaning liquid that splashes from the surface of the substrate to the surrounding area is caught in a cup and then collected in a drainage section. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 4347785 Summary of the Invention [Problem to be solved by the invention]

[0005] In substrate processing apparatuses, various components such as cups, nozzles, and piping are made of resin. These resin components gradually corrode or deteriorate when in contact with processing liquids. Therefore, maintenance, such as replacing the resin components or replacing the substrate processing apparatus, is required before the corrosion or deterioration of the resin components becomes severe. However, because the degree of corrosion or deterioration of the resin components varies depending on the operating period of the substrate processing apparatus and the type of substrate processing, it is not easy to perform maintenance on the substrate processing apparatus at the appropriate time.

[0006] An object of the present invention is to provide a substrate processing apparatus and a substrate processing method that are capable of maintaining a resin member at an appropriate timing. [Means for solving the problem]

[0007] A substrate processing apparatus according to one aspect of the present invention includes a processing unit including a resin member that comes into contact with a processing liquid and is configured to be able to perform substrate processing using the processing liquid, a sensor that measures the vibration frequency of the resin member, and a weight acquisition unit that acquires the weight of the resin member based on the vibration frequency of the resin member.

[0008] A substrate processing method according to another aspect of the present invention is included in a processing unit configured to be able to perform substrate processing using a processing liquid, and includes measuring the vibration frequency of a resin member that comes into contact with the processing liquid using a sensor, and obtaining the weight of the resin member based on the vibration frequency of the resin member. [Effects of the Invention]

[0009] According to the present invention, it is possible to perform maintenance on the resin member at an appropriate time. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a diagram showing an example of a configuration of a substrate processing apparatus according to an embodiment of the present invention; [Figure 2] FIG. 1 illustrates an example of a configuration of an information processing device. [Figure 3] FIG. 2 is a schematic diagram showing the inside of the processing unit as viewed horizontally. [Figure 4] FIG. 2 is a block diagram showing the functional configuration of the learning device of FIG. [Figure 5] FIG. 2 is a diagram illustrating an example of a data set acquired by a data acquisition unit. [Figure 6] FIG. 3 is a block diagram showing a functional configuration of the information processing device of FIG. 2. [Figure 7] 10A and 10B are diagrams showing an example of substrate processing conditions and liquid contact times determined by a condition determination unit. [Figure 8] FIG. 10 is a diagram showing an example of a prediction of a weight change. [Figure 9] FIG. 10 is a diagram illustrating an example of calculation of a contribution rate. [Figure 10] FIG. 10 is a diagram showing an example of a screen displayed on a display device by a presentation unit. [Figure 11] 10A and 10B are diagrams illustrating other examples of screens displayed on the display device by the presentation unit. [Figure 12] FIG. 10 is a diagram showing yet another example of a screen displayed on the display device by the presentation unit. [Figure 13] 10A and 10B are diagrams illustrating an example of changing the transport conditions of the substrate by the transport mechanism. [Figure 14] FIG. 10 is a diagram showing an example of a calibration curve. [Figure 15] FIG. 1 illustrates a discrete distribution of force field parameters. [Figure 16] FIG. 1 illustrates a continuous distribution of force field parameters. [Figure 17] FIG. 10 is a diagram illustrating an example of extraction of molecular descriptors. [Figure 18] 10 is a flowchart illustrating an example of the flow of a model generation process. [Figure 19] 10 is a flowchart illustrating an example of the flow of a model update process. [Figure 20] 10 is a flowchart showing an example of the flow of a weight acquisition process. [Figure 21] 10 is a flowchart showing an example of the flow of a life evaluation process. DETAILED DESCRIPTION OF THE INVENTION

[0011] 1. Substrate processing equipment A substrate processing apparatus and a substrate processing method according to an embodiment of the present invention will be described below with reference to the drawings. In the following description, the term "substrate" refers to a semiconductor substrate (wafer), a substrate for an FPD (Flat Panel Display) such as a liquid crystal display device or an organic EL (Electro Luminescence) display device, a substrate for an optical disk, a substrate for a magnetic disk, a substrate for a magneto-optical disk, a substrate for a photomask, a ceramic substrate, or a substrate for a solar cell. FIG. 1 is a diagram showing an example of the configuration of a substrate processing apparatus according to an embodiment of the present invention. As shown in FIG. 1, a substrate processing apparatus 500 includes a processing unit 100, a database storage device 200, an information processing device 300, and a learning device 400.

[0012] The processing unit 100, the database storage device 200, the information processing device 300, and the learning device 400 are connected to a network 501 and are capable of transmitting and receiving data to and from each other. The network 501 may be, for example, a local area network (LAN) or a wide area network (WAN). Alternatively, the network 501 may be the Internet. The connection form of the network 501 may be a wired connection or a wireless connection. Furthermore, the processing unit 100 and the information processing device 300 may be connected by a dedicated network instead of the network 501.

[0013] The processing unit 100 includes multiple processing sections 110 and a transport mechanism 120. The transport mechanism 120 sequentially transports substrates to be processed to the multiple processing sections 110 according to predetermined transport conditions. Each processing section 110 includes various resin members that come into contact with the processing liquid. The structure of the processing sections 110 will be described later. Each processing section 110 sequentially performs a series of processes using the processing liquid on multiple substrates according to a processing recipe that describes the processing details for the substrates. In this example, each processing section 110 performs a series of processes to form a solid or liquid film on the substrate by supplying the processing liquid to the substrate under predetermined processing conditions, and then removes the solid or liquid film from the substrate and dries the substrate.

[0014] Specifically, each processing unit 110 applies a processing liquid, which is a mixture of a sublimation agent and a solvent at a predetermined concentration, to the surface of a substrate on which a pattern has been formed, and then rotates the substrate while evaporating the solvent to deposit the sublimation agent on the substrate. Then, each processing unit 110 sprays an inert gas onto the substrate to dry it by sublimation. However, the substrate processing in each processing unit 110 is not limited to sublimation drying of the substrate, and may be, for example, development, cleaning, or coating film formation.

[0015] The database storage device 200 includes a large-capacity storage device such as a server. Molecular descriptors of various resin materials and various solutions are stored in the database storage device 200. The molecular descriptors will be described in detail later.

[0016] Information processing device 300 is configured, for example, by a general-purpose computer. Fig. 2 is a diagram showing an example of the configuration of information processing device 300. As shown in Fig. 2, information processing device 300 includes a control unit 310, a RAM (random access memory) 320, a ROM (read-only memory) 330, a storage device 340, an operation unit 350, a display device 360, an input / output I / F (interface) 370, and a bus 380. Control unit 310, RAM 320, ROM 330, storage device 340, operation unit 350, display device 360, and input / output I / F 370 are connected to bus 380.

[0017] The control unit 310 includes a processor such as a central processing unit (CPU), a micro processing unit (MPU), a graphics processing unit (GPU), or a digital signal processor (DSP). The control unit 310 may be configured by combining two or more of the above processors, or may be configured as a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC).

[0018] The RAM 320 is used as a working area for the control unit 310. The ROM 330 stores a system program. The storage device 340 includes a storage medium such as a hard disk or a semiconductor memory, and stores a data processing program for executing the data processing described below. The data processing program may be stored in the ROM 330 or another external storage device. The storage device 340 may also store a processing recipe or transport conditions. Furthermore, the storage device 340 may store history information indicating the history of substrate processing in the processing unit 100.

[0019] The operation unit 350 is an input device such as a keyboard, a mouse, or a touch panel. A user can give predetermined instructions to the information processing device 300 by operating the operation unit 350. The display device 360 ​​is a display device such as a liquid crystal display device, and displays a GUI (Graphical User Interface) for accepting instructions from the user or processing results of data processing, etc. The input / output I / F 370 is connected to the network 501 in FIG. 1.

[0020] The learning device 400 is configured, for example, by a general-purpose computer. Therefore, the learning device 400 has basically the same configuration as the information processing device 300 except for the following points, and therefore a detailed description of the configuration of the learning device 400 will be omitted. The storage device of the learning device 400 stores a learning program for executing the learning process described below. The learning program may be stored in the ROM of the learning device 400 or in another external storage device. Note that the information processing device 300 and the learning device 400 may be configured by the same hardware. In this case, the programs installed in the information processing device 300 and the learning device 400 are different. Therefore, the processes executed by the information processing device 300 and the learning device 400 are different from each other.

[0021] 2. Processing Unit The configuration of the processing section 110 of the processing unit 100 shown in FIG. 1 will be described below. FIG. 3 is a horizontal schematic diagram of the interior of the processing section 110. While FIG. 3 shows the configuration of one processing section 110, the configurations of the other processing sections 110 are the same as that shown in FIG. 3. As shown in FIG. 3, the processing section 110 includes a processing mechanism 1 and a control device 2. The control device 2 is capable of communicating with the information processing device 300 shown in FIG. 2 and controls the processing mechanism 1 based on commands from the information processing device 300. The processing mechanism 1 includes a box-shaped chamber 3, a spin chuck 10 that holds a single substrate W horizontally within the chamber 3 and rotates the substrate W about a vertical rotation axis A1 that passes through the center of the substrate W, and a cylindrical processing cup 21 that surrounds the spin chuck 10 about the rotation axis A1.

[0022] The spin chuck 10 includes a disk-shaped spin base 12 held in a horizontal position, a plurality of chuck pins 11 that hold the substrate W in a horizontal position above the spin base 12, a spin shaft 13 that extends downward from the center of the spin base 12, and a spin motor 14 that rotates the spin shaft 13 to rotate the spin base 12 and the plurality of chuck pins 11.

[0023] Processing cup 21 is an example of a resin member, and includes an outer wall member 22, a plurality of cups 23, and a plurality of guards 24. The plurality of guards 24 can be individually raised and lowered between an upper position and a lower position below the upper position by a guard lifting unit 27. In the upper position, the plurality of guards 24 receive processing liquid discharged outward from substrate W. The plurality of cups 23 each receive processing liquid guided downward by the plurality of guards 24. The outer wall member 22 has a cylindrical shape and surrounds the plurality of guards 24 and the plurality of cups 23.

[0024] The processing mechanism 1 includes a chemical liquid nozzle 31 that discharges a chemical liquid, a rinse liquid nozzle 35 that discharges a rinse liquid, a processing liquid nozzle 39 that discharges a processing liquid, and a substitute liquid nozzle 43 that discharges a substitute liquid. The chemical liquid nozzle 31, the rinse liquid nozzle 35, the processing liquid nozzle 39, and the substitute liquid nozzle 43 can be independently moved horizontally within the chamber 3 by nozzle movement units 34, 38, 42, and 46 provided corresponding to the nozzles.

[0025] The processing liquid nozzle 39 is connected to a processing liquid pipe 40 that guides the processing liquid to the processing liquid nozzle 39. The processing liquid nozzle 39 and the processing liquid pipe 40 are another example of a resin member. When a processing liquid valve 41 disposed in the processing liquid pipe 40 is opened, the processing liquid is continuously discharged downward from the discharge port of the processing liquid nozzle 39. The processing liquid is a mixed liquid containing a sublimation agent and a solvent that is soluble in the sublimation agent.

[0026] The processing liquid nozzle 39 is connected to a nozzle movement unit 42. The nozzle movement unit 42 moves the processing liquid nozzle 39 in at least one of the vertical and horizontal directions. The nozzle movement unit 42 moves the processing liquid nozzle 39 horizontally between a processing position where the processing liquid ejected from the processing liquid nozzle 39 is supplied to the upper surface of the substrate W, and a standby position where the processing liquid nozzle 39 is positioned around the processing cup 21 in a plan view.

[0027] The processing mechanism 1 includes a disk-shaped blocking member 51 arranged above the spin chuck 10. The blocking member 51 includes a disk portion 52 arranged horizontally above the spin chuck 10. The blocking member 51 is supported horizontally by a cylindrical support shaft 53 extending upward from the center of the disk portion 52. The center line of the disk portion 52 is arranged on the rotation axis A1 of the substrate W. The lower surface of the disk portion 52 corresponds to the lower surface 51L of the blocking member 51. The lower surface 51L of the blocking member 51 is parallel to the upper surface of the substrate W and has an outer diameter equal to or greater than the diameter of the substrate W.

[0028] The blocking member 51 is connected to a blocking member lifting unit 54 that vertically raises and lowers the blocking member 51. The blocking member lifting unit 54 moves the blocking member 51 to any position between an upper position (the position shown in FIG. 3) and a lower position.

[0029] A center nozzle 55 is disposed in a through-hole that passes vertically through the center of the blocking member 51. The center nozzle 55 rises and falls together with the blocking member 51. The center nozzle 55 is connected to an upper gas pipe 56 that guides inert gas to the center nozzle 55. The processing unit 110 is equipped with an upper temperature regulator 59 that heats or cools the inert gas discharged from the center nozzle 55. When an upper gas valve 57 installed in the upper gas pipe 56 is opened, the inert gas is continuously discharged downward from the discharge port of the center nozzle 55 at a flow rate that corresponds to the opening of a flow rate adjustment valve 58 that changes the flow rate of the inert gas. The inert gas discharged from the center nozzle 55 is nitrogen gas.

[0030] The processing mechanism 1 includes a sensor 60 that measures the vibration frequency of the resin member. In this embodiment, the sensor 60 includes an event-based vision camera and measures the vibration frequency of the resin member by capturing an image of the resin member without contacting the resin member. A plurality of sensors 60 may be provided corresponding to the plurality of resin members. On the other hand, as in the example of FIG. 3 , when a plurality of resin members, such as the processing cup 21, the processing liquid nozzle 39, and the processing liquid pipe 40, are arranged within the angle of view of the sensor 60, one sensor 60 may be provided for the plurality of resin members. The measurement result by the sensor 60 is used in the information processing device 300 to obtain the weight of the resin member. Details will be described later.

[0031] 3. Learning Device Fig. 4 is a block diagram showing the functional configuration of learning device 400 in Fig. 1. As shown in Fig. 4, learning device 400 includes, as functional units, a data acquisition unit 401, a descriptor acquisition unit 402, a model generation unit 403, an additional data acquisition unit 404, and an additional learning unit 405. The functional units of learning device 400 are realized by a control unit of learning device 400 executing a learning program. Some or all of the functional units of learning device 400 may be realized by hardware such as electronic circuits.

[0032] The data acquisition unit 401 acquires multiple data sets that have been generated in advance. Each data set is generated by a user of the processing unit 100 through a preliminary experiment. The user may input the generated data set to the data acquisition unit 401 using an operation unit (not shown). Alternatively, the user may store the generated multiple data sets in a storage device (not shown) of the learning device 400. In this case, the multiple data sets are acquired from the storage device.

[0033] Fig. 5 is a diagram showing an example of a data set acquired by the data acquisition unit 401. As shown in Fig. 5, each data set includes a set of processing conditions for the substrate and a weight change of the resin member corresponding to the processing conditions. The processing conditions for the substrate include, for example, the resin member, processing liquid, temperature of the processing liquid, or concentration of the processing liquid in the processing unit 110 of Fig. 3.

[0034] The weight change of the resin member corresponding to each processing condition is a value obtained by subtracting the weight of the resin member at the time when the processing solution under that processing condition comes into contact with the resin member for a predetermined time from the initial weight of the resin member. In this example, the weight of the resin member is normalized so that the initial weight is 1 (100%). Therefore, the weight of the resin member in this example is a relative value based on the initial weight and is treated as a dimensionless quantity without units.

[0035] In the example of Figure 5, the data set includes the weight changes of the resin member when the time the processing liquid is in contact with the resin member (hereinafter referred to as the liquid contact time) is one day, one week, and two weeks. The weight of the resin member decreases as the liquid contact time increases due to corrosion or deterioration. Therefore, the weight change of the resin member increases as the liquid contact time increases.

[0036] In a preliminary experiment for generating a data set, a resin member having a known initial weight and made of the same material as the resin member in the processing unit 110 is immersed in the same processing liquid as the processing liquid under the processing conditions. The weight of the resin member is then measured when the immersion time (liquid contact time) of the resin member reaches a predetermined time. This determines the weight change of the resin member for each liquid contact time. A set of each processing condition and the weight change of the resin member for each liquid contact time determined under the processing condition is generated as a data set.

[0037] The descriptor acquisition unit 402 acquires the molecular descriptor of the resin material used as the resin component in the data set from the molecular descriptors of various resin materials stored in the database storage device 200. In this case, the data acquisition unit 401 associates the molecular descriptor of the resin material acquired by the descriptor acquisition unit 402 with the resin component in the data set.

[0038] Similarly, the descriptor acquisition unit 402 acquires the molecular descriptor of the solution used as the treatment liquid in the data set from the molecular descriptors of various solutions stored in the database storage device 200. In this case, the data acquisition unit 401 associates the molecular descriptor of the solution acquired by the descriptor acquisition unit 402 with the treatment liquid in the data set.

[0039] The model generation unit 403 trains a predetermined machine learning model using the substrate processing conditions and wetted time in each data set acquired by the data acquisition unit 401 as explanatory variables and the weight change of the resin member as a response variable. This generates a weight change prediction model that predicts the weight change of the resin member from the substrate processing conditions and wetted time. The machine learning model used to generate the weight change prediction model may be, for example, a decision tree or a neural network.

[0040] It is desirable to predict the weight change of resin members in the processing unit 110, even for those members whose weight change has not been measured in a preliminary experiment, from the processing conditions of the substrate and the contact time. Therefore, the additional data acquisition unit 404 acquires a plurality of additional data sets. Each additional data set includes a set of the processing conditions of the substrate and the weight change of the resin member, which is the difference between the weight of the resin member before and after the substrate processing is performed by the processing unit 110 under the processing conditions. The processing conditions of the substrate are acquired based on, for example, a processing recipe. The weights of the resin members before and after the substrate processing are acquired from the information processing device 300.

[0041] The additional learning unit 405 updates the weight change prediction model by additionally learning the weight change prediction model from the multiple additional data sets acquired by the additional data acquisition unit 404. This makes it possible to predict weight changes from the substrate processing conditions and liquid contact time using the weight change prediction model, even for resin members whose weight changes have not been measured in preliminary experiments.

[0042] The weight change prediction model generated or updated by the learning device 400 is stored, for example, in the database storage device 200 of FIG. 1. The information processing device 300 of FIG. 2 acquires the weight change prediction model from the database storage device 200 and stores it in the storage device 340. This causes the weight change prediction model to be installed in the information processing device 300. The weight change prediction model generated or updated by the learning device 400 may be stored in a cloud server or the like. In this case, the weight change prediction model generated or updated in the substrate processing device 500 can be easily used in other substrate processing devices.

[0043] 4. Information Processing Device Fig. 6 is a block diagram showing the functional configuration of the information processing device 300 in Fig. 2. As shown in Fig. 6, the information processing device 300 includes, as functional units, a condition determination unit 301, a weight prediction unit 302, a calculation unit 303, a life evaluation unit 304, a presentation unit 305, a process control unit 306, a vibration unit 307, a calibration curve acquisition unit 308, and a weight acquisition unit 309. The functional units of the information processing device 300 are realized by the control unit 310 of the information processing device 300 in Fig. 2 executing a data processing program. Some or all of the functional units of the information processing device 300 may be realized by hardware such as electronic circuits.

[0044] The condition determination unit 301 determines the processing conditions and liquid contact time for the substrate. FIG. 7 is a diagram showing an example of the processing conditions and liquid contact time for the substrate determined by the condition determination unit 301. As shown in FIG. 7, the processing conditions for the substrate include a descriptor of the processing liquid, a descriptor of the resin member, the temperature of the processing liquid, and the concentration of the processing liquid. The temperature and concentration of the processing liquid are described in the processing recipe. Therefore, the temperature of the processing liquid or the concentration of the processing liquid may be determined based on the processing recipe.

[0045] The descriptor of the treatment liquid is obtained from molecular descriptors of various treatment liquids stored in the database storage device 200 based on information about the treatment liquid (type, temperature, or concentration). Alternatively, it is determined by calculation by the information processing device 300 based on information about the treatment liquid (type, temperature, or concentration). Similarly, the descriptor of the resin component is obtained from molecular descriptors of various resin components stored in the database storage device 200 based on information about the resin component. Alternatively, it is determined by calculation by the information processing device 300 based on information about the resin component.

[0046] The wetted time is proportional to the supply time of the processing liquid to the substrate. The supply time of the processing liquid to the substrate is specified in the processing recipe. Therefore, the wetted time can also be determined based on the processing recipe. For example, the wetted time may be determined by multiplying the supply time of the processing liquid by a predetermined proportionality coefficient. Alternatively, the user can specify the processing conditions and the wetted time for the substrate to the condition determination unit 301 by operating the operation unit 350. Therefore, some or all of the processing conditions and the wetted time for the substrate may be determined based on the user's instructions. The wetted time shown in FIG. 7 is the cumulative wetted time when a substrate is processed multiple times using a certain processing recipe.

[0047] The weight prediction unit 302 uses a weight change prediction model installed in the information processing device 300 to predict a weight change of the resin member after a series of substrate processing operations based on the processing conditions and wet time determined by the condition determination unit 301. Furthermore, when a processing recipe scheduled to be executed in the processing unit is changed, the weight prediction unit 302 updates the predicted weight change based on the changed processing conditions or wet time. Furthermore, the weight prediction unit 302 may calculate the remaining weight of the resin member after a series of substrate processing operations. In this example, similar to the weight change, the remaining weight is treated as a relative value based on the initial weight of the resin member. In this case, the remaining weight is calculated by subtracting the predicted weight change from 1 (100%).

[0048] FIG. 8 is a diagram showing an example of weight change prediction. In this example, processing units 110A to 110C are provided as three processing units 110. Individual processing conditions and liquid contact times are determined for each of the processing units 110A to 110C by the condition determination unit 301. Therefore, as shown in FIG. 8, the weight change of the resin member in processing unit 110A is predicted from the processing conditions and liquid contact time determined for processing unit 110A. The weight change of the resin member in processing unit 110B is predicted from the processing conditions and liquid contact time determined for processing unit 110B. The weight change of the resin member in processing unit 110C is predicted from the processing conditions and liquid contact time determined for processing unit 110C.

[0049] The calculation unit 303 calculates reference information for the prediction of the weight change of the resin member by the weight prediction unit 302. The reference information includes the contribution rate and prediction accuracy of each processing condition. The contribution rate may be calculated by calculating a SHAP (Shapley Additive exPlanations) value or the importance of an explanatory variable of an extra tree. FIG. 9 is a diagram showing an example of calculation of the contribution rate. In the example of FIG. 9, the processing condition with the highest contribution rate among the multiple processing conditions of each processing unit 110 is highlighted. Specifically, in processing unit 110A, the resin member has the highest contribution rate. In processing unit 110B, the processing liquid has the highest contribution rate. In processing unit 110C, the concentration of the processing liquid has the highest contribution rate.

[0050] The life evaluation unit 304 evaluates the life of the resin member against the processing liquid based on the weight change of the resin member predicted by the weight prediction unit 302. In this example, the time when the integrated value of the weight change predicted by the weight prediction unit 302 reaches a preset threshold value is evaluated as the life. For example, the threshold value is set to 0.1. That is, the time when the remaining weight of the resin member reaches 0.9 (90%) is evaluated as the life of the resin member. In this example, if the weight change is predicted to be 0.01 for 100 hours of liquid contact time, the life is evaluated to be 100 hours × 0.1 / 0.01 = 1000 hours. The evaluated life serves as an index indicating when to replace the resin member.

[0051] The presentation unit 305 presents to the user various information such as the weight change of the resin member predicted by the weight prediction unit 302, the reference information calculated by the calculation unit 303, or the lifespan of the resin member evaluated by the lifespan evaluation unit 304. In this example, a predetermined screen is displayed on the display device 360, thereby presenting various information to the user.

[0052] Fig. 10 is a diagram showing an example of a screen displayed on the display device 360 ​​by the presentation unit 305. The screen in Fig. 10 is referred to as a lifespan prediction screen 361. As shown in Fig. 10, the lifespan prediction screen 361 displays the processing conditions used to predict the weight change by the weight prediction unit 302, the change over time in the remaining weight of the resin member calculated by the lifespan evaluation unit 304, and the evaluation result of the lifespan of the resin member predicted by the lifespan evaluation unit 304.

[0053] Fig. 11 is a diagram showing another example of a screen displayed on the display device 360 ​​by the presentation unit 305. The screen in Fig. 11 is called a history screen 362. As shown in Fig. 11, the history screen 362 displays the history of substrate processing in the processing unit 110 selected by the user, based on the history information stored in the storage device 340 of Fig. 2. In the example of Fig. 11, the history of substrate processing includes sets of the resin member and processing liquid under the processing conditions, the period, and the remaining weight.

[0054] Fig. 12 is a diagram showing yet another example of a screen displayed on the display device 360 ​​by the presentation unit 305. The screen in Fig. 12 is called a reference information screen 363. As shown in Fig. 12, the reference information screen 363 displays the contribution rate and prediction accuracy of the processing conditions calculated by the calculation unit 303 for the processing unit 110 selected by the user. By appropriately viewing the screen such as the life prediction screen 361, the history screen 362, or the reference information screen 363, the user can recognize the processing conditions of the desired processing unit 110, the life of the resin member, the history of substrate processing, or reference information.

[0055] The process control unit 306 controls the operation of the transport mechanism 120 in FIG. 1 to transport the substrate to one of the multiple processing units 110 based on the transport conditions. Furthermore, the process control unit 306 controls the operation of the processing mechanism 1 of each processing unit 110 via the control device 2 in FIG. 3 of that processing unit 110 based on the processing recipe. The process control unit 306 may change the transport conditions of the substrates by the transport mechanism 120 based on the lifetime evaluated by the lifetime evaluation unit 304 so as to change the transport order of the substrates. When the transport conditions are changed, the processing unit to which the substrate is loaded changes. Since the processing recipes are linked to the substrates, if the processing recipes linked to each substrate are different, changing the processing unit to which the substrate is loaded results in a change in the processing content performed in the processing unit. Furthermore, the process control unit 306 may change the processing conditions of the substrate based on the lifetime evaluated by the lifetime evaluation unit 304. When the processing conditions are changed, the processing recipe related to the processing conditions is changed.

[0056] Fig. 13 is a diagram showing an example of changes in the transport conditions for substrates by the transport mechanism 120. In the example of Fig. 13, initially, in the substrate transport order, the processing unit 110A is set as number 1, the processing unit 110B is set as number 2, and the processing unit 110C is set as number 3. Furthermore, the cumulative liquid contact time in each of the processing units 110A to 110C according to the initial processing recipe is 1500 hours.

[0057] In such a processing unit 100, as shown in FIG. 13, it is assumed that the lifespans of the resin members in processing sections 110A to 110C evaluated at a certain point in time are 2 months, 1 month, and 1.5 months, respectively. That is, the resin members in processing section 110A have the longest lifespan, and the resin members in processing section 110B have the shortest lifespan. In this case, the order in which substrates are transported is switched between processing section 110B and processing section 110C. Therefore, in the order in which substrates are transported, processing section 110B is changed from second to third, and processing section 110C is changed from third to second. This extends the lifespan of the resin members in processing section 110B.

[0058] Furthermore, the liquid contact time in processing unit 110A is changed from 1500 hours to 2000 hours, and the liquid contact time in processing unit 110B is changed from 1500 hours to 1000 hours. This shortens the lifespan of the resin members in processing unit 110A and lengthens the lifespan of the resin members in processing unit 110B. These changes in transport conditions make it possible to equalize the lifespans of the resin members in processing units 110A to 110C. In the example of Figure 13, the lifespans of the resin members in processing units 110A to 110C evaluated after the change in processing conditions are all 1.5 months.

[0059] The vibration unit 307 vibrates the resin member of each processing unit 110 via the control device 2 of that processing unit 110 (see FIG. 3). As a result, the vibration frequency of the vibrated resin member is measured by the sensor 60 of FIG. 3. In this example, the vibration unit 307 vibrates the resin member during a period when the processing unit 110 is not processing a substrate. The period when the processing unit 110 is not processing a substrate includes a period before a substrate processing is performed, a period after a substrate processing is performed, and a period after a substrate processing is performed and before the next substrate processing is performed.

[0060] As a method of vibrating the resin member, the resin member may be vibrated by moving it between two different positions. For example, if the resin member is the processing cup 21 in FIG. 3, each guard 24 of the processing cup 21 is moved (raised and lowered) between an upper position and a lower position by the guard lifting unit 27. Alternatively, if the resin member is the processing liquid nozzle 39 in FIG. 3, the processing liquid nozzle 39 is moved between a processing position and a standby position by the nozzle moving unit 42. This vibrates the resin member.

[0061] Alternatively, as another method of vibrating the resin member, the resin member may be vibrated by introducing a processing liquid. For example, when the resin member is the processing liquid pipe 40 in FIG. 3, the processing liquid is introduced into the processing liquid pipe 40 by opening the processing liquid valve 41. Similarly, when the resin member is the processing liquid nozzle 39, the processing liquid is introduced into the processing liquid nozzle 39 through the processing liquid pipe 40 by opening the processing liquid valve 41, and is then discharged from the processing liquid nozzle 39. This vibrates the resin member.

[0062] The calibration curve acquisition unit 308 acquires a calibration curve for the resin member from the storage device 340 or the like, depending on the vibration method of the resin member by the vibration unit 307. FIG. 14 is a diagram showing an example of the calibration curve. As shown in FIG. 14, the calibration curve shows the relationship between the vibration frequency of the resin member and the weight of the resin member. A user conducts experiments in advance to create calibration curves similar to those shown in FIG. 14 for each resin member and each vibration method of the resin member, and stores the calibration curves in the storage device 340 or the like.

[0063] The weight acquisition unit 309 acquires the weight of the resin member based on the vibration frequency of the resin member measured by the sensor 60 in FIG. 3 and the calibration curve acquired by the calibration curve acquisition unit 308. In this example, the vibration frequency measured by the sensor 60 is the vibration frequency of the resin member during a period when the processing unit 110 in FIG. 3 does not process a substrate, i.e., before or after the substrate processing is performed. Therefore, the weight of the resin member acquired by the weight acquisition unit 309 is the weight of the resin member before or after the substrate processing is performed. The weight of the resin member acquired by the weight acquisition unit 309 is provided to the additional data acquisition unit 404 of the learning device 400 in FIG. 4.

[0064] In this embodiment, the weight acquisition unit 309 acquires the weight of the resin member using a calibration curve, but the embodiment is not limited to this. The weight acquisition unit 309 may acquire the weight of the resin member without using a calibration curve. For example, if the vibration frequency of the resin member measured by the sensor 60 is f, the spring constant of the resin member is k, and the mass of the resin member is m, the weight acquisition unit 309 may acquire the weight of the resin member based on the following formula (1). The weight of the resin member can be acquired by multiplying the mass m of the resin material in formula (1) by the acceleration of gravity.

[0065]

number

[0066] 5.Database Storage The molecular descriptors of the resin material stored in the database storage device 200 of Fig. 1 will be described below. The molecular descriptors of the solvent stored in the database storage device 200 are the same as the molecular descriptors of the resin material. In this example, the molecular descriptors of the resin material are molecular descriptors included in the force field descriptor or the mass of the resin material. The force field descriptors are force field parameters used in MD (molecular dynamics) calculations that describe the behavior of molecules in the resin material.

[0067] Specifically, the force field potential used in MD calculations is expressed by the following formula (2). The first term in formula (2) is the van der Waals interaction energy, and the second term is the Coulomb interaction energy. The third term in formula (2) is the interaction energy of stretching vibration bonds, the fourth term is the interaction energy of bending vibration bonds, and the fifth term is the interaction energy of bonds associated with dihedral angle changes.

[0068]

number

[0069] An example of a molecular descriptor for polypropylene as a resin material is explained below. The following formula (3) shows the chemical structure of polypropylene in SMILES notation. As shown in formula (3), polypropylene is a polymer compound in which many structural units consisting of carbon atoms and hydrogen atoms are bonded together.

[0070]

number

[0071] Next, kernel mean embedding is performed on the force field parameter distribution of Fig. 15, thereby converting the force field parameter distribution into a continuous distribution. Fig. 16 is a diagram showing the continuous distribution of force field parameters. As shown in Fig. 16, multiple kernel functions (Gaussian functions in this example) are calculated using multiple probabilities appearing in the discrete distribution as weights. Next, the discrete distribution is replaced with a continuous function that is the sum of the calculated kernel functions.

[0072] Then, the probabilities of a predetermined number of force field parameters are extracted as molecular descriptors from the continuous distribution curve of the force field parameters in Fig. 16. Fig. 17 is a diagram showing an example of molecular descriptor extraction. As shown in Fig. 17, the probabilities of 10 force field parameter values ​​g1 to g2 determined at equal intervals are extracted from the continuous distribution curve of the force field parameters. 10 The probabilities of the 10 force field parameters in are extracted as components of the molecular descriptor. Therefore, in the example of Fig. 17, the molecular descriptor has a 10-dimensional vector structure.

[0073] In the above example, polarity is used as a molecular descriptor. However, other force field parameters such as ε ij ,σ ij ,q i q j ,K bond ,r0,K angle ,θ 0,i ,K dihedral Alternatively, even when the mass of a resin material is used, a molecular descriptor with any number of dimensions can be extracted by a similar process. The molecular descriptors extracted from various resin materials are stored in the database storage device 200 in association with the resin material.

[0074] 6. Learning process The learning process is performed by a control unit included in the learning device 400 as the control unit executes a learning program. The learning process includes a model generation process and a model update process. Each of the model generation process and the model update process will be described below with reference to the learning device 400 in FIG.

[0075] 18 is a flowchart showing an example of the flow of a model generation process. First, the data acquisition unit 401 acquires a data set generated in advance (step S1). Next, the descriptor acquisition unit 402 acquires, from the database storage device 200, a molecular descriptor of the resin material used as the resin component in the data set acquired in step S1 (step S2). Subsequently, the data acquisition unit 401 associates the molecular descriptor of the resin material acquired in step S2 with the resin component in the data set acquired in step S1 (step S3).

[0076] Similarly, the descriptor acquisition unit 402 acquires from the database storage device 200 a molecular descriptor of the solution used as the treatment liquid in the data set acquired in step S1 (step S4). Subsequently, the data acquisition unit 401 associates the molecular descriptor of the solution acquired in step S4 with the treatment liquid in the data set acquired in step S1 (step S5). Either steps S2 and S3 or steps S4 and S5 may be executed first, or they may be executed simultaneously.

[0077] Thereafter, the data acquisition unit 401 determines whether or not to terminate acquisition of the data sets (step S6). If a sufficient number of data sets have been acquired, the data acquisition unit 401 may determine to terminate acquisition of the data sets. Alternatively, the data acquisition unit 401 may determine to terminate acquisition of the data sets based on a user instruction. The user can instruct the data acquisition unit 401 to terminate acquisition of the data sets by operating an operation unit (not shown).

[0078] If the acquisition of the dataset is not completed, the process returns to step S1. Steps S1 to S5 are repeated until the acquisition of the dataset is completed. If the acquisition of the dataset is completed, the model generation unit 403 generates a weight change prediction model by training a machine learning model using the dataset in which the molecular descriptors of the resin material and the molecular descriptors of the solution are associated in steps S3 and S5 (step S7). This completes the model generation process.

[0079] 19 is a flowchart showing an example of the flow of the model update process. First, the additional data acquisition unit 404 acquires processing conditions for the substrate based on, for example, a processing recipe (step S11). The additional data acquisition unit 404 also acquires a weight change of the resin member (step S12). The weight change of the resin member is acquired by the information processing device 300 executing a weight acquisition process (described below) before and after the substrate processing is performed. Either step S11 or step S12 may be performed first, or they may be performed simultaneously.

[0080] Next, the additional data acquisition unit 404 acquires an additional data set by associating the substrate processing conditions acquired in step S11 with the weight change of the resin member acquired in step S12 (step S13). Subsequently, the additional learning unit 405 updates the weight change prediction model generated in step S7 of the model generation process by additionally learning the multiple additional data sets acquired in step S13. Thereafter, the process returns to step S1. Thereafter, steps S11 to S14 are repeated.

[0081] If the processing conditions for the substrate do not change, the execution of step S11 may be omitted after the process returns from step S14 to step S11. Furthermore, steps S11 and S12 may be executed by the information processing device 300. In this case, the additional data is generated by the information processing device 300, and therefore the execution of steps S11 and S12 is omitted. Furthermore, the additional data acquisition unit 404 acquires the additional data from the information processing device 300 in step S13. If the weight change prediction model becomes capable of predicting the weight change of the resin member with sufficient accuracy, the model update process may be terminated after step S14 is executed.

[0082] 7. Data Processing The data processing is performed by a control unit included in the information processing device 300 as the control unit executes a data processing program. The data processing includes a weight acquisition process and a lifespan evaluation process. Each of the weight acquisition process and the lifespan evaluation process will be described below with reference to the information processing device 300 in FIG. 6.

[0083] 20 is a flowchart showing an example of the flow of the weight acquisition process. First, the vibration unit 307 vibrates the resin member of one of the processing units 110 (step S21). As a result, the vibration frequency of the resin member is measured by the sensor 60 in that processing unit 110. Next, the weight acquisition unit 309 acquires the vibration frequency of the resin member measured by the sensor 60 in response to step S21 (step S22). Steps S21 and S22 are performed during a period when the processing unit 110 is not processing substrates W before or after substrate processing is performed.

[0084] Furthermore, the calibration curve acquisition unit 308 acquires a calibration curve for the resin member from the storage device 340 or the like in accordance with the vibration method of the resin member in step S21 (step S23). Step S23 may be executed before step S21 or step S22. Alternatively, step S23 may be executed simultaneously with step S21 or step S22.

[0085] Next, the weight acquisition unit 309 acquires the weight of the resin member based on the vibration frequency acquired in step S22 and the calibration curve acquired in step S23 (step S24). The weight of the resin member acquired in step S24 is provided to the learning device 400. This causes step S12 of the model update process in FIG. 19 to be executed. Thereafter, the process returns to step S21. Thereafter, steps S21 to S24 are repeated.

[0086] If the vibration mode of the resin member does not change, the execution of step S23 may be omitted after the process returns from step S24 to step S21. Also, in step S24, the weight acquisition unit 309 may acquire the weight of the resin member based on formula (1) without using the calibration curve. In this case, step S23 is not executed.

[0087] 21 is a flowchart showing an example of the flow of the life evaluation process. First, the condition determination unit 301 determines the processing conditions and wet time of the substrate in each processing unit 110 (step S31). Next, the weight prediction unit 302 predicts the weight change of the resin member after a series of substrate processing in each processing unit 110 from the processing conditions and wet time determined in step S31, using the weight change prediction model generated in step S7 of the model generation process or the weight change prediction model updated in step S14 of the model update process (step S32). Furthermore, the calculation unit 303 calculates reference information for predicting the weight change of the resin member in step S32 (step S33).

[0088] Next, the life evaluation unit 304 evaluates the life of the resin member of each processing unit 110 based on the weight change of the resin member predicted in step S32 (step S34). In addition, the presentation unit 305 presents to the user various information such as the weight change of the resin member predicted in step S32, the reference information calculated in step S33, and the life of the resin member evaluated in step S34 (step S35).

[0089] Thereafter, the process control unit 306 determines whether the evaluated lifespans of the resin components are uniform for all the processing units 110 (step S36). If the difference between the longest and shortest lifespans among the evaluated lifespans is equal to or less than a predetermined threshold, it is determined that the lifespans of the resin components are uniform. If the lifespans of the resin components are uniform, the process control unit 306 determines whether the process recipe has been changed (step S37).

[0090] If the processing recipe has not been changed, the process returns to step S36. In this case, steps S36 and S37 are repeated until the lifespans of the resin members become uneven or the processing recipe is changed. On the other hand, if the processing recipe has been changed, the process returns to step S31. In this case, in step S31, the condition determination unit 301 determines the processing conditions and liquid contact time for the substrate in each processing unit 110 so as to correspond to the changed processing recipe. Thereafter, the processing from step S32 onwards is executed again.

[0091] If it is determined in step S36 that the lifespans of the resin components are not uniform, the process control unit 306 changes the substrate transport conditions or the substrate processing conditions in any of the processing units 110 based on the lifespans evaluated in step S36 (step S38). Specifically, the substrate transport conditions are changed so that the lifespans of the resin components evaluated by the life evaluation unit 304 become closer to uniform. Since the processing recipes are linked to the substrates, if the processing recipes linked to each substrate are different, the processing unit 110 including the resin components evaluated to have a longer lifespan than the other processing units 110 may be changed so that the substrates to which the processing recipes associated with the processing units 110 associated with the resin components have a longer liquid contact time are transported. Furthermore, the processing unit 110 including the resin components evaluated to have a shorter lifespan than the other processing units 110 may be changed so that the substrates to which the processing recipes associated with the processing units 110 associated with the resin components have a shorter liquid contact time are transported.

[0092] Even if the processing recipes associated with the respective substrates are the same, the substrate transport conditions may be changed so that the substrate is moved up in the transport order for a processing unit 110 that includes a resin member that is evaluated to have a longer lifespan than the other processing units 110. Alternatively, the substrate transport conditions may be changed so that the substrate is moved down in the transport order for a processing unit 110 that includes a resin member that is evaluated to have a shorter lifespan than the other processing units 110.

[0093] Furthermore, the substrate processing conditions may be changed so that the liquid contact time is longer than before the change for a processing unit 110 including a resin member that is evaluated to have a longer lifespan than the other processing units 110. Alternatively, the substrate processing conditions may be changed so that the liquid contact time is shorter than before the change for a processing unit 110 including a resin member that is evaluated to have a shorter lifespan than the other processing units 110.

[0094] After step S38 is executed, the process returns to step S32. In this case, in step S32, the weight prediction unit 302 predicts the weight change of the resin member after a series of substrate processing in each processing unit 110 based on the changed substrate processing conditions. Then, the processes from step S33 onward are executed again.

[0095] 8. Variations As described above, in this embodiment, a machine learning model is trained using the substrate processing conditions and liquid contact time as explanatory variables and the weight change of the resin member as a target variable, thereby generating a weight change prediction model that predicts the weight change of the resin member from the substrate processing conditions and liquid contact time. The substrate processing conditions include, for example, the resin member, processing liquid, and the temperature or concentration of the processing liquid in processing unit 110 of FIG.

[0096] In a modified example, a crystallinity label is added to the resin component, which is one of the substrate processing conditions in the explanatory variables. The crystallinity label is a label for identifying whether the polymer compound (polymer) that constitutes the resin component is crystalline or amorphous. If the polymer compound is crystalline, the crystallinity label is set to "1," and if the polymer compound is amorphous, the crystallinity label is set to "0."

[0097] Specifically, crystalline polymer compounds have a relatively high degree of crystallinity. Here, the degree of crystallinity is the ratio of the crystalline region to the sum of the crystalline region and the amorphous region of a polymer compound. The crystalline region and the amorphous region of a polymer compound can be identified by analyzing the components of the polymer compound. For example, when observing a polymer compound by XRD (X-ray diffraction), the area of ​​the peak due to the crystalline component is the crystalline region, and the area of ​​the peak due to the amorphous component is the amorphous region. Alternatively, crystalline polymer compounds have a melting point, whereas amorphous polymer compounds do not have a melting point.

[0098] Therefore, in this example, multiple data sets corresponding to multiple resin components are prepared. In each data set, a crystallinity label of "1" is assigned to a resin component formed from a polymer compound whose crystallinity is equal to or greater than a predetermined crystallinity threshold, or a polymer compound having a melting point. On the other hand, a crystallinity label of "0" is assigned to a resin component formed from a polymer compound whose crystallinity is less than the predetermined crystallinity threshold, a polymer compound whose crystallinity cannot be measured, or a polymer compound without a melting point. The crystallinity threshold is, for example, 0.1 (10%), but is not limited to this value.

[0099] 4 trains a predetermined machine learning model using the substrate processing conditions and wetted time in each of the above data sets acquired by the data acquisition unit 401 as explanatory variables and the weight change of the resin member as a response variable. This makes it possible to generate a weight change prediction model that predicts the weight change of the resin member with higher accuracy from the substrate processing conditions and wetted time.

[0100] 9.Effects In the substrate processing apparatus 500 according to this embodiment, the weight prediction unit 302 predicts a weight change of the resin member from the processing conditions when the substrate processing is performed by the processing unit 110, using a weight change prediction model obtained by machine learning of a learning dataset. The dataset includes a set of the processing conditions of the substrate and the weight change of the resin member after the substrate processing is performed by the processing unit under the processing conditions. According to this configuration, the weight change prediction model is used to predict a weight change of the resin member when a substrate is processed using a processing liquid in the processing unit 110. In this case, a user can uniformly determine the timing for maintenance of the resin member by recognizing the predicted weight change of the resin member. This allows the resin member to be maintained at an appropriate timing.

[0101] Furthermore, the additional data set is used by the additional learning unit 405 to additionally learn the weight change prediction model. The additional data set includes a set of substrate processing conditions and a weight change of the resin component, which is the difference between the weights acquired by the weight acquisition unit 309 before and after the substrate processing is performed by the processing unit 110 under the processing conditions. In this case, the prediction accuracy of the weight change prediction model can be improved. Furthermore, since the weight acquisition unit 309 can acquire the weight of the resin component while the resin component is installed in the processing unit 110, there is no need to disassemble the processing unit 110 to acquire the weight of the resin component. Therefore, the additional data set can be easily prepared.

[0102] A calibration curve indicating the relationship between the vibration frequency of the resin member and the weight of the resin member is acquired by the calibration curve acquisition unit 308. Based on the calibration curve acquired by the calibration curve acquisition unit 308, the weight of the resin member is acquired by the weight acquisition unit 309 from the vibration frequency of the resin member measured by the sensor 60. Alternatively, by calculating the mass of the resin member using equation (1), the weight acquisition unit 309 acquires the weight of the resin member from the vibration frequency of the resin member measured by the sensor 60. In these cases, the weight of the resin member can be easily acquired.

[0103] The sensor 60 measures the vibration frequency of the resin member without coming into contact with the resin member. In this case, the location where the sensor 60 is installed is not limited. Therefore, the sensor 60 can be easily installed even in a small substrate processing apparatus 500. For example, the sensor 60 may include an event-based vision camera. In this case, the vibration frequency of the resin member can be easily measured without the sensor 60 coming into contact with the resin member.

[0104] The resin member is vibrated by the vibrating unit 307. The vibration frequency of the resin member vibrated by the vibrating unit 307 is measured by the sensor 60. In this case, the vibration frequency of the resin member can be measured at any timing. This makes it possible to obtain the weight of the resin member at any timing. In this example, the resin member is vibrated by the vibrating unit 307 during a period when the processing unit 110 is not processing a substrate. In this case, it is possible to easily obtain the weight of the resin member at any timing when the processing unit 110 is not processing a substrate.

[0105] The vibration unit 307 may vibrate the resin member by moving the resin member between a first position and a second position that are different from each other. Alternatively, the vibration unit 307 may vibrate the resin member by introducing a treatment liquid into the resin member. In these cases, the resin member can be easily vibrated.

[0106] 10. Other Embodiments (1) In the above embodiment, the weight of the resin member acquired by the weight acquisition unit 309 is used to acquire the additional data set, but the embodiment is not limited to this. The weight of the resin member acquired by the weight acquisition unit 309 does not have to be used to acquire the additional data set. In this case, the substrate processing apparatus 500 does not have to include the learning device 400, and the information processing apparatus 300 does not have to include the weight prediction unit 302, etc. Even in this case, the user can uniformly determine the timing for maintenance of the resin member by recognizing the change in the weight of the resin member acquired by the weight acquisition unit 309. This allows the resin member to be maintained at an appropriate time.

[0107] (2) In the above embodiment, the sensor 60 includes an event-based vision camera, but the embodiment is not limited to this. When the sensor 60 measures the vibration frequency of the resin member while in contact with the resin member, the sensor 60 may include a vibration sensor, an acceleration sensor, or the like.

[0108] (3) In the above embodiment, the weight and remaining weight of the resin member are treated as relative values ​​based on the initial weight, but the embodiment is not limited to this. The weight and remaining weight of the resin member may be treated as absolute values ​​having units.

[0109] (4) In the above embodiment, the substrate processing apparatus 500 includes a plurality of processing units 110, but the embodiment is not limited to this. The substrate processing apparatus 500 may include a single processing unit 110. Even in this case, the processing control unit 306 can adjust the timing for maintenance of the resin member in the single processing unit 110 by changing the processing conditions for the substrate in the processing unit 110 based on the life of the resin member evaluated by the life evaluation unit 304.

[0110] (5) In the above embodiment, the substrate processing apparatus 500 includes the life evaluation unit 304, but the embodiment is not limited to this. By recognizing the weight change of the resin member predicted by the weight prediction unit 302, a user can uniformly determine the timing for maintenance of the resin member. Therefore, the substrate processing apparatus 500 does not need to include the life evaluation unit 304.

[0111] (6) In the above embodiment, the substrate processing apparatus 500 includes the process control unit 306. However, the embodiment is not limited to this. If the processing unit 110 or the transport mechanism 120 is not controlled based on the weight change of the resin member predicted by the weight prediction unit 302, the substrate processing apparatus 500 does not need to include the process control unit 306.

[0112] (7) In the above embodiment, the processing liquid is a mixture containing a sublimation agent and a solvent that dissolves in the sublimation agent, but the embodiment is not limited to this. The processing liquid may be sulfuric acid, nitric acid, hydrochloric acid, hydrofluoric acid, phosphoric acid, acetic acid, ammonia water, hydrogen peroxide water, an organic acid (e.g., citric acid or oxalic acid), an organic alkali (e.g., TMAH: tetramethylammonium hydroxide), or an organic solvent (e.g., IPA: isopropyl alcohol). Alternatively, the processing liquid may be a mixture of any of these liquids.

[0113] (8) In the above embodiment, the substrate processing conditions include a processing liquid descriptor, a resin member descriptor, the temperature of the processing liquid, and the concentration of the processing liquid, but the embodiment is not limited to this. The processing conditions may also include other parameters described in the processing recipe. For example, the processing conditions may include the supply time of the processing liquid, the discharge flow rate of the processing liquid, the substrate rotation speed, the substrate rotation speed, the processing time, the behavior of the supply nozzle, the behavior of the shielding plate, the supply time of the inert gas, or the discharge flow rate of the inert gas.

[0114] (9) In the above embodiment, the force field parameter K bond ,K angle ,K dihedral is used as a molecular descriptor, but the embodiment is not limited to this. ij ,σ ij ,q i q j ,K bond ,r0,K angle ,θ 0,i ,K dihedral One or more of the molecular weight, the polarity, and the mass of the resin member (resin material) may be used as a molecular descriptor.

[0115] 11. Correspondence between each element of the claims and each part of the embodiment Below, examples of correspondence between each element of the claims and each element of the embodiments will be described, but the present invention is not limited to the following examples. Various other elements having the configuration or function described in the claims can also be used as each element of the claims.

[0116] In the above-described embodiment, processing cup 21, processing liquid nozzle 39, or processing liquid pipe 40 is an example of a resin member, processing unit 110 is an example of a processing unit, sensor 60 is an example of a sensor, and weight acquisition unit 309 is an example of a weight acquisition unit. Processing unit 100 is an example of a substrate processing apparatus, calibration curve acquisition unit 308 is an example of a calibration curve acquisition unit, vibration unit 307 is an example of a vibration unit, weight prediction unit 302 is an example of a weight prediction unit, and additional learning unit 405 is an example of an additional learning unit.

[0117] 12. Summary of the embodiment (Item 1) The substrate processing apparatus according to item 1 comprises: a processing section including a resin member that comes into contact with a processing liquid and configured to perform substrate processing using the processing liquid; a sensor that measures the vibration frequency of the resin member; and a weight acquisition unit that acquires the weight of the resin member based on the vibration frequency of the resin member.

[0118] In this substrate processing apparatus, a sensor measures the vibration frequency of the resin member in the processing section. Furthermore, a weight acquisition unit acquires the weight of the resin member based on the vibration frequency of the resin member measured by the sensor. In this case, a user can uniformly determine the timing for maintenance of the resin member by recognizing the acquired weight change of the resin member. This allows the resin member to be maintained at an appropriate timing.

[0119] (Item 2) The substrate processing apparatus according to item 1, a calibration curve acquisition unit that acquires a calibration curve showing a relationship between the vibration frequency of the resin member and the weight of the resin member; The weight acquisition unit may acquire the weight of the resin member further based on the calibration curve acquired by the calibration curve acquisition unit.

[0120] In this case, the weight of the resin member can be easily obtained from the vibration frequency of the resin member measured by the sensor based on the calibration curve obtained by the calibration curve obtaining unit.

[0121] (Item 3) In the substrate processing apparatus according to item 1, When the vibration frequency of the resin member measured by the sensor is f, the spring constant of the resin member is k, and the mass of the resin member is m, the weight acquisition unit may acquire the weight of the resin member based on equation (1).

[0122] In this case, by calculating the mass of the resin member using equation (1), the weight of the resin member can be easily obtained from the vibration frequency of the resin member measured by the sensor.

[0123] (Item 4) In the substrate processing apparatus according to any one of items 1 to 3, The sensor may measure the vibration frequency of the resin member without coming into contact with the resin member.

[0124] In this case, the position where the sensor is provided is not limited, and therefore the sensor can be easily provided even in a small substrate processing apparatus.

[0125] (Item 5) In the substrate processing apparatus according to item 4, The sensor may include an event-based vision camera.

[0126] In this case, the vibration frequency of the resin member can be easily measured without bringing the sensor into contact with the resin member.

[0127] (Item 6) The substrate processing apparatus according to any one of Items 1 to 5, Further, a vibration unit that vibrates the resin member is provided, The sensor may measure a frequency of the resin member vibrated by the vibrating portion.

[0128] In this case, the vibration frequency of the resin member can be measured at any timing, thereby making it possible to obtain the weight of the resin member at any timing.

[0129] (Item 7) In the substrate processing apparatus according to item 6, The vibrating section may vibrate the resin member by moving the resin member between a first position and a second position that are different from each other.

[0130] In this case, the resin member can be easily vibrated.

[0131] (Item 8) In the substrate processing apparatus according to item 6 or 7, The vibration section may vibrate the resin member by introducing a treatment liquid into the resin member.

[0132] In this case, the resin member can be easily vibrated.

[0133] (Item 9) In the substrate processing apparatus according to any one of items 6 to 8, The vibration section may vibrate the resin member during a period when the processing section is not processing a substrate.

[0134] In this case, the weight of the resin member at any timing when the processing section is not processing the substrate can be easily obtained.

[0135] (Item 10) The substrate processing apparatus according to any one of Items 1 to 9, The apparatus may further include a weight prediction unit that predicts a weight change in the resin member from the processing conditions under which the processing unit performs substrate processing, using a weight change prediction model that has been machine-learned from a learning dataset that includes a pair of processing conditions for the substrate and a weight change in the resin member after the processing unit performs substrate processing under those processing conditions.

[0136] According to this configuration, the weight change prediction model is used to predict the weight change of the resin member when substrate processing is performed using the processing liquid in the processing section. In this case, by recognizing the predicted weight change of the resin member, a user can uniformly determine the timing for maintenance of the resin member. This allows the resin member to be maintained at an appropriate timing.

[0137] (Item 11) The substrate processing apparatus according to item 10, The apparatus may further include an additional learning unit that additionally learns an additional data set including a set of substrate processing conditions and a weight change of the resin component, which is the difference between the weights acquired by the weight acquisition unit before and after substrate processing is performed by the processing unit under the processing conditions, into the weight change prediction model.

[0138] In this case, the weight change prediction model is additionally trained using an additional data set based on the weight of the resin member acquired by the weight acquisition unit. This improves the prediction accuracy of the weight change prediction model. Furthermore, since the weight acquisition unit can acquire the weight of the resin member while the resin member is installed in the processing unit, there is no need to disassemble the processing unit to acquire the weight of the resin member. Therefore, the additional data set can be easily prepared.

[0139] (Item 12) The substrate processing method according to item 12 includes: a processing unit configured to perform substrate processing using a processing liquid, the processing unit measuring a vibration frequency of a resin member that comes into contact with the processing liquid by a sensor; and obtaining a weight of the resin member based on a vibration frequency of the resin member.

[0140] According to this substrate processing method, the vibration frequency of the resin member in the processing section is measured by a sensor. Furthermore, the weight of the resin member is acquired based on the vibration frequency of the resin member measured by the sensor. In this case, a user can uniformly determine the timing for maintenance of the resin member by recognizing the acquired weight change of the resin member. This allows the resin member to be maintained at an appropriate timing. [Explanation of symbols]

[0141] 1... processing mechanism, 2... control device, 3... chamber, 10... spin chuck, 11... chuck pin, 12... spin base, 13... spin shaft, 14... spin motor, 21... processing cup, 22... outer wall member, 23... cup, 24... guard, 27... guard lifting unit, 31... chemical solution nozzle, 34, 38, 42, 46... nozzle moving unit, 35... rinse liquid nozzle, 39... processing liquid nozzle, 40... processing liquid piping, 41... processing liquid valve, 43... replacement liquid nozzle, 51... shutoff member, 51L... lower surface, 52... disc portion, 53... support shaft, 54... shutoff member lifting unit, 55... center nozzle, 56... upper gas piping, 57... upper gas valve, 58... flow rate adjustment valve, 59... upper temperature regulator, 60... sensor, 100... processing unit, 110, 110A~ 110C... processing unit, 120... transport mechanism, 200... database storage device, 300... information processing device, 301... condition determination unit, 302... weight prediction unit, 303... calculation unit, 304... life evaluation unit, 305... presentation unit, 306... processing control unit, 307... vibration unit, 308... calibration curve acquisition unit, 309... weight acquisition unit, 310... control unit, 320... RAM, 330... ROM, 340... storage device, 350... operation unit, 360... display device, 361... life prediction screen, 362... history screen, 363... reference information screen, 370... input / output I / F, 380... bus, 400... learning device, 401... data acquisition unit, 402... descriptor acquisition unit, 403... model generation unit, 404... additional data acquisition unit, 405... additional learning unit, 500... substrate processing device, 501... network

Claims

1. a processing section including a resin member that comes into contact with a processing liquid and configured to perform substrate processing using the processing liquid; a sensor that measures the vibration frequency of the resin member; a weight acquisition unit that acquires a weight of the resin member based on a vibration frequency of the resin member.

2. a calibration curve acquisition unit that acquires a calibration curve showing a relationship between the vibration frequency of the resin member and the weight of the resin member; The substrate processing apparatus according to claim 1 , wherein the weight acquisition unit acquires the weight of the resin member further based on the calibration curve acquired by the calibration curve acquisition unit.

3. 2. The substrate processing apparatus according to claim 1, wherein the weight acquisition unit acquires the weight of the resin member based on the following formula (1): where f is the vibration frequency of the resin member measured by the sensor, k is the spring constant of the resin member, and m is the mass of the resin member. [Equation 1]

4. 4. The substrate processing apparatus according to claim 1, wherein the sensor measures the vibration frequency of the resin member without coming into contact with the resin member.

5. The substrate processing apparatus of claim 4 , wherein the sensor comprises an event-based vision camera.

6. Further, a vibration unit that vibrates the resin member is provided, 4. The substrate processing apparatus according to claim 1, wherein the sensor measures a vibration frequency of the resin member vibrated by the vibration unit.

7. The substrate processing apparatus according to claim 6 , wherein the vibration section vibrates the resin member by moving the resin member between a first position and a second position that are different from each other.

8. The substrate processing apparatus according to claim 6 , wherein the vibration unit vibrates the resin member by introducing a processing liquid into the resin member.

9. The substrate processing apparatus according to claim 6 , wherein the vibration section vibrates the resin member during a period when the processing section is not processing a substrate.

10. The substrate processing apparatus according to any one of claims 1 to 3, further comprising a weight prediction unit that predicts a weight change of the resin member from the processing conditions when the substrate processing is performed by the processing unit, using a weight change prediction model that has been machine-learned from a learning dataset that includes a pair of processing conditions for the substrate and a weight change of the resin member after the substrate processing is performed by the processing unit under the processing conditions.

11. The substrate processing apparatus according to claim 10, further comprising an additional learning unit that additionally learns an additional data set including a pair of substrate processing conditions and a weight change of the resin member, which is the difference between the weights acquired by the weight acquisition unit before and after substrate processing is performed by the processing unit under the processing conditions, into the weight change prediction model.

12. a processing unit configured to perform substrate processing using a processing liquid, the processing unit measuring a vibration frequency of a resin member that comes into contact with the processing liquid by a sensor; and obtaining a weight of the resin member based on a vibration frequency of the resin member.

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