Patent Publication Number: US-2023161301-A1

Title: Substrate processing apparatus, substrate processing method, training data generation method, training method, training device, trained model creation method, and trained model

Description:
TECHNICAL FIELD 
     The present invention relates to a substrate processing apparatus, a substrate processing method, a training data generation method, a training method, a training device, trained model creation method, and trained model. 
     BACKGROUND ART 
     A substrate processing apparatus for substrate processing is favorably used for semiconductor device production. In substrate production, formation of a resist layer into a specific pattern may be followed by ion implantation on a substrate for modifying a characteristic of the substrate. In that case, the resist layer is peeled off with a chemical liquid after ion implantation. It is known that when ions are implanted into the resist layer in the course of substrate production, a hardened layer or a characteristic-modified layer is formed in the resist layer (see Patent Literature 1). 
     Patent Literature 1 discloses peeling and removal of the resist layer with a cleaning agent for semiconductor substrate use obtained by adding alkylene carbonate to sulfuric acid and hydrogen peroxide water. According to Patent Literature 1, the cleaning agent for semiconductor substrate use has a cleaning power comparable to that of a SPM cleaning agent and can reduce damage to semiconductor substrates in resist layer peeling. 
     CITATION LIST 
     Patent Literature 
     
         
         Patent Literature 1 
       
    
     Japanese Patent Application Laid-Open Publication No. 2012-67254 
     SUMMARY OF INVENTION 
     Technical Problem 
     However, the hardened layer in the resist layer of the substrate may be inappropriately removed by the method disclosed in Patent Literature 1 in some cases. For example, an excessively small amount of the chemical liquid relative to the resist layer subjected to ion implantation leads to insufficient removal of the resist layer. By contrast, an excessively large amount of the chemical liquid relative to the resist layer subjected to ion implantation leads to an increase in cost. 
     The present invention has been made in view of the foregoing and has its object of providing a substrate processing apparatus, a substrate processing method, a training data generation method, a training method, a training device, a trained model creation method, and a trained model that are capable of appropriately removing the hardened layer in the resist layer of the substrate. 
     Solution to Problem 
     According to an aspect of the present invention, a substrate processing device includes: a substrate holding section configured to hold a processing target substrate in a rotatable manner, the processing target substrate including a resist layer with a hardened layer formed therein; a chemical liquid supply section configured to supply a chemical liquid to the processing target substrate; a substrate information acquiring section configured to acquire substrate information of the processing target substrate including hardened layer thickness information of the processing target substrate or ion implantation condition information of the processing target substrate, the hardened layer thickness information of the processing target substrate indicating a thickness of the hardened layer in the processing target substrate, the ion implantation condition information of the processing target substrate indicating a condition for ion implantation by which the hardened layer in the processing target substrate has been formed in the resist layer of the processing target substrate; a chemical liquid processing condition information acquiring section configured to acquire chemical liquid processing condition information of the processing target substrate from a trained model based on the substrate information of the processing target substrate, the chemical liquid processing condition information of the processing target substrate indicating a chemical liquid processing condition of the processing target substrate; and a controller configured to control the substrate holding section and the chemical liquid supply section to perform processing with a chemical liquid on the processing target substrate based on the chemical liquid processing condition information of the processing target substrate acquired in the chemical liquid processing condition information acquiring section. The trained model is built through machine training of training data in which substrate information of a training target substrate including a resist layer with a hardened layer formed therein, chemical liquid processing condition information of the training target substrate, and processing result information of the training target substrate are associated with each other, the substrate information of the training target substrate including hardened layer thickness information indicating a thickness of the hardened layer in the training target substrate or ion implantation condition information indicating a condition for ion implantation by which the hardened layer in the training target substrate has been formed in the resist layer of the training target substrate, the chemical liquid condition information of the training target substrate indicating a condition for the processing with the chemical liquid having been performed on the training target substrate, the processing result information indicating a result of the processing with the chemical liquid having been on the training target substrate. 
     In an embodiment, the substrate processing device further includes storage that stores the trained model therein. 
     In an embodiment, the hardened layer thickness information of the processing target substrate contains hardened layer height information indicating a height of the hardened layer in the processing target substrate or hardened layer width information indicating a width of the hardened layer in the processing target substrate, and the hardened layer thickness information of the training target substrate contains hardened layer height information indicating a height of the hardened layer in the training target substrate or hardened layer width information indicating a width of the hardened layer in the training target substrate. 
     In an embodiment, the chemical liquid processing condition information of the processing target substrate contains information indicating any of a concentration of the chemical liquid, a temperature of the chemical liquid, a supply amount of the chemical liquid, a discharge pattern of the chemical liquid, and a rotational speed of the processing target substrate in supply of the chemical liquid, and the chemical liquid processing condition information of the training target substrate contains information indicating any of the concentration of the chemical liquid, the temperature of the chemical liquid, the supply amount of the chemical liquid, the discharge pattern of the chemical liquid, and a rotational speed of the training target substrate when the chemical liquid has been supplied. 
     In an embodiment, the information indicating the concentration of the chemical liquid of the processing target substrate indicates a concentration profile in which the concentration of the chemical liquid changes over time, and the information indicating the concentration of the chemical liquid of the training target substrate indicates a concentration profile in which the concentration of the chemical liquid has changed over time. 
     In an embodiment, the information indicating the temperature of the chemical liquid of the processing target substrate indicates a temperature profile in which the temperature of the chemical liquid changes over time, and the information indicating the temperature of the chemical liquid of the training target substrate indicates a temperature profile in which the temperature of the chemical liquid has changed over time. 
     According to another aspect of the present invention, a substrate processing method includes: holding a processing target substrate in a rotatable manner, the processing target substrate including a resist layer with a hardened layer formed therein; acquiring substrate information of the processing target substrate including hardened layer thickness information of the processing target substrate or ion implantation condition information of the processing target substrate, the hardened layer thickness information of the processing target substrate indicating a thickness of the hardened layer in the processing target substrate, the ion implantation condition information of the processing target substrate indicating a condition for ion implantation by which the hardened layer in the processing target substrate has been formed in the resist layer of the processing target substrate; acquiring chemical liquid processing condition information of the processing target substrate from a trained model based on the substrate information of the processing target substrate, the chemical liquid processing condition information of the processing target substrate indicating a chemical liquid processing condition of the processing target substrate; and performing processing with a chemical liquid on the processing target substrate according to the chemical liquid processing condition indicated in the chemical liquid processing condition information of the processing target substrate. In the acquiring chemical liquid processing condition information of the processing target substrate, the trained model is built through machine training of training data in which substrate information of a training target substrate including a resist layer with a hardened layer formed therein, chemical liquid processing condition information of the training target substrate, and processing result information of the training target substrate are associated with each other, the substrate information of the training target substrate including hardened layer thickness information indicating a thickness of the hardened layer in the training target substrate or ion implantation condition information indicating a condition for ion implantation by which the hardened layer in the training target substrate has been formed in the resist layer of the training target substrate, the chemical liquid condition information of the training target substrate indicating a condition for the processing with the chemical liquid having been performed on the training target substrate, the processing result information indicating a result of the processing with the chemical liquid having been on the training target substrate. 
     According to still another aspect of the present invention, a training data generation method includes: acquiring substrate information including hardened layer thickness information or ion implantation condition information from time-series data output from a substrate processing apparatus that processes a training target substrate including a resist layer with a hardened layer formed therein, the hardened layer thickness information indicating a thickness of the hardened layer, the ion implantation condition information indicating a condition for ion implantation by which the hardened layer has been formed in the resist layer; acquiring chemical liquid processing condition information from the time-series data, the chemical liquid processing condition information indicating a condition under which the training target substrate has undergone processing with a chemical liquid in the substrate processing apparatus; acquiring processing result information from the time-series data, the processing result information indicating a result of the processing with the chemical liquid on the training target substrate in the substrate processing apparatus; and storing in storage the substrate information of the training target substrate, the chemical liquid processing condition information of the training target substrate, and the processing result information of the training target substrate in association with each other as training data. 
     According to a still another aspect of the present invention, a training method includes: acquiring the training data generated according to the training data generation method described above; and performing machine training of the training data by inputting the training data to a training program. 
     According to a still another aspect of the present invention, a training device includes: storage that stores therein the training data generated according to the training data generation method described above; and a training section configured to perform machine training of the training data by inputting the training data to a training program. 
     According to a still another aspect of the present invention, a trained model generating method includes: acquiring the training data generated according to the training data generation method described above; and creating a trained model built through machine training of the training data. 
     According to a still another aspect of the present invention, a trained model is built through machine training of the training data generated according to the training data generation method described above. 
     According to a still another aspect of the present invention, a substrate processing apparatus includes: a substrate holding section configured to hold a substrate in a rotatable manner, the substrate including a resist layer with a hardened layer formed therein; a chemical liquid supply section configured to supply a chemical liquid to the substrate; storage that stores therein a conversion table in which substrate information of substrates and chemical liquid processing condition information of the substrates are associated with each other, the substrate information of the substrates including hardened thickness information of the substrates or ion implantation condition information of the substrates that indicates conditions for ion implantation of the substrates, the chemical liquid processing condition information of the substrates indicating conditions for chemical liquid processing on the substrates; a substrate information acquiring section configured to acquire substrate information of the substrate including hardened layer thickness information of the substrate or ion implantation condition information of the substrate, the hardened layer thickness information of the substrate indicating a thickness of the hardened layer of the substrate, the ion implantation condition information of the substrate indicating a condition for ion implantation by which the hardened layer of the substrate has been formed in the resist layer of the substrate; a chemical liquid processing condition information acquiring section configured to acquire chemical liquid processing condition information of the substrate indicating a chemical liquid processing condition of the substrate using the conversion table based on the substrate information of the substrate; and a controller configured to control the substrate holding section and the chemical liquid supply section to perform processing with a chemical liquid on the substrate based on the chemical liquid processing condition information of the substrate acquired in the chemical liquid processing condition information acquiring section. Advantageous Effects of Invention 
     According to the present invention, the hardened layer in the resist layer of the processing target substrate can be appropriately removed. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    is a schematic diagram of a substrate processing training system including a substrate processing apparatus of an embodiment. 
         FIG.  2    is a schematic diagram of the substrate processing system including substrate processing apparatuses of the embodiment. 
         FIG.  3    is a schematic diagram of a substrate processing apparatus of the embodiment. 
         FIG.  4    is a block diagram of a substrate processing system including a substrate processing apparatus of the embodiment. 
         FIGS.  5 A and  5 B  is a flowchart depicting a substrate processing method of the embodiment, and  FIG.  5 B  is a flowchart depicting chemical liquid processing in the substrate processing method of the embodiment. 
         FIGS.  6 A to  6 C  each are a schematic diagram illustrating a formation process of a hardened layer formed in a substrate to be processed in the substrate processing apparatus of the embodiment. 
         FIGS.  7 A to  7 D  each are a schematic diagram illustrating a formation process of a hardened layer formed in a substrate to be processed in the substrate processing apparatus of the embodiment. 
         FIG.  8    is a block diagram of a training data generating device and a substrate processing system including a substrate processing apparatus of the embodiment. 
         FIG.  9    is a flowchart depicting a training data generation method of the embodiment. 
         FIG.  10    is a block diagram of the training data generating device and a training device of the embodiment. 
         FIG.  11    is a flowchart depicting a training method and a trained model creation method of the embodiment. 
         FIG.  12    is a diagram of training data input to the training device of the embodiment. 
         FIG.  13    is a diagram of training data input to the training device of the embodiment. 
         FIG.  14    is a diagram of training data input to the training device of the embodiment. 
         FIGS.  15 A and  15 B  is a schematic diagram illustrating a processing target substrate including a resist layer, which is a processing target of the substrate processing apparatus of the embodiment, with a hardened layer formed therein, and  FIG.  15 B  is a diagram of a chemical liquid processing condition acquired based on substrate information. 
         FIG.  16    is a diagram of training data input to the training device of the embodiment. 
         FIG.  17    is a schematic diagram of a substrate processing apparatus of the embodiment. 
         FIG.  18    is a diagram of training data input to the training device of the embodiment. 
         FIG.  19    is a block diagram of a substrate processing apparatus of the embodiment. 
         FIG.  20    is a diagram of a conversion table in a substrate processing apparatus of the embodiment. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     The following describes an embodiment of a substrate processing apparatus, a substrate processing method, a training data generation method, a training method, a training device, a trained model creation method, and a trained model according to the present invention with reference to the accompanying drawings. Note that elements that are the same or equivalent are indicated by the same reference signs in the drawings and description thereof is not repeated. Also, an X direction, a Y direction, and a Z direction perpendicular to one another may be described for the sake of easy understanding in the present description. Typically, the X direction and the Y direction are parallel to the horizontal direction and the Z direction is parallel to the vertical direction. 
     First, a substrate processing training system  200  including a substrate processing apparatus  100  of the present embodiment will be described with reference to  FIG.  1   . The substrate processing training system  200  will be described first with reference to  FIG.  1   . 
       FIG.  1    is a schematic diagram of the substrate processing training system  200 . As illustrated in  FIG.  1   , the substrate processing training system  200  includes a substrate processing apparatus  100 , a substrate processing apparatus  100 L, a training data generating device  300 , and a training device  400 . 
     The substrate processing apparatus  100  processes a processing target substrate. Here, the processing target substrate includes a resist layer with a hardened layer formed therein and the substrate processing apparatus  100  that performs processing with a chemical liquid on the resist layer of the processing target substrate. Note that the substrate processing apparatus  100  may perform processing on the processing target substrate besides the chemical liquid processing. The substrate processing apparatus  100  is of a single-wafer type that processes processing target substrates one at a time. Typically, the processing target substrate is substantially disk shaped. 
     The substrate processing apparatus  100 L processes a training target substrate. Here, the training target substrate includes a resist layer with a hardened layer formed therein and the substrate processing apparatus  100 L processes the resist layer of the training target substrate with the chemical liquid. Note that the substrate processing apparatus  100 L may perform processing on the training target substrate besides the chemical liquid processing. The training target substrate has the same configuration as the processing target substrate. The substrate processing apparatus  100 L is of a single-wafer type that processes processing target substrates one at a time. Typically, the processing target substrate is substantially disk shaped. The substrate processing apparatus  100 L has the same configuration as the substrate processing apparatus  100 . The substrate processing apparatus  100 L may be identical to the substrate processing apparatus  100 . For example, a single substrate processing apparatus having processed a training target substrate may process a processing target substrate thereafter. Alternatively, the substrate processing apparatus  100 L may be another product having the same configuration as that of the substrate processing apparatus  100 . 
     In the following description of the present description, the training target substrate may be referred to as “training target substrate WL” and the processing target substrate may be referred to as “processing target substrate Wp”. Also, when it is not necessary to distinguish between the training target substrate WL and the processing target substrate Wp, the training target substrate WL and the processing target substrate Wp may each be referred to as “substrate W”. 
     Examples of the substrates W include a semiconductor wafer, a substrate for liquid crystal display use, a substrate for plasma display use, a substrate for field emission display (FED) use, a substrate for optical disk use, a substrate for magnetic disk use, a substrate for magneto-optical disk use, a substrate for photomask use, a ceramic substrate, and a substrate for solar cell use. 
     The substrate processing apparatus  100 L outputs time-series data TDL. The time-series data TDL is data indicating change in physical quantity over time in the substrate processing apparatus  100 L. The time-series data TDL indicates time change in physical quantity (value) that chronologically changes over a predetermined time period. For example, the time-series data TDL is data indicating time change in physical quantity in processing that the substrate processing apparatus  100 L has performed on a training target substrate. Alternatively, the time-series data TDL is data indicating time change in physical quantity of a characteristic of a training target substrate processed by the substrate processing apparatus  100 L. Alternatively, the time-series data TDL may contain data indicating a production process before a training target substrate is processed by the substrate processing apparatus  100 L. 
     Note that a value indicated in the time-series data TDL may be a value directly measured by a measuring instrument. Alternatively, the value indicated in the time-series data TDL may be a value obtained by arithmetic processing of the value directly measured by the measuring instrument. Alternatively, the value indicated in the time-series data TDL may be a value obtained by arithmetic processing of values measured by a plurality of measuring instruments. 
     The training data generating device  300  generates training data LD based on the time-series data TDL or at least a portion of the time-series data TDL. The training data generating device  300  outputs the training data LD. The training data LD contains substrate information of a training target substrate WL, chemical liquid processing condition information indicating a processing condition of the chemical liquid processing performed on the training target substrate WL, and processing result information indicating a result of the chemical liquid processing performed on the training target substrate WL. Furthermore, the substrate information of the training target substrate WL contains substrate information of the training target substrate WL measured before the training target substrate WL undergoes the chemical liquid processing. 
     The training device  400  creates a trained model LM through machine training of the training data LD. The training device  400  outputs the trained model LM. 
     The substrate processing apparatus  100  outputs time-series data TD. The time-series data TD is data indicating change in physical quantity over time in the substrate processing apparatus  100 . The time-series data TDL indicates time change in physical quantity (value) that chronologically changes over the predetermined time period. For example, the time-series data TD is data indicating time change in physical quantity in processing that the substrate processing apparatus  100  has performed on a processing target substrate. Alternatively, the time-series data TD is data indicating time change in physical quantity of the characteristic of a processing target substrate processed by the substrate processing apparatus  100 . 
     Note that a value indicated in the time-series data TD may be a value directly measured by a measuring instrument. Alternatively, the value indicated in the time-series data TD may be a value obtained by arithmetic processing of the value directly measured by the measuring instrument. Alternatively, the value indicated in the time-series data TD may be a value obtained by arithmetic processing of values measured by a plurality of measuring instruments. Alternatively, the time-series data TD may contain data indicating a production process before a processing target substrate is processed by the substrate processing apparatus  100 . 
     An object used by the substrate processing apparatus  100  corresponds to an object used by the substrate processing apparatus  100 L. As such, the object used by the substrate processing apparatus  100  has the same configuration as the object used by the substrate processing apparatus  100 L. Furthermore, a physical quantity of the object used by the substrate processing apparatus  100  in the time-series data TD corresponds to a physical quantity of the object used by the substrate processing apparatus  100 L. As such, the physical quantity of the object used by the substrate processing apparatus  100 L is the same as the physical quantity of the object use by the substrate processing apparatus  100 . 
     Substrate information Cp of a processing target substrate Wp is generated from the time-series data TD. The substrate information Cp of the processing target substrate Wp corresponds to the substrate information of the training target substrate WL. The substrate information Cp of the processing target substrate Wp contains substrate information of the processing target substrate Wp. The substrate information of the processing target substrate Wp may be information obtained by measuring the processing target substrate Wp before the processing target substrate Wp undergoes the chemical liquid processing. Alternatively, the substrate information of the processing target substrate Wp may be information about processing performed on the processing target substrate Wp before the chemical liquid processing. 
     Chemical liquid processing condition information Rp is output from the trained model LM based on the substrate information Cp of the processing target substrate Wp. The chemical liquid processing condition information Rp indicates a chemical liquid processing condition suitable for the processing target substrate Wp in the substrate processing apparatus  100 . 
     According to the present embodiment, the training device  400  performs machine training as has been described so far with reference to  FIG.  1   . As such, a highly accurate trained model LM can be created from extremely complex time-series data TDL of a huge number of analysis targets. Furthermore, the substrate information Cp from the time-series data TD is input to the trained model LM and the chemical liquid processing condition information Rp indicating a chemical liquid processing condition is output from the trained model LM. As such, the chemical liquid processing can be performed according to the processing target substrate Wp. 
     Next, a substrate processing system  10  including substrate processing apparatuses  100  of the present embodiment will be described with reference to  FIG.  2   .  FIG.  2    is a schematic plan view of the substrate processing system  10 . 
     The substrate processing system  10  processes substrates W. The substrate processing system  10  includes a plurality of substrate processing apparatuses  100 . The substrate processing apparatuses  100  each process a substrate W in a manner to perform at least one of etching, surface treatment, characteristic assignment, processing film formation, and a combination of removal of at least a part of a film and washing. 
     As illustrated in  FIG.  1   , the substrate processing system  10  includes a fluid cabinet  32 , fluid boxes  34 , a plurality of load ports LP, an indexer robot IR, a center robot CR, and a control device  20  in addition to the substrate processing apparatuses  100 . The control device  20  controls the load ports LP, the indexer robot IR, and the center robot CR. 
     Each of the load ports LP accommodates a plurality of substrates W in a stacked manner. The indexer robot IR transports the substrates W between the load ports LP and the center robot CR. Note that it is possible that a placement table (path) on which the substrates W are temporarily placed is provided between the indexer robot IR and the center robot CR to provide an apparatus configuration by which each substrate W is indirectly delivered between the indexer robot IR and the center robot CR via the placement table. The center robot CR transports the substrates W between the indexer robot IR and the substrate processing apparatuses  100 . The substrate processing apparatuses  100  process the substrates W by discharging a liquid toward the substrates W. The liquid includes either or both a chemical liquid and a rinse liquid. Alternatively, the liquid may include another processing liquid. The fluid cabinet  32  accommodates the liquid. Note that the fluid cabinet  32  may accommodate air. 
     Specifically, the substrate processing apparatuses  100  form a plurality of towers TW (four towers TW in  FIG.  2   ) arranged so as to surround the center robot CR in a plan view. Each of the towers TW includes a plurality of substrate processing apparatuses  100  (three substrate processing apparatuses  100  in  FIG.  1   ) stacked in the vertical direction. The fluid boxes  34  correspond to the respective towers TW. The liquid in the fluid cabinet  32  is supplied to all the substrate processing apparatuses  100  included in the towers TW, each of which corresponds to one of the fluid boxes  34 , via the fluid box  34 . Furthermore, the air in the fluid cabinet  32  is supplied to all the substrate processing apparatuses  100  included in the towers TW, each of which corresponds to one of the fluid boxes  34 , via the fluid box  34 . 
     The control device  20  controls various operations of the substrate processing system  10 . The control device  20  includes a controller  22  and storage  24 . The controller  22  includes a processor. The controller  22  includes a central processing unit (CPU), for example. Alternatively, the controller  22  may include a general purpose computer. 
     The storage  24  stores data and a computer program therein. The data includes recipe data. The recipe data contains information indicating a plurality of recipes. Each of the recipes defines processing contents and a processing sequence of a substrate W. 
     The storage  24  includes a main storage device and an auxiliary storage device. The main storage device is semiconductor memory, for example. The auxiliary storage device is either or both semiconductor memory and a hard disk drive, for example. The storage  24  may include a removable medium. The controller  22  executes the computer program stored in the storage  24  to execute substrate processing operation. 
     The computer program in which procedures are pre-defined is stored in the storage  24 . The substrate processing apparatuses  100  each operate in accordance with the procedures defined in the computer program. 
     Note that although one control device  20  is provided in the substrate processing system  10  in  FIG.  2   , the control device  20  may be provided in each of the substrate processing apparatuses  100 . However, in a case as above, the substrate processing system  10  is preferably provided with an additional controller for controlling the substrate processing apparatuses  100  and elements other than the substrate processing apparatuses  100 . 
     Next, the substrate processing apparatus  100  of the present embodiment will be described with reference to  FIG.  3   .  FIG.  3    is a schematic diagram of the substrate processing apparatus  100  of the present embodiment. Note that although a configuration of the substrate processing apparatus  100  is described here, the substrate processing apparatus  100 L has the same configuration as that of the substrate processing apparatus  100 . 
     The substrate processing apparatus  100  processes a substrate W. The substrate processing apparatus  100  includes a chamber  110 , a substrate holding section  120 , a chemical liquid supply section  130 , and a rinse liquid supply section  140 . The chamber  110  accommodates the substrate W. The substrate holding section  120  holds the substrate W. The substrate holding section  120  holds the substrate W in a rotatable manner. 
     The chamber  110  is substantially box shaped with a hollow therein. The chamber  110  accommodates the substrate W. Here, the substrate processing apparatus  100  is of single-wafer type that processes substrates W one at a time and the substrates W are accommodated one by one in the chamber  110 . The substrate W is accommodated inside the chamber  110  and processed inside the chamber  110 . At least a part of each of the substrate holding section  120 , the chemical liquid supply section  130 , and the rinse liquid supply section  140  is accommodated in the chamber  110 . 
     The substrate holding section  120  holds the substrate W. The substrate holding section  120  holds the substrate W in a horizontal posture so that an upper surface Wa of the substrate W faces upward while a reverse surface (lower surface) Wb of the substrate W faces vertically downward. Furthermore, the substrate holding section  120  rotates the substrate W while holding the substrate W. 
     For example, the substrate holding section  120  may be of pinching type by which the end of the substrate W is pinched. Alternatively, the substrate holding section  120  may have an optional mechanism for holding the substrate W from the reverse surface Wb. For example, the substrate holding section  120  may be of vacuum type. In this case, the substrate holding section  120  holds the substrate W in a horizontal posture by sucking the central part of the reverse surface Wb, which is a non-device-forming surface, of the substrate W to the upper surface of the substrate holding section  120 . Alternatively, the substrate holding section  120  may be a combinational type of the pinching type and the vacuum type that makes a plurality of chuck pins in contact with the circumferential end surface of the substrate W. 
     For example, the substrate holding section  120  includes a spin base  121 , a chuck member  122 , a shaft  123 , and an electric motor  124 . The chuck member  122  is provided at the spin base  121 . The chuck member  122  chucks the substrate W. Typically, the spin base  121  is provided with a plurality of chuck members  122 . 
     The shaft  123  is a hollow shaft. The shaft  123  extends in the vertical direction along a rotational axis Ax thereof The shaft  123  has an upper end to which the spin base  121  is connected. The reverse surface of the substrate W is in contact with the spin base  121 . The substrate W is placed above the spin base  121 . 
     The spin base  121  is disk shaped and supports the substrate W in a horizontal posture. The shaft  123  extends downward from the central part of the spin base  121 . The electric motor  124  provides rotational force to the shaft  123 . The electric motor  124  rotates the shaft  123  in a rotational direction to rotate the substrate W and the spin base  121  about the rotational axis Ax as a center. Here, the rotational direction is an anticlockwise direction. 
     The chemical liquid supply section  130  supplies the chemical liquid to the substrate W. Through the above, the substrate W is processed with the chemical liquid. 
     For example, the chemical liquid contains hydrofluoric acid (hydrofluoric water (HF)). Alternatively, the chemical liquid may be a liquid containing at least one of sulfuric acid, acetic acid, nitric acid, hydrochloric acid, citric acid, buffered hydrofluoric acid (BHF), dilute hydrofluoric acid (DHF), ammonia water, dilute ammonia water, hydrogen peroxide water, organic alkali (e.g., tetramethyl ammonium hydroxide (TMAH)), a surfactant, and a corrosion inhibitor. Alternatively, the chemical liquid may be a mixed liquid obtained by mixing any of the above liquids. For example, examples of a chemical liquid obtained by mixing these include a sulfuric acid-hydrogen peroxide mixture (SPM), a mixed liquid (SC 1 ) of ammonium hydrogen peroxide mixture, and a mixed liquid (SC 2 ) of hydrochloric acid hydrogen peroxide. 
     The chemical liquid supply section  130  includes a nozzle  132 , a pipe  134 , and a valve  136 . The nozzle  132  is directed to the upper surface Wa of the substrate W and discharges the chemical liquid toward the upper surface Wa of the substrate W. The pipe  134  is connected to the nozzle  132 . The nozzle  132  is provided at the tip end of the pipe  134 . The chemical liquid is supplied to the pipe  134  from a supply source. The valve  136  is provided in the pipe  134 . The valve  136  opens and closes the flow channel of the pipe  134 . 
     The chemical liquid supply section  130  further includes a nozzle moving section  138 . The nozzle moving section  138  moves the nozzle  132  between a discharge point and a retraction point. When the nozzle  132  is located at the discharge point, the nozzle  132  is located above the substrate W. When the nozzle  132  is located at the discharge point, the nozzle  132  discharges the chemical liquid toward the upper surface Wa of the substrate W. When the nozzle  132  is located at the retraction point, the nozzle  132  is located outside the substrate W in the radial direction of the substrate W. 
     The nozzle moving section  138  includes an arm  138   a , a pivot shaft  138   b , and a moving mechanism  138   c . The arm  138   a  extends in a substantially horizontal direction. The arm  138   a  has a tip end on which the nozzle  132  is mounted. The arm  138   a  is connected to the pivot shaft  138   b . The pivot shaft  138   b  extends in a substantially vertical direction. The moving mechanism  138   c  turns the pivot shaft  138   b  about the pivot axis thereof extending in a substantially vertical direction to turn the arm  138   a  along a substantially horizontal plane. As a result, the nozzle  132  moves along a substantially horizontal plane. For example, the moving mechanism  138   c  includes an arm swaying motor that turns the pivot shaft  138   b  about the pivot axis thereof. The arm swaying motor is a servomotor, for example. Furthermore, the moving mechanism  138   c  moves the pivot shaft  138   b  up and down in a substantially vertical direction to move up and down the arm  138   a . As a result, the nozzle  132  moves in a substantially vertical direction. For example, the moving mechanism  138   c  includes a ball screw mechanism and an arm raising and lowering motor that provides drive power to the ball screw mechanism. The arm raising and lowering motor is a servomotor, for example. 
     The rinse liquid supply section  140  supplies a rinse liquid to the substrate W. The rinse liquid may contain any of deionized water (DIW), carbonated water, electrolytic ionized water, ozone water, ammonia water, hydrochloric acid water at a diluted concentration (e.g., about  10  ppm to  100  ppm), and reduced water (hydrogen water). 
     The rinse liquid supply section  140  includes a nozzle  142 , a pipe  144 , and a valve  146 . The nozzle  142  is directed to the upper surface Wa of the substrate W and discharges the rinse liquid toward the upper surface Wa of the substrate W. The pipe  144  is connected to the nozzle  142 . The nozzle  142  is provided at the tip end of the pipe  144 . The rinse liquid is supplied to the pipe  144  from a supply source. The valve  146  is provided in the pipe  144 . The valve  146  opens and closes the flow channel of the pipe  144 . 
     The substrate processing apparatus  100  further includes a cap  180 . The cap  180  collects liquid scattered from the substrate W. The cap  180  ascends vertically to the side of the substrate W. Furthermore, the cap  180  may descend vertically from the side of the substrate W. 
     The control device  20  controls various operations of the substrate processing device  100 . The controller  22  controls any of the substrate holding section  120 , the chemical liquid supply section  130 , the rinse liquid supply section  140 , and the cap  180 . In one example, the controller  22  controls any of the electric motor  124 , the valves  136  and  146 , the moving mechanism  138   c , and the cap  180 . 
     The substrate processing apparatus  100  of the present embodiment performs the chemical liquid processing and rinsing processing on the substrate W. 
     A substrate processing apparatus  100  of the present embodiment will be described next with reference to  FIGS.  1  to  4   .  FIG.  4    is a block diagram of a substrate processing system  10  including the substrate processing apparatus  100 . 
     As illustrated in  FIG.  4   , the control device  20  controls various operations of the substrate processing system  10 . The control device  20  controls the indexer robot IR, the center robot CR, the substrate holding section  120 , the chemical liquid supply section  130 , the rinse liquid supply section  140 , and the cap  180 . In detail, the control device  20  transmits control signals to the indexer robot IR, the center robot CR, the substrate holding section  120 , the chemical liquid supply section  130 , the rinse liquid supply section  140 , and the cap  180  to control the indexer robot IR, the center robot CR, the substrate holding section  120 , the chemical liquid supply section  130 , the rinse liquid supply section  140 , and the cap  180 . 
     Specifically, the controller  22  controls the indexer robot IR to deliver a substrate W. 
     The controller  22  controls the center robot CR to deliver the substrate W. For example, the center robot CR receives a non-processed substrate W and carries the substrate W into any one of the chambers  110 . Also, the center robot CR receives a processed substrate W from the chamber  110  and carries out the substrate W. 
     The controller  22  controls the substrate holding section  120  to control rotation start of the substrate W, change in rotational speed, and rotation stop of the substrate W. For example, the controller  22  controls the substrate holding section  120  to change the rotational speed of the substrate holding section  120 . Specifically, the controller  22  changes the rotational speed of the electric motor  124  of the substrate holding section  120  to change the rotational speed of the substrate W. 
     The controller  22  controls the valve  136  of the chemical liquid supply section  130  and the valve  146  of the rinse liquid supply section  140  to switch each state of the valves  136  and  146  between the open state and the closed state. Specifically, the controller  22  controls the valves  136  and  146  to set the valves  136  and  146  in the open state, thereby allowing the chemical liquid and the rinse liquid respectively flowing in the pipes  134  and  144  to flow toward the nozzles  132  and  142 . Furthermore, the controller  22  controls the valve  136  of the chemical liquid supply section  130  and the valve  146  of the rinse liquid supply section  140  to set the valves  136  and  146  in the closed state, thereby stopping the chemical liquid and the rinse liquid respectively flowing in the pipes  134  and  144  toward the nozzles  132  and  142 . 
     Furthermore, the controller  22  controls the moving mechanism  138   c  to move the arm  138   a  in either or both the horizontal direction and the vertical direction. The controller  22  accordingly moves the nozzle  132  mounted on the tip end of the arm  138   a  above the upper surface Wa of the substrate W. Furthermore, the controller  22  moves the nozzle  132  mounted on the tip end of the arm  138   a  between the discharge point and the retraction point. The substrate processing apparatus  100  of the present embodiment is favorably used for semiconductor device formation. 
     In the substrate processing apparatus  100  of the present embodiment, the storage  24  stores a trained model LM and a control program PG therein. The substrate processing apparatus  100  operates in accordance with the procedures defined in the computer program PG. 
     Furthermore, the controller  22  includes a substrate information acquiring section  22   a  and a chemical liquid processing condition information acquiring section  22   b . The substrate information acquiring section  22   a  acquires substrate information of a processing target substrate Wp. The substrate information of the processing target substrate Wp includes hardened layer thickness information indicating the thickness of a hardened layer in the processing target substrate Wp or ion implantation condition information indicating a condition for ion implantation having been performed on the processing target substrate Wp. Note that the substrate information acquiring section  22   a  may acquire, as the substrate information, information other than the hardened layer thickness information and the ion implantation condition information from the storage  24 . 
     The trained model LM generates chemical liquid processing condition information based on the substrate information. Typically, when the substrate information is input to the trained model LM, chemical liquid processing condition information corresponding to the substrate information is output. In one example, when the hardened layer thickness information or the ion implantation condition information is input to the trained model LM, chemical liquid processing condition information corresponding to the hardened layer thickness information or the ion implantation condition information is output. 
     The chemical liquid processing condition information acquiring section  22   b  acquires chemical liquid processing condition information from the trained model LM. The chemical liquid processing condition information acquiring section  22   b  acquires the chemical liquid processing condition information corresponding to the substrate information of the processing target substrate Wp from the trained model LM. 
     The controller  22  controls the substrate holding section  120  and the chemical liquid supply section  130  according to a chemical liquid processing condition indicated in the chemical liquid processing condition information. 
     Preferably, the substrate processing system  10  further includes a display section  42 , an input section  44 , and a communication section  46 . 
     The display section  42  displays an image. The display section  42  is a liquid-crystal display or an organic electroluminescent display, for example. 
     The input section  44  is an input device through which various information is input to the controller  22 . For example, the input section  44  is a keyboard, a pointing device, or a touch panel. 
     The communication section  46  is connected to a network to communicate with an external device. Examples of the network in the present embodiment include the Internet, a local area network (LAN), s public telephone network, and a short-range wireless network. The communication section  46  is a communication device and may be a network interface controller, for example. 
     Preferably, the substrate processing system  10  further includes a sensor  50 . Typically, a plurality of sensors  50  detect the states of respective elements of the substrate processing system  10 . For example, at least some of the sensors  50  detect the states of respective parts of the substrate processing apparatus  100 . 
     The storage  24  stores as time-series data TD results output from the sensors  50  and a control parameter in the control program therein. Typically, the time-series data are stored separately for each substrate W. 
     The sensors  50  detect physical quantities of respective objects used by the substrate processing apparatus  100  during a period from a processing start to a processing end for the substrate W each time processing is performed on one substrate W, and output detection signals indicating the physical quantities to the controller  22 . Each time the processing is performed on one substrate W, the controller  22  causes the storage  24  to store, for the substrate W, in association with time the physical quantities indicated by the detection signals output from the sensors  50  during the period from the processing start to the processing end as time-series data TD. 
     The controller  22  acquires the time-series data TD from the sensors  50  and causes the storage  24  to store the time-series data TD. In this case, the controller  22  causes the storage  24  to store the time-series data TD in association with lot identification information, substrate identification information, processing sequence information, and lot interval information. The lot identification information is information for lot identification (e.g., a lot number). The lot indicates a processing unit of substrates W. One lot is constituted by a predetermined number of substrates W. The substrate identification information is information for identifying a substrate W. The processing sequence information is information indicating a sequence of processing on the predetermined number of substrates W constituting one lot. The lot interval information is information indicating a time interval from a processing end of a lot to a processing start of the next lot. 
     Next, a substrate processing method implemented by the substrate processing apparatus  100  of the present embodiment will be described with reference to  FIGS.  1  to  5 B .  FIG.  5 A  is a flowchart depicting the substrate processing method implemented by the substrate processing apparatus  100  of the present embodiment, and  FIG.  5 B  is a flowchart depicting the chemical liquid processing in the substrate processing method of the present embodiment. 
     As depicted in  FIG.  5 A , a processing target substrate Wp is carried into the substrate processing apparatus  100  in Step S 10 . The carried processing target substrate Wp is fitted on the substrate holding section  120 . Typically, the processing target substrate Wp is carried into the substrate processing apparatus  100  by the center robot CR. 
     In Step S 20 , the processing target substrate Wp is processed with the chemical liquid. The chemical liquid supply section  130  supplies the chemical liquid to the processing target substrate Wp. The chemical liquid is discharged from the nozzle  132  of the chemical liquid supply section  130  toward the upper surface Wa of the processing target substrate Wp. The chemical liquid covers the upper surface Wa of the processing target substrate Wp. Through the above, the processing target substrate Wp is processed with the chemical liquid. Note that the substrate W is rotated by the substrate holding section  120  during the processing with the chemical liquid on the processing target substrate Wp. Rotation of the processing target substrate Wp may be continued directly before carry-out of the processing target substrate Wp. 
     In step S 30 , the processing target substrate Wp is rinsed with the rinse liquid. The rinse liquid supply section  140  supplies the rinse liquid to the processing target substrate Wp. The rinse liquid is discharged from the nozzle  142  of the rinse liquid supply section  140  toward the upper surface Wa of the processing target substrate Wp. The rinse liquid covers the supper surface Wa of the processing target substrate Wp. Through the above, the processing target substrate Wp is processed with the rinse liquid. 
     In Step S 40 , the processing target substrate Wp is separated from the substrate holding section  120  and carried out. Typically, the processing target substrate Wp is carried out of the substrate processing apparatus  100  by the center robot CR. The processing target substrate Wp is processed with the chemical liquid in the manner described above. 
     In the substrate processing apparatus  100  of the present embodiment, the processing target substrate Wp is processed with the chemical liquid as depicted in  FIG.  5 B . 
     In Step S 22 , the substrate information of the processing target substrate Wp is acquired. The substrate information acquiring section  22   a  acquires the substrate information of the processing target substrate Wp. The substrate information includes the hardened layer thickness information or the ion implantation condition information of the processing target substrate Wp. 
     For example, the controller  22  acquires the hardened layer thickness information or the ion implantation condition information of the processing target substrate Wp from the storage  24 . Note that the thickness of the hardened layer in the processing target substrate Wp may be measured in the substrate processing system  10  or the substrate processing apparatus  100 . Alternatively, the thickness of the hardened layer in the processing target substrate Wp may be measured outside the substrate processing system  10  and the substrate processing apparatus  100 . 
     Alternatively, the ion implantation on the processing target substrate Wp may be performed in the substrate processing system  10  or the substrate processing apparatus  100 . Alternatively, the ion implantation on the processing target substrate Wp may be performed outside the substrate processing system  10  and the substrate processing apparatus  100 . Note that that the controller  22  may acquire substrate information other than the hardened layer thickness information and the ion implantation condition information. 
     In Step S 24 , the substrate information of the processing target substrate Wp is input to a trained model LM. Although described later in detail, the trained model LM is built based on training data containing substrate information of a training target substrate WL, chemical liquid processing condition information indicating a processing condition of the chemical liquid processing performed on the training target substrate WL, and processing result information indicating a result of the chemical liquid processing performed on the training target substrate WL. The trained model LM outputs chemical liquid processing condition information Rp corresponding to the substrate information of the processing target substrate Wp. 
     In Step S 26 , chemical liquid processing condition information is acquired from the trained model LM. The chemical liquid processing condition information acquiring section  22   b  acquires the chemical liquid processing condition information corresponding to the substrate information from the trained model LM. 
     In Step S 28 , the substrate holding section  120  and the chemical liquid supply section  130  perform the chemical liquid processing on the processing target substrate Wp according to the chemical liquid processing condition information. In the substrate processing apparatus  100  illustrated in  FIG.  3   , the chemical liquid supply section  130  supplies the chemical liquid to the processing target substrate Wp according to the chemical liquid processing condition information. The processing target substrate Wp is processed with the chemical liquid in the manner described above. 
     In the present embodiment, the chemical liquid processing condition information corresponding to the substrate information including the hardened layer thickness information or the ion implantation condition information of the processing target substrate Wp is acquired from the trained model LM built through machine training, and the chemical liquid processing is performed according to the chemical liquid processing condition indicated in the chemical liquid processing condition information. According to the present embodiment, the chemical liquid processing can be appropriately performed according to the thickness of the hardened layer in the processing target substrate Wp. 
     The hardened layer formed in a substrate W, which is the target for the substrate processing method of the present embodiment, will be described next with reference to  FIGS.  6 A to  6 C .  FIGS.  6 A to  6 C  each are a schematic diagram explaining formation of a hardened layer in a resist layer R of the substrate W. 
     As illustrated in  FIG.  6 A , the resist layer R is formed on the upper surface of the substrate W. The resist layer R extends in a direction perpendicular to the main surface of the substrate W. The resist layer R is patterned into a specific shape. 
     As illustrated in  FIG.  6 B , ions are implanted into the substrate W. Here, the ions are implanted in a direction parallel to the normal direction of the main surface of the substrate W. Ion implantation changes a characteristic of the surface of the substrate W. At this time, the characteristic of the surface of the resist layer R is also changed to form a hardened layer Rc. Note that the characteristic in the interior of the resist layer R remains unchanged and remains as an inner layer Ri. The hardened layer Rc is harder than the inner layer Ri. 
     As illustrated in  FIG.  6 C , the hardened layer Rc has a specific thickness d. In detail, the thickness d of the hardened layer Rc includes a height dt of the hardened layer Rc and a width of dw of the hardened layer Rc. In a case in which the ions are isotropiccally implanted into the resist layer R, the height dt is approximately equal to the width dw. 
     By contrast, when the ions are implanted anisotropically into the resist layer R, the height dt differs from the width dw. For example, as illustrated in  FIG.  6 B , when the ions are implanted in a direction parallel to the normal direction of the main surface of the substrate W, the height dt is greater than the width dw. 
     The hardened layer formed in a substrate W, which is a target for the substrate processing method of the present embodiment, will be described next with reference to  FIGS.  7 A to  7 D .  FIGS.  7 A to  7 D  each are a schematic diagram explaining formation of a hardened layer in the resist layer R of the substrate W. 
     As illustrated in  FIG.  7 A , the resist layer R is formed on the upper surface of the substrate W. The resist layer R extends in a direction perpendicular to the main surface of the substrate W. 
     As illustrated in  FIG.  7 B , ions are implanted into the substrate W. Here, the ions are implanted in a direction inclined in a specific direction (leftward with respect to the paper surface) with respect to the normal direction of the main surface of the substrate W. Ion implantation changes the characteristic of the surface of the substrate W. At this time, the characteristic of the surface of the resist layer R is changed to form the hardened layer Rc. In this case, the left part of the hardened layer Rc is relatively thick while the right part of the hardened layer Rc is relatively thin relative to the resist layer R. 
     As illustrated in  FIG.  7 C , the ions are implanted into the substrate W. Here, the ions are implanted in a direction inclined in another direction (rightward with respect to the paper surface) with respect to the normal direction of the main surface of the substrate W. Ion implantation thickens the hardened layer Rc being a surface portion of the resist layer R of which characteristic has been changed. 
     As illustrated in  FIG.  7 D , the hardened layer Rc has a specific thickness d. In detail, the thickness d of the hardened layer Rc includes a height dt of the hardened layer Rc and a width of dw of the hardened layer Rc. The hardened layer Rc is formed in the manner described above. 
     Note that ion implantation is performed in the left and right oblique directions of the resist layer R, with a result that the widths of the left part and the right part of the hardened layer Rc can be approximately equalized. Furthermore, ion implantation in the oblique directions on the resist layer R can increase not only the height dt of the hardened layer Rc but also the width dw of the hardened layer Rc. 
     As described above, any of the thickness d, the height dt, and the width dw of the hardened layer Rc may be measured using a measurement instrument. Alternatively, any of the thickness d, the height dt, and the width dw of the hardened layer Rc defined according to either or both a characteristic (e.g., composition, thickness, or width) of the resist layer R and a condition (e.g., ion species, acceleration energy, implantation amount, or direction of implantation) for the ion implantation. Therefore, any of the thickness d, the height dt, and the width dw of the hardened layer Rc can be determined according to a condition for ion implantation. 
     As has been described with reference to  FIG.  1   , the trained model LM is created from the training data LD and the training data LD is generated from the time-series data TDL of the substrate processing apparatus  100 L. 
     Generation of the training data LD will be described next with reference to  FIG.  8   .  FIG.  8    is a block diagram of the training data generating device  300  and a substrate processing system  10 L including a substrate processing apparatus  100 L. Here, the training data generating device  300  is connected to the substrate processing apparatus  100 L in a communicable manner. The substrate processing system  10 L including the substrate processing apparatus  100 L illustrated in  FIG.  8    is the same as the substrate processing system  10  illustrated in the block diagram of  FIG.  4    in all aspects other than that a controller  22 L includes neither the substrate information acquiring section  22   a  nor the chemical liquid processing condition information acquiring section  22   b  and that storage  24 L does not store the trained model LM and stores a test recipe TR therein. Therefore, duplicate description is omitted for avoiding redundancy. 
     The substrate processing system  10 L includes a plurality of substrate processing apparatuses  100 L, an indexer robot IRL, a center robot CRL, a control device  20 L, a display section  42 L, an input section  44 L, a communication section  46 L, and a sensor  50 L. The substrate processing apparatuses  100 L, the indexer robot IRL, the center robot CRL, the control device  20 L, the display section  42 L, the input section  44 L, and the communication section  46 L respectively have the same configurations as those of the substrate processing apparatuses  100 , the indexer robot IR, the center robot CR, the control device  20 , the display section  42 , the input section  44 , and the communication section  46  of the substrate processing system  10  illustrated in  FIG.  4   . 
     Also, the substrate processing apparatuses  100 L each include a substrate holding section  120 L, a chemical liquid supply section  130 L, a rinse liquid supply section  140 L, and a cap  180 L. Preferably, the chamber  110 L, the substrate holding section  120 L, the chemical liquid supply section  130 L, the rinse liquid supply section  140 L, and the cap  180 L respectively have the same configurations as those of the substrate holding section  120 , the chemical liquid supply section  130 , the rinse liquid supply section  140 , and the cap  180  illustrated in  FIGS.  3  and  4   . 
     The control device  20 L includes a controller  22 L and storage  24 L. The storage  24 L stores a control program PGL therein. The substrate processing apparatuses  100 L each operate according to procedures defined in the control program PGL. 
     The storage  24 L further stores a plurality of test recipes TR therein. The test recipes TR include recipes different in chemical liquid processing condition. Therefore, when the controller  22 L controls processing on training target substrates WL according to the test recipes TR, the different training target substrates WL undergo processing with different chemical liquids. 
     The storage  24 L stores time-series data TDL of the training target substrates WL therein. The time-series data TDL is data indicating change in physical quantity in the substrate processing apparatuses  100 L over time. The time-series data TDL indicates a plurality of physical quantities detected by the sensor  50 L. The time-series data TDL may contain data indicating a production process before the training target substrates WL are processed by the substrate processing apparatuses  100 L. Note that the time-series data TDL contains substrate information including hardened layer thickness information or ion implantation condition information, chemical liquid processing condition information indicating conditions for chemical liquid processing having been performed on the training target substrates WL, and processing result information indicating results of the chemical liquid processing having been performed on the training target substrates WL. 
     The training data generating device  300  is connected to the substrate processing apparatuses  100 L in a communicable manner. The training data generating device  300  receives at least a portion of the time-series data TDL of each substrate processing apparatus  100 L. 
     The training data generating device  300  includes a control device  320 , a display section  342 , an input section  344 , and a communication section  346 . The training data generating device  300  communicates with the control device  20 L of each of the substrate processing apparatuses  100 L via the communication section  346 . The display section  342 , the input section  344 , and the communication section  346  respectively have the same configurations as those of the display section  42 , the input section  44 , and the communication section  46 . 
     The control device  320  includes a controller  322  and storage  324 . The storage  324  stores a control program PG 3  therein. The training data generating device  300  operates in accordance with the procedures defined in the computer program PG 3 . 
     The controller  322  receives at least a portion of the time-series data TDL from each substrate processing apparatus  100 L, and causes the storage  324  to store the received time-series data TDL. The storage  324  stores at least a portion of the time-series data TDL of the training target substrates WL therein. The time-series data TDL is transmitted from each substrate processing apparatus  100 L to the training data generating device  300  via the communication section  46 L and the communication section  346 . The controller  322  causes the storage  324  to store at least a portion of the transmitted time-series data TDL. The time-series data TDL stored in the storage  324  contains the substrate information of the time-series data TDL, the chemical liquid processing condition information, and the processing result information. 
     The controller  322  acquires the substrate information, the chemical liquid processing condition information, and the processing result information of the training target substrates WL from the time-series data TDL stored in the storage  324 . Furthermore, the controller  322  groups the substrate information, the chemical liquid processing condition information, and the processing result information of the training target substrates WL to generate training data LD and the storage  324  stores the training data LD. 
     With reference to  FIGS.  8  and  9   , a training data generation method of the present embodiment will be described next.  FIG.  9    is a flowchart depicting the training data generation method of the present embodiment. Training data generation is performed in the training data generating device  300 . 
     As illustrated in  FIG.  9   , the time-series data TDL of the training target substrates WL are acquired in Step S 110 . Typically, the training data generating device  300  receives at least a portion of time-series data TDL of each training target substrate WL from the substrate processing apparatus  100 L. The storage  324  stores the received time-series data TDL. 
     In Step S 112 , substrate information is extracted from the time-series data TDL of the training target substrates WL stored in the storage  324 . The substrate information includes hardened layer thickness information or ion implantation condition information. The controller  322  acquires the substrate information of the training target substrates WL from the time-series data TDL stored in the storage  324 . 
     In Step S 114 , chemical liquid processing condition information of the training target substrates WL is extracted from the time-series data TDL of the training target substrates WL stored in the storage  324 . The controller  322  acquires the chemical liquid processing condition information of the training target substrates WL from the time-series data TDL stored in the storage  324 . 
     In Step S 116 , processing result information of the training target substrates WL is extracted from the time-series data TDL of the training target substrates WL stored in the storage  324 . The controller  322  acquires the processing result information of the training target substrates WL from the time-series data TDL stored in the storage  324 . 
     In step S 118 , the substrate information, the chemical liquid processing condition information, and the processing result information of the training target substrates WL are associated with one another to generate training data LD and the storage  324  stores the training data LD for each of the training target substrates WL. 
     The generated training data contains the substrate information, the chemical liquid processing condition information, and the processing result information of each of the training target substrates WL associated with each other in the present embodiment. Training data such as above is favorably used for training processing. 
     Note that the training data generating device  300  in  FIG.  8    is connected to one substrate processing apparatus  100 L in a communicable manner in the present embodiment, which should not be taken to limit the present embodiment. The training data generating device  300  may be connected to the substrate processing apparatuses  100 L in a communicable manner. 
     Furthermore, although the time-series data TDL generated in the substrate processing apparatus  100 L is transmitted to the training data generating device  300  via the communication section  46 L and the communication section  346  in the description made with reference to  FIGS.  8  and  9   , which should not be taken to limit the present embodiment. It is possible that the control device  320  of the training data generating device  300  is incorporated in the control device  20  of the substrate processing system  10  that includes the substrate processing apparatuses  100 L and the training data LD is generated from the time-series data TDL in the substrate processing system  10  without transmission of the time-series data TDL to the network. 
     Creation of the trained model LM in the present embodiment will be described next with reference to  FIG.  10   .  FIG.  10    is a schematic diagram of the training data generating device  300  and the training device  400  of the present embodiment. The training data generating device  300  and the training device  400  communicate with each other. 
     The training device  400  is connected to the training data generating device  300  in a communicable manner. The training device  400  receives training data LD from the training data generating device  300 . The training device  400  creates a trained model LM through machine training based on the training data LD. 
     The training device  400  includes a control device  420 , a display section  442 , an input section  444 , and a communication section  446 . The display section  442 , the input section  444 , and the communication section  446  respectively have the same configurations as those of the display section  42 , the input section  44 , and the communication section  46  of the substrate processing system  10  illustrated in  FIG.  4   . 
     The control device  420  includes a controller  422  and storage  424 . The storage  424  stores a control program PG 4  therein. The training device  400  operates in accordance with the procedures defined in the computer program PG 4 . 
     The storage  424  stores the training data LD therein. The training data LD is transmitted from the training data generating device  300  to the training device  400  via the communication section  346  and the communication section  446 . The controller  422  causes the storage  424  to store the transmitted training data LD. In the training data LD stored in the storage  424 , the substrate information, the chemical liquid processing condition information, and the processing result information of the time-series data TDL are associated with one another. 
     The storage  424  stores a training program LPG therein. The training program LPG is a program used for finding out a certain rule in plural pieces of training data LD and executing a machine training algorithm for creating a trained model LM expressing the found rule. The controller  422  creates trained model LM in a manner to perform machine training of the training data LD through execution of the training program LPG stored in the storage  424  to adjust a parameter of an inference program. 
     The machine training algorithm is not limited specifically as long as it is supervised training algorithm, and may be a decision tree, a nearest neighbor method, a simple Bayesian classifier, a support vector machine, or a neural network, for example. Therefore, the trained model LM is created using the decision tree, the nearest neighbor method, the simple Bayesian classifier, the support vector machine, or the neural network. Machine training for creating the trained model LM may use an error back propagation method. 
     For example, the neural network includes an input layer, a single or plurality of intermediate layers, and an output layer. Specifically, the neural network is a deep neural network (DNN), a recurrent neural network (RNN), or a convolutional neural network (CNN), for example, and performs deep learning. For example, the deep neural network includes an input layer, a plurality of intermediate layers, and an output layer. 
     The controller  422  includes an acquiring section  422   a  and a training section  422   b . The acquiring section  422   a  acquires the training data LD from the storage  424 . The training section  422   b  performs machine training of the training data LD through execution of the training program LPG stored in the storage  424  to create a trained model LM from the training data LD. 
     The training section  422   b  performs machine training of plural pieces of training data LD based on the training program LPG. As a result, a certain rule is found out among the plural pieces of training data LD to create a trained model LM. That is, the trained model LM is built through machine training of the training data LD. The storage  424  stores the trained model LM. 
     Thereafter, the trained model LM is typically transmitted to the substrate processing system  10  and the storage  24  stores the trained model LM. In this case, as described above with reference to  FIG.  4   , the storage  24  of the control device  20  in the substrate processing system  10  stores the trained model LM therein and the chemical liquid processing condition information acquiring section  22   b  acquires the chemical liquid processing condition information from the trained model LM stored in the storage  24 . 
     However, the present disclosure is not limited to the above. It is possible that the storage  24  does not store the trained model LM therein and the chemical liquid processing condition information acquiring section  22   b  acquires the chemical liquid processing condition information from the outside of the substrate processing system  10 . For example, it is possible that the chemical liquid processing condition information acquiring section  22   b  transmits the substrate information of a processing target substrate Wp to the trained model LM of the training device  400  via the communication section  46  and the communication section  446  and receives the chemical liquid processing condition information output from the trained model LM of the training device  400 via the communication section  446  and the communication section  46 . 
     A training method implemented by the training device  400  of the present embodiment will be described next with reference to  FIGS.  1  to  11   .  FIG.  11    is a flowchart depicting the training method of the present embodiment. Training of the training data LD and creation of the trained model LM are performed in the training device  400 . 
     As depicted in  FIG.  11   , the acquiring section  422   a  of the training device  400  acquires plural pieces of training data LD from the storage  424  in Step S 122 . In the training data LD, the substrate information, the chemical liquid processing condition information, and the processing result information of the training target substrates WL are associated with one another. 
     Next, in Step S 124 , the training section  422   b  performs machine training of the plural pieces of training data LD based on the training program LPG. 
     Next, in Step S 126 , the training section  422   b  determines whether or not machine training of the training data LD has been completed. Whether or not the machine training has been completed is determined according to a prescribed condition. For example, machine training will be completed when machine training is performed on a predetermined number or more of pieces of training data LD. 
     If machine training has not been completed (No in Step S 126 ), the routine returns to Step S 122 . If so, machine training is repeated. If machine training has been completed (Yes in Step S 126 ) by contrast, the routine proceeds to Step S 128 . 
     In Step S 128 , the training section  422   b  outputs as a trained model LM a model (at least one function) to which the latest parameters (coefficients), that is, a plurality of trained parameters (coefficients) are applied. The storage  424  stores the trained model LM. 
     The training method ends and the trained model LM is created in the manner described above. According to the present embodiment, machine training of the training data LD can create the trained model LM. 
     Note that although the training device  400  in  FIG.  10    is connected to a single training data generating device  300  in a communicable manner, which should not be taken to limit the present embodiment. The training device  400  may be connected to a plurality of training data generating devices  300  in a communicable manner. 
     Furthermore, the training data LD generated in the training data generating device  300  is transmitted to the training device  400  via the communication section  346  and the communication section  446  in the description made with reference to  FIGS.  10  and  11   , which should not be taken to limit the present embodiment. It is possible that the control device  420  of the training device  400  is incorporated in the control device  320  of the training data generating device  300  and the trained model LM is created from the training data LD in the training data generating device  300  without transmission of the training data LD to the network. 
     Furthermore, the time-series data TDL generated in the substrate processing apparatus  100 L is transmitted to the training data generating device  300  via the communication section  46 L and the communication section  346  and the training data LD generated in the training data generating device  300  is transmitted to the training device  400  via the communication section  346  and the communication section  446  in the description made with reference to  FIGS.  8  to  11   , which should not be taken to limit the present embodiment. It is possible that the control device  320  of the training data generating device  300  and the control device  420  of the training device  400  are incorporated in the control device  20  of the substrate processing system  10 L and the trained model LM is created from the time-series data TDL through the training data LD in the substrate processing system  10 L without transmission of the time-series data TDL and the training data LD to the network. 
     One example of the training data LD will be described next with reference to  FIG.  12   .  FIG.  12    is a diagram indicating an example of the training data LD. 
       FIG.  12    indicates the substrate information of each of training target substrates WL, the chemical liquid processing conditions of the training target substrate WL, and the processing results of the training target substrate WL. Here, the substrate information is information about a hardened layer thickness of the training target substrate WL. The thickness of a hardened layer in the resist layer of the training target substrate WL can be acquired by measuring the hardened layer. The chemical liquid processing condition indicates a condition for chemical liquid processing performed on the training target substrate WL. The chemical liquid processing condition includes a concentration of the chemical liquid, a temperature of the chemical liquid, a supply amount of the chemical liquid, and a rotational speed of the training target substrate WL when the chemical liquid is supplied, for example. The processing result indicates a result after the chemical liquid processing on the training target substrate WL. The training data LD in  FIG.  12    includes training data LD 1  to training data LD 1000 . 
     The training data LD 1  indicates substrate information, a chemical liquid processing condition, and a processing result of a training target substrate WL 1 . Here, Ld 1  of the training data LD 1  indicates the thickness of the hardened layer in the training target substrate WL 1 . Also, Lp 1  indicates a condition for the chemical liquid processing having been performed on the training target substrate WL 1 . 
     The processing result information indicates a processing result of the chemical liquid processing on the training target substrate WL 1 . The processing result may be determined according to whether or not any anomalies of the characteristic have been found in the training target substrate WL 1 . Since the result of chemical liquid processing performed on the training target substrate was good, GOOD is indicated in the training data LD 1 . If the result of chemical liquid processing performed on a training target substrate is not good by contrast, POOR is indicated in a training data. 
     The training data LD 2  to LD 1000  are generated in correspondence with the respective training target substrates WL 2  to WL 1000 . Chemical liquid processing conditions for the training target substrates WL may be the same as or different from each other. Processing results significantly vary depending on the thicknesses of the hardened layers in the training target substrates WL. 
     The number of pieces of the training data LD indicated in  FIG.  12    is  1000 , which should not be taken to limit the present embodiment. The number of pieces of the data may be smaller than  1000  or greater than  1000 . However, the number of pieces of the data should be as large as possible. 
     Note that the substrate information in the training data LD preferably contains a plurality of items. For example, the hardened layer thickness information may contain hardened layer height information indicating the height of the hardened layer and hardened layer width information indicating the width of the hardened layer. 
       FIG.  13    is a diagram indicating an example of the training data LD. As indicated in  FIG.  13   , the training data LD contains substrate information of each training target substrate WL, chemical liquid processing condition information of the training target substrate WL, and processing result information of the training target substrate WL. The training data LD in  FIG.  13    is the same as the training data LD described with reference to  FIG.  12    in all aspects other than that the substrate information of the training target substrates WL contains hardened layer height information and hardened layer width information. Therefore, duplicate description is omitted for avoiding redundancy. 
     Here, the substrate information contains the hardened layer thickness information and the hardened layer width information of each training target substrate WL. The thickness and the width of a hardened layer in the resist layer of the training target substrate WL can be acquired by measuring the hardened layer. The training data LD in  FIG.  13    includes training data LD 1  to LD 1000 . 
     The training data LD 1  indicates substrate information, a chemical liquid processing condition, and a processing result of a training target substrate WL 1 . Here, Ldt 1  in the training data LD 1  indicates the height of the hardened layer in the training target substrate WL 1 . Ldw 1  indicates the width of the hardened layer in the training target substrate WL 1 . 
     The training data LD 2  to LD 1000  are generated in correspondence with the respective training target substrate WL 2  to WL 1000 . The processing results significantly vary depending on the heights and the widths in the hardened layers of the training target substrates WL. The training data LD preferably contains an item that greatly contributes to variation in results of the chemical liquid processing on the training target substrates WL. 
     Note that in the description made with reference to  FIGS.  12  and  13   , GOOD is indicated if the processing result is good while POOR is indicated if the processing result is not good and the processing results in the training data LD 1  to LD 1000  are binarized, which should not be taken to limit the present embodiment. The processing results may be classified into three or more values. Alternatively, the processing results may be classified into any value between the minimum value and the maximum value. For example, the processing results may be converted into numerals in consideration of the use amount (supply amount) of the chemical liquid or time require for the chemical liquid processing, in addition to the characteristic of the training target substrates WL. 
     Note that the chemical liquid processing condition preferably includes a plurality of items in the training data LD. For example, the chemical liquid processing condition may include a concentration, a temperature, and a supply amount of the chemical liquid, a discharge pattern of the chemical liquid for a training target substrate, and the rotational speed of the substrate holding section  120  in the chemical liquid processing. 
     The training data LD used in the training method of the present disclosure will be described next with reference to  FIG.  14   .  FIG.  14    is a diagram indicating an example of the training data LD. 
     As illustrated in  FIG.  14   , the training data LD includes training data LD 1  to LD 1000 . The training data LD indicates substrate information of each training target substrate WL, a chemical liquid processing condition of the training target substrate WL, and processing result information of the training target substrate WL. Here, the chemical liquid processing condition includes the concentration of the chemical liquid, the temperature of the chemical liquid, the supply amount of the chemical liquid, the discharge pattern of the chemical liquid for a training target substrate, and the rotational speed of the training target substrate when the chemical liquid is supplied. 
     The concentration of the chemical liquid indicates a concentration of the chemical liquid having been used for the training target substrate WL. The temperature of the chemical liquid indicates a temperature of the chemical liquid having been used for the training target substrate WL. The supply amount of the chemical liquid indicates a supply amount of the chemical liquid having been used for the training target substrate WL. The discharge pattern of the chemical liquid for the training target substrate indicates a path (travel path of the nozzle  132 ) where the chemical liquid has been discharged toward the training target substrate WL. The rotational speed of the training target substrate indicates a rotational speed of the training target substrate WL when the chemical liquid has been supplied. Furthermore, in the training data LD, “GOOD” is indicated if the result of the chemical liquid processing on a training target substrate is good while “POOR” is indicated if the result of the chemical liquid processing on the training target substrate is not good. 
     In the training data LD 1 , Lc 1  indicates a concentration of the chemical liquid having been used for the training target substrate WL 1  and Lt 1  indicates a temperature of the chemical liquid having been used for the training target substrate WL 1 . Also, Ls 1  indicates a supply amount of the chemical liquid having been used for the training target substrate WL 1 , Lel indicates a discharge pattern of the chemical liquid for the training target substrate WL 1 , and Lvl indicates a rotational speed of the training target substrate WL 1  when the chemical liquid has been supplied to the training target substrate WL 1 . In the training data LD 1 , “GOOD” is indicated as the processing result since the processing result of the chemical liquid processing on the training target substrate WL 1  was good. 
     The same applies to the training data LD 2  to LD 1000 . Note that chemical liquid processing conditions for the respective training target substrates WL may be the same as or different from each other. For example, at least some of the items of the chemical liquid processing conditions may be the same as or different from each other. Alternatively, all the items of the chemical liquid processing conditions may be the same as or different from each another. The processing results vary depending on the substrate states and the chemical liquid processing conditions of the training target substrates WL. 
     The chemical liquid processing performed in a substrate processing apparatus  100  of the present embodiment will be described next with reference to  FIGS.  1  to  15 B .  FIG.  15 A  is a schematic diagram of a processing target substrate Wp, and  FIG.  15 B  indicates chemical liquid processing condition information Rp generated in the trained model LM. 
     As illustrated in  FIG.  15 A , the processing target substrate Wp includes a resist layer R with a hardened layer Rc formed therein. Here, the hardened layer Rc has a height of dt and a width of dw. 
       FIG.  15 B  is a diagram indicating the chemical liquid processing condition information Rp. The chemical liquid processing condition information Rp contains the concentration of the chemical liquid, the temperature of the chemical liquid, the supply amount of the chemical liquid, the discharge pattern of the chemical liquid for a training target substrate, and the rotational speed of the training target substrate in supply of the chemical liquid. 
     In the chemical liquid processing condition information Rp, Rc indicates a concentration of the chemical liquid to be used for the processing target substrate Wp and Rt indicates a temperature of the chemical liquid to be used for the processing target substrate Wp. Also, Rs indicates a supply amount of the chemical liquid to be used for the processing target substrate Wp, Pe indicates a discharge pattern of the chemical liquid to be used for the processing target substrate Wp, and Rv indicates a rotational speed of the processing target substrate Wp in supply of the chemical liquid to the processing target substrate Wp. 
     In this case, the controller  22  controls the substrate holding section  120  and the chemical liquid supply section  130  to perform the chemical liquid processing on the processing target substrate Wp according to the chemical liquid processing condition indicated in the chemical liquid processing condition information Rp. 
     Note that the chemical liquid processing condition includes five items of the concentration of the chemical liquid, the temperature of the chemical liquid, the supply amount of the chemical liquid, the discharge pattern of the chemical liquid, and the rotational speed of a processing target substrate in the description made with reference to  FIGS.  14  to  15 B , which should not be taken to limit the present embodiment. The chemical liquid processing condition may include any one or more of the five items. Alternatively, the chemical liquid processing condition may be a combination of any one or more of the five items and another item. Alternatively, the chemical liquid processing condition may include one or more items different from the five items. 
     Note that the substrate information of each training target substrate WL is information obtained by measuring the thickness of the hardened layer in the above description made with reference to  FIGS.  12  to  15 B , which should not be taken to limit the present embodiment. The substrate information of each training target substrate WL may contain information about a condition for ion implantation for hardened layer formation. 
     The training data LD used in the training method of the present embodiment will be described next with reference to  FIG.  16   .  FIG.  16    is a diagram indicating an example of the training data LD. Note that the training data LD in  FIG.  16    is the same as the training data LD describe above with reference to  FIG.  12    in all aspects other than that the substrate information indicates an ion implantation condition for hardened layer formation in training target substrates WL. Therefore, duplicate description is omitted for avoiding redundancy. 
     As indicated in  FIG.  16   , the training data LD includes training data LD 1  to LD 1000 . The training data LD indicates the substrate information of each training target substrate WL, the chemical liquid processing condition of the training target substrate WL, and the processing result information of the training target substrate WL. Here, the substrate information of the training target substrate WL contains ion implantation condition information indicating an ion implantation condition for forming a hardened layer in the training target substrate WL. 
     Furthermore, the ion implantation condition information contains an ion species, an acceleration energy, an implantation amount, and an implantation direction. The ion species indicates a species of the ions having been used for ion implantation, and the acceleration energy indicates an acceleration energy of the ion species in ion implantation. Furthermore, the implantation amount indicates an amount of the implanted ion species, and the implantation direction indicates a direction in which the ions have been implanted to the training target substrate WL. 
     In the training data LD 1 , Lk 1  indicates an ion species having been used for ion implantation in formation of a hardened layer in a training target substrate WL 1  and Lal indicates an acceleration energy in ion implantation for forming the hardened layer in the training target substrate WL 1 . Also, Lul indicates an ion implantation amount in formation of the hardened layer in the training target substrate WL 1  and Ld 1  indicates a direction in which the ions have been implanted to the training target substrate WL for forming the hardened layer in the training target substrate WL 1 . 
     The same applies to the training data LD 2  to LD 1000 . As described above, the substrate information may indicate a condition for ion implantation for hardened layer formation rather than the thickness of the hardened layer itself. Note that the ion implantation condition information may include any items of the ion species, the acceleration energy, the implantation amount, and the implantation direction. Furthermore, the ion implantation condition information may include a combination of any items of the ion species, the acceleration energy, the implantation amount, and the implantation direction and another item. Alternatively, the ion implantation condition information may include one or more items other than the ion species, the acceleration energy, the implantation amount, and the implantation direction. Alternatively, the substrate information may be a combination of the hardened layer thickness information and the ion implantation condition information. 
     Note that the chemical liquid supply section  130  of the substrate processing apparatus  100  illustrated in  FIG.  3    supplies the chemical liquid in a given state, which should not be taken to limit the present embodiment. The concentration and the temperature of the chemical liquid may be changed as appropriate. 
     Next, a substrate processing apparatus  100  of the present embodiment will be described with reference to  FIG.  17   .  FIG.  17    is a schematic diagram of the substrate processing apparatus  100  of the present embodiment. Note that the substrate processing apparatus  100  illustrated in  FIG.  17    is the same as the substrate processing apparatus  100  described with reference to  FIG.  3    in all aspects other than that the concentration and the temperature of the chemical liquid supplied from the chemical liquid supply section  130  is adjustable. Therefore, duplicate description is omitted for avoiding redundancy. 
     In the substrate processing apparatus  100  of the present embodiment illustrated in  FIG.  17   , the chemical liquid supply section  130  supplies an SPM as the chemical liquid to a substrate W. The SPM is produced by mixing sulfuric acid and hydrogen peroxide water. For example, the chemical liquid supply section  130  supplies the SPM to a substrate W with the concentration (mixing ratio) of the SPM changed. Thus, the substrate W is processed with the SPM. 
     The chemical liquid supply section  130  includes a nozzle  132 , a pipe  134   a , a valve  136   a , an adjusting valve  137   a , a pipe  134   b , a valve  136   b , and an adjusting valve  137   b . The nozzle  132  is directed to an upper surface Wa of the substrate W and discharges the chemical liquid toward the upper surface Wa of the substrate W. 
     The pipe  134   a  is connected to the nozzle  132 . The nozzle  132  is provided at the tip end of the pipe  134   a . The hydrogen peroxide water is supplied to the pipe  134   a  from a supply source. The valve  136   a  and the adjusting valve  137   a  are provided in the pipe  134   a . The valve  136   a  opens and closes the flow channel of the pipe  134   a . The adjusting valve  137   a  adjusts the flow rate of the hydrogen peroxide water passing through the flow channel of the pipe  134   a.    
     The pipe  134   b  is connected to the nozzle  132 . The nozzle  132  is provided at the tip end of the pipe  134   b . The sulfuric acid is supplied to the pipe  134   b  from a supply source. The temperature of the sulfuric acid may be the same as or different from the temperature of the hydrogen peroxide water. The valve  136   b  and the adjusting valve  137   b  are provided in the pipe  134   b . The valve  136   b  opens and closes the flow channel of the pipe  134   b . The adjusting valve  137   b  adjusts the flow rate of the sulfuric acid passing through the flow channel of the pipe  134   b.    
     According to the present embodiment, adjustment of the adjusting valves  137   a  and  137   b  can change the flow rates of the hydrogen peroxide water and the sulfuric acid respectively flowing in the pipes  134   a  and  134   b . Therefore, the concentration and the temperature of the SPM produced by mixing the hydrogen peroxide water and the sulfuric acid can be adjusted. 
     Note that where the resist layer R of the substrate W is to remove, the inner layer Ri is relatively easy to remove with the chemical liquid while the hardened layer Rc is relatively difficult to remove with the chemical liquid as described with reference to  FIGS.  6  and  7   . Therefore, it is preferable that the concentration of the SPM is controlled so as to increase the removing power of the SPM for removing the hardened layer Rc and controlled so as to reduce the removing power of the SPM for removing the inner layer Ri. 
     For example, the concentration of the SPM is controlled to increase the ratio of the hydrogen peroxide water in the SPM in removal of the hardened layer Rc and controlled to reduce the ratio of the hydrogen peroxide water in the SPM in removal of the inner layer Ri. This can remove the resist layer while suppressing damage to the resist layer R. 
     The training data LD used in the training method of the present embodiment will be described next with reference to  FIG.  18   .  FIG.  18    is a diagram indicating an example of the training data LD. The training data LD in  FIG.  18    are favorably used for creating a trained model LM used in the substrate processing apparatus  100  illustrated in  FIG.  17   . Note that the training data LD in  FIG.  18    is the same as the training data LD described with reference to  FIG.  14    in all aspects other than that a value of at least one item of the training data indicates a profile of time variation in physical property. Therefore, duplicate description is omitted for avoiding redundancy. 
     As indicated in  FIG.  18   , the training data LD includes training data LD 1  to LD 1000 . The chemical liquid processing condition includes the concentration profile of the chemical liquid, the temperature profile of the chemical liquid, the supply amount of the chemical liquid, the discharge pattern of the chemical liquid for each training target substrate, and the rotational speed of the training target substrate when the chemical liquid has been supplied. The concentration profile indicates change in concentration of the SPM over time. The temperature profile indicates change in temperature of the SPM having been used for the training target substrate WL over time. Furthermore, the supply amount indicates a supply amount of the SPM having been used for the training target substrate WL and the discharge pattern indicates a discharge pattern and discharge timing of the SPM for the training target substrate WL. In addition, the rotational speed indicates a rotational speed of the training target substrate WL when the SPM has been supplied to the training target substrate WL. 
     In the substrate processing apparatus  100  illustrated in  FIG.  17   , the ratio between the hydrogen peroxide water and the sulfuric acid in the SPM can be changed by the adjusting valves  137   a  and  137   b . As such, in  FIG.  18   , the concentration of the chemical liquid indicates change in concentration of the chemical liquid having been used for the training target substrate WL over time and the temperature of the chemical liquid indicates change in temperature of the chemical liquid having been used for the training target substrate WL over time. 
     In the training data LD 1 , Lcpl indicates a concentration profile of the SPM having been supplied to a training target substrate WL 1  and Ltp 1  indicates a temperature profile of the SPM having been supplied to the training target substrate WL 1 . Ls 1  indicates a supply amount of the SPM having been supplied to the training target substrate WL 1 , and Le 1  indicates a discharge pattern of the SPM for the training target substrate WL 1 . Furthermore, Lv 1  indicates a rotational speed of the training target substrate WL when the SPM has been supplied to the training target substrate 
     The same applies to the training data LD 2  to LD 1000 . The results of the chemical liquid processing on the training target substrates WL significantly vary depending on the concentration profiles and the temperature profiles of the training target substrates WL. Therefore, the training data LD preferably contains an item that greatly contributes to variation in results of the chemical liquid processing on the training target substrates WL. 
     Note that the storage  24  of each substrate processing apparatus  100  or the storage  424  of the training device  400  stores the trained model LM built through machine training in the above description made with reference to  FIGS.  1  to  18   , which should not be taken to limit the present embodiment. The storage  24  of each substrate processing apparatus  100  or the storage  424  of the training device  400  may store therein a conversion table CT in place of the trained model LM. 
     Next, a substrate processing apparatus  100  of the present embodiment will be described with reference to  FIG.  19   . The substrate processing apparatus  100  in  FIG.  19    has the same configuration as the substrate processing apparatus  100  described with reference to  FIG.  4    in all aspects other than that the storage  24  stores therein the conversion table CT in place of the trained model LM. Therefore, duplicate description is omitted for avoiding redundancy. 
     As illustrated in  FIG.  19   , the storage  24  of the substrate processing apparatus  100  stores the conversion table CT therein. In the conversion table CT, substrate information is associated with chemical liquid processing condition information for each processing target substrate Wp. The substrate information of the processing target substrates Wp contains hardened layer height information and hardened layer width information, for example. Note that the conversion table CT is created based on the substrate information, the chemical liquid processing condition information, and the processing result information of the training target substrate WL. 
     The substrate information acquiring section  22   a  acquires substrate information from the storage  24 . For example, the substrate information acquiring section  22   a  acquires the hardened layer height information and the hardened layer width information from the storage  24 . 
     The chemical liquid processing condition information acquiring section  22   b  acquires chemical liquid processing condition information based on the substrate information using the conversion table CT. Typically, the chemical liquid processing condition information acquiring section  22   b  extracts a value corresponding to the substrate information from the conversion table CT and acquires chemical liquid processing condition information based on the relationship between the substrate information and the chemical liquid processing condition information associated with each other in the conversion table CT. The chemical liquid processing condition information acquiring section  22   b  acquires the chemical liquid processing condition information corresponding to the substrate information using the conversion table CT in the manner described above. 
     Thereafter, the controller  22  controls the substrate holding section  120  and the chemical liquid supply section  130  according to the chemical liquid processing condition indicated in the chemical liquid processing condition information. 
       FIG.  20    is a diagram indicating an example of the conversion table CT. As indicated in  FIG.  20   , the conversion table CT indicates the substrate information of a processing target substrate Wp and the chemical liquid processing condition. In the conversion table CT, the substrate information contains information about the hardened layer. Here, the substrate information of the processing target substrate Wp contains hardened layer height information and hardened layer width information. 
     The conversion table CT 1  indicates a chemical liquid processing condition corresponding to certain substrate information. Here, the substrate information contains hardened layer height information and the hardened layer width information of the processing target substrate Wp in the conversion table CT 1 . dt 1  indicates a height of the hardened layer in the processing target substrate Wp. dw 1  indicates a width of the hardened layer in the processing target substrate Wp. Rp 1  indicates a chemical liquid processing condition for the chemical liquid processing to be performed on the processing target substrate Wp. As such, where the height of the hardened layer in the processing target substrate Wp is dt 1  and the width of the hardened layer therein is dw 1 , the substrate processing apparatus  100  performs chemical liquid processing under the chemical liquid processing condition indicated by Rpl. 
     The same applies to the conversion tables CD 2  to CD 1000 . Typically, at least one of the height of the hardened layer and the width of the hardened layer differs among the conversion tables CT 1  to CD 1000 . 
     Note that when the height of the hardened layer in the processing target substrate Wp and the width of the hardened layer therein do not match any of the values indicated in the conversion table CT, a chemical liquid processing condition for the processing target substrate Wp may be determined by linear interpolation of the values of the chemical liquid processing condition indicated in the conversion table CT. Alternatively, a chemical liquid processing condition for the processing target substrate Wp may be determined by interpolation of values of the chemical liquid processing condition indicated in the conversion table using a polynomial equation. 
     An embodiment of the present invention has been described so far with reference to the drawings. However, the present invention is not limited to the above embodiment and can be implemented in various forms within a scope not departing from the gist thereof. Also, appropriate combination of some elements of configuration disclosed in the above embodiment can form various inventions. For example, some of all the elements of configuration indicated in the embodiment may be removed. Alternatively or additionally, elements of configuration in different embodiments may be combined as appropriate. The drawings schematically illustrate elements of configuration in order to facilitate understanding, and properties of elements of configuration illustrated in the drawings, such as thickness, length, number, and intervals thereof, may differ from actual properties thereof in order to facilitate preparation of the drawings. The properties of each of the elements of configuration, such as material, shape, and dimension thereof, described in the above embodiment are mere examples and not limited specifically, and various alterations thereof are possible within a range substantially not departing from the effects of the present invention. 
     INDUSTRIAL APPLICABILITY 
     The present invention is favorably used in a substrate processing apparatus, a substrate processing method, a training data generation method, a training method, a training device, a trained model creation method, and a trained model. 
     REFERENCE SIGNS LIST 
       10  Substrate processing system 
       20  Control device 
       22  Controller 
       22   a  Substrate information acquiring section 
       22   b  Chemical liquid processing condition information acquiring section 
       24  Storage 
     LM Trained model 
       100  Substrate processing apparatus 
       130  Chemical liquid supply section 
       140  Rinse liquid supply section 
       200  Substrate processing training system 
       300  Training data generating device 
       400  Training device