Training device, information processing apparatus, substrate processing apparatus, substrate processing system, training method and processing condition determining method

An information processing apparatus that manages a substrate processing apparatus, wherein

• the substrate processing apparatus
• processes a film formed on a substrate by supplying a processing liquid to the substrate on which the film is formed, according to processing conditions including a variable condition indicating a relative position of a nozzle with respect to the substrate, with the relative position varying over time, and
• includes
• a converter that converts the variable condition into compressed data representing a nozzle work amount for each of a plurality of movement sections, with the plurality of movement sections being obtained when a movement range in which the nozzle moves during a scanning period from a time when the substrate processing apparatus starts a nozzle work for moving the nozzle with respect to the substrate until a time when the substrate processing apparatus ends the nozzle work is divided into a number smaller than a data count of the variable condition, and
• a processing condition determiner that determines processing conditions for driving the substrate processing apparatus using a learning model, with the learning model predicting a second processing amount that indicates a difference between a film thickness obtained before a process for the film and a film thickness obtained after the process for the film in regard to the film formed on the substrate before being processed by the substrate processing apparatus,
• the learning model is an inference model that has executed machine training using training data, with the training data including compressed data that is obtained when the variable condition included in processing conditions according to which the substrate processing apparatus has executed a process for the film formed on the substrate is converted by the converter, and a first processing amount indicating a difference between a film thickness obtained before the process for the film and a film thickness obtained after the process for the film in regard to the film formed on the substrate that has been processed by the substrate processing apparatus, and
• the processing condition determiner, in a case in which compressed data obtained when a temporary variable condition is converted by the converter is provided to the learning model and the second processing amount predicted by the learning model satisfies an allowable condition, determines processing conditions including the temporary variable condition as processing conditions for driving the substrate processing apparatus.

A substrate processing system managing a substrate processing apparatus that processes a substrate, comprising a training device and an information processing apparatus, wherein

• the substrate processing apparatus processes a film formed on a substrate by supplying a processing liquid to the substrate on which the film is formed, according to processing conditions including a variable condition indicating a relative position of a nozzle with respect to the substrate, with the relative position varying over time,
• the training device includes
• an experimental data acquirer that acquires a first processing amount indicating a difference between a film thickness obtained before a process for a film and a film thickness obtained after the process for the film, after the substrate processing apparatus is driven according to processing conditions and executes the process for the film formed on the substrate,
• a first converter that converts the variable condition into compressed data representing a nozzle work amount for each of a plurality of movement sections, with the plurality of movement sections being obtained when a movement range in which the nozzle moves during a scanning period from a time when the substrate processing apparatus starts a nozzle work for moving the nozzle with respect to a substrate until a time when the substrate processing apparatus ends the nozzle work is divided into a number smaller than a data count of the variable condition, and
• a model generator that generates a learning model, with the learning model executing machine learning using training data that includes the compressed data obtained when the variable condition is converted by the first converter and the first processing amount corresponding to the processing conditions and predicting a second processing amount that indicates a difference between a film thickness obtained before a process for the film and a film thickness obtained after the process for the film in regard to the film formed on the substrate before being processed by the substrate processing apparatus,
• the information processing apparatus includes
• a second converter that is same as the first converter, and
• a processing condition determiner that determines processing conditions for driving the substrate processing apparatus using the learning model generated by the training device, and
• the processing condition determiner, in a case in which a conversion result obtained when a temporary variable condition is converted by the second converter is provided to the learning model and a second processing amount predicted by the learning model satisfies an allowable condition, determines processing conditions including the temporary variable condition as processing conditions for driving the substrate processing apparatus.

A training method of causing a computer to execute the processes of:

• acquiring a first processing amount indicating a difference between a film thickness obtained before a process for a film and a film thickness obtained after the process for the film, after a substrate processing apparatus is driven according to processing conditions including a variable condition indicating a relative position of a nozzle with respect to a substrate and executes the process for the film formed on the substrate, the relative position varying over time, the substrate processing apparatus moving the nozzle for supplying a processing liquid to the substrate on which the film is formed and supplying the processing liquid to the substrate;
• converting the variable condition into compressed data representing a nozzle work amount for each of a plurality of movement sections, the plurality of movement sections being obtained when a movement range in which the nozzle moves during a scanning period from a time when the substrate processing apparatus starts a nozzle work for moving the nozzle with respect to the substrate until a time when the substrate processing apparatus ends the nozzle work is divided into a number smaller than a data count of the variable condition;
• and
• generating a learning model, the learning model executing machine learning using training data that includes the compressed data and the first processing amount corresponding to the processing conditions and predicting a second processing amount that indicates a difference between a film thickness obtained before a process for the film and a film thickness obtained after the process for the film in regard to the film formed on the substrate before being processed by the substrate processing apparatus.

A processing condition determining method executed by a computer that manages a substrate processing apparatus, wherein

• the substrate processing apparatus processes a film formed on a substrate by supplying a processing liquid to the substrate on which the film is formed, according to processing conditions including a variable condition indicating a relative position of a nozzle with respect to the substrate, with the relative position varying over time,
• the processing condition determining method includes
• a process of converting the variable condition into compressed data representing a nozzle work amount for each of a plurality of movement sections, with the plurality of movement sections being obtained when a movement range in which the nozzle moves during a scanning period from a time when the substrate processing apparatus starts a nozzle work for moving the nozzle with respect to the substrate until a time when the substrate processing apparatus ends the nozzle work is divided into a number smaller than a data count of the variable condition, and
• a process of determining processing conditions for driving the substrate processing apparatus using a learning model, with the learning model predicting a second processing amount that indicates a difference between a film thickness obtained before a process for the film and a film thickness obtained after the process for the film in regard to the film formed on the substrate before being processed by the substrate processing apparatus,
• the learning model is an inference model that has executed machine training using training data, with the training data including compressed data that is obtained when the variable condition included in processing conditions according to which the substrate processing apparatus has executed a process for the film formed on the substrate is converted in the process of converting, and a first processing amount indicating a difference between a film thickness obtained before the process for the film and a film thickness obtained after the process for the film in regard to the film formed on the substrate that has been processed by the substrate processing apparatus, and
• the process of determining processing conditions, in a case in which compressed data obtained when a temporary variable condition is converted in the process of converting is provided to the learning model and the second processing amount predicted by the learning model satisfies an allowable condition, includes determining processing conditions including the temporary variable condition as processing conditions for driving the substrate processing apparatus.