Method of training neural network model, and plating apparatus

A method of training a neural network model used for control of a plating apparatus including a plating bath for accommodating a plating solution, a substrate holder for holding a substrate, and an anode disposed in the plating bath so as to be opposite the substrate held by the substrate holder, wherein

• upon accepting input data including at least data representing a current density data at a peripheral portion of the substrate, the neural network model is configured to perform inference processing on the input data and calculate inference data representing a potential of the plating solution in a vicinity of the peripheral portion of the substrate, and
• the method comprises:
• reading training input data and supervisory data corresponding to the training input data from a training data storage in which training data is stored;
• causing the training input data to be input to the neural network model;
• outputting, by the neural network model, the inference data in accordance with the training input data;
• calculating, as a first loss metric, a residual between the supervisory data and the output inference data;
• calculating, as a second loss metric, a residual obtained by substituting the output inference data into an equation describing physics phenomena in a region of the plating solution accommodated between the substrate and the anode in the plating bath;
• and
• optimizing parameters of the neural network model by using the first loss metric and the second loss metric.