Method for predicting misalignment data of a wafer using an improved neural network learning method

A method for obtaining misalignment data of an exposure equipment for a wafer using a neural network model, the method comprising:

• converting alignment data of a plurality of shots measured for each of a plurality of light sources into input data comprising a positional relationship between the plurality of shots;
• inputting the input data to a graph neural network (GNN) to obtain a first latent vector;
• obtaining a third latent vector by reflecting an importance of each light source and an importance of each shot in the first latent vector;
• predicting misalignment data for each of the plurality of shots from the third latent vector using a multilayer perceptron (MLP) neural network;
• and
• adjusting an equipment control value of the exposure equipment based on the misalignment data for each of the plurality of shots in the wafer.

A method for obtaining misalignment data for an exposure equipment, the method comprising:

• converting alignment data of a plurality of shots within a wafer, measured using a plurality of light sources having different wavelengths, into a graphical input data;
• inputting the graphical input data to a graph neural network (GNN) to obtain a first latent vector;
• obtaining a third latent vector by reflecting an importance of the plurality of light sources and the plurality of shots in the first latent vector;
• flattening the third latent vector;
• inputting the flattened third latent vector to an x-axis MLP neural network to predict x-axis misalignment data comprising an x-axis misalignment component;
• inputting the flattened third latent vector to a y-axis MLP neural network to predict y-axis misalignment data comprising a y-axis misalignment component;
• and
• adjusting an equipment control value of the exposure equipment based on the y-axis misalignment data and the x-axis misalignment data for each of the plurality of shots in the wafer.