Predicting photoresist sensitivity using machine learning

Abstract

We introduce a scheme for predicting photoresist sensitivity using machine learning (ML) work flow on the basis of previously reported experimental data. Different ML models, specifically Linear Regression, Kernel Ridge Regression, Gaussian Process Regressor, Random Forest Regressor, and Multilayer Perceptron Regressor, were evaluated to rapidly identify the best sensitivity prediction model. The experiment was carried out on the Google Colab platform using the Materials Simulation Toolkit for Machine Learning and Sci-kit Learn. Different ensemble models were utilized without splitting the dataset to determine the prediction accuracy of the ML models. The hyperparameter optimization was established with a 70/30 ratio, followed by a K-Fold cross-validation to improve the model prediction performance. The optimized ML model showed a prediction performance of R-2 = 0.83, RMSE = 10.53, and MARE = 0.68. Hence, by optimizing the hyperparameters used in the ML model, the sensitivity of the photoresist materials can be predicted with improved prediction performance.