SPIE - Education Machine Learning for Lithography SC1264

This course provides background on supervised learning applied to microlithography. A primary goal of the course is to illustrate supervised learning, inference, and validation workflow to practitioners of microlithography, using datasets and problems with which they are familiar. Example applications will include photoresist models and inverse lithography models. Example model types include linear regressions, logistic classifiers and deep neural networks. Training methodology will utilize prepared datasets with Jupyter notebooks.