Machine Learning Topography Prediction and Optimization in Maskless Grayscale Laser Lithography

A machine learning (ML) framework was developed for the prediction of surface topography obtained with maskless grayscale laser lithography based on the spatial distribution of the applied laser energy dose, or virtual photomask. Artificial neural networks (ANNs) were employed, with the virtual photomask and its radial averages selected as input variables and the surface elevation selected as the output variable. Training of ANNs was carried out with data acquired from the production of models comprising a wide range of representative geometries. Hyperparameter optimization was performed by assessing the accuracy of trained ANNs, with the final configuration comprising a single hidden layer with 15 neurons and a Sigmoid activation function. The trained ANN was then employed within an iterative optimization algorithm to determine the best virtual photomask for the production of new objects by updating the virtual photomask based on the predicted error, thus automatically compensating for proximity effects and sharp dose transitions. The developed approach achieved a reduction in average build error from 2.8 µm to 1.3-1.5 µm compared to standard experimental approaches in a single build, improving not only accuracy but also greatly reducing time requirements for optimization of the process.