Proximity Effect Corrections in Electron Beam Lithography Using a Neural Network

Abstract

We have used a neural network to compute corrections for images written by electron beams to eliminate the proximity effects caused by electron Iterative methods are effective. but require prohibitively scattering. computation time. We have instead trained a neural network to perform equivalent corrections. resulting in a significant speed-up. We have examined hardware implementations using both analog and digital electronic networks. Both had an acceptably small error of 0.5% compared to the iterative results. Additionally. we verified that the neural network correctly generalized the solution of the problem to include patterns not contained in its training set. We have experimentally verified this approach on a Cambridge Instruments EBMF 10.5 exposure system.