Computation of proximity effect corrections in electron beam lithography by a neural network

The proximity effect, caused by electron-beam backscattering during resist exposure, can be compensated for by appropriate local changes in the incident beam dose, but the optimal correction, found iteratively, requires a prohibitively long time for realistic pattern sizes. A neural network has been used to perform these corrections, resulting in a significant decrease in computation time. The correction was first computed for a small test pattern using an iterative method. This solution was used as a training set for an adaptive, feedforward neural network, using back-propagation learning. After training, the network computed the same correction as the iterative method, but in a much shorter time