The proximity effect, caused by electron beam backscattering during resist exposure, is an important concern in writing submicron features. It can be compensated by appropriate local changes in the incident beam dose, but computation of the optimal correction usually requires a prohibitively long time. We present an example of such a computation on a small test pattern, which we performed by an iterative method. We then used this solution as a training set for an adaptive neural network. After training, the network computed the same correction as the iterative method, but in a much shorter time. Correcting the image with a software based neural network resulted in a decrease in the computation time by a factor of 30, and a hardware based network enhanced the computation speed by more than a factor of 1000. Both methods had an acceptably small error of 0.5% compared to the results of the iterative computation. Additionally, we verified that the neural network correctly generalized the solution of the problem to include patterns not contained in its training set.

Date Published: 1 May 1990
PDF: 12 pages
Proc. SPIE 1263, Electron-Beam, X-Ray, and Ion-Beam Technology: Submicrometer Lithographies IX, (1 May 1990); doi: 10.1117/12.20157

Published in SPIE Proceedings Vol. 1263:
Electron-Beam, X-Ray, and Ion-Beam Technology: Submicrometer Lithographies IX
Douglas J. Resnick, Editor(s)