We investigated the possibility of hotspot detection after lithography simulation by using Neural Networks (NN). We applied the image recognition technique by the NN for hotspot detection and confirmed the possibility by its recognition rate of the device pattern defects after NN learning. Various test patterns were prepared for NN learning and we investigated the convergence and the learning time of the NN. The compositions of the input and the hidden-layers of the NN do not have so much influence on the convergence of NN, but the initial parameter values of weight setting have predominant effect on the convergence of the NN. There are correlations among the learning time of the NN, the number of input samples and the number of hidden-layers, so a certain consideration is required for NN design. The hotspot recognition rate ranged from 90% to 42%, depending pattern type and learning sample number. Increasing learning sample number improves the recognition rate. But learning all type patterns leads to 55% recognition, so learning single type pattern leads to better recognition rate.

Date Published: 14 May 2007
PDF: 6 pages
Proc. SPIE 6607, Photomask and Next-Generation Lithography Mask Technology XIV, 66071B (14 May 2007); doi: 10.1117/12.728959

Published in SPIE Proceedings Vol. 6607:
Photomask and Next-Generation Lithography Mask Technology XIV
Hidehiro Watanabe, Editor(s)