Share Email Print

Proceedings Paper

Automatic layout feature extraction for lithography hotspot detection based on deep neural network
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Lithography hotspot detection in the physical verification phase is one of the most important techniques in today's optical lithography based manufacturing process. Although lithography simulation based hotspot detection is widely used, it is also known to be time-consuming. To detect hotspots in a short runtime, several machine learning based methods have been proposed. However, it is difficult to realize highly accurate detection without an increase in false alarms because an appropriate layout feature is undefined. This paper proposes a new method to automatically extract a proper layout feature from a given layout for improvement in detection performance of machine learning based methods. Experimental results show that using a deep neural network can achieve better performance than other frameworks using manually selected layout features and detection algorithms, such as conventional logistic regression or artificial neural network.

Paper Details

Date Published: 16 March 2016
PDF: 10 pages
Proc. SPIE 9781, Design-Process-Technology Co-optimization for Manufacturability X, 97810H (16 March 2016); doi: 10.1117/12.2217746
Show Author Affiliations
Tetsuaki Matsunawa, Toshiba Corp. (Japan)
Shigeki Nojima, Toshiba Corp. (Japan)
Toshiya Kotani, Toshiba Corp. (Japan)

Published in SPIE Proceedings Vol. 9781:
Design-Process-Technology Co-optimization for Manufacturability X
Luigi Capodieci, Editor(s)

© SPIE. Terms of Use
Back to Top