Share Email Print
cover

Proceedings Paper

Accurate lithography simulation model based on convolutional neural networks
Author(s): Yuki Watanabe; Taiki Kimura; Tetsuaki Matsunawa; Shigeki Nojima
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Lithography simulation is an essential technique for today's semiconductor manufacturing process. In order to calculate an entire chip in realistic time, compact resist model is commonly used. The model is established for faster calculation. To have accurate compact resist model, it is necessary to fix a complicated non-linear model function. However, it is difficult to decide an appropriate function manually because there are many options. This paper proposes a new compact resist model using CNN (Convolutional Neural Networks) which is one of deep learning techniques. CNN model makes it possible to determine an appropriate model function and achieve accurate simulation. Experimental results show CNN model can reduce CD prediction errors by 70% compared with the conventional model.

Paper Details

Date Published: 30 March 2017
PDF: 9 pages
Proc. SPIE 10147, Optical Microlithography XXX, 101470K (30 March 2017); doi: 10.1117/12.2257871
Show Author Affiliations
Yuki Watanabe, Toshiba Corp. (Japan)
Taiki Kimura, Toshiba Corp. (Japan)
Tetsuaki Matsunawa, Toshiba Corp. (Japan)
Shigeki Nojima, Toshiba Corp. (Japan)


Published in SPIE Proceedings Vol. 10147:
Optical Microlithography XXX
Andreas Erdmann; Jongwook Kye, Editor(s)

© SPIE. Terms of Use
Back to Top