Accurate lithography simulation model based on convolutional neural networks

Yuki Watanabe; Taiki Kimura; Tetsuaki Matsunawa; Shigeki Nojima

doi:10.1117/12.2257871

30 March 2017 Accurate lithography simulation model based on convolutional neural networks

Yuki Watanabe, Taiki Kimura, Tetsuaki Matsunawa, Shigeki Nojima

Proceedings Volume 10147, Optical Microlithography XXX; 101470K (2017) https://doi.org/10.1117/12.2257871
Event: SPIE Advanced Lithography, 2017, San Jose, California, United States

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.

Citation Download Citation

Yuki Watanabe, Taiki Kimura, Tetsuaki Matsunawa, and Shigeki Nojima "Accurate lithography simulation model based on convolutional neural networks", Proc. SPIE 10147, Optical Microlithography XXX, 101470K (30 March 2017); https://doi.org/10.1117/12.2257871

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