Accurate lithography simulation model based on convolutional neural networks

Yuki Watanabe; Taiki Kimura; Tetsuaki Matsunawa; Shigeki Nojima

doi:10.1117/12.2279780

13 July 2017 Accurate lithography simulation model based on convolutional neural networks

Yuki Watanabe, Taiki Kimura, Tetsuaki Matsunawa, Shigeki Nojima

Author Affiliations +

Proceedings Volume 10454, Photomask Japan 2017: XXIV Symposium on Photomask and Next-Generation Lithography Mask Technology; 104540I (2017) https://doi.org/10.1117/12.2279780
Event: Photomask Japan 2017, 2017, Yokohama, Japan

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.

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Yuki Watanabe, Taiki Kimura, Tetsuaki Matsunawa, and Shigeki Nojima "Accurate lithography simulation model based on convolutional neural networks", Proc. SPIE 10454, Photomask Japan 2017: XXIV Symposium on Photomask and Next-Generation Lithography Mask Technology, 104540I (13 July 2017); https://doi.org/10.1117/12.2279780

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