How GPU-accelerated simulation enables applied deep learning for masks and wafers

Linyong Pang; Mariusz Niewczas; Mike Meyer; Ryan Pearman; Abhishek Shendre; Aki Fujimura

doi:10.1117/12.2538244

27 June 2019 How GPU-accelerated simulation enables applied deep learning for masks and wafers

Linyong Pang, Mariusz Niewczas, Mike Meyer, Ryan Pearman, Abhishek Shendre, Aki Fujimura

Proceedings Volume 11178, Photomask Japan 2019: XXVI Symposium on Photomask and Next-Generation Lithography Mask Technology; 111780A (2019) https://doi.org/10.1117/12.2538244
Event: Photomask Japan 2019, 2019, Yokohama, Japan

Abstract

Deep Learning (DL) is one of the most exciting fields in artificial intelligence (AI) right now. It’s still early days, but DL will completely change the lithography and photomask industry to automate or optimize the efficiency of equipment and processes. The key element required for building applied DL is a GPU-accelerated simulation environment. In this paper, we will present a Deep Learning Kit (DLK), an artificial intelligence platform that allows semiconductor manufacturing companies and mask shops to do such simulations for DL training, and show a case study with DLK. DLK provides accurate physical models for masks and lithography that are fully accelerated by CUDA on GPUs, the de facto DL training platform, a GPU accelerated Computational Design Platform (CDP), fully integrated and distributed TensorFlowTM on CDP, and pre-trained neural network models for wafer and mask problems. Using DLK, semiconductor manufacturing companies and mask shops can quickly build their deep neural network model, connect the simulator of their choice (either provided by D2S or its partners), and train the neural network model in that environment to learn a desired behavior.

Citation Download Citation

Linyong Pang, Mariusz Niewczas, Mike Meyer, Ryan Pearman, Abhishek Shendre, and Aki Fujimura "How GPU-accelerated simulation enables applied deep learning for masks and wafers", Proc. SPIE 11178, Photomask Japan 2019: XXVI Symposium on Photomask and Next-Generation Lithography Mask Technology, 111780A (27 June 2019); https://doi.org/10.1117/12.2538244

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