Machine learning based recursive partitioning for simplifying OPC model building complexity

Apoorva Oak; Soobin Hwang; Ruoxia Chen; Shinill Kang; Ryan Ryoung-han Kim

doi:10.1117/12.2584704

22 February 2021 Machine learning based recursive partitioning for simplifying OPC model building complexity

Apoorva Oak, Soobin Hwang, Ruoxia Chen, Shinill Kang, Ryan Ryoung-han Kim

Proceedings Volume 11614, Design-Process-Technology Co-optimization XV; 116140O (2021) https://doi.org/10.1117/12.2584704
Event: SPIE Advanced Lithography, 2021, Online Only

Abstract

We propose the use of machine learning based analytics to simplify OPC (Optical Proximity Correction) model building process which demands concurrent optimization of more than 70 parameters as nodes shrink. We first built a deep neural network architecture to predict the RMS error, for a given set of model parameters. The neural network was trained on existing OPC model parameters and corresponding output RMS data of simulations to achieve an accurate prediction of output RMS for given set of OPC model parameters. Later, a sensitivity analysis-based methodology for recursive partitioning of OPC modelling parameters was employed to reduce the total search space of OPC model simulations. This resulted in reduction of the number of OPC model iterations performed during model tuning by orders of magnitude.

Conference Presentation

Citation Download Citation

Apoorva Oak, Soobin Hwang, Ruoxia Chen, Shinill Kang, and Ryan Ryoung-han Kim "Machine learning based recursive partitioning for simplifying OPC model building complexity", Proc. SPIE 11614, Design-Process-Technology Co-optimization XV, 116140O (22 February 2021); https://doi.org/10.1117/12.2584704

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