Dr. Elsley Tan Profile

Dr. Elsley Tan

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Proceedings Article | 10 April 2024 Poster + Paper

Advanced regression OPC model setup

Tony Hu, Terry Hsuan, Elvis Yang, Li-Jin Chen, Tony Chao, Y. Liao, Elsley Tan

Proceedings Volume 12954, 1295419 (2024) https://doi.org/10.1117/12.3008958

KEYWORDS: Autoregressive models, Optical proximity correction, Performance modeling, Education and training, 3D modeling, Calibration, Neurons, Data modeling, Modeling, Overfitting

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The calibration of optical proximity correction (OPC) models has become increasingly challenging, especially when the behavior of photoresist on wafers cannot be adequately interpreted using conventional model terms assembled in a linear fashion. Additionally, fine-tuning such linearly separable physical components proves difficult due to evidence of nonlinear interactions among physical effects. In this study, we propose leveraging an advanced regression technique that progressively augments the linear model assembly with perturbative nonlinear neural network units the sharing same set of physics-inspired model terms as its base model, aiming to enhance model accuracy while maintaining stability. The research approach involves setting up initial models using conventional model calibration techniques, including optical model optimization and resist model optimization. Subsequently, we incorporate the Synopsys Advanced Regression (AR) neural network to identify essential non-linear interactions among modeling components. We selectively include these non-linear components into the existing linear model to capture on-wafer behavior. The entire process is designed to integrate seamlessly into the existing OPC production flow, ensuring a balance between model accuracy and efficiency. To evaluate the efficacy of the Synopsys AR method, we conduct tests on layers from 3D-NAND. The results demonstrate that this approach significantly reduces calibration costs due to its simpler calibration requirements.

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