Mr. Guojin Chen Profile

Guojin Chen

at UT Austin

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Proceedings Article | 10 April 2024 Presentation + Paper

Open-source differentiable lithography imaging framework

Guojin Chen, Hao Geng, Bei Yu, David Pan

Proceedings Volume 12954, 129540J (2024) https://doi.org/10.1117/12.3009980

KEYWORDS: Lithography, Latex, Source mask optimization, Photomasks, Performance modeling, Systems modeling, Computational lithography, Computer programming, Optical lithography, Mathematical optimization

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The rapid evolution of the electronics industry, driven by Moore’s law and the proliferation of integrated circuits, has led to significant advancements in modern society, including the Internet, wireless communication, and artificial intelligence (AI). Central to this progress is optical lithography, a critical technology in semiconductor manufacturing that accounts for approximately 30% to 40% of production costs. As semiconductor nodes shrink and transistor numbers increase, optical lithography becomes increasingly vital in current integrated circuit (IC) fabrication technology. This paper introduces an open-source differentiable lithography imaging framework that leverages the principles of differentiable programming and the computational power of GPUs to enhance the precision of lithography modeling and simplify the optimization of resolution enhancement techniques (RETs). The framework models the core components of lithography as differentiable segments, allowing for the implementation of standard scalar imaging models, including the Abbe and Hopkins models, as well as their approximation models. The paper introduces a computational lithography framework that optimizes semiconductor manufacturing processes using advanced computational techniques and differentiable programming. It compares imaging models and provides tools for enhancing resolution, demonstrating improved semiconductor patterning performance. The open-sourced framework represents a significant advancement in lithography technology, facilitating collaboration in the field. The source code is available at https://github.com/TorchOPC/TorchLitho.

Proceedings Article | 28 April 2023 Poster + Paper

GPU-accelerated matrix cover algorithm for multiple patterning layout decomposition

Guojin Chen, Haoyu Yang, Bei Yu

Proceedings Volume 12495, 124951P (2023) https://doi.org/10.1117/12.2657904

KEYWORDS: Optical lithography, Design and modelling, Lithography, Computer programming, Algorithm development, Performance modeling, Double patterning technology, Design rules

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Multiple patterning lithography (MPL) is regarded as one of the most promising ways of overcoming the resolution limitations of conventional optical lithography due to the delay of next-generation lithography technology. As the feature size continues to decrease, layout decomposition for multiple patterning lithography (MPLD) technology is becoming increasingly crucial for improving the manufacturability in advanced nodes. The decomposition process refers to assigning the layout features to different mask layers according to the design rules and density requirements. When the number of masks k ≥ 3, the MPLD problems are N P-hard and thus may suffer from runtime overhead for practical designs. However, the number of layout patterns is increasing exponentially in industrial layouts, which hinders the runtime performance of MPLD models. In this research, we substitute the CPU’s dance link data structure with parallel GPU matrix operations to accelerate the solution for exact coverbased MPLD algorithms. Experimental results demonstrate that our system is capable of full-scale, lightningfast layout decomposition, which can achieve more than 10× speed-up without quality degradation compared to state-of-the-art layout decomposition methods.

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