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Regular Paper

SegNet-OPC: A Mask Optimization Framework in VLSI Design Flow Based on Semantic Segmentation Network

School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China
School of Artificial Intelligence, Anhui University of Science and Technology, Huainan 232001, China
School of Microelectronics, Hefei University of Technology, Hefei 230009, China
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Abstract

With the continuous decrease in the critical dimensions of integrated circuits, mask optimization has become the main challenge in VLSI design. In recent years, thriving machine learning has been gradually introduced in the field of optical proximity correction (OPC). Currently, advanced learning-based frameworks have been limited by low mask printability or large computational overhead. To address these limitations, this paper proposes a learning-based framework named SegNet-OPC, which can generate optimized masks from the target layout at shorter training and turnaround time with higher mask printability. The proposed framework consists of a backbone network and loss terms suitable for mask optimization tasks, followed by a fine-tuning network. The framework yields remarkable improvements over conventional methods, delivering significantly faster turnaround time and superior mask printability and manufacturability. With just 1.25 hours of training, the framework achieves comparable mask complexity while surpassing the state-of-the-art methods, achieving a minimum 3% enhancement in mask printability and an impressive 16.7% improvement in mask manufacturability.

Keywords

learning-based frameworkmask optimizationmask printabilityoptical proximity correction

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Journal of Computer Science and Technology
Volume 40 Issue 2,
March 2025
Pages 500-512
DOI: 10.1007/s11390-023-3002-7
Cite this article:
Xu H, Qi P, Tang F-X, et al. SegNet-OPC: A Mask Optimization Framework in VLSI Design Flow Based on Semantic Segmentation Network. Journal of Computer Science and Technology, 2025, 40(2): 500-512. https://doi.org/10.1007/s11390-023-3002-7

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Received: 09 January 2023
Accepted: 16 November 2023
Published: 31 March 2025
© Institute of Computing Technology, Chinese Academy of Sciences 2025
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