research-article

Robustify ML-Based Lithography Hotspot Detectors

Authors:

Chen-Chia Chang,

Yiran ChenAuthors Info & Claims

ICCAD '22: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

Article No.: 134, Pages 1 - 7

https://doi.org/10.1145/3508352.3549389

Published: 22 December 2022 Publication History

Abstract

Deep learning has been widely applied in various VLSI design automation tasks, from layout quality estimation to design optimization. Though deep learning has shown state-of-the-art performance in several applications, recent studies reveal that deep neural networks exhibit intrinsic vulnerability to adversarial perturbations, which pose risks in the ML-aided VLSI design flow. One of the most effective strategies to improve robustness is regularization approaches, which adjust the optimization objective to make the deep neural network generalize better. In this paper, we examine several adversarial defense methods to improve the robustness of ML-based lithography hotspot detectors. We present an innovative design rule checking (DRC)-guided curvature regularization (CURE) approach, which is customized to robustify ML-based lithography hotspot detectors against white-box attacks. Our approach allows for improvements in both the robustness and the accuracy of the model. Experiments show that the model optimized by DRC-guided CURE achieves the highest robustness and accuracy compared with those trained using the baseline defense methods. Compared with the vanilla model, DRC-guided CURE decreases the average attack success rate by 53.9% and increases the average ROC-AUC by 12.1%. Compared with the best of the defense baselines, DRC-guided CURE reduces the average attack success rate by 18.6% and improves the average ROC-AUC by 4.3%.

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Cited By

Zhang TYang HLiu KXie ZKim T(2024)APPLE: An Explainer of ML Predictions on Circuit Layout at the Circuit-Element LevelProceedings of the 29th Asia and South Pacific Design Automation Conference10.1109/ASP-DAC58780.2024.10473805(374-379)Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1109/ASP-DAC58780.2024.10473805
Xie ZZhang TPeng Y(2023)Security and Reliability Challenges in Machine Learning for EDA: Latest Advances2023 24th International Symposium on Quality Electronic Design (ISQED)10.1109/ISQED57927.2023.10129359(1-6)Online publication date: 5-Apr-2023
https://doi.org/10.1109/ISQED57927.2023.10129359

Index Terms

Robustify ML-Based Lithography Hotspot Detectors
1. Hardware

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cover image ACM Conferences

ICCAD '22: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

October 2022

1467 pages

ISBN:9781450392174

DOI:10.1145/3508352

Conference Chair:
Tulika Mitra
National University of Singapore
,
Program Chairs:
Evangeline Young
The Chinese University of Hong Kong
,
Jinjun Xiong
University at Buffalo (UB)

Copyright © 2022 ACM.

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New York, NY, United States

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Published: 22 December 2022

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ICCAD '22: IEEE/ACM International Conference on Computer-Aided Design

October 30 - November 3, 2022

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Cited By

Zhang TYang HLiu KXie ZKim T(2024)APPLE: An Explainer of ML Predictions on Circuit Layout at the Circuit-Element LevelProceedings of the 29th Asia and South Pacific Design Automation Conference10.1109/ASP-DAC58780.2024.10473805(374-379)Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1109/ASP-DAC58780.2024.10473805
Xie ZZhang TPeng Y(2023)Security and Reliability Challenges in Machine Learning for EDA: Latest Advances2023 24th International Symposium on Quality Electronic Design (ISQED)10.1109/ISQED57927.2023.10129359(1-6)Online publication date: 5-Apr-2023
https://doi.org/10.1109/ISQED57927.2023.10129359

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