Robustify ML-Based Lithography Hotspot Detectors-Reference-Cited by-同舟云学术

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Robustify ML-Based Lithography Hotspot Detectors

Published:2022-10-30 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design
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Author:

Pan Jingyu¹,Chang Chen-Chia¹,Xie Zhiyao²,Hu Jiang³,Chen Yiran¹

Affiliation:

1. Duke University

2. Hong Kong University of Science and Technology

3. Texas A&M University

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3508352.3549389

Reference24 articles.

1. Wild patterns: Ten years after the rise of adversarial machine learning

2. Coupling-aware Dummy Metal Insertion for Lithography

3. Empirical Study of the Topology and Geometry of Deep Networks

4. SRAF Insertion via Supervised Dictionary Learning

5. Ian J Goodfellow , Jonathon Shlens , and Christian Szegedy . 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 ( 2014 ). Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014).

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. APPLE: An Explainer of ML Predictions on Circuit Layout at the Circuit-Element Level;2024 29th Asia and South Pacific Design Automation Conference (ASP-DAC);2024-01-22

2. Security and Reliability Challenges in Machine Learning for EDA: Latest Advances;2023 24th International Symposium on Quality Electronic Design (ISQED);2023-04-05

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