Efficient Layout Hotspot Detection via Neural Architecture Search-Reference-Cited by-同舟云学术

Efficient Layout Hotspot Detection via Neural Architecture Search

Published:2022-06-27 Issue:6 Volume:27 Page:1-16
ISSN:1084-4309
Container-title:ACM Transactions on Design Automation of Electronic Systems
language:en
Short-container-title:ACM Trans. Des. Autom. Electron. Syst.

Author:

Jiang Yiyang¹^ORCID,Yang Fan¹^ORCID,Yu Bei²^ORCID,Zhou Dian³^ORCID,Zeng Xuan¹^ORCID

Affiliation:

1. State Key Lab of AISC & System, School of Microelectronics, Fudan University, China

2. Chinese University of Hong Kong, China

3. University of Texas at Dallas

Abstract

Layout hotspot detection is of great importance in the physical verification flow. Deep neural network models have been applied to hotspot detection and achieved great success. Despite their success, high-performance neural networks are still quite difficult to design. In this article, we propose a bayesian optimization-based neural architecture search scheme to automatically do this time-consuming and fiddly job. Experimental results on ICCAD 2012 and ICCAD 2019 Contest benchmarks show that the architectures designed by our proposed scheme achieve higher performance on hotspot detection task compared with state-of-the-art manually designed neural networks.

Funder

National Key R&D Program of China

National Natural Science Foundation of China (NSFC) Research Projects

Research Grants Council of Hong Kong SAR

Publisher

Association for Computing Machinery (ACM)

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Science Applications

Link

https://dl.acm.org/doi/pdf/10.1145/3517130

Reference33 articles.

1. Faster Region-based Hotspot Detection

2. Ying Chen Yibo Lin Tianyang Gai Yajuan Su Yayi Wei and David Z. Pan. 2019. Semisupervised hotspot detection with self-paced multitask learning. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 39 7 (2019) 1511–1523.

3. Improved regularization of convolutional neural networks with cutout;DeVries Terrance;arXiv:1708.04552,2017

4. Duo Ding Andres J. Torres Fedor G. Pikus and David Z. Pan. 2011. High performance lithographic hotspot detection using hierarchically refined machine learning. In Proceedings of the 16th Asia and South Pacific Design Automation Conference (ASP-DAC 2011) .

5. EPIC: Efficient prediction of IC manufacturing hotspots with a unified meta-classification formulation

Cited by 5 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. Feature Fusion based Hotspot Detection with R-EfficientNet;Proceedings of the Great Lakes Symposium on VLSI 2024;2024-06-12

2. Low-cost architecture performance evaluation strategy based on pixel difference degree contrast measurement;Applied Soft Computing;2024-04

3. Towards Full Forward On-Tiny-Device Learning: A Guided Search for a Randomly Initialized Neural Network;Algorithms;2024-01-05

4. Automated and Agile Design of Layout Hotspot Detector via Neural Architecture Search;2023 Design, Automation & Test in Europe Conference & Exhibition (DATE);2023-04

5. CmpCNN: CMP Modeling with Transfer Learning CNN Architecture;ACM Transactions on Design Automation of Electronic Systems;2022-10-27