Machine Learning-Based Hotspot Detection: Fallacies, Pitfalls and Marching Orders-Reference-Cited by-同舟云学术

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Machine Learning-Based Hotspot Detection: Fallacies, Pitfalls and Marching Orders

Published:2019-11 Issue: Volume: Page:
ISSN:
Container-title:2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)
language:
Short-container-title:

Author:

Reddy Gaurav Rajavendra¹,Madkour Kareem²,Makris Yiorgos¹

Affiliation:

1. The University of Texas,Department of Electrical and Computer Engineering,Dallas,USA

2. MentorA Siemens Business,Egypt

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8931666/8942037/08942128.pdf?arnumber=8942128

Cited by 25 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. 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

3. Hierarchical Attention Module-Based Hotspot Detection in Wafer Fabrication Using Convolutional Neural Network Model;IEEE Access;2024

4. Analysis and Characterization of Defects in FeFETs;2023 IEEE International Test Conference (ITC);2023-10-07

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

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