Machine Learning Approaches for IC Fault Localization and Diagnosis-Reference-Cited by-同舟云学术

Machine Learning Approaches for IC Fault Localization and Diagnosis

Published:2024-04-04 Issue: Volume: Page:1-6
ISSN:
Container-title:2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM)
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Author:

Saidala Ravi Kumar¹,Dwivedi Yagya Dutta²,P Aparna³,Prasad S. J. Suji⁴,Dineshkumar S.⁵,Hemalatha R.⁶

Affiliation:

1. Alliance University,Department of Computer Science Engineering,Bangalore,Karnataka,India,560076

2. Institute of Aeronautical Engineering,Department of Aeronautical Engineering,Hyderabad,Telangana,India,500043

3. RMK College of Engineering and Technology,Department of Computer Science and Engineering,Puduvoyal,Tamil Nadu,601206

4. Kongu Engineering College,Department of Electronics and Instrumentation Engineering,Erode,Tamil Nadu,India,638060

5. M.Kumarasamy College of Engineering,Department of Electrical and Electronics Engineering,Karur,Tamil Nadu,India,639113

6. St. Joseph's College of Engineering, OMR,Department of Computer Science and Engineering,Chennai,Tamil Nadu,India,600119

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10568576/10568577/10568636.pdf?arnumber=10568636

Reference13 articles.

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2. Study of the performance signature based ternary and quaternary III- V compounds for laser diodes in optical communication system;Govindaraj;Journal of Optical Communications,2023

3. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD);Chakrabarty;IEEE XploreMachine Learning Approach to Efficient Test Data Compression for Fault Diagnosis,1995