Vmin Shift Prediction Using Machine Learning-Based Methodology for Automotive Products-Reference-Cited by-同舟云学术

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Vmin Shift Prediction Using Machine Learning-Based Methodology for Automotive Products

Published:2024-04-14 Issue: Volume: Page:3C.1-1-3C.1-5
ISSN:
Container-title:2024 IEEE International Reliability Physics Symposium (IRPS)
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Author:

Yang Y.L.¹,Tsao P.C.¹,Lin C.W.¹,Chen H.Q.¹,Huang B.J.²,Hsieh Hank³,Chen Kerwin³,Lee Ross⁴,Koh Khim⁴,Ting Y.J.⁴,Hsu B.C.¹,Huang Y.S.¹,Lai Citi⁴,Lee M.Z.¹,Lee T.H.¹

Affiliation:

1. MediaTek Inc.,Product Engineering,Hsinchu,Taiwan

2. MediaTek Inc.,High Performance Computing,Hsinchu,Taiwan

3. MediaTek Inc.,Quality & Reliability,Hsinchu,Taiwan

4. MediaTek Inc.,AI & Data Engineering,Hsinchu,Taiwan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10529283/10529298/10529430.pdf?arnumber=10529430

Reference10 articles.

1. Fast chip aging prediction by product-like VMIN drift characterization on test structures

2. In-Product BTI Aging Sensor for Reliability Screening and Early Detection of Material at Risk

3. NBTI/PBTI separated BTI monitor with 4.2x sensitivity by standard cell based unbalanced ring oscillator

4. Performing Machine Learning Based Outlier Detection for Automotive Grade Products

5. Degradation Monitoring –– from a Vision to Reality

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