TY - GEN
T1 - Machine Learning for Detection of Competing Wearout Mechanisms
AU - Hsu, Shu Han
AU - Yang, Kexin
AU - Milor, Linda
N1 - Funding Information:
ACKNOWLEDGMENTS The authors would like to acknowledge Dr. Yi-da Wu and Li-Hsiang Lin for their discussions. The authors would like to thank the NSF for support under Award Number 1700914.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/5/22
Y1 - 2019/5/22
N2 - Because data from a variety of wearout mechanisms is confounded in circuits, we apply machine learning techniques to detect the parameters of competing failure mechanisms in ring oscillators, which more closely mimic circuit behavior than test structures. This is the first known application using data analysis to distinguish competing wearout mechanisms in circuit-level data. To quickly and efficiently analyze failure data, we propose to use maximum likelihood estimation to separately determine the parameters of each underlying distribution by only observing the time-to-failure of samples. The quasi-Newton method is used to update and optimize the parameter extraction.
AB - Because data from a variety of wearout mechanisms is confounded in circuits, we apply machine learning techniques to detect the parameters of competing failure mechanisms in ring oscillators, which more closely mimic circuit behavior than test structures. This is the first known application using data analysis to distinguish competing wearout mechanisms in circuit-level data. To quickly and efficiently analyze failure data, we propose to use maximum likelihood estimation to separately determine the parameters of each underlying distribution by only observing the time-to-failure of samples. The quasi-Newton method is used to update and optimize the parameter extraction.
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U2 - 10.1109/IRPS.2019.8720533
DO - 10.1109/IRPS.2019.8720533
M3 - Conference contribution
AN - SCOPUS:85066735848
T3 - IEEE International Reliability Physics Symposium Proceedings
BT - 2019 IEEE International Reliability Physics Symposium, IRPS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Reliability Physics Symposium, IRPS 2019
Y2 - 31 March 2019 through 4 April 2019
ER -