Circuits may fail in the field due to a wide variety of failure modes. If there are frequent failures in the field, circuits are returned to the manufacturer, and the causes of failure must be identified. The challenge is that wearout mechanisms are confounded in circuit and system-level failure data. Using such failure data, it is often hard to separate the underlying failure causes without time-consuming and expensive physical failure analysis. To distinguish the wearout mechanisms for each failure sample, we have developed a quick and low-cost methodology using maximum likelihood estimation and probability analysis to determine the origin of the failure distributions, region of error, and sorting accuracy. We apply our methodology to analyze the competing wearout mechanisms in 14nm FinFET ring oscillators, as an example, using simulation. We also consider the problem of Trojan detection.
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Machine Learning for Detection of Competing Wearout Mechanisms
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
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- Award ID(s):
- 1700914
- NSF-PAR ID:
- 10104497
- Date Published:
- Journal Name:
- IEEE International Reliability Physics Symposium
- Page Range / eLocation ID:
- 1 to 9
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IRPS.2019.8720533
