Machine Learning-Based Aging Analysis-Reference-Cited by-同舟云学术

Machine Learning-Based Aging Analysis

Published:2019 Issue: Volume: Page:265-289
ISSN:
Container-title:Machine Learning in VLSI Computer-Aided Design
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Author:

Vijayan Arunkumar,Chakrabarty Krishnendu,Tahoori Mehdi B.

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-04666-8_9

Reference46 articles.

1. M. Agarwal, B.C. Paul, M. Zhang, S. Mitra, Circuit failure prediction and its application to transistor aging, in IEEE VLSI Test Symposium (2007), pp. 277–286

2. J. Benesty, J. Chen, Y. Huang, I. Cohen, Pearson correlation coefficient, in Noise Reduction in Speech Processing (Springer, Berlin, 2009), pp. 1–4

3. S. Borkar, Designing reliable systems from unreliable components: the challenges of transistor variability and degradation. IEEE Micro 25(6), 10–16 (2005)

4. K. Bowman et al., Circuit techniques for dynamic variation tolerance, in ACM/IEEE Design Automation Conference (2009), pp. 4–7

5. T.-B. Chan, J. Sartori, P. Gupta, R. Kumar, On the efficacy of NBTI mitigation techniques, in Design, Automation and Test in Europe Conference and Exhibition (IEEE, Piscataway, 2011), pp. 1–6

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