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Title: Equity2Vec: End-to-end Deep Learning Framework for Cross-sectional Asset Pricing
Award ID(s):
2008557 1755769 1835821
NSF-PAR ID:
10299569
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
2nd ACM International Conference on AI in Finance
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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  1. Advanced FinFET SRAMs undergo reliability degradation due to various front-end and back-end wearout mechanisms. The design of reliable SRAMs benefits from accurate wearout models that are calibrated by accelerated test. With respect to testing, the accelerated conditions which can help separate the dominant wearout mechanisms related to circuit failure is crucial for model calibration and reliability prediction. In this paper, the estimation of optimal accelerated test regions for a 14nm FinFET SRAM under various wearout mechanisms is presented. The dominant regions for specific mechanisms are compared and analyzed for effective testing. It is observed that for our SRAM example circuit only bias temperature instability (BTI) and middle-of-line time-dependent dielectric breakdown (MTDDB) have test regions where their failures can be isolated, while the other mechanisms can’t be extracted individually due to acceptable regions’ overlap. Meanwhile, the SRAM cell activity distribution has a small influence on test regions and selectivity. 
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