Extraction of Wearout Model Parameters Using On-Line Test of an SRAM
To accurately determine the reliability of SRAMs, we propose a method to estimate the wearout parameters of FEOL TDDB using on-line data collected during operations. Errors in estimating lifetime model parameters are determined as a function of time, which are based on the available failure sample size. Systematic errors are also computed due to uncertainty in estimation of temperature and supply voltage during operations, as well as uncertainty in process parameters and use conditions.
Authors:
; ; ; ; ;
Award ID(s):
Publication Date:
NSF-PAR ID:
10205517
Journal Name:
Microelectronics reliability
Volume:
114
Page Range or eLocation-ID:
p. 113756
ISSN:
1872-941X
2. Today’s systems, rely on sending all the data to the cloud, and then use complex algorithms, such as Deep Neural Networks, which require billions of parameters and many hours to train a model. In contrast, the human brain can do much of this learning effortlessly. Hyperdimensional (HD) Computing aims to mimic the behavior of the human brain by utilizing high dimensional representations. This leads to various desirable properties that other Machine Learning (ML) algorithms lack such as: robustness to noise in the system and simple, highly parallel operations. In this paper, we propose $\mathsf {HyDREA}$ , a Hy permore »