Improving Nano-circuit Reliability Estimates by Using Neural Methods

Azam Beg

Towards Brain-inspired Interconnects and Circuits

Research Article

Improving Nano-circuit Reliability Estimates by Using Neural Methods

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@INPROCEEDINGS{10.1007/978-3-642-04850-0_35,
    author={Azam Beg},
    title={Improving Nano-circuit Reliability Estimates by Using Neural Methods},
    proceedings={Towards Brain-inspired Interconnects and Circuits},
    proceedings_a={TBIC},
    year={2012},
    month={5},
    keywords={Reliability estimation reliability model probability of failure nano-metric circuits neural network model},
    doi={10.1007/978-3-642-04850-0_35}
}

Azam Beg
Year: 2012
Improving Nano-circuit Reliability Estimates by Using Neural Methods
TBIC
Springer
DOI: 10.1007/978-3-642-04850-0_35

Azam Beg¹^,*

1: United Arab Emirates University

*Contact email: abeg@uaeu.ac.ae

Abstract

The reliability of nano-sized combinational circuits can be estimated by using different techniques, such as mathematical equations, Monte Carlo simulations, algorithmic approaches, and combinations of these. Commonly used equations are functions of gate count, and of the reliability and number of devices that make up the gates. The aim of this paper is to present a(n alternative) neural-based approach which is more accurate than applying simple equations, while being faster than the time-consuming Monte Carlo technique.

Keywords: Reliability estimation reliability model probability of failure nano-metric circuits neural network model

Published: 2012-05-28

: http://dx.doi.org/10.1007/978-3-642-04850-0_35