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A Modular Approach for Reliable Nanoelectronic and Very-Deep Submicron Circuit Design Based on Analog Neural Network Principles
BibTeX
@MISC{_amodular,
author = {},
title = {A Modular Approach for Reliable Nanoelectronic and Very-Deep Submicron Circuit Design Based on Analog Neural Network Principles},
year = {}
}
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Abstract
Abstract — Reliability of nanodevices is expected to be a central issue with the advent of very-deep submicon devices and future single-electron transistors. We propose a new approach based on the assumption that a number of circuit-level devices are to be expected to fail. Artificial neural networks can be trained to resists to errors and be used for synthesizing fault-tolerant Boolean functions. The development method is outlined; results based on the feed-forward artificial neural network implementation are presented, while future research directions are discussed with possible applications. Fault-tolerance; robust very-deep submicron design; artificial neural networks I.
Keyphrases
analog neural network principle very-deep submicron circuit design reliable nanoelectronic modular approach artificial neural network feed-forward artificial neural network implementation robust very-deep submicron design development method fault-tolerant boolean function central issue new approach circuit-level device abstract reliability very-deep submicon device possible application future single-electron transistor future research direction
