A Modular Approach for Reliable Nanoelectronic and Very-Deep Submicron Circuit Design Based on Analog Neural Network Principles

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