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 submicron 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.