On fault probabilities and yield models for VLSI neural networks

We investigate the estimation of fault probabilities and yield for very large scale integration (VLSI) implementations of neural computational models. Our analysis is limited to structures that can be mapped directly onto silicon as truly distributed parallel processing systems. Our work improves on the framework suggested by Feltham and Maly and is also applicable to analog or mixed analog/digital VLSI systems.