An analytical approach based on neural computation to estimate the lifetime of deep submicron MOSFETs

Abstract

The VLSI-component industry requires more financial investment than ever in order to measure the growing sophistication of the manufactured products and for the equipment necessary to their development. So, the modelling of electric components constitutes a research field that is currently very important throughout the world. To continue this evolution, the existing models must be improved and new models have to be developed. Hence, we regularly see improvements of simulation software. In this paper, we present the applicability of artificial neural networks for the development of an analytical approach allowing the evaluation of the time degradation at deep submicron level of MOSFETs devices. This approach can be implemented in electronics simulators (SPICE, PSPICE, CADENCE, ...). Our results are compared with the experimental ones, analysed and discussed in order to draw some useful information and decisive conclusions about the VLSI technology.