Modeling of the Degradation of Resonant-Tunneling Diodes Using Artificial Neural Networks

Devices based on nanoscale nonlinear elements with the transverse transport of charge carriers are widely used in various modern radio-electronic systems. However, at present, neither a time-optimal nor an accuracy-optimal approach has been developed for predicting the operating parameters with regard to the time factor and external influence of such devices. In this paper, we propose a variant of solving the problem of modeling the degradation of a nanoelectronic device with the transverse transport of carriers on the basis of a feed-forward artificial neural network. The use of neural-network approaches in the simulation of resonant-tunneling diodes can significantly (by several orders) increase the speed of such models. Training of the developed artificial neural network by data obtained from the reliability tests of low-dimensional semiconductor heterostructure diodes allows an increase in the model accuracy by several times by taking into account the influence of technological errors (occurring during manufacturing) and various degradation changes (occurring with time and under the influence of external factors during the operation of resonant-tunneling diodes).