Artificial neural network design for compact modeling of generic transistors
(J Comput Electron; pp. 1-8; 2017)
Lining Zhang and Mansun Chan
Department of ECE, Hong Kong University of Science and
Technology, Kowloon, Hong Kong
Technology, Kowloon, Hong Kong
Abstract: A methodology to develop artificial neural network (ANN) models to quickly incorporate the characteristics of emerging devices for circuit simulation is described in this work. To improve the model accuracy, a current and voltage data preprocessing scheme is proposed to derive a minimum dataset to train the ANN model with sufficient accuracy. To select a proper network size, four guidelines are developed from the principles of two-layer network. With that, a reference ANN size is proposed as a generic three-terminal transistor model. The ANN model formulated using the proposed approach has been verified by physical device data. Both the device and circuit-level tests show that the ANN model can reproduce and predict various device and circuits with high accuracy [read more...]
(Published online April 9, 2017 http://dx.doi.org/10.1007/s10825-017-0984-9)
