Modeling of CMOS transistors from 0.18 μm process by artificial neural network

Abstract

In this work, we developed a new transistor model that can be used to simulate analog circuits. Our model uses artificial neural network to capture the electrical characteristics of transistors instead of the traditional physics-driven model. By preprocessing the transistor data, high-precision modeling is achieved. N-type and p-type transistors with various widths and lengths are fabricated with the 0.18 μm analog process in the foundry. ANN modeling on these transistors shows high precision compared with the BSIM3 transistor model. The results show that the DC characteristics of the ANN model are more accurate than the BSIM3 transistor model, and have better capture on the output-resistance of the MOSFET.