Comparative Study on III-V MOSFET and Si-MOSFET Model Parameters based on BP Neural Networks Algorithm

Abstract

MOSFET is the basic unit of modern integrated circuit, and main basic characteristics are: current-voltage (IV) characteristics, capacitor-voltage (CV) characteristics and source-drain contact characteristics. The introduction of group III-V semi-conductor, high k- metal date and SBSD allows MOSFET device characteristic device may keep downsizing based on Moore’s Law. However, as II-V MOSFET devices keep downsizing, the short-channel effect and quantum effect are more obvious so that it is more complicate to calculate and extract characteristic parameters of III-V MOSFET. This paper proposes a kind of III-V MOSFET characteristic parameter modeling method based on BP neutral networks algorithm. Compared to other semi-empirical models, this method needs not to calculate characteristic parameters of devices. In stead, it calculates current and voltage output characteristics and transfer characteristics of devices through BP neutral network models according to test data. Through verification, the trained and predicted output relative error is within 5%. The model has short operation time, high calculation precision and good stability. The models established may be applied extensively to other types of transistor, and feasible for practical engineering application.