A Random Forest Model for Predicting and Analyzing the Performance of CNT TFET with Highly Doped Pockets

Abstract

This paper presents a Random Forest (RF) machine learning model that relates the DC characteristics and high‐frequency response of a carbon nanotube (CNT) tunnel field‐effect transistor (TFET) with highly doped pockets to the transistor parameters. The analysis of multiple factors for a complex structure as the one studied here becomes expensive with the ordinary simulation techniques and hence machine learning (ML) offers a proficient method to model and enhance the understanding of the key factors that influence the CNT TFET with pockets in considerably reduced time. Numerical simulations are used to generate the data on which the model is trained. This dataset comprises ten input features and four output attributes. The tuned model is capable of predicting the output characteristics of the device with minimal mean squared error (MSE). The RF model is also compared to other ML algorithms to demonstrate its advantage.