An ANFIS-based Computation to Study the Degradation-related Ageing effects in Nanoscale GAA-TFETs

Abstract

The degradation aspects associated with Tunneling Field-Effect Transistors (TFETs) have become an active research area that needs further development. In this work, a new computation methodology based on an adaptive neuro-fuzzy inference system (ANFIS) is proposed for predicting the switching capabilities of Gate All Around (GAA) TFET devices including interface trap effects. Accordingly, several performance criteria including the current ratio (Ion/Ioff) ratio and the swing factor (S) are considered to analyze the device degradation phenomenon. ATLAS 2-D numerical simulator has been exploited for the elaboration of the training dataset of the neuro-fuzzy system. Based on the obtained outcomes, the proposed approach can provide new pathways for accurately predicting the performance of nanoelectronic circuits based on GAA-TFETs including the ageing effects.