Machine-learning-assisted rational design of 2D doped tellurene for fin field-effect transistor devices.

Abstract

Fin field-effect transistors (FinFETs) have been widely used in electronic devices on account of their excellent performance, but this new type of device is facing many challenges because of size constraints. Two-dimensional (2D) materials with a layer structure can meet the required thickness of FinFETs and provide ideal carrier transport performance. In this work, we used 2D tellurene as the parent material and modified it with doping techniques to improve electronic device performance. High-performance FinFET devices were prepared with 23 systems screened from 385 doping systems by a combination of first-principle calculations and a machine-learning (ML) model. Moreover, theoretical calculations demonstrated that 1S1@Te and 2S2@Te have high carrier mobility and stability with an electron mobility and a hole mobility of 6.211 × 10⁴ cm² V^-1 S^-1 and 1.349 × 10⁴ cm² V^-1 S^-1, respectively. This work can provide a reference for subsequent experiments and advance the development of functional materials by using an ML-assisted design paradigm.