Low Resistance Asymmetric III-Nitride Tunnel Junctions Designed by Machine Learning

Yue Wang,Ronghui Lin,Rongyu Lin,Peng Han,Xiangliang Zhang,Xiaohang Li,Zhiyuan Liu,Yi Lu

doi:10.3390/nano11102466

Yue Wang, Ronghui Lin + Show 6 more

Open Access

https://doi.org/10.3390/nano11102466

Copy DOI

Abstract

The tunnel junction (TJ) is a crucial structure for numerous III-nitride devices. A fundamental challenge for TJ design is to minimize the TJ resistance at high current densities. In this work, we propose the asymmetric p-AlGaN/i-InGaN/n-AlGaN TJ structure for the first time. P-AlGaN/i-InGaN/n-AlGaN TJs were simulated with different Al or In compositions and different InGaN layer thicknesses using TCAD (Technology Computer-Aided Design) software. Trained by these data, we constructed a highly efficient model for TJ resistance prediction using machine learning. The model constructs a tool for real-time prediction of the TJ resistance, and the resistances for 22,254 different TJ structures were predicted. Based on our TJ predictions, the asymmetric TJ structure (p-Al0.7Ga0.3N/i-In0.2Ga0.8N/n-Al0.3Ga0.7N) with higher Al composition in p-layer has seven times lower TJ resistance compared to the prevailing symmetric p-Al0.3Ga0.7N/i-In0.2Ga0.8N/n-Al0.3Ga0.7N TJ. This study paves a new way in III-nitride TJ design for optical and electronic devices.

Highlights

We investigated the configurations with relatively low resistance; and we discovered that the asymmetric tunnel junction (TJ) design such as pAl0.7 Ga0.3 N/i-In0.2 Ga0.8 N/n-Al0.3 Ga0.7 N with different Al compositions in the p-type and n-type layers, could lead to considerably lower TJ resistance compared with conventional symmetric p-Al0.3 Ga0.7 N/i-In0.2 Ga0.8 N/n-Al0.3 Ga0.7 N design
Our ML model is based on the XG-Boost algoapproximation rithm and trained with the data calculated by TCAD simulations, which rapidly predicted s
We developed an efficient model for III-nitride TJ resistance prediction to instruct the TJ device design

Summary

Introduction

The use of tunnel junction (TJ) is crucial for many advanced III-nitride electronic and optical devices, including tunnel field-effect transistors (TFETs), light-emitting diodes (LEDs), and solar cells [1,2,3]. The interplay of material compositions, polarization effects, and the thicknesses of each layer in the TJ structures provide enormous design parameter space for TJ designs, which increases the difficulties associated with TJ optimization. The machine-learning (ML) technique has demonstrated its significant effectiveness to these TCAD convergence issues including for III-nitride LED and nanophotonics designs [15,16,17,18]. To exclude the outliers caused by the convergence issues, all the configurations were calculated twice with two different iterations and cross-validated by the results.

Machine Learning Model for TJ Resistance Prediction

Higher Al

Summary