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

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

doi:10.3390/nano11102466

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

Nanomaterials (Basel). 2021 Sep 22;11(10):2466. doi: 10.3390/nano11102466.

Authors

Rongyu Lin¹, Peng Han², Yue Wang¹, Ronghui Lin¹, Yi Lu¹, Zhiyuan Liu¹, Xiangliang Zhang^{2

3}, Xiaohang Li¹

Affiliations

¹ Advanced Semiconductor Laboratory, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia.
² Laboratory Machine, Intelligence and kNowledge Engineering (MINE), King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia.
³ Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA.

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-Al_0.7Ga_0.3N/i-In_0.2Ga_0.8N/n-Al_0.3Ga_0.7N) with higher Al composition in p-layer has seven times lower TJ resistance compared to the prevailing symmetric p-Al_0.3Ga_0.7N/i-In_0.2Ga_0.8N/n-Al_0.3Ga_0.7N TJ. This study paves a new way in III-nitride TJ design for optical and electronic devices.

Keywords: III-nitride; machine learning; tunnel junction.

Abstract

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