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Article

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

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
*
Authors to whom correspondence should be addressed.
Academic Editors: Vladimir G. Dubrovskii and Vladimir S. Bystrov
Nanomaterials 2021, 11(10), 2466; https://doi.org/10.3390/nano11102466
Received: 7 August 2021 / Revised: 9 September 2021 / Accepted: 13 September 2021 / Published: 22 September 2021
(This article belongs to the Special Issue Simulation and Modeling of Nanomaterials)
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. View Full-Text
Keywords: tunnel junction; machine learning; III-nitride tunnel junction; machine learning; III-nitride
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MDPI and ACS Style

Lin, R.; Han, P.; Wang, Y.; Lin, R.; Lu, Y.; Liu, Z.; Zhang, X.; Li, X. Low Resistance Asymmetric III-Nitride Tunnel Junctions Designed by Machine Learning. Nanomaterials 2021, 11, 2466. https://doi.org/10.3390/nano11102466

AMA Style

Lin R, Han P, Wang Y, Lin R, Lu Y, Liu Z, Zhang X, Li X. Low Resistance Asymmetric III-Nitride Tunnel Junctions Designed by Machine Learning. Nanomaterials. 2021; 11(10):2466. https://doi.org/10.3390/nano11102466

Chicago/Turabian Style

Lin, Rongyu, Peng Han, Yue Wang, Ronghui Lin, Yi Lu, Zhiyuan Liu, Xiangliang Zhang, and Xiaohang Li. 2021. "Low Resistance Asymmetric III-Nitride Tunnel Junctions Designed by Machine Learning" Nanomaterials 11, no. 10: 2466. https://doi.org/10.3390/nano11102466

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