Low Resistance Asymmetric III-Nitride Tunnel Junctions Designed by Machine Learning
KAUST DepartmentAdvanced Semiconductor Laboratory
Computer Science Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Electrical and Computer Engineering
Electrical and Computer Engineering Program
Laboratory Machine, Intelligence and kNowledge Engineering (MINE), King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia.
Machine Intelligence & kNowledge Engineering Lab
KAUST Grant NumberBAS/1/1664-01-01
KAUST AI Initiative
Permanent link to this recordhttp://hdl.handle.net/10754/672017
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AbstractThe 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.
CitationLin, R., Han, P., Wang, Y., Lin, R., Lu, Y., Liu, Z., … Li, X. (2021). Low Resistance Asymmetric III-Nitride Tunnel Junctions Designed by Machine Learning. Nanomaterials, 11(10), 2466. doi:10.3390/nano11102466
SponsorsThis research was funded by KAUST Baseline Fund BAS/1/1664-01-01, GCC Research Council Grant REP/1/3189-01-01, Competitive Research Grants URF/1/3437-01-01 and URF/1/3771-01-01, and KAUST AI Initiative.
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