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

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(0.7)Ga(0.3)N/i-In(0.2)Ga(0.8)N/n-Al(0.3)Ga(0.7)N) with higher Al composition in p-layer has seven times lower TJ resistance compared to the prevailing symmetric p-Al(0.3)Ga(0.7)N/i-In(0.2)Ga(0.8)N/n-Al(0.3)Ga(0.7)N TJ. This study paves a new way in III-nitride TJ design for optical and electronic devices.