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

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

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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.

Original language	English (US)
Pages (from-to)	2466
Journal	Nanomaterials
Volume	11
Issue number	10
DOIs	https://doi.org/10.3390/nano11102466
State	Published - Sep 22 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-09-29
Acknowledged KAUST grant number(s): BAS/1/1664-01-01, KAUST AI Initiative, REP/1/3189-01-01, URF/1/3437-01-01, URF/1/3771-01-01
Acknowledgements: This 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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10.3390/nano11102466

https://repository.kaust.edu.sa/bitstream/10754/672017/1/nanomaterials-11-02466.pdf

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@article{493c9e2522b84810a1558c18232553d1,

title = "Low Resistance Asymmetric III-Nitride Tunnel Junctions Designed by Machine Learning",

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.",

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

note = "KAUST Repository Item: Exported on 2021-09-29 Acknowledged KAUST grant number(s): BAS/1/1664-01-01, KAUST AI Initiative, REP/1/3189-01-01, URF/1/3437-01-01, URF/1/3771-01-01 Acknowledgements: This 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.",

year = "2021",

month = sep,

day = "22",

doi = "10.3390/nano11102466",

language = "English (US)",

volume = "11",

pages = "2466",

journal = "Nanomaterials",

issn = "2079-4991",

publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",

number = "10",

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TY - JOUR

T1 - Low Resistance Asymmetric III-Nitride Tunnel Junctions Designed by Machine Learning

AU - Lin, Rongyu

AU - Han, Peng

AU - Wang, Yue

AU - Lin, Ronghui

AU - Lu, Yi

AU - Liu, Zhiyuan

AU - Zhang, Xiangliang

AU - Li, Xiaohang

N1 - KAUST Repository Item: Exported on 2021-09-29 Acknowledged KAUST grant number(s): BAS/1/1664-01-01, KAUST AI Initiative, REP/1/3189-01-01, URF/1/3437-01-01, URF/1/3771-01-01 Acknowledgements: This 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.

PY - 2021/9/22

Y1 - 2021/9/22

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/672017

UR - https://www.mdpi.com/2079-4991/11/10/2466

U2 - 10.3390/nano11102466

DO - 10.3390/nano11102466

M3 - Article

C2 - 34684907

SN - 2079-4991

VL - 11

SP - 2466

JO - Nanomaterials

JF - Nanomaterials

IS - 10

ER -

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

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