High-performance green micro-LED array with isolated n-GaN layers for 6.58 Gbps high-speed visible light communication

Z Zhen Zhang Z Zuxin Jin (AI for Science Institute 1 , Beijing 100080,) Y Yuandong Ruan (College of Intelligent Robotics and Advanced Manufacturing, Fudan University , Shanghai 200438,) R Runze Lin T Tianyang Ren (College of Intelligent Robotics and Advanced Manufacturing, Fudan University , Shanghai 200438,) E Erdan Gu X Xugao Cui (College of Intelligent Robotics and Advanced Manufacturing, Fudan University , Shanghai 200438,) P Pengfei Tian

Abstract

GaN-based micro-light-emitting diodes (micro-LEDs) have great advantages in visible light communication (VLC). However, the limited external quantum efficiency (EQE) and the modulation bandwidth of green micro-LEDs have been the obstacles of improvement in the data rates of the VLC system. In this work, a straightforward and efficacious approach is employed to improve the VLC performance of the micro-LEDs, that is, etching epitaxial layers to a sapphire substrate using deep-etching processes to fabricate each micro-LED electrically isolated arrays. Compared to micro-LEDs with a common n-type GaN layer, the deep-etched micro-LEDs exhibit enhanced light output power from the sapphire side and increased −3 dB bandwidth, owing to the reduced n-GaN waveguiding effect and parasitic capacitance, resulting in 47.03% and 57.33% improvements in EQE and −3 dB bandwidth at 4 kA/cm2, respectively. As a result, the deep-etched green micro-LED based VLC system achieved a maximum data rate of 6.58 Gbps by employing an orthogonal frequency division multiplexing modulation scheme and a pre-equalization method, which represents the highest data rate for a single green micro-LED.

Article Details

Volume / Issue Vol. 138, Issue 14
Published October 14, 2025
ISSN 0021-8979
Publisher American Institute of Physics

Journal Info

Journal of Applied Physics

American Institute of Physics

ISSN: 0021-8979 Physical Sciences

Authors (8)

Z

Zhen Zhang

Z

Zuxin Jin

AI for Science Institute 1 , Beijing 100080,

Y

Yuandong Ruan

College of Intelligent Robotics and Advanced Manufacturing, Fudan University , Shanghai 200438,

R

Runze Lin

T

Tianyang Ren

College of Intelligent Robotics and Advanced Manufacturing, Fudan University , Shanghai 200438,

E

Erdan Gu

X

Xugao Cui

College of Intelligent Robotics and Advanced Manufacturing, Fudan University , Shanghai 200438,

P

Pengfei Tian