InGaN/GaN multi-quantum well LED array for short-distance optical links

Abstract

Visible-light communication is increasingly being regarded as a pivotal complementary paradigm for emerging wireless infrastructures, while optical interconnects are widely acknowledged as a disruptive enabler of energy-efficient, ultra-dense data-center architectures. Based on this idea, we proposed an InGaN/GaN LED array for high-speed, short-distance optical links. By utilizing InGaN/GaN multi-quantum-well (MQW) devices and matrix electrode injection structures, the LED array (10 × 10) was fabricated for multi-channel communication using an on–off keying modulation scheme. The electroluminescence (EL), modulation bandwidth, and signal transmission properties of individual LEDs were thoroughly characterized. Experimental results demonstrated that the individual unit was about 200 × 200 μm in size, and it showed a green emission peak near 530 nm, achieving a −3 dB bandwidth in the region of 5.3 MHz to 13.7 MHz with currents in the region of 10 to 70 mA. The data rate remained stable at 50 Mbps with a bit error rate below 2 × 10−5 and can reach a limiting data rate exceeding 100 Mbps for drive currents over 80 mA. We also confirmed that these values are high enough to support video and audio transmissions. Furthermore, as the LED array was designed in a row-common-ground configuration with individually addressable columns, the total data rate in a multiple-input multiple-output (MIMO) mode theoretically exceeded 500 Mbps. Our study reveals the promising potential of compact LED arrays for integrated optical links, with significant room remaining for improvement, particularly in reducing power consumption.

Graphical abstract: InGaN/GaN multi-quantum well LED array for short-distance optical links

Supplementary files

Article information

Article type
Paper
Submitted
30 Oct 2025
Accepted
04 Feb 2026
First published
06 Feb 2026

Nanoscale, 2026, Advance Article

InGaN/GaN multi-quantum well LED array for short-distance optical links

X. Lu, X. Gao, X. Wang, Y. Chen, X. Li, F. Shi, F. Liu, X. Wang, F. Qin and Y. Wang, Nanoscale, 2026, Advance Article , DOI: 10.1039/D5NR04586J

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