GaN Optoelectronic Integrated Chip with Multifunctions of Communication and Neuromorphic Computing

Abstract The ultimate neuromorphic chip based on light‐stimulated artificial synapses requires suitable materials and platforms for optoelectronic integration. Herein, a GaN optoelectronic integrated chip with multifunctions of communication, sensing, and neuromorphic computing is proposed and fabricated on a GaN‐on‐Si light‐emitting diode (LED) epitaxial wafer. The monolithically integrated chip consists of an InGaN/GaN multiple‐quantum‐well LED and a GaN‐based optoelectronic synaptic metal‐oxide‐semiconductor field‐effect transistor connected in series. It can achieve multi‐mode switching, including self‐powered photodetector (PD), light‐emitting synapse, and gate‐voltage‐controlled light transmitter. For the PD mode, a great responsivity of 4393 A W −1 and a high specific detectivity of 1.56 × 10 14 Jones are demonstrated. Notably, due to varying absorption of short wavelength light by different epitaxial layers, the integrated device shows differentiated wavelength selectivity for light illuminated from the front and back sides. When the integrated device is operating in synapse mode, excitatory postsynaptic currents induced by light stimuli can be read both optically and electrically with the assistance of built‐in optical‐to‐electrical‐to‐optical conversion. Moreover, direct bias‐voltage modulation and gate‐voltage modulation are both configured into this integrated device, achieving 60 and 5 Mbps modulation rates for visible light communication applications, respectively.

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Publication Details

Journal
Advanced Optical Materials
Published
2025-03-22
DOI
https://doi.org/10.1002/adom.202403514
Citations
4
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
9.92

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GaN Optoelectronic Integrated Chip with Multifunctions of Communication and Neuromorphic Computing

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4 citations
Advanced Optical Materials
Neural Networks and Reservoir Computing
9.92
article

GaN Optoelectronic Integrated Chip with Multifunctions of Communication and Neuromorphic Computing

Zhihang Sun, Yuhuai Liu, Jiabin Yan, Fan Shi, Hoi Wai Choi, Zheng Shi, Yongjin Wang
article en
4 citations

Abstract

Abstract The ultimate neuromorphic chip based on light‐stimulated artificial synapses requires suitable materials and platforms for optoelectronic integration. Herein, a GaN optoelectronic integrated chip with multifunctions of communication, sensing, and neuromorphic computing is proposed and fabricated on a GaN‐on‐Si light‐emitting diode (LED) epitaxial wafer. The monolithically integrated chip consists of an InGaN/GaN multiple‐quantum‐well LED and a GaN‐based optoelectronic synaptic metal‐oxide‐semiconductor field‐effect transistor connected in series. It can achieve multi‐mode switching, including self‐powered photodetector (PD), light‐emitting synapse, and gate‐voltage‐controlled light transmitter. For the PD mode, a great responsivity of 4393 A W −1 and a high specific detectivity of 1.56 × 10 14 Jones are demonstrated. Notably, due to varying absorption of short wavelength light by different epitaxial layers, the integrated device shows differentiated wavelength selectivity for light illuminated from the front and back sides. When the integrated device is operating in synapse mode, excitatory postsynaptic currents induced by light stimuli can be read both optically and electrically with the assistance of built‐in optical‐to‐electrical‐to‐optical conversion. Moreover, direct bias‐voltage modulation and gate‐voltage modulation are both configured into this integrated device, achieving 60 and 5 Mbps modulation rates for visible light communication applications, respectively.

Advanced Optical MaterialsVol. 13(18)
Zhengzhou University (CN), Nanjing University of Posts and Telecommunications (CN), University of Hong Kong (HK)
National Natural Science Foundation of China, Natural Science Foundation of Jiangsu Province, National Key Research and Development Program of China
Openalex Percentile: Top 2%
Neural Networks and Reservoir Computing
9.92
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