4.6 Article

MXene-Based Memristor for Artificial Optoelectronic Neuron

期刊

IEEE TRANSACTIONS ON ELECTRON DEVICES
卷 70, 期 3, 页码 1359-1365

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TED.2023.3234881

关键词

MXene; memristor; optoelectronic neuron

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With high efficiency and low energy consumption, bio-inspired artificial neuromorphic systems have attracted tremendous attention. In this work, an artificial optoelectronic neural device based on an Ag/MXene/SiO2/Si structure is demonstrated. By introducing an oxidized MXene (O-MXene) layer, photoelectric integration can be realized on a single device, which efficiently accelerates the firing behavior of neurons. Additionally, a 64 x 64 sensing array based on optoelectronic neurons is successfully designed and demonstrated for signal recognition and sharpening.
With high efficiency and low energy consumption, bio-inspired artificial neuromorphic systems are regarded as the next generation of computing methods and have attracted tremendous attention in recent years. In bio-inspired artificial neuromorphic systems, multi-functional artificial neurons function as processing units to process complex information with high efficiency. However, the majority of reports on artificial neurons are based on electrical stimulation, whereas light-simulated neurons receive less attention. In this work, an artificial optoelectronic neural device based on an Ag/MXene/SiO2 /Si structure is demonstrated. By introducing an oxidized MXene (O-MXene) layer, photoelectric integration can be realized on a single device. Compared to pure electrical stimulation, the synergistic effect of light and electrical stimulation can efficiently accelerate the firing behavior of neurons. In addition, we successfully designed and demonstrated a 64 x 64 sensing array based on optoelectronic neurons to recognize and sharpen the input signal trajectory. The proposed artificial optoelectronic neural device shows great potential for optoelectronic neuromorphic systems and is expected to promote the development of multifunctional, high-performance neuromorphic systems.

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