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Realize ultralow-energy-consumption photo-synaptic device based on a single(Al,Ga)N nanowire for neuromorphic computing

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Nano Research 2024年第3期17卷 1933-1941页

作者： xiushuo gu Min Zhou Yukun Zhao Qianyi Zhang Jianya Zhang Yonglin Huang Shulong Lu College of Electronic and Optical Engineering&College of Flexible Electronics(Future Technology) Nanjing University of Posts and TelecommunicationsNanjing 210023China Key Lab of Nanodevices and Applications Suzhou Institute of Nano-Tech and Nano-Bionics(SINANO)Chinese Academy of Sciences(CAS)Suzhou 215123China School of Nano-Tech and Nano-Bionics University of Science and Technology of ChinaHefei 230026China Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and TechnologySuzhou University of Science and TechnologySuzhou 215009China

The rapid development of artificial intelligence poses an urgent need for low-energy-consumption and small-sized artificial photonic ***,it is pretty novel to demonstrate a light-stimulated synaptic device based on a single(Al,Ga)N nanowire *** to the presence of vacancy defects in the single nanowire,the artificial synaptic device can simulate multiple functions of biological synapses under stimulation of both 310 and 365 nm light photons,including paired-pulse facilitation,spike timing dependent plasticity,and memory learning *** energy consumption of artificial synaptic device can be reduced as little as 5.58×10^(-13) J,which is close to that of the biological synapse in human ***,the synaptic device is demonstrated to have the high stability for both long-time stimulation and long-time *** on the experimental conductance of long-term potentiation and long-term depression,the simulated three-layer neural network can achieve a high recognition rate of 92%after only 10 training *** a brain-like behavior,the single-nanowire-based synaptic devices can promote the development of visual neuromorphic computing technology and artificial intelligence systems requiring ultralow energy consumption.

关键词： single(Al Ga)N nanowire light-stimulated synaptic device low-energy-consumption neural network

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Light-stimulated low-power artificial synapse based on a single GaN nanowire for neuromorphic computing

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Photonics Research 2023年第10期11卷 1667-1677页

作者： MIN ZHOU YUKUN ZHAO xiushuo gu QIANYI ZHANG JIANYA ZHANG MIN JIANG SHULONG LU Key Laboratory of Nanodevices and Applications Suzhou Institute of Nano-Tech and Nano-Bionics(SINANO)Chinese Academy of Sciences(CAS)Suzhou 215123China School of Nano-Tech and Nano-Bionics University of Science and Technology of ChinaHefei 230026China Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and TechnologySuzhou University of Science and TechnologySuzhou 215009China

The fast development of the brain-inspired neuromorphic computing system has ignited an urgent demand for artificial synapses with low power consumption. In this work, it is the first time a light-stimulated low-power synaptic device based on a single GaN nanowire has been demonstrated successfully. In such an artificial synaptic device, the incident light, the electrodes, and the light-generated carriers play the roles of action potential,presynaptic/postsynaptic membrane, and neurotransmitter in a biological synapse, respectively. Compared to those of other synaptic devices based on GaN materials, the energy consumption of the single-GaN-nanowire synaptic device can be reduced by more than 92%, reaching only 2.72 × 10^(-12)J. It is proposed that the oxygen element can contribute to the synaptic characteristics by taking the place of the nitrogen site. Moreover, it is found that the dynamic “learning-forgetting” performance of the artificial synapse can resemble the behavior of the human brain, where less time is required to relearn the missing information previously memorized and the memories can be strengthened after relearning. Based on the experimental conductance for long-term potentiation(LTP) and long-term depression(LTD), the simulated network can achieve a high recognition rate up to 90%after only three training epochs. Such few training times can reduce the energy consumption in the supervised learning processes substantially. Therefore, this work paves an effective way for developing single-nanowire-based synapses in the fields of artificial intelligence systems and neuromorphic computing technology requiring lowpower consumption.

关键词： power artificial computing

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