In the era of accelerated development in artificial intelligence as well as explosive growth of information and data throughput,underlying hardware devices that can integrate perception and memory while simultaneously...In the era of accelerated development in artificial intelligence as well as explosive growth of information and data throughput,underlying hardware devices that can integrate perception and memory while simultaneously offering the bene-fits of low power consumption and high transmission rates are particularly valuable.Neuromorphic devices inspired by the human brain are considered to be one of the most promising successors to the efficient in-sensory process.In this paper,a homojunction-based multi-functional optoelectronic synapse(MFOS)is proposed and testified.It enables a series of basic electri-cal synaptic plasticity,including paired-pulse facilitation/depression(PPF/PPD)and long-term promotion/depression(LTP/LTD).In addition,the synaptic behaviors induced by electrical signals could be instead achieved through optical signals,where its sen-sitivity to optical frequency allows the MFOS to simulate high-pass filtering applications in situ and the perception capability integrated into memory endows it with the information acquisition and processing functions as a visual system.Meanwhile,the MFOS exhibits its performances of associative learning and logic gates following the illumination with two different wave-lengths.As a result,the proposed MFOS offers a solution for the realization of intelligent visual system and bionic electronic eye,and will provide more diverse application scenarios for future neuromorphic computing.展开更多
Traditional transistors confront severe challenges of insufficient computing capability and excessive power consumption in large-scale neuromorphic systems.To address these critical bottlenecks,we propose an optoelect...Traditional transistors confront severe challenges of insufficient computing capability and excessive power consumption in large-scale neuromorphic systems.To address these critical bottlenecks,we propose an optoelectronic memristor based on zinc oxide-indium tin oxide/tungsten oxide(ZnO-ITO/WO_(3-x))heterojunctions as a promising solution.Through applying different types of electrical and optical signals,the device successfully emulates diverse synaptic functions including short-term/long-term synaptic plasticity,alongside short-term and long-term memory.Introducing the ZnO-ITO functional layer enhances the photoresponse of the WO_(3-x)-based memristor and demonstrates“learning-forgetting-relearning”behavior under optical modulation.Furthermore,based on the photoelectric cooperative memristor array,a convolutional neural network for vehicle type recognition is constructed,which solves the problem of zero weight and negative weight complexity.In regard to energy efficiency,the neural network built with this device operates at a power level of only 10^(-3)W,representing a reduction of more than 4 orders of magnitude compared with a standard central processor.Hence,the photoelectric memristor proposed in this work provides a new idea for neuromorphic computing and is expected to promote the development of energy-efficient brain-like computing.展开更多
基金supported by the National Natural Science Foundation of China under Grant(62174068,61625404).
文摘In the era of accelerated development in artificial intelligence as well as explosive growth of information and data throughput,underlying hardware devices that can integrate perception and memory while simultaneously offering the bene-fits of low power consumption and high transmission rates are particularly valuable.Neuromorphic devices inspired by the human brain are considered to be one of the most promising successors to the efficient in-sensory process.In this paper,a homojunction-based multi-functional optoelectronic synapse(MFOS)is proposed and testified.It enables a series of basic electri-cal synaptic plasticity,including paired-pulse facilitation/depression(PPF/PPD)and long-term promotion/depression(LTP/LTD).In addition,the synaptic behaviors induced by electrical signals could be instead achieved through optical signals,where its sen-sitivity to optical frequency allows the MFOS to simulate high-pass filtering applications in situ and the perception capability integrated into memory endows it with the information acquisition and processing functions as a visual system.Meanwhile,the MFOS exhibits its performances of associative learning and logic gates following the illumination with two different wave-lengths.As a result,the proposed MFOS offers a solution for the realization of intelligent visual system and bionic electronic eye,and will provide more diverse application scenarios for future neuromorphic computing.
基金supported by the National Natural Science Foundation of China(62174068,62311540155,62174068,and 61804063)Jinan City-University Integrated Development Strategy Project(JNSX2023017)+2 种基金Taishan Scholars Project Special Funds(tsqn202312035)the National Key Research and Development Program of China(2019YFA0705900)funded by MOSTthe Natural Science Foundation of Jilin Province(20220201070GX)。
文摘Traditional transistors confront severe challenges of insufficient computing capability and excessive power consumption in large-scale neuromorphic systems.To address these critical bottlenecks,we propose an optoelectronic memristor based on zinc oxide-indium tin oxide/tungsten oxide(ZnO-ITO/WO_(3-x))heterojunctions as a promising solution.Through applying different types of electrical and optical signals,the device successfully emulates diverse synaptic functions including short-term/long-term synaptic plasticity,alongside short-term and long-term memory.Introducing the ZnO-ITO functional layer enhances the photoresponse of the WO_(3-x)-based memristor and demonstrates“learning-forgetting-relearning”behavior under optical modulation.Furthermore,based on the photoelectric cooperative memristor array,a convolutional neural network for vehicle type recognition is constructed,which solves the problem of zero weight and negative weight complexity.In regard to energy efficiency,the neural network built with this device operates at a power level of only 10^(-3)W,representing a reduction of more than 4 orders of magnitude compared with a standard central processor.Hence,the photoelectric memristor proposed in this work provides a new idea for neuromorphic computing and is expected to promote the development of energy-efficient brain-like computing.
