Spiking neural network(SNN)consisting of memristor-based artificial neurons and synapses has emerged as a compact and energy-efficient hardware solution for spatiotemporal information processing.However,it is challeng...Spiking neural network(SNN)consisting of memristor-based artificial neurons and synapses has emerged as a compact and energy-efficient hardware solution for spatiotemporal information processing.However,it is challenging to develop memristive neurons and synapses based on the same material system because the required resistive switching(RS)characteristics are different.Here,it is shown that SrFeO_(x)(SFO),an intriguing material system exhibiting topotactic phase transformation between insulating brownmillerite(BM)SrFeO_(2).5 phase and conductive perovskite(PV)SrFeO_(3) phase,can be engineered into both neuronal and synaptic devices.Using a BM-SFO single layer as the RS medium,the Au/BM-SFO/SrRuO_(3)(SRO)memristor exhibits nonvolatile RS behavior originating from the formation/rupture of PV-SFO filaments in the BM-SFO matrix.By contrast,using a PV-SFO(matrix)/BM-SFO(interfacial layer)bilayer as the RS medium,the Au/PV-SFO/BM-SFO/SRO memristor exhibits volatile RS behavior originating from the interfacial BM-PV phase transformation.Synaptic and neuronal characteristics are further demonstrated in the Au/BM-SFO/SRO and Au/PV-SFO/BM-SFO/SRO memristors,respectively.Using the SFO-based synapses and neurons,fully memristive SNNs are constructed by simulation,which show good performance on unsupervised image recognition.Our study suggests that SFO is a versatile material platform on which both neuronal and synaptic devices can be developed for constructing fully memristive SNNs.展开更多
基金The authors would like to thank the National Natural Science Foundation of China(Nos.92163210,U1932125,52172143)Science and Technology Program of Guangzhou(No.2019050001)Natural Science Foundation of Guangdong Province(No.2020A1515010996).
文摘Spiking neural network(SNN)consisting of memristor-based artificial neurons and synapses has emerged as a compact and energy-efficient hardware solution for spatiotemporal information processing.However,it is challenging to develop memristive neurons and synapses based on the same material system because the required resistive switching(RS)characteristics are different.Here,it is shown that SrFeO_(x)(SFO),an intriguing material system exhibiting topotactic phase transformation between insulating brownmillerite(BM)SrFeO_(2).5 phase and conductive perovskite(PV)SrFeO_(3) phase,can be engineered into both neuronal and synaptic devices.Using a BM-SFO single layer as the RS medium,the Au/BM-SFO/SrRuO_(3)(SRO)memristor exhibits nonvolatile RS behavior originating from the formation/rupture of PV-SFO filaments in the BM-SFO matrix.By contrast,using a PV-SFO(matrix)/BM-SFO(interfacial layer)bilayer as the RS medium,the Au/PV-SFO/BM-SFO/SRO memristor exhibits volatile RS behavior originating from the interfacial BM-PV phase transformation.Synaptic and neuronal characteristics are further demonstrated in the Au/BM-SFO/SRO and Au/PV-SFO/BM-SFO/SRO memristors,respectively.Using the SFO-based synapses and neurons,fully memristive SNNs are constructed by simulation,which show good performance on unsupervised image recognition.Our study suggests that SFO is a versatile material platform on which both neuronal and synaptic devices can be developed for constructing fully memristive SNNs.
