Versatile SrFeO for memristive neurons and synapses

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.