Analog memristive devices based on La2NiO4+ δ as synapses for spiking neural networks

Abstract

Neuromorphic computing has recently emerged as a potential alternative to the conventional von Neumann computer paradigm, which is inherently limited due to its architectural bottleneck. Thus, new artificial components and architectures for brain-inspired computing hardware implementation are required. Bipolar analog memristive devices, whose resistance (or conductance) can be continuously tuned (as a synaptic weight), are potential candidates for artificial synapse applications. In this work, lanthanum nickelate (La2NiO4+δ , L2NO4), a mixed ionic electronic conducting oxide, is used in combination with TiN and Pt electrodes. The TiN/L2NO4/Pt devices show bipolar resistive switching with gradual transitions both for the SET and RESET processes. The resistance (conductance) can be gradually modulated by the pulse amplitude and duration, showing good data retention characteristics. A linear relationship between the resistance change and total applied pulse duration is experimentally measured. Moreover, synaptic depression and potentiation characteristics, one of the important functions of bio-synapses, are artificially reproduced for these devices, then modeled and successfully tested in a spiking neural network environment. These results indicate the suitability of using TiN/L2NO4/Pt memristive devices as long-term artificial synapses in neuromorphic computing.