Effect of weight overlap region on neuromorphic system with memristive synaptic devices

Recently, hardware-based neural network using memristive devices, so called neuromorphic system, has been extensively studied. Especially, on-chip (in situ) learning methods where training occurs inside hardware structure itself have been proposed and optimized based on memristor crossbar arrays regarding the linearity of weight-update characteristics. In this study, we analyze the effect of conductance overlap region of memristor on the recognition accuracy for on-chip learning simulation. The effect of conductance overlap region on recognition accuracy for modified national institute of standards and technology (MNIST) dataset is studied with an identical potentiation/depression pulse applied to Pt/Al₂O₃/TiO_x/Ti/Pt stacked memristor. The overlap range can be varied by different pulse amplitude, and the training characteristics of memristive neural network is significantly dependent on the weight-update overlap region.