Arrays of Nanocomposite Crossbar Memristors for the Implementation of Formal and Spiking Neuromorphic Systems

Abstract

Memristors are promising devices for use as synapses in hardware-based artificial formal and spiking neural networks. Their use can increase the energy efficiency of such networks when solving various cognitive tasks. At the same time, for the implementation of hardware-based neural networks, a large number of rather densely packed structures are required, which can be achieved by creating memristive matrices in a crossbar architecture. Crossbar arrays of nanocomposite memristors on silicon substrates with protection against edge effects are developed and manufactured, their resistive switching is investigated, a formal neural network is created on their basis, which is capable of recognizing simple patterns after loading a pretrained weight map, and the possibility of precise adjustment of the conductivity of the crossbar memristors by various means applicable in the creation of more complex spiking and formal neural networks is demonstrated.