Nanoscale Conductive Filament with Alternating Rectification as an Artificial Synapse Building Block.

A popular approach for resistive memory (RRAM)-based hardware implementation of neural networks utilizes one (or two) device that functions as an analog synapse in a crossbar structure of perpendicular pre- and postsynaptic neurons. An ideal fully automated, large-scale artificial neural network, which matches a biologic counterpart (in terms of density and energy consumption), thus requires nanosized, extremely low power devices with a wide dynamic range and multilevel functionality. Unfortunately the trade-off between these traits proves to be a serious obstacle in the realization of brain-inspired computing platforms yet to be overcome.