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. This study demonstrates an alternative manner for the implementation of artificial synapses in which the local stoichiometry of metal oxide materials is delicately manipulated to form a single nanoscale conductive filament that may be used as a synaptic gap building block in an equivalent manner to the functionality of a single connexon (a signaling pore between synapses) with dynamic rectification direction. The structure, of a few nanometers in size, is based on the formation of defect states and shows current rectification properties that can be consecutively flipped to a forward or reverse direction to create either an excitatory or inhibitory (positive or negative) weight parameter. Alternatively, a plurality of these artificial connexons may be used to create a synthetic rectifying synaptic gap junction. In addition, the junction plasticity may be altered in a differential digital scheme (opposed to conventional analog RRAM conductivity manipulation) by changing the ratio of forward to reverse rectifying connexons.