In this work, nanoscale wedge-structured silicon nitride (SiNx)-based resistive-switching random-access memory with data non-volatility and conductance graduality has been designed, fabricated, and characterized for its application in the hardware neuromorphic system. The process integration with full Si-processing-compatibility for constructing the unique wedge structure by which the electrostatic effects in the synaptic device operations are maximized is demonstrated. The learning behaviors of the fabricated synaptic devices are shown. In the end, vector-matrix multiplication is experimentally verified in the array level for application in more energy-efficient hardware-driven neuromorphic systems.
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