Monolithic 3D neuromorphic computing system with hybrid CMOS and memristor-based synapses and neurons

Abstract

Because of fabrication compatibility to current semiconductor technology, three-dimensional integrated circuits (3D-ICs) offer promising near-term solutions for maintaining Moore’s Law. 3D-ICs proffer high system speeds, massively parallel processing, low power consumption, and their high densities result in small footprints. In this paper, a novel 3D neuromorphic IC architecture which combines monolithic 3D integration and a synaptic array based on vertical resistive random-access memory structure (V-RRAM) is proposed. To analyze the electrical characteristics of the proposed synaptic array, a concise equivalent circuit model of the system is developed, and analytical calculations for each parameter of the equivalent circuit are provided. Moreover, a novel signal intensity encoding neuron design that can directly convert analog signal into a spiking waveform sequence is proposed and analyzed. A feasible 3D neuromorphic computing architecture is demonstrated. Applying the monolithic 3D integration technology on neuromorphic computing system hardware implementation can reduce the power consumption by 50%, and shrink die areas by 35%.