(Invited) Memcomputing (Memristor + Computing) in Intrinsic SiOx-Based Resistive Switching Memory

Abstract

This study focuses on memcomputing (Memristor + Computing) in intrinsic SiOx-based resistive switching memory. Specifically, we investigate neuromorphic computing by a device with biological synaptic behaviors in integrating silicon oxide (SiOx) resistive switching memory with Si diodes. Minimal synaptic power consumption due to sneak-path current has been achieved and the capability for spike-induced synaptic behaviors has been demonstrated, representing critical milestones for the application of SiO2–based materials in future neuromorphic computing. The detailed biological synaptic behaviors such as long-term potentiation (LTP), long-term depression (LTD) and spike-timing dependent plasticity (STDP) are demonstrated systematically using a comprehensive analysis of spike-induced waveforms. We will also discuss a implementation technique of implication operations by using SiOx-based memristors in TaN/SiOx/Si. The implication function and its truth table have been implemented. The key factors for the operation of material implication, such as load resistance, characteristics of the memristor, and design tradeoffs have been investigated. Furthermore, we have demonstrated implication operation using a circuit with two SiOx-based memristor and CMOS transistor. A circuit with two one-diode and one resistor (1D-1R) memory elements and a transistor are designed to perform implication operations. A circuit consisting of a 4 × 4 crossbar structure 1D1R memristor array together with the select transistors is proposed. Based on this circuit, a total of 48 steps of operations are performed to realize the functionality of a one bit full adder. The advantages and disadvantages of the memristor enabled logic circuit are compared with CMOS logic circuits. The experimental results suggest a simple, robust approach to realize programmable memcomputing chips compatible with large-scale CMOS manufacturing technology. (* This paper is invited by ECS 2016 Honolulu Meeting.)