Graphene-based RRAM devices for neural computing

TL;DR

This paper explores the use of graphene and 2D materials in RRAM devices to enhance their electrical endurance, stability, and suitability for neural computing applications, addressing variability issues in traditional designs.

Contribution

It provides a comprehensive analysis of graphene-based RRAM structures, fabrication techniques, and potential applications in neural computing, highlighting improvements over conventional oxide-based RRAM.

Findings

Graphene-based RRAM shows improved endurance and stability.

Potential for high-speed, low-power neural computing applications.

Addresses variability issues in traditional RRAM devices.

Abstract

Resistive random access memory (RRAM) is very well known for its potential application in in-memory and neural computing. However, they often have different types of device-to-device and cycle-to-cycle variability. This makes it harder to build highly accurate crossbar arrays.Traditional RRAM designs make use of various filament-based oxide materials for creating a channel which is sandwiched between two electrodes to form a two-terminal structure. They are often subjected to mechanical and electrical stress over repeated read-and-write cycles. The behavior of these devices often varies in practice across wafer arrays over these stress when fabricated. The use of emerging 2D materials is explored to improve electrical endurance, long retention In review time, high switching speed, and fewer power losses. This study provides an in-depth exploration of neuro-memristive computing and its potential applications, focusing specifically on the utilization of graphene and 2D materials in resistive random-access memory (RRAM) for neural computing. The paper presents a comprehensive analysis of the structural and design aspects of graphene-based RRAM, along with a thorough examination of commercially available RRAM models and their fabrication techniques. Furthermore, the study investigates the diverse range of applications that can benefit from graphene-based RRAM devices.