  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.
