# Advances on MXene-Based Memristors for Neuromorphic Computing: A Review on Synthesis, Mechanisms, and Future Directions

## TL;DR

This paper reviews MXene-based memristors for neuromorphic computing, focusing on their synthesis, mechanisms, and potential for brain-like computing systems.

## Contribution

The paper provides a comprehensive review of MXene-based memristors, highlighting their unique properties and potential for neuromorphic applications.

## Key findings

- MXene-based memristors show diverse neuromorphic characteristics with improved stability and low power consumption.
- Modifications to MXene properties enable tunable performance for artificial synapses.
- The paper identifies key challenges and future directions for MXene-based neuromorphic devices.

## Abstract

Neuromorphic computing
seeks to replicate the capabilities of parallel
processing, progressive learning, and inference while retaining low
power consumption by drawing inspiration from the human brain. By
further overcoming the constraints imposed by the traditional von
Neumann architecture, this innovative approach has the potential to
revolutionize modern computing systems. Memristors have emerged as
a solution to implement neuromorphic computing in hardware, with research
based on developing functional materials for resistive switching performance
enhancement. Recently, two-dimensional MXenes, a family of transition
metal carbides, nitrides, and carbonitrides, have begun to be integrated
into these devices to achieve synaptic emulation. MXene-based memristors
have already demonstrated diverse neuromorphic characteristics while
enhancing the stability and reducing power consumption. The possibility
of changing the physicochemical properties through modifications of
the surface terminations, bandgap, interlayer spacing, and oxidation
for each existing MXene makes them very promising. Here, recent advancements
in MXene synthesis, device fabrication, and characterization of MXene-based
neuromorphic artificial synapses are discussed. Then, we focus on
understanding the resistive switching mechanisms and how they connect
with theoretical and experimental data, along with the innovations
made during the fabrication process. Additionally, we provide an in-depth
review of the neuromorphic performance, making a connection with the
resistive switching mechanism, along with a compendium of each relevant
performance factor for nonvolatile and volatile applications. Finally,
we state the remaining challenges in MXene-based devices for artificial
synapses and the next steps that could be taken for future development.