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Brain Mapping Using a Graphene Electrode Array

Brain Mapping Using a Graphene Electrode Array

Published on: October 20, 2023

Graphene-based RRAM devices for neural computing.

Rajalekshmi T R¹, Rinku Rani Das¹, Chithra Reghuvaran¹

¹Digital University, Thiruvananthapuram, Kerala, India.

Frontiers in Neuroscience

|October 27, 2023

Summary

This summary is machine-generated.

Emerging 2D materials, like graphene, offer solutions to variability issues in resistive random-access memory (RRAM) for advanced neural computing. This research explores their potential to enhance device performance and enable more accurate crossbar arrays.

Keywords:

chemical vapor deposition (CVD)cryptography graphene neuromorphic computing resistive random access memory (RRAM)

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Area of Science:

Materials Science and Engineering
Electrical Engineering
Computer Science (Artificial Intelligence/Neuromorphic Computing)

Background:

Resistive random-access memory (RRAM) shows promise for in-memory and neural computing applications.
Traditional RRAM faces challenges with device-to-device and cycle-to-cycle variability, hindering the accuracy of crossbar arrays.
Existing filament-based oxide RRAM designs are susceptible to mechanical and electrical stress, leading to inconsistent performance.

Purpose of the Study:

To explore the use of emerging 2D materials, specifically graphene, to overcome variability in RRAM.
To investigate the potential of graphene-based RRAM for enhanced electrical endurance, retention time, switching speed, and reduced power consumption.
To provide an in-depth analysis of neuro-memristive computing applications utilizing graphene and 2D materials in RRAM.

Main Methods:

Comprehensive analysis of the structural and design aspects of graphene-based RRAM.
Thorough examination of commercially available RRAM models and their fabrication techniques.
Investigation into the diverse applications benefiting from graphene-based RRAM devices.

Main Results:

2D materials, particularly graphene, demonstrate potential to significantly improve RRAM performance metrics.
Graphene-based RRAM offers a pathway to mitigate device variability, enabling more reliable crossbar array construction.
The study highlights the suitability of these advanced RRAM structures for next-generation neural computing paradigms.

Conclusions:

Graphene and other 2D materials are promising candidates for next-generation RRAM, addressing key limitations of current technologies.
The integration of 2D materials in RRAM is crucial for advancing neuro-memristive computing and in-memory computing architectures.
Further research and development in graphene-based RRAM fabrication and application are warranted for realizing its full potential.