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Conductive Bridge Random Access Memory (CBRAM): Challenges and Opportunities for Memory and Neuromorphic Computing

Haider Abbas¹, Jiayi Li¹, Diing Shenp Ang¹

¹School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore.

|May 28, 2022

Summary

Emerging conductive-bridging random-access memory (CBRAM) offers promising solutions for high-density storage and efficient computing. This review explores CBRAM principles, materials, and applications in neuromorphic computing.

Keywords:

CBRAM RRAM artificial neurons artificial synapses emerging memory technologies memristor neuromorphic computing

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Area of Science:

Materials Science
Computer Engineering
Neuroscience

Background:

The exponential growth in data necessitates advanced memory and computing solutions beyond conventional transistor limitations.
Emerging technologies are crucial to overcome scaling and technical hurdles in current storage and computing systems.
Conductive-bridging random-access memory (CBRAM) presents a significant opportunity for next-generation applications.

Purpose of the Study:

To provide an in-depth review of Conductive-Bridging Random-Access Memory (CBRAM) technology.
To explore the principles, materials, switching mechanisms, and challenges of CBRAM.
To discuss the potential of CBRAM in memory and neuromorphic computing applications.

Main Methods:

Review of existing literature on CBRAM principles and materials.
Analysis of switching mechanisms and material-related issues in CBRAM devices.
Examination of CBRAM applications in memory and brain-inspired computing.

Main Results:

CBRAM technology offers a viable path for high-density memory and efficient computing.
Various materials and device engineering approaches are being explored for CBRAM fabrication.
CBRAM devices show potential for emulating biological synapses and neurons.

Conclusions:

CBRAM technology is a key candidate for future memory and neuromorphic computing systems.
Material innovation and device engineering are critical for overcoming CBRAM challenges.
Further research into CBRAM is essential for realizing its full potential in advanced computing paradigms.