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Resistive random access memory: introduction to device mechanism, materials and application to neuromorphic
Furqan Zahoor1, Fawnizu Azmadi Hussin2, Usman Bature Isyaku3
1School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.
Discover Nano
|June 29, 2023
Summary
Resistive random-access memory (RRAM) offers a fast, energy-efficient alternative to conventional computing memory. This review details RRAM
Area of Science:
- Materials Science and Engineering
- Electrical Engineering
- Computer Science
Background:
- Conventional silicon-based complementary metal oxide semiconductor (CMOS) technologies face scaling limitations for data-intensive applications.
- Emerging memory technologies are crucial for developing faster, more energy-efficient, and durable computing systems.
- Resistive random-access memory (RRAM) is a promising candidate for next-generation computing due to its unique properties.
Purpose of the Study:
- To review the development and device engineering of Resistive Random-Access Memory (RRAM).
- To explore the resistive switching mechanisms within RRAM devices.
- To highlight the potential of two-dimensional (2D) materials in RRAM and their applications in neuromorphic computing.
Main Methods:
- Review of existing literature on RRAM technology and its advancements.
- Analysis of RRAM device structures and resistive switching mechanisms.
- Investigation of RRAM applications, particularly in neuromorphic computing and the integration of 2D materials.
Main Results:
- RRAM demonstrates potential to replace current integrated electronic devices for advanced computing.
- RRAM offers advantages such as simple structure, high speed, low power consumption, and scalability for high-density applications.
- Two-dimensional (2D) materials enhance RRAM performance due to their unique properties and structural characteristics.
Conclusions:
- RRAM is a key technology for the post-CMOS era, enabling efficient, intelligent, and secure computing systems.
- The integration of 2D materials in RRAM offers significant opportunities for device innovation.
- RRAM shows strong potential for applications in neuromorphic computing architectures.
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