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Updated: Jun 12, 2025

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A Standard and Reliable Method to Fabricate Two-Dimensional Nanoelectronics
Published on: August 28, 2018
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Revolutionizing Nonvolatile Memory: Advances and Future Prospects of 2D Floating-Gate Technology
Junjie Shi1,2, Zhewei Liu1,2, Jie Wei1,2
1UNSW Materials and Manufacturing Futures Institute, University of New South Wales, Sydney, NSW 2052, Australia.
ACS Nano
|June 10, 2025
Summary
Two-dimensional (2D) materials offer a promising solution to overcome scaling limits in traditional nonvolatile floating-gate memory. This review highlights their advantages for next-generation computing and identifies manufacturing challenges for industrial adoption.
Area of Science:
- Materials Science
- Electrical Engineering
- Computer Engineering
Background:
- Traditional silicon-based nonvolatile floating-gate memory faces fundamental scaling limitations.
- Next-generation computing demands power-efficient and miniaturized memory solutions.
Purpose of the Study:
- To review the current landscape of two-dimensional (2D) material-based floating-gate memory.
- To analyze the performance advantages and manufacturing challenges of 2D floating-gate memory.
- To explore future potential for 2D materials in high-performance in-memory computing.
Main Methods:
- Comprehensive analysis of existing 2D floating-gate memory research.
- Integration of foundational principles with materials and fabrication techniques.
- Performance benchmarking of 2D devices against conventional counterparts.
Main Results:
- 2D materials demonstrate significant advantages over traditional silicon for floating-gate memory.
- Key performance metrics show the potential of 2D materials.
- Identified critical manufacturing challenges including interface engineering and scalable synthesis.
Conclusions:
- 2D floating-gate memory is crucial for overcoming current scaling limits and enabling next-generation computing.
- Addressing manufacturing challenges like wafer-scale growth and 3D integration is essential.
- Further advancements in 2D materials will drive transformative progress in nonvolatile memory and in-memory computing.
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