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Updated: Aug 9, 2026
08:07
Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
Published on: March 9, 2019
Robust Nonvolatile Memory and Synaptic Emulation via Dual-Mode Modulation in ReS2/h-BN/Ta2NiSe5 Floating-Gate Device
Yuhang Jia1, Zunfa Wang1, Dong Li1
1Center of Low Dimensional Materials and Intelligent Devices, Key Laboratory of Applied Surface and Colloid Chemistry of Ministry of Education, Shaanxi Provincial Basic Discipline (Surface and Interface Chemistry) Research Center, Shaanxi Key Laboratory for Advanced Energy Devices, School of Materials Science and Engineering, Shaanxi Normal University, Xi'an, People's Republic of China.
Small (Weinheim an Der Bergstrasse, Germany)
|August 8, 2026
Summary
This study presents a novel 2D heterostructure memory device for neuromorphic computing. It demonstrates efficient optical and electrical control for advanced AI applications, overcoming key data processing challenges.
Area of Science:
- Materials Science
- Nanotechnology
- Computer Engineering
Background:
- The von Neumann bottleneck hinders AI performance in data-intensive applications.
- Developing nonvolatile storage with dual electrical and optical modulation in 2D materials is challenging.
Purpose of the Study:
- To demonstrate a high-performance floating-gate memory using a ReS2/h-BN/Ta2NiSe5 van der Waals heterostructure.
- To explore photo-electrical cooperative modulation for advanced memory functions.
- To validate the device's potential in neuromorphic computing and AI.
Main Methods:
- Fabrication of a van der Waals heterostructure device.
- Characterization of nonvolatile memory properties under photo-electrical modulation.
- Evaluation of synaptic plasticity and artificial neural network performance.
Main Results:
- Achieved a large memory window (96.2 V), long retention (>10^4 s), and high endurance (>1000 cycles).
- Demonstrated multi-bit storage via optical erasure and electrical writing.
- Successfully mimicked biological synaptic plasticity and achieved 96.43% MNIST recognition accuracy.
Conclusions:
- The ReS2/h-BN/Ta2NiSe5 heterostructure offers a promising platform for integrated sensing, storage, and computation.
- This device advances the development of efficient neuromorphic computing systems.
- The findings pave the way for next-generation 2D material-based AI applications.
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