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Updated: Jun 25, 2025
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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
Published on: March 9, 2019
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Programmable Retention Characteristics in MoS2-Based Atomristors for Neuromorphic and Reservoir Computing Systems
Yoonseok Lee1,2, Yifu Huang2, Yao-Feng Chang3
1Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Seoul 04620, Korea.
ACS Nano
|May 20, 2024
Summary
This study shows a novel 2D atomristor device exhibits both short- and long-term memory, enabling advanced neuromorphic computing and artificial intelligence applications through programmable retention.
Area of Science:
- Materials Science
- Neuroscience
- Computer Science
Background:
- Neuromorphic systems require memory components mimicking biological synapses.
- Atomristor devices offer potential for compact and efficient synaptic emulation.
Purpose of the Study:
- Investigate short- and long-term memory effects in a 2D Au/MoS2/Au atomristor.
- Determine the impact of these memory effects on synaptic properties for neuromorphic applications.
Main Methods:
- Fabrication of a bilayer MoS2 crossbar atomristor device.
- Programmable multilevel retention tests to validate memory characteristics.
- Implementation and testing of various synaptic plasticity behaviors.
Main Results:
- Demonstrated coexistence of short- and long-term memory effects.
- Validated synaptic properties including EPSC, PPF, and plasticity.
- Emulated synaptic learning, forgetting, and reservoir computing.
Conclusions:
- The 2D atomristor device shows significant potential for neuromorphic systems.
- Programmable retention properties are key for AI-based computing applications.
- The device can function as a physical reservoir for reservoir computing.
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