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Superlow Power Consumption Artificial Synapses Based on WSe2 Quantum Dots Memristor for Neuromorphic Computing.
Zhongrong Wang1, Wei Wang1, Pan Liu1
1Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, China.
Research (Washington, D.C.)
|October 7, 2022
Summary
This study presents a novel memristor using tungsten diselenide (WSe2) quantum dots (QDs) for highly efficient artificial synapses. The device achieves super low power consumption and demonstrates excellent performance in neuromorphic computing tasks like digit recognition.
Area of Science:
- Materials Science: Zero-dimension transition metal dichalcogenide (TMD) quantum dots (QDs).
- Nanotechnology: Application of WSe2 QDs in advanced electronic devices.
- Condensed Matter Physics: Resistive switching phenomena and device physics.
Background:
- Tungsten diselenide (WSe2) quantum dots (QDs) are emerging materials for memristors.
- Existing WSe2 QD memristors face challenges in low power consumption and high reliability for synaptic applications.
- Memristors are crucial for developing energy-efficient neuromorphic computing systems.
Purpose of the Study:
- To develop a high-performance memristor device with super low power consumption using WSe2 QDs.
- To investigate the resistive switching characteristics and synaptic functions of the novel memristor.
- To evaluate the potential of the WSe2 QD memristor for artificial neural network applications.
Main Methods:
- Fabrication of a memristor device with the structure Ag/WSe2 QDs/La0.3Sr0.7MnO3/SrTiO3.
- Experimental characterization of resistive switching behavior, including R_OFF/R_ON ratio, switching voltages, and endurance.
- Density functional theory (DFT) calculations to verify low power consumption; simulation of synaptic functions and digit recognition using an artificial neural network.
Main Results:
- Achieved a high R_OFF/R_ON ratio (~5 × 10^3) and extremely low power consumption (~0.16 nW per switching).
- Demonstrated excellent cycling stability, reproducibility, and data retention with low set/reset voltages (~0.52 V / -0.19 V).
- Successfully simulated synaptic functions and achieved 94.05% digit recognition accuracy in a three-layer artificial neural network.
Conclusions:
- The Ag/WSe2 QDs/LSMO/STO memristor exhibits superior performance for low-power neuromorphic computing.
- WSe2 QDs offer a promising pathway for developing next-generation synaptic devices.
- This research advances the development of energy-efficient artificial intelligence hardware.
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