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Anisotropic Optoelectronic Synapses in 2D Nb2GeTe4 for Direction-Programmable Neuromorphic Perception and
Tianle Zeng1,2, Zishen Zhao3, Kun Ye4
1Center for High Pressure Science (CHiPS), State Key Laboratory of Metastable Materials Science and Technology, Yanshan University, Qinhuangdao, 066004, China.
Advanced Materials (Deerfield Beach, Fla.)
|September 4, 2025
Summary
Researchers developed novel 2D Nb2GeTe4 synaptic devices with directional plasticity. These devices enable efficient, intelligent sensing for neuromorphic computing and adaptive AI applications.
Area of Science:
- Materials Science
- Neuroscience
- Computer Engineering
Background:
- Neuromorphic computing addresses the von Neumann bottleneck for energy-efficient intelligent systems.
- Two-dimensional (2D) materials offer potential for bioinspired neuromorphic devices.
- Challenges remain in achieving multifunctional synaptic operations with simple configurations and linear weight updates in 2D materials.
Purpose of the Study:
- To exploit the in-plane anisotropy of 2D Nb2GeTe4 for developing dual electronic-optical synaptic devices.
- To achieve directional synaptic plasticity and multimodal sensing capabilities.
- To advance 2D material-based neuroelectronics for edge computing and artificial intelligence.
Main Methods:
- Utilized the in-plane anisotropy of 2D Nb2GeTe4.
- Developed dual electronic-optical synaptic devices.
- Investigated anisotropic hole mobilities and wavelength-dependent photoresponse.
Main Results:
- Demonstrated anisotropic hole mobilities (137.97 cm^2 V^-1s^-1 along a-axis, 78.29 cm^2V^-1s^-1 along b-axis).
- Achieved directional synaptic plasticity under electrical-optical co-stimulation with high accuracy in adaptive image processing (98.3% along a-axis, 88.3% along b-axis).
- Showcased a machine vision system (89.6% object recognition) and intelligent vehicle navigation (90.2% decision-making).
Conclusions:
- The integration of anisotropic transport and spectrally tunable responses in Nb2GeTe4 enables compact neuromorphic hardware.
- Paves the way for multimodal sensing and parallel processing capabilities in neuroelectronic devices.
- Advances 2D material-based neuroelectronics for edge computing, autonomous robotics, and adaptive AI systems.
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