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Perception, Synaptic Plasticity, and Spiking Neuron Function Enabled by a 2D Ferroelectric NbOBr2 for Neuromorphic
Zhipeng Yu1, Zixuan Zhao2, Qingchen Han1,3
1Nanofabrication facility, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou, China.
Advanced Materials (Deerfield Beach, Fla.)
|July 11, 2026
Summary
This study introduces a novel 2D ferroelectric material, NbOBr₂, for advanced neuromorphic visual recognition. This multifunctional material enables compact, bio-inspired hardware for efficient perception and computation.
Area of Science:
- Materials Science
- Neurotechnology
- Condensed Matter Physics
Background:
- 2D ferroelectrics offer tunable polarization and anisotropic light interaction for sensing and computing.
- Current neuromorphic systems use single-function components, increasing complexity and cost.
- Multifunctional materials are needed for integrated perception-computation hardware.
Purpose of the Study:
- To develop a device-algorithm co-design for neuromorphic visual recognition using multifunctional NbOBr₂.
- To leverage the unique properties of 2D ferroelectrics for bio-inspired neuromorphic functions.
- To create compact and efficient hardware for high-performance neuromorphic vision.
Main Methods:
- Utilized NbOBr₂'s anisotropic photoresponse, ferroelectricity, and graphene integration.
- Implemented anisotropic photoelectric synaptic preprocessing for retina-like encoding.
- Demonstrated ferroelectric synaptic weight modulation and leaky integrate-and-fire (LIF) neuronal emulation.
- Developed a device-aware spiking neural network (SNN) using encoded driving scene data.
Main Results:
- Achieved 94.2% recognition accuracy on driving scenes, surpassing the SNN baseline of 91.8%.
- Successfully integrated three bio-inspired functions: synaptic preprocessing, weight modulation, and neuronal emulation.
- Demonstrated the potential of multifunctional 2D ferroelectrics in neuromorphic vision.
Conclusions:
- Multifunctional 2D ferroelectrics like NbOBr₂ can create compact, biologically plausible hardware.
- Device-algorithm co-design is effective for harnessing material properties in neuromorphic systems.
- This approach reduces system complexity and cost for advanced visual recognition.
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