Demonstrates improved recognition accuracy in neuromorphic visual systems using Mf NbOBr2.
ABSTRACT 2D ferroelectrics integrate electrically tunable polarization dynamics with anisotropic light–matter interaction, providing a unified platform for co‐localized sensing and in‐memory computing. However, most neuromorphic visual systems still rely on heterogeneous components that exploit only a single material functionality at a time, thereby increasing system complexity and fabrication costs. Here, we demonstrate a device–algorithm co‐design for neuromorphic visual recognition based on multifunctional NbOBr 2 . By leveraging its anisotropic broadband photoresponse, robust in‐plane ferroelectricity, and integration with multilayer graphene, we realize three bio‐inspired core functions: anisotropic photoelectric synaptic preprocessing for retina‐like encoding, ferroelectric synaptic weight modulation for in‐memory weighted operations, and leaky integrate‐and‐fire (LIF) neuronal emulation for spike generation. With BDD100K driving scenes converted to drivable maps and encoded into two orthogonal polarization channels (c‐ and b‐axis; 0°/90°), a device‐aware spiking neural network achieves a recognition accuracy of up to 94.2%, outperforming a standard SNN baseline (91.8%). These results illustrate how the intrinsic multifunctionality of a 2D ferroelectric can be harnessed to create compact, biologically plausible perception–computation hardware for high‐performance neuromorphic vision.
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Yu et al. (2026) studied this question.
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