Polarization-sensitive neuromorphic vision sensing enabled by pristine black arsenic-phosphorus

Abstract Polarization-sensitive neuromorphic vision sensing excels in distinguishing light polarization states, offering intrinsic advantages in reducing glare and enhancing visual clarity in complex lighting environments, enabling advanced applications in autonomous driving, optical communication, and bioinspired imaging across the visible-to-infrared spectrum. Here, we present a polarization-sensitive neuromorphic phototransistor based on a high-quality, intrinsically anisotropic two-dimensional black arsenic-phosphorus nanosheet, which exhibits exceptional optoelectronic performance with a peak responsivity of 2.88 A W -1 , a polarization ratio of 4.7 and a dynamic range of 40 dB within the near-infrared communication band. Through multidimensional input control, including polarization and gate voltage, the phototransistor successfully simulates synaptic behaviors analogous to human neural responses to visual stimuli, with paired-pulse facilitation values reaching 201%. Critically, the device demonstrates gate-tunable short-term plasticity, with optical persistence triggering stable long-term plasticity states that underpin memory consolidation. The neuromorphic properties enable the development of a hybrid optical-electronic neural network which achieves a classification accuracy of over 90% on the Fashion-MNIST dataset and a reconstruction accuracy of 71.38% using data from the Yale Face Database under 0º linear polarization. We demonstrate a polarization-resolved imaging approach utilizing the black arsenic-phosphorus phototransistor to reconstruct hidden targets with high fidelity through Stokes parameter extraction and degree of linear polarization mapping, revealing intricate polarization features invisible to conventional imaging systems. Our work establishes a foundational platform for high-performance neuromorphic vision systems with integrated polarization imaging, computation, and communication functionalities, addressing critical challenges in scalable brain-inspired optoelectronic technologies.