Scalable 2D Spectral-Spatial Associated Vision Sensor for Multidimensional Feature Fusion

The perception of multidimensional information (e.g., spatial, temporal, and spectral domains) plays a vital role in fields like remote sensing that require high optical resolution and precision. The current approach typically relies on hyperspectral imaging, a band-by-band image acquisition mode with subsequent feature learning and fusion through post-processing algorithms. Such an asynchronous workflow introduces substantial data redundancy, transmission latency, and high energy consumption, limiting its practical deployment. Here, we propose a novel spectral-spatial associated vision sensor that enables the synchronous acquisition and feature fusion of spectral and spatial information at the hardware level. Specifically, scalable highly oriented 2D Bi₂Te₃ thin films with broadband response are employed for the fabrication of highly uniform device arrays, thus achieving simultaneous capture of spectral-spatial information. The arrays perform enhanced synaptic behavior under multi-wavelength stimuli, with a maximum enhanced ratio of more than 20, facilitating feature discriminability and recognition efficiency. By leveraging such a synergistic enhancement characteristic, an increased recognition accuracy of 91.12% is achieved for topography recognition on the Indian Pines dataset. These findings demonstrate that the proposed vision sensor streamlines hardware-level data acquisition while improving processing efficiency, thereby establishing a new paradigm for multidimensional information fusion, particularly in scenarios with massive data streams.