Quaternary PdPS_0.55Se_0.45 for SBUV-to-SWIR Broadband Photodetection and Tri-Band In-Sensor Processing

Post-fire remote sensing for accurate damage assessment critically relies on multi-band perception spanning from solar-blind ultraviolet (SBUV) to short-wave infrared (SWIR) and effective in-sensor image pre-processing. However, most vdW photodetectors still operate over limited spectral response, which constrains unified ultraviolet-visible-infrared sensing and computing within a single device platform. Here, we develop a stable quaternary vdW semiconductor, PdPS_0.55Se_0.45, and demonstrate a single photodetector enabling broadband sensing from SBUV (266 nm) to SWIR (1550 nm) while supporting in-sensor convolutional processing for remote-sensing images. The device achieves a peak responsivity (R) of 98.13 (84.81) A W^-1 and a specific detectivity (D*) exceeding 10¹³ Jones at 266 nm (638 nm). We further exploit the intrinsic power-density-dependent responsivity to program band-specific convolution kernels, where responsivity differences under 266, 638, and 1550 nm illumination are mapped into analog multiply-accumulate weights. Coupled with a convolutional neural network (CNN), this tri-band in-sensor pre-processing enables robust post-fire target recognition on noise-corrupted remote sensing images, achieving a recognition accuracy of ∼96% for post-fire scenes. This work offers a practical route to SBUV-to-SWIR photodetector for in-sensor computing, advancing broadband perception-computation integration and creating new opportunities for remote sensing vision under complex environments.