Tellurium Sub‐Oxides Infrared Phototransistors for Adaptive Super‐Resolution Image Reconstruction

Abstract

ABSTRACT Infrared (IR) detection using crystalline silicon or III‐V compounds is commonly utilized but often challenged by bulkiness and inefficiency. With the development of autonomous driving and machine vision, there is a growing need for IR technology to incorporate compact neural architectures. In this study, IR‐sensitive p ‐type disordered tellurium sub‐oxides (TeO x ) thin films are deposited via an inorganic blending strategy. By integrating a luminescent dielectric layer, synergistic charge transfer and photon‐induced secondary excitation endow TeO x ‐based IR‐visible adaptive sensors (IVAS) with broadband detection and memory capabilities. The IR‐driven modulation of IVAS convolutional weights enables super‐resolution image reconstruction even under suboptimal conditions. This IVAS‐based system achieves a peak signal‐to‐noise ratio of 27.55 dB (compared to 26.85 dB conventionally), a structural similarity index measure of 0.94 (compared to 0.88 conventionally), and a 13.8% reduction in mean absolute error. These findings highlight TeO x ‐based IVAS as a robust and adaptive solution for IR machine vision systems.