Recent Advances in Synaptic Photodetectors based on Colloidal Semiconductor Nanocrystals: Materials, Devices, Mechanisms, and Applications

Recent advances in machine vision require efficient acquisition and processing of large‐scale image data. Conventional machine vision systems, however, remain constrained by the physical isolation of image sensors and processors, which requires extensive data transfer and increases both power consumption and latency. Synaptic photodetectors address this limitation by integrating photodetection with history‐dependent conductance modulation, thereby enabling image preprocessing at the sensor level. Colloidal semiconductor nanocrystals (CSNs) are particularly attractive for these devices because they combine broad spectral tunability with controllable synaptic behavior, all of which can be tailored through dimensions, compositions, and surface chemistry of CSNs. In this review, we summarize recent advances in CSN‐based synaptic photodetectors, including inorganic semiconductor quantum dots, metal halide perovskite nanoparticles, and metal oxide nanoparticles. We first discuss their material‐design principles, focusing on compositions, dimensions, crystal structures, and colloidal synthesis. We then review device engineering strategies, including solution‐processed fabrication, device architecture, and performance metrics. Next, we examine the mechanisms underlying synaptic plasticity, focusing on carrier trapping associated with intrinsic defects and heterojunction interfaces. Finally, we highlight applications in neuromorphic perception, in‐sensor computing, and optoelectronic logic processing, and conclude this review by discussing challenges and outlook.