Colloidal quantum dot (CQD)–based photodetectors are promising alternatives to bulk semiconductor-based detectors to be monolithically integrated with complementary metal-oxide semiconductor readout integrated circuits avoiding high-cost epitaxial growth methods and complicated flip-bonding processes. To date, photovoltaic (PV) single-pixel detectors have led to the best performance with background-limit infrared photodetection performance. However, the nonuniform and uncontrollable doping methods and complex device configuration restrict the focal plane array (FPA) imagers to operate in PV mode. Here, we propose a controllable in situ electric field–activated doping method to construct lateral p-n junctions in the short-wave infrared (SWIR) mercury telluride (HgTe) CQD–based photodetectors with a simple planar configuration. The planar p-n junction FPA imagers with 640 × 512 pixels (15-μm pixel pitch) are fabricated and exhibit substantially improved performance compared with photoconductor imagers before activation. High-resolution SWIR infrared imaging is demonstrated with great potential for various applications including semiconductor inspection, food safety, and chemical analysis.

Infrared detection technology plays an important role in remote sensing, imaging, monitoring, and other fields. So far, most infrared photodetectors are based on InGaAs and HgCdTe materials, which are limited by high fabrication costs, complex production processes, and poor compatibility with silicon-based readout integrated circuits. This hinders the wider application of infrared detection technology. Therefore, reducing the cost of high-performance photodetectors is a research focus. Colloidal quantum dot photodetectors have the advantages of solution processing, low cost, and good compatibility with silicon-based substrates. In this paper, we summarize the recent development of infrared photodetectors based on mainstream lead chalcogenide colloidal quantum dots.

In-memory sensing and computing devices integrate the functionalities of sensors, memory, and processors, offering advantages such as low power consumption, high bandwidth, and zero latency, making them particularly suitable for simulating synaptic behavior in biological neural networks. As the pace of digital transformation accelerates, the demand for efficient information processing technologies is increasing, and in-memory sensing and computing devices show great potential in AI, machine learning, and edge computing. In recent years, with the continuous advancement of infrared detector technology, infrared in-memory sensing and computing devices have also seen new opportunities for development. This article reviews the latest research progress in infrared in-memory sensing and computing devices. It first introduces the working principles and performance metrics of in-memory sensing and computing devices, then discusses in detail transistors and memristor-type devices with infrared band response, and finally looks forward to the development prospects of the field. Through innovation in new semiconductor materials and structures, the development trajectory of infrared in-memory sensing and computing devices has been significantly expanded, providing new impetus for the development of a new generation of information technology.

Memristors, as transformative electronic devices designed to transcend the von Neumann architecture, enable the physical unification of information storage and computation, thereby offering a foundational hardware pathway toward energy-efficient, brain-inspired computing. Their intrinsic analog resistive switching, non-volatility, and history-dependent learning capabilities allow them to natively implement in-memory computing and emulate synaptic plasticity, addressing the critical bottlenecks of energy and speed in conventional systems. Notably, the evolution from electrically controlled memristors to optoelectronic memristors marks a paradigm shift from pure computing to integrated sensing-processing, opening new dimensions for high-speed, parallel, and adaptive signal processing. In recent years, significant progress has been made in the development of memristor-based neuromorphic vision and tactile systems, on-chip signal processors, and dynamic trajectory trackers, demonstrating their potential in edge intelligence, adaptive robotics, and real-time perceptual tasks. This review systematically summarizes the latest advances in memristor technology, providing a comprehensive analysis of their operating mechanisms, material and structural innovations, and cutting-edge applications in neuromorphic perception and computing. Furthermore, it discusses the key challenges and future directions for the development and integration of memristor-based systems in the post-von Neumann era.

Flexible upconversion (UC) devices, owing to their unique combination of high–efficiency optical energy conversion and mechanical flexibility, have attracted increasing attention in the fields of optoelectronics, wearable devices, flexible displays, and biomedical applications. However, significant challenges remain in balancing optical performance, mechanical adaptability, long–term stability, and scalable fabrication, which limit their practical deployment. This review systematically introduces five representative upconversion mechanisms—excited–state absorption (ESA), energy transfer upconversion (ETU), energy migration upconversion (EMU), triplet–triplet annihilation upconversion (TTA–UC), and photon avalanche (PA)—highlighting their energy conversion principles, performance characteristics, and applicable scenarios. The article further delves into the flexible transition of upconversion devices, detailing not only the evolution of the luminescent layer from bulk crystals and nanoparticles to polymer composites and hybrid systems, but also the optimization of electrodes from rigid metal films to metal grids, carbon–based materials, and stretchable polymers. These developments significantly enhance the stability and reliability of flexible upconversion devices under bending, stretching, and complex mechanical deformation. Finally, emerging research directions are outlined, including multi–mechanism synergistic design, precise nanostructure engineering, interface optimization, and the construction of high–performance composite systems, emphasizing the broad potential of flexible UC devices in flexible displays, wearable health monitoring, solar energy harvesting, flexible optical communications, and biomedical photonic applications. This work provides critical insights for the design and application of high–performance flexible optoelectronic devices.