Dandan Hao

Achieving self-powered dual-mode optoelectronic devices that integrate fast photodetection with optoelectronic synaptic functionality remains challenging because signal sensing and memory rely on different response dynamics. Here, a self-powered wavelength-selective bipolar dual-mode optoelectronic device based on a SnO2/CsBi3I10–IR820 heterojunction is fabricated. At zero bias, the device exhibits a positive and rapid photodetection response under 650 nm illumination, with a responsivity of 0.18 A/W and a specific detectivity of 1.2 × 1013 Jones. Under 820 nm illumination, the photocurrent polarity reverses, and the device mimics optoelectronic synaptic behaviors, including short-term and long-term plasticity. The wavelength-selective bipolar dual-mode operation is due to the competition between the built-in photovoltaic effect in the SnO2/CsBi3I10–IR820 heterojunction and the photothermoelectric effect introduced by IR820. These results provide a simple strategy for self-powered multifunctional optoelectronic devices with integrated sensing and memory functions.

Conventional frame-based vision systems inevitably generate large amounts of redundant data owing to the continuous capture of absolute light intensity. Here, we present a bioinspired vision system that converts dynamic self-powered photocurrents into sparse event-driven spikes using MoS₂-based Schottky photodiodes with asymmetric electrodes integrated with a dual-branch differential circuit. These self-powered devices exhibit a high rectification ratio (>10⁵), fast response time (<200 μs), and ultralow dark current (<5 pA). This photon-to-spike conversion pathway enables bioinspired bidirectional temporal encoding with positive and negative spikes, matching biological synaptic time scales. When directly interfaced with spiking neural networks, the system tracks only intensity changes of dynamic targets with no response to the static background. This significantly reduces computational overhead while maintaining 93.3% accuracy in dynamic gesture recognition. This work establishes a hardware foundation for direct photon-to-spike conversion at the sensor level, enabling ultralow-power neuromorphic vision systems for real-time edge applications.

Photoelectric synaptic transistors (PSTs) based on metal–oxide semiconductors are endowed with advantages such as stability and scalability. However, they encounter difficulties in the modulation under visible light. In this study, a visible light-driven PST was proposed, in which the indium gallium zinc oxide was employed as the channel and gallium oxide was utilized as the modification layer. All layers of the PST were fabricated using the solution-processed method. The PST device exhibits persistent photoconductivity under illumination at a wavelength of 532 nm, which enables the synaptic plasticity behaviors such as excitatory postsynaptic current, paired-pulse facilitation, and the transition from short-term memory to long-term memory. Moreover, the role of the Ga2O3 modification layer was investigated, and the formation of oxygen vacancies is effectively enhanced due to the modification layer. Both the short-term and long-term plasticity of the PST are significantly enhanced, and its cyclic stability is also ensured. An artificial neural network was constructed based on the long-term plasticity of the PST, and 93.1% accuracy is achieved in image recognition tasks. The development of oxide-based PSTs is promoted, and its broad potential in artificial vision technologies is highlighted.

The development of photoelectric synaptic transistors (PSTs) using visible light-driven mimicking synaptic behaviors represents a key advancement toward biomimetic visual systems. This study proposes a PST based on bilayer indium-gallium-zinc-oxide (IGZO) homojunctions with tunable gallium ratios. By optimizing the gallium content, oxygen vacancy concentrations in the channel were precisely controlled, suppressing deionization processes and enhancing device performance. The IGZO homojunction PST demonstrated outstanding electrical characteristics (Ion/Ioff = 1.2 × 107, μ = 3.88 cm2/Vs, Vth = 0 V) and exhibited high photocurrent and robust persistent photoconductivity under visible light. The device mimicked various synaptic behaviors, including excitatory postsynaptic current, paired-pulse facilitation, the transition from short-term plasticity to long-term plasticity, spiking-rate-dependent plasticity, and spike-timing-dependent plasticity. Furthermore, leveraging the potentiation and depression behaviors of the IGZO homojunction PST, a triple-layer neural network achieved 96.8% accuracy in pattern recognition tasks. These results underscore the IGZO homojunction PST's immense potential for advancing artificial vision systems.

Humidity sensors are widely utilized in meteorological research, industrial production, precision instrument maintenance, agriculture, health care, and other fields. However, the long response time and low sensitivity of current metal oxide and hybrid humidity sensors limit their practical applications. In this study, a humidity sensor was prepared using a simple drop-casting method with 2-hydroxy-2-methylpropiophenone (HOMPP) and 1-vinyl-3-butylimidazolium bromide (C₉H₁₅BrN₂) as the humidity sensing materials. This approach offers advantages such as low cost, high chemisorption capacity, and excellent moisture-sensitive performance. The prepared humidity sensors demonstrate high sensitivity, good repeatability, excellent flexibility, low hysteresis, and response/recovery times of 6/12.5 s, respectively, over a wide relative humidity (RH) range (2–97%). Additionally, the sensor exhibits potential for various multifunctional applications, including humidity detection in daily life, respiratory monitoring, non-contact sensing, and flexible electronics applications.

The review examines the recent progress in gas sensors with different perovskite structures and offers suggestions for future research.

Massive data processing with high computing efficiency and low operating power is required owing to the rapid development of artificial intelligence and information technology. However, the von Neumann structure computing system with the separated memory and processor can cause large energy consumption and a low running speed during massive data processing. Therefore, the brain‐inspired neuromorphic computing system is developed, that can provide hardware support for emulating biological synaptic functions and realizing highly intensive data processing with low power consumption. As a neuromorphic device, the optoelectronic synaptic device (OSD) is regarded as an ideal device to replace the von Neumann‐based computer because of its ultrafast signal transmission, large bandwidth, low energy consumption, and wireless communication. Owing to their unique optoelectronic property, metal halide perovskites (MHPs) have received growing attention as effective photosensitive materials in OSDs. Therefore, the review introduces the recent progress on OSDs based on MHPs (MHPs‐OSDs) including the structures and properties of MHPs, and the architectures and performance characteristics of MHPs‐OSDs. Furthermore, applications of MHPs‐OSDs are presented. Finally, the outlook and opportunity of MHPs‐OSDs are discussed.