The rapid growth of artificial intelligence has accelerated data generation, which increasingly exposes the limitations faced by traditional computational architectures, particularly in terms of energy consumption and data latency. In contrast, data-centric computing that integrates processing and storage has the potential of reducing latency and energy usage. Organic optoelectronic synaptic transistors have emerged as one type of promising devices to implement the data-centric computing paradigm owing to their superiority of flexibility, low cost, and large-area fabrication. However, sophisticated functions including vector-matrix multiplication that a single device can achieve are limited. Thus, the fabrication and utilization of organic optoelectronic synaptic transistor arrays (OOSTAs) are imperative. Here, we summarize the recent advances in OOSTAs. Various strategies for manufacturing OOSTAs are introduced, including coating and casting, physical vapor deposition, printing, and photolithography. Furthermore, innovative applications of the OOSTA system integration are discussed, including neuromorphic visual systems and neuromorphic computing systems. At last, challenges and future perspectives of utilizing OOSTAs in real-world applications are discussed.

To address the increasing demand for massive data storage and processing, brain-inspired neuromorphic computing systems based on artificial synaptic devices have been actively developed in recent years. Among the various materials investigated for the fabrication of synaptic devices, silicon carbide (SiC) has emerged as a preferred choices due to its high electron mobility, superior thermal conductivity, and excellent thermal stability, which exhibits promising potential for neuromorphic applications in harsh environments. In this review, the recent progress in SiC-based synaptic devices is summarized. Firstly, an in-depth discussion is conducted regarding the categories, working mechanisms, and structural designs of these devices. Subsequently, several application scenarios for SiC-based synaptic devices are presented. Finally, a few perspectives and directions for their future development are outlined.

Photonic neural networks have garnered significant attention in recent years due to their ultra-high computational speed, broad bandwidth, and parallel processing capabilities. However, compared to conventional electronic nonlinear activation function (NAF), progress on efficient and easily implementable optical nonlinear activation function (ONAF) was barely reported. To address this issue, we proposed a programmable, low-loss ONAF device based on a silicon micro-ring resonator capped with the Antimony selenide (Sb₂Se₃) thin films, and with indium tin oxide (ITO) used as the microheater. Leveraging our self-developed phase-transformation kinetic and optical models, we successfully simulated the phase-transition behavior of Sb₂Se₃ and three different ONAFs—ELU, ReLU, and radial basis function (RBF) were achieved according to discernible optical responses of proposed devices under different phase-change extents. Classification results from the Fashion MNIST dataset demonstrated that these ONAFs can be considered as appropriate substitutes for traditional NAF. This indicated the bright prospect of the proposed device for nonlinear activation function in future photonic neural networks.

High-quality AlN epitaxial layers with low dislocation densities and uniform crystal quality are essential for next-generation optoelectronic and power devices. This study reports the epitaxial growth of 6-inch AlN films on 17 nm AlN/sapphire templates using metal−organic chemical vapor deposition (MOCVD). Comprehensive characterization reveals significant advancements in crystal quality and uniformity. Atomic force microscopy (AFM) shows progressive surface roughness reduction during early growth stages, achieving stabilization at a root mean square (RMS) roughness of 0.216 nm within 3 min, confirming successful 2D growth mode. X-ray rocking curve (XRC) analysis indicates a marked reduction in the (0002) reflection full width at half maximum (FWHM), from 445 to 96 arcsec, evidencing effective dislocation annihilation. Transmission electron microscopy (TEM) demonstrates the elimination of edge dislocations near the AlN template interface. Stress analysis highlights the role of a highly compressive 17 nm AlN template (5.11 GPa) in facilitating threading dislocation bending and annihilation, yielding a final dislocation density of ~1.5 × 10⁷ cm⁻². Raman spectroscopy and XRC mapping confirm excellent uniformity of stress and crystal quality across the wafer. These findings demonstrate the feasibility of this method for producing high-quality, large-area, atomically flat AlN films, advancing applications in optoelectronics and power electronics.

The traditional von Neumann architecture faces inherent limitations due to the separation of memory and computation, leading to high energy consumption, significant latency, and reduced operational efficiency. Neuromorphic computing, inspired by the architecture of the human brain, offers a promising alternative by integrating memory and computational functions, enabling parallel, high-speed, and energy-efficient information processing. Among various neuromorphic technologies, ion-modulated optoelectronic devices have garnered attention due to their excellent ionic tunability and the availability of multidimensional control strategies. This review provides a comprehensive overview of recent progress in ion-modulation optoelectronic neuromorphic devices. It elucidates the key mechanisms underlying ionic modulation of light fields, including ion migration dynamics and capture and release of charge through ions. Furthermore, the synthesis of active materials and the properties of these devices are analyzed in detail. The review also highlights the application of ion-modulation optoelectronic devices in artificial vision systems, neuromorphic computing, and other bionic fields. Finally, the existing challenges and future directions for the development of optoelectronic neuromorphic devices are discussed, providing critical insights for advancing this promising field.

In the present work, the high uniform 6-inch single-crystalline AlN template is successfully achieved by high temperature annealing technique, which opens up the path towards industrial application in power device. Moreover, the outstanding crystalline-quality is confirmed by Rutherford backscattering spectrometry (RBS). In accompanied with the results from X-ray diffraction, the RBS results along both [0001] and $ [1\bar{2}13] $ reveal that the in-plane lattice is effectively reordered by high temperature annealing. In addition, the constant Φ_epi angle between [0001] and $ [1\bar{2}13] $ at different depths of 31.54° confirms the uniform compressive strain inside the AlN region. Benefitting from the excellent crystalline quality of AlN template, we can epitaxially grow the enhanced-mode high electron mobility transistor (HEMT) with a graded AlGaN buffer as thin as only ~300 nm. Such an ultra-thin AlGaN buffer layer results in the wafer-bow as low as 18.1 μm in 6-inch wafer scale. The fabricated HEMT devices with 16 μm-L_GD exhibits a threshold voltage (V_TH) of 1.1 V and a high OFF-state breakdown voltage (V_BD) over 1400 V. Furthermore, after 200 V high-voltage OFF-state stress, the current collapse is only 13.6%. Therefore, the advantages of both 6-inch size and excellent crystallinity announces the superiority of single-crystalline AlN template in low-cost electrical power applications.

The traditional von Neumann architecture has demonstrated inefficiencies in parallel computing and adaptive learning, rendering it incapable of meeting the growing demand for efficient and high-speed computing. Neuromorphic computing with significant advantages such as high parallelism and ultra-low power consumption is regarded as a promising pathway to overcome the limitations of conventional computers and achieve the next-generation artificial intelligence. Among various neuromorphic devices, the artificial synapses based on electrolyte-gated transistors stand out due to their low energy consumption, multimodal sensing/recording capabilities, and multifunctional integration. Moreover, the emerging optoelectronic neuromorphic devices which combine the strengths of photonics and electronics have demonstrated substantial potential in the neuromorphic computing field. Therefore, this article reviews recent advancements in electrolyte-gated optoelectronic neuromorphic transistors. First, it provides an overview of artificial optoelectronic synapses and neurons, discussing aspects such as device structures, operating mechanisms, and neuromorphic functionalities. Next, the potential applications of optoelectronic synapses in different areas such as artificial visual system, pain system, and tactile perception systems are elaborated. Finally, the current challenges are summarized, and future directions for their developments are proposed.

In this data explosion era, ensuring the secure storage, access, and transmission of information is imperative, encompassing all aspects ranging from safeguarding personal devices to formulating national information security strategies. Leveraging the potential offered by dual-type carriers for transportation and employing optical modulation techniques to develop high reconfigurable ambipolar optoelectronic transistors enables effective implementation of information destruction after reading, thereby guaranteeing data security. In this study, a reconfigurable ambipolar optoelectronic synaptic transistor based on poly (3-hexylthiophene) (P3HT) and poly [[N,N-bis(2-octyldodecyl)-napthalene-1,4,5,8-bis(dicarboximide)-2,6-diyl]-alt-5,5′-(2,2′-bithiophene)] (N2200) blend film was fabricated through solution-processed method. The resulting transistor exhibited a relatively large ON/OFF ratio of 10³ in both n- and p-type regions, and tunable photoconductivity after light illumination, particularly with green light. The photo-generated carriers could be effectively trapped under the gate bias, indicating its potential application in mimicking synaptic behaviors. Furthermore, the synaptic plasticity, including volatile/non−volatile and excitatory/inhibitory characteristics, could be finely modulated by electrical and optical stimuli. These optoelectronic reconfigurable properties enable the realization of information light assisted burn after reading. This study not only offers valuable insights for the advancement of high-performance ambipolar organic optoelectronic synaptic transistors but also presents innovative ideas for the future information security access systems.

Optoelectronic memristor is generating growing research interest for high efficient computing and sensing-memory applications. In this work, an optoelectronic memristor with Au/a-C:Te/Pt structure is developed. Synaptic functions, i.e., excitatory post-synaptic current and pair-pulse facilitation are successfully mimicked with the memristor under electrical and optical stimulations. More importantly, the device exhibited distinguishable response currents by adjusting 4-bit input electrical/optical signals. A multi-mode reservoir computing (RC) system is constructed with the optoelectronic memristors to emulate human tactile-visual fusion recognition and an accuracy of 98.7% is achieved. The optoelectronic memristor provides potential for developing multi-mode RC system.

Recently, for developing neuromorphic visual systems, adaptive optoelectronic devices become one of the main research directions and attract extensive focus to achieve optoelectronic transistors with high performances and flexible functionalities. In this review, based on a description of the biological adaptive functions that are favorable for dynamically perceiving, filtering, and processing information in the varying environment, we summarize the representative strategies for achieving these adaptabilities in optoelectronic transistors, including the adaptation for detecting information, adaptive synaptic weight change, and history-dependent plasticity. Moreover, the key points of the corresponding strategies are comprehensively discussed. And the applications of these adaptive optoelectronic transistors, including the adaptive color detection, signal filtering, extending the response range of light intensity, and improve learning efficiency, are also illustrated separately. Lastly, the challenges faced in developing adaptive optoelectronic transistor for artificial vision system are discussed. The description of biological adaptive functions and the corresponding inspired neuromorphic devices are expected to provide insights for the design and application of next-generation artificial visual systems.

In recent years, research focusing on synaptic device based on phototransistors has provided a new method for associative learning and neuromorphic computing. A TiO₂/AlGaN/GaN heterostructure-based synaptic phototransistor is fabricated and measured, integrating a TiO₂ nanolayer gate and a two-dimensional electron gas (2DEG) channel to mimic the synaptic weight and the synaptic cleft, respectively. The maximum drain to source current is 10 nA, while the device is driven at a reverse bias not exceeding −2.5 V. A excitatory postsynaptic current (EPSC) of 200 nA can be triggered by a 365 nm UVA light spike with the duration of 1 s at light intensity of 1.35 μW∙cm⁻². Multiple synaptic neuromorphic functions, including EPSC, short-term/long-term plasticity (STP/LTP) and paried-pulse facilitation (PPF), are effectively mimicked by our GaN-based heterostructure synaptic device. In the typical Pavlov’s dog experiment, we demonstrate that the device can achieve "retraining" process to extend memory time through enhancing the intensity of synaptic weight, which is similar to the working mechanism of human brain.

The hierarchical and coordinated processing of visual information by the brain demonstrates its superior ability to minimize energy consumption and maximize signal transmission efficiency. Therefore, it is crucial to develop artificial visual synapses that integrate optical sensing and synaptic functions. This study fully leverages the excellent photoresponsivity properties of the PM6 : Y6 system to construct a vertical photo-tunable organic memristor and conducts in-depth research on its resistive switching performance, photodetection capability, and simulation of photo-synaptic behavior, showcasing its excellent performance in processing visual information and simulating neuromorphic behaviors. The device achieves stable and gradual resistance change, successfully simulating voltage-controlled long-term potentiation/depression (LTP/LTD), and exhibits various photo-electric synergistic regulation of synaptic plasticity. Moreover, the device has successfully simulated the image perception and recognition functions of the human visual nervous system. The non-volatile Au/PM6 : Y6/ITO memristor is used as an artificial synapse and neuron modeling, building a hierarchical coordinated processing SLP-CNN cascade neural network for visual image recognition training, its linear tunable photoconductivity characteristic serves as the weight update of the network, achieving a recognition accuracy of up to 93.4%. Compared with the single-layer visual target recognition model, this scheme has improved the recognition accuracy by 19.2%.

Synaptic nano-devices have powerful capabilities in logic, memory and learning, making them essential components for constructing brain-like neuromorphic computing systems. Here, we have successfully developed and demonstrated a synaptic nano-device based on Ga₂O₃ nanowires with low energy consumption. Under 255 nm light stimulation, the biomimetic synaptic nano-device can stimulate various functionalities of biological synapses, including pulse facilitation, peak time-dependent plasticity and memory learning ability. It is found that the artificial synaptic device based on Ga₂O₃ nanowires can achieve an excellent "learning−forgetting−relearning" functionality. The transition from short-term memory to long-term memory and retention of the memory level after the stepwise learning can attribute to the great relearning functionality of Ga₂O₃ nanowires. Furthermore, the energy consumption of the synaptic nano-device can be lower than 2.39 × 10^‒11 J for a synaptic event. Moreover, our device demonstrates exceptional stability in long-term stimulation and storage. In the application of neural morphological computation, the accuracy of digit recognition exceeds 90% after 12 training sessions, indicating the strong learning capability of the cognitive system composed of this synaptic nano-device. Therefore, our work paves an effective way for advancing hardware-based neural morphological computation and artificial intelligence systems requiring low power consumption.

Memristors have a synapse-like two-terminal structure and electrical properties, which are widely used in the construction of artificial synapses. However, compared to inorganic materials, organic materials are rarely used for artificial spiking synapses due to their relatively poor memrisitve performance. Here, for the first time, we present an organic memristor based on an electropolymerized dopamine-based memristive layer. This polydopamine-based memristor demonstrates the improvements in key performance, including a low threshold voltage of 0.3 V, a thin thickness of 16 nm, and a high parasitic capacitance of about 1 μF∙mm⁻². By leveraging these properties in combination with its stable threshold switching behavior, we construct a capacitor-free and low-power artificial spiking neuron capable of outputting the oscillation voltage, whose spiking frequency increases with the increase of current stimulation analogous to a biological neuron. The experimental results indicate that our artificial spiking neuron holds potential for applications in neuromorphic computing and systems.

Lead chalcohalides (PbYX, X = Cl, Br, I; Y = S, Se) is an extension of the classic Pb chalcogenides (PbY). Constructing the heterogeneous integration with PbYX and PbY material systems makes it possible to achieve significantly improved optoelectronic performance. In this work, we studied the effect of introducing halogen precursors on the structure of classical PbS nanocrystals (NCs) during the synthesis process and realized the preparation of PbS/Pb₃S₂X₂ core/shell structure for the first time. The core/shell structure can effectively improve their optical properties. Furthermore, our approach enables the synthesis of Pb₃S₂Br₂ that had not yet been reported. Our results not only provide valuable insights into the heterogeneous integration of PbYX and PbY materials to elevate material properties but also provide an effective method for further expanding the preparation of PbYX material systems.