A high-speed c-plane InGaN/GaN blue micro-scale light-emitting diodes (Micro-LEDs) with a pixel diameter of $60~\mu \text{m}$ , and a high −3 dB modulation bandwidth of 1.53 GHz at the current density of 5996 A/cm ² were demonstrated. The ultra-thin quantum well (QW) structure with InGaN layer of 1 nm was designed, and optimized variable temperature growth processes were adopted to increase In incorporation and localization effect in the quantum wells, which greatly reduce the polarization field and increase the overlap of electron-hole wave functions. The differential carrier lifetime was effectively shortened, increasing the modulation bandwidth of the Micro-LED. And a data rate of 5.27 Gbps with a bit error rate (BER) of $3.1\times 10^{-{3}}$ was demonstrated with orthogonal frequency-division multiplexing (OFDM) signal for a 1 m free space real-time visible light communication (VLC) based on such high-speed micro-LED. Promising results in this work may support a great development of high-speed VLC applications.

Utilizing electronic devices to emulate biological synapses for the construction of artificial neural networks has provided a feasible research approach for the future development of artificial intelligence systems. Until now, different kinds of electronic devices have been proposed in the realization of biological synapse functions. However, the device stability and the power consumption are major challenges for future industrialization applications. Herein, an electronic synapse of MXene/SiO₂ structure-based resistive random-access memory (RRAM) devices has been designed and fabricated by taking advantage of the desirable properties of SiO₂ and 2D MXene material. The proposed RRAM devices, Ag/MXene/SiO₂/Pt, exhibit the resistance switching characteristics where both the volatile and nonvolatile behaviors coexist in a single device. These intriguing features of the Ag/MXene/SiO₂/Pt devices make them more applicable for emulating biological synaptic plasticity. Additionally, the conductive mechanisms of the Ag/MXene/SiO₂/Pt RRAM devices have been discussed on the basis of our experimental results.

In this investigation, we experimentally demonstrated a high-speed Gbps long distance real-time visible light communication system based on non-return-to-zero on-off keying modulation by using a high-bandwidth Gallium nitride-based micro-LED with a modulation bandwidth of 230 MHz and a size of 40 μm x 40 μm. The maximum real-time data rate obtained is up to 1.3 Gbps at a 3 m free-space transmission distance with a bit-error rate (BER) of 3.4 x 10 ^-3 and 1 Gbps at a 10 m distance with a BER of 3.2 x 10 ^-3 , both of which are underneath the forward error correction threshold of 3.8 x 10 ^-3 required for free-error operation. In addition, when the transmission distance is increased to 16 m in free space through reflecting the emission light beam by blue reflectors mounted on the wall, a data rate of 0.87 Gbps with a BER of 3.5 x 10 ^-3 is achieved successfully.

Conventional von Newmann-based computers face severe challenges in the processing and storage of the large quantities of data being generated in the current era of “big data.” One of the most promising solutions to this issue is the development of an artificial neural network (ANN) that can process and store data in a manner similar to that of the human brain. To extend the limits of Moore’s law, memristors, whose electrical and optical behaviors closely match the biological response of the human brain, have been implemented for ANNs in place of the traditional complementary metal-oxide-semiconductor (CMOS) components. Based on their different operation modes, we classify the memristor family into electronic, photonic, and optoelectronic memristors, and review their respective physical principles and state-of-the-art technologies. Subsequently, we discuss the design strategies, performance superiorities, and technical drawbacks of various memristors in relation to ANN applications, as well as the updated versions of ANN, such as deep neutral networks (DNNs) and spike neural networks (SNNs). This paper concludes by envisioning the potential approaches for overcoming the physical limitations of memristor-based neural networks and the outlook of memristor applications on emerging neural networks.

The environmental pollution generated by electronic waste (e-waste), waste-gas, and wastewater restricts the sustainable development of society. Environmental-friendly electronics made of degradable, resorbable, and compatible thin-film materials were utilized and explored, which was beneficial for e-waste dissolution and sustainable development. In this paper, we present a literature review about the development of various degradable and disposable thin-films for electronic applications. The corresponding preparation methods were simply reviewed and one of the most exciting and promising methods was discussed: Printing electronics technology. After a short introduction, detailed applications in the environment sensors and eco-friendly devices based on these degradable and compatible thin-films were mainly reviewed, finalizing with the main conclusions and promising perspectives. Furthermore, the future on these upcoming environmental-friendly electronic devices are proposed and prospected, especially on resistive switching devices, showing great potential applications in artificial intelligence (AI) and the Internet of Thing (IoT). These resistive switching devices combine the functions of storage and computations, which can complement the off-shelf computing based on the von Neumann architecture and advance the development of the AI.

The development of vision bionic systems is indispensable for the perception, memory, and processing of optical signals, which promotes the exploration of efficient visual perception systems. In this work, a simple and novel two‐terminal optoelectronic memristor based on the CuAlAgCr/TiO₂/W (CTW) structure is prepared, where the CuAlAgCr high‐entropy alloys are employed as the top electrode for the first time. Before annealing, the CTW optoelectronic memristor exhibited fascinating performance, including uniformly distributed operating voltage, reliable data retention, and a higher switching ratio. Moreover, the optoelectronic memristor can be reversibly switched between volatile and nonvolatile memories by adjusting compliance currents. The CTW optoelectronic memristor annealed in air exhibits various artificial synaptic functions, such as short‐term memory, optical learning, and forgetting behavior under the illumination of the laser. The photo‐response current is increased from nano‐ampere to micro‐ampere level. Furthermore, a logic function unit based on CTW optoelectronic memristor is proposed, which realizes “AND” operation. Furthermore, first‐principles calculations of the CTW structure are performed to describe the influence of photocarriers on the barrier height at the CuAlAgCr‐TiO₂ interface, revealing the working mechanism of the CTW optoelectronic memristor. This work has greatly facilitated the development of optically operated artificial synaptic devices and vision bionic systems.