Recent progress in the development of artificial intelligence technologies, aided by deep learning algorithms, has led to an unprecedented revolution in neuromorphic circuits, bringing us ever closer to brain-like computers. However, the vast majority of advanced algorithms still have to run on conventional computers. Thus, their capacities are limited by what is known as the von-Neumann bottleneck, where the central processing unit for data computation and the main memory for data storage are separated. Emerging forms of non-volatile random access memory, such as ferroelectric random access memory, phase-change random access memory, magnetic random access memory, and resistive random access memory, are widely considered to offer the best prospect of circumventing the von-Neumann bottleneck. This is due to their ability to merge storage and computational operations, such as Boolean logic. This paper reviews the most common kinds of non-volatile random access memory and their physical principles, together with their relative pros and cons when compared with conventional CMOS-based circuits (Complementary Metal Oxide Semiconductor). Their potential application to Boolean logic computation is then considered in terms of their working mechanism, circuit design and performance metrics. The paper concludes by envisaging the prospects offered by non-volatile devices for future brain-inspired and neuromorphic computation.

The development and utilization of perovskites are beneficial to improve or even change the optical properties of devices and obtain fascinating performances such as higher photoelectric conversion efficiency, better thermal stability, higher external quantum efficiency, more excellent remodeling, and flexibility. So, there are many articles on perovskite reviews having been reported from synthesis, properties to various applications (such as optoelectronic devices, electrical memristor, etc. ). Based on the reported review of perovskites, this study will make a further supplement to the research progress of perovskites in visible light communication (VLC), optical neuromorphic devices, and highlight huge development prospects in these emerging fields in recent years. First, we briefly reviewed the preparation methods of common perovskite materials, followed by the optical and electrical characteristics. Then, the specific applications of optical properties based on perovskite materials are emphatically investigated, in addition to traditional photovoltaic devices, especially the latest cutting-edge fields of information encryption and decryption, VLC as well as optical memristive devices for photonic synapse and photonic neuromorphic computing. Finally, the main conclusions and prospects are given. Perovskite-based optical memristive devices are enabled to assist photonic neuromorphic calculations, showing huge potential application prospects in intelligent integrated chip fusing sensing, storage, and computing.

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.

Ferroelectric, phase-change, and magnetic materials are considered promising candidates for advanced memory devices. Under the development dilemma of traditional silicon-based memory devices, ferroelectric materials stand out due to their unique polarization properties and diverse manufacturing techniques. On the occasion of the 100th anniversary of the birth of ferroelectricity, scandium-doped aluminum nitride, which is a different wurtzite structure, was reported to be ferroelectric with a larger coercive, remanent polarization, curie temperature, and a more stable ferroelectric phase. The inherent advantages have attracted widespread attention, promising better performance when used as data storage materials and better meeting the needs of the development of the information age. In this paper, we start from the characteristics and development history of ferroelectric materials, mainly focusing on the characteristics, preparation, and applications in memory devices of ferroelectric wurtzite AlScN. It compares and analyzes the unique advantages of AlScN-based memory devices, aiming to lay a theoretical foundation for the development of advanced memory devices in the future.

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.

Artificial synapses and neurons are two critical, fundamental bricks for constructing hardware neural networks. Owing to its high-density integration, outstanding nonlinearity, and modulated plasticity, memristors have attracted emerging attention on emulating biological synapses and neurons. However, fabricating a low-power and robust memristor-based artificial neuron without extra electrical components is still a challenge for brain-inspired systems. In this work, we demonstrate a single two-dimensional (2D) MXene(V₂C)-based threshold switching (TS) memristor to emulate a leaky integrate-and-fire (LIF) neuron without auxiliary circuits, originating from the Ag diffusion-based filamentary mechanism. Moreover, our V₂C-based artificial neurons faithfully achieve multiple neural functions including leaky integration, threshold-driven fire, self-relaxation, and linear strength-modulated spike frequency characteristics. This work demonstrates that three-atom-type MXene (e.g., V₂C) memristors may provide an efficient method to construct the hardware neuromorphic computing systems.