With the emergence of the Internet of Things (IoT) and the rapid growth of big data generated by edge devices, there has been a growing need for electronic devices that are capable of processing and transmitting data at low power and high speeds. Traditional Complementary Metal-Oxide-Semiconductor (CMOS) devices are nonvolatile and often limited by their ability for certain IoT applications due to their unnecessary power consumption for data movement in von Neuman architecture-based systems. This has led to a surge in research and development efforts aimed at creating innovative electronic components and systems that can overcome these shortcomings and meet the evolving needs of the information era, which share features such as improved energy efficiency, higher processing speeds, and increased functionality. Memristors are a novel type of electronic device that has the potential to break down the barrier between storage and computing. By storing data and processing information within the same device, memristors can minimize the need for data movement, which allows for faster processing speeds and reduced energy consumption. To further improve the energy efficiency and reliability of memristors, there has been a growing trend toward diversifying the selection of dielectric materials used in memristors. Halide perovskites (HPs) have unique electrical and optical properties, including ion migration, charge trapping effect caused by intrinsic defects, excellent optical absorption efficiency, and high charge mobility, which makes them highly promising in applications of memristors. In this paper, we provide a comprehensive overview of the recent development in resistive switching behaviors of HPs and the underlying mechanisms. Furthermore, we summarize the diverse range of HPs, their respective performance metrics, as well as their applications in various fields. Finally, we critically evaluate the current bottlenecks and possible opportunities in the future research of HP memristors.

Memristors have received much attention for their ability to achieve multi-level storage and synaptic learning. However, the main factor that hinders the application of memristors to simulate neural synapses is the instability of the formation and breakage of conductive filaments inside traditional memristors, which makes it difficult to simulate the function of biological synapses in practice. However, the resistance change of ferroelectric memristors relies on the polarization inversion of the ferroelectric thin film, thus avoiding the above problem. In this study, a Pd/HfAlO/LSMO/STO/Si ferroelectric memristor is proposed, which can achieve resistive switching properties through the combined action of ferroelectricity and oxygen vacancies. The I−V curves show that the device has good stability and uniformity. In addition, the effect of pulse sequence modulation on the conductance was investigated, and the biological synaptic function and learning behavior were simulated successfully. The results of the above studies provide a basis for the development of ferroelectric memristors with neurosynaptic-like behaviors.

In this big data era, the explosive growth of information puts ultra-high demands on the data storage/computing, such as high computing power, low energy consumption, and excellent stability. However, facing this challenge, the traditional von Neumann architecture-based computing system is out of its depth owing to the separated memory and data processing unit architecture. One of the most effective ways to solve this challenge is building brain inspired computing system with in-memory computing and parallel processing ability based on neuromorphic devices. Therefore, there is a research trend toward the memristors, that can be applied to build neuromorphic computing systems due to their large switching ratio, high storage density, low power consumption, and high stability. Two-dimensional (2D) ferroelectric materials, as novel types of functional materials, show great potential in the preparations of memristors because of the atomic scale thickness, high carrier mobility, mechanical flexibility, and thermal stability. 2D ferroelectric materials can realize resistive switching (RS) because of the presence of natural dipoles whose direction can be flipped with the change of the applied electric field thus producing different polarizations, therefore, making them powerful candidates for future data storage and computing. In this review article, we introduce the physical mechanisms, characterizations, and synthetic methods of 2D ferroelectric materials, and then summarize the applications of 2D ferroelectric materials in memristors for memory and synaptic devices. At last, we deliberate the advantages and future challenges of 2D ferroelectric materials in the application of memristors devices.

Recently, with the emergence of ChatGPT, the field of artificial intelligence has garnered widespread attention from various sectors of society. Reservoir Computing (RC) is a neuromorphic computing algorithm used to analyze time-series data. Unlike traditional artificial neural networks that require the weight values of all nodes in the trained network, RC only needs to train the readout layer. This makes the training process faster and more efficient, and it has been used in various applications, including speech recognition, image classification, and control systems. Its flexibility and efficiency make it a popular choice for processing large amounts of complex data. A recent research trend is to develop physical RC, which utilizes the nonlinear dynamic and short-term memory properties of physical systems (photonic modules, spintronic devices, memristors, etc.) to construct a fixed random neural network structure for processing input data to reduce computing time and energy. In this paper, we introduced the recent development of memristors and demonstrated the remarkable data processing capability of RC systems based on memristors. Not only do they possess excellent data processing ability comparable to digital RC systems, but they also have lower energy consumption and greater robustness. Finally, we discussed the development prospects and challenges faced by memristors-based RC systems.

The intrinsic variability of memristor switching behavior can be used as a natural source of randomness, this variability is valuable for safe applications in hardware, such as the true random number generator (TRNG). However, the speed of TRNG is still be further improved. Here, we propose a reliable Ag/SiN_x/n-Si volatile memristor, which exhibits a typical threshold switching device with stable repeat ability and fast switching speed. This volatile-memristor-based TRNG is combined with nonlinear feedback shift register (NFSR) to form a new type of high-speed dual output TRNG. Interestingly, the bit generation rate reaches a high speed of 112 kb/s. In addition, this new TRNG passed all 15 National Institute of Standards and Technology (NIST) randomness tests without post-processing steps, proving its performance as a hardware security application. This work shows that the SiN_x-based volatile memristor can realize TRNG and has great potential in hardware network security.

Neuromorphic computing aims to achieve artificial intelligence by mimicking the mechanisms of biological neurons and synapses that make up the human brain. However, the possibility of using one reconfigurable memristor as both artificial neuron and synapse still requires intensive research in detail. In this work, Ag/SrTiO₃(STO)/Pt memristor with low operating voltage is manufactured and reconfigurable as both neuron and synapse for neuromorphic computing chip. By modulating the compliance current, two types of resistance switching, volatile and nonvolatile, can be obtained in amorphous STO thin film. This is attributed to the manipulation of the Ag conductive filament. Furthermore, through regulating electrical pulses and designing bionic circuits, the neuronal functions of leaky integrate and fire, as well as synaptic biomimicry with spike-timing-dependent plasticity and paired-pulse facilitation neural regulation, are successfully realized. This study shows that the reconfigurable devices based on STO thin film are promising for the application of neuromorphic computing systems.

Conduction filament formation, redox reaction, and mobile ion migration in solid electrolytes underpin the memristive devices, all of which are partially influenced or fully dominated by the moisture. The moisture-based physical-chemistry mechanism provides an electric tunable method to create enough dissociate conductance states for neuromorphic computing, but overconcentration moisture will corrode electrode and then causes device invalidation. This perspective goal is that surveys the moisture-dependency of dynamic at interfaces or/and switching function layer, clarifies the bottlenecks that the memristive device facing in terms of water molecule-related reaction, and gives the possible solutions.