Memristors have been considered to be more efficient than traditional Complementary Metal Oxide Semiconductor (CMOS) devices in implementing artificial synapses, which are fundamental yet very critical components of neurons as well as neural networks. Compared with inorganic counterparts, organic memristors have many advantages, including low-cost, easy manufacture, high mechanical flexibility, and biocompatibility, making them applicable in more scenarios. Here, we present an organic memristor based on an ethyl viologen diperchlorate [EV(ClO₄)]₂/triphenylamine-containing polymer (BTPA-F) redox system. The device with bilayer structure organic materials as the resistive switching layer (RSL) exhibits memristive behaviors and excellent long-term synaptic plasticity. Additionally, the device’s conductance states can be precisely modulated by consecutively applying voltage pulses between the top and bottom electrodes. A three-layer perception neural network with in situ computing enabled was then constructed utilizing the proposed memristor and trained on the basis of the device’s synaptic plasticity characteristics and conductance modulation rules. Recognition accuracies of 97.3% and 90% were achieved, respectively, for the raw and 20% noisy handwritten digits images from the Modified National Institute of Standards and Technology (MNIST) dataset, demonstrating the feasibility and applicability of implementing neuromorphic computing applications utilizing the proposed organic memristor.

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.

Conductance quantization (QC) phenomena occurring in metal oxide based memristors demonstrate great potential for high-density data storage through multilevel switching, and analog synaptic weight update for effective training of the artificial neural networks. Continuous, linear and symmetrical modulation of the device conductance is a critical issue in QC behavior of memristors. In this contribution, we employ the scanning probe microscope (SPM) assisted electrode engineering strategy to control the ion migration process to construct single conductive filaments in Pt/HfO_x/Pt devices. Upon deliberate tuning and evolution of the filament, 32 half integer quantized conductance states in the 16 G₀ to 0.5 G₀ range with enhanced distribution uniformity was achieved. Simulation results revealed that the numbers of the available QC states and fluctuation of the conductance at each state play an important role in determining the overall performance of the neural networks. The 32-state QC behavior of the hafnium oxide device shows improved recognition accuracy approaching 90% for handwritten digits, based on analog type operation of the multilayer perception (MLP) neural network.

Resistive random access memory (RRAM) holds great promise for in-memory computing, which is considered the most promising strategy for solving the von Neumann bottleneck. However, there are still significant problems in its application due to the non-uniform performance of RRAM devices. In this work, a bilayer dielectric layer memristor was designed based on the difference in the Gibbs free energy of the oxide. We fabricated Au/Ta₂O₅/HfO₂/Ta/Pt (S3) devices with excellent uniformity. Compared with Au/HfO₂/Pt (S1) and Au/Ta₂O₅/Pt (S2) devices, the S3 device has a low reset voltage fluctuation of 2.44%, and the resistive coefficients of variation are 13.12% and 3.84% in HRS and LRS, respectively, over 200 cycles. Otherwise, the bilayer device has better linearity and more conductance states in multi-state regulation. At the same time, we analyze the physical mechanism of the bilayer device and provide a physical model of ion migration. This work provides a new idea for designing and fabricating resistive devices with stable performance.