Recent advancements in AI have spurred interest in ferroelectric memristors for neuromorphic chips due to their ability to precisely control resistive states through polarization flip-flop without electroforming. However, oxygen vacancies in these devices often cause high leakage current, low endurance, and dispersed switching voltages. Here, we introduce a silicon-based integrated (Ba_0.6Sr_0.4TiO₃)_0.5(Nd₂O₃)_0.5 (BSTN) nanoscaffolded ferroelectric thin film memristor with a vertically self-assembled nanocomposite structure (VSNs) optimally oriented on La_0.67Sr_0.33MnO₃/SrTiO₃/PSi substrates. This device demonstrates a widely tunable ferroelectric domain range (0°–180°), high remnant polarization (21.04 μC/cm²), and a greater number of unitary states (16 states or 4 bits). It exhibits high durability, enduring over 10⁹ switching cycles. The switching mechanism combines ferroelectric polarization and oxygen vacancy migration, enabling the simulation of biological synaptic functions via bi-directional conductance tunability. Additionally, we implemented a low-power (0.57 pJ per event) multi-factor secure encryption system for smart locks using 16×16 BSTN memristor crossbar arrays and a pressure sensor. Under multiple factors (disordered inputs, specific users, and corresponding passwords) the system recognized passwords with 97.6% accuracy and a 3.8% loss rate after 500 iterations. Overall, this work establishes a robust foundation for advancing multilevel storage, neuromorphic computing, and AI chip applications based on ferroelectric memristors.

Mott insulator material, as a kind of strongly correlated electronic system with the characteristic of a drastic change in electrical conductivity, shows excellent application prospects in neuromorphological calculations and has attracted significant attention in the scientific community. Especially, computing systems based on Mott insulators can overcome the bottleneck of separated data storage and calculation in traditional artificial intelligence systems based on the von Neumann architecture, with the potential to save energy, increase operation speed, improve integration, scalability, and three-dimensionally stacked, and more suitable to neuromorphic computing than a complementary metal-oxide-semiconductor. In this review, we have reviewed Mott insulator materials, methods for driving Mott insulator transformation (pressure-, voltage-, and temperature-driven approaches), and recent relevant applications in neuromorphic calculations. The results in this review provide a path for further study of the applications in neuromorphic calculations based on Mott insulator materials and the related devices.

Ferroelectric memristors, as one of the most potential non-volatile memory to meet the rapid development of the artificial intelligence era, have the comprehensive function of simulating brain storage and calculation. However, due to the high dielectric loss of traditional ferroelectric materials, the durability of ferroelectric memristors and Si based integration have a great challenge. Here, we report a silicon-based epitaxial ferroelectric memristor based on self-assembled vertically aligned nano-composites BaTiO₃(BTO)-CeO₂ films. The BTO-CeO₂ memristors exhibit a stable resistance switching behavior at a high temperature of 100 °C due to higher Curie temperatures of BTO-CeO₂ films with in-plane compressive strain. And the endurance of the device can reach the order of magnitude of 1 × 10⁶ times. More importantly, the device has excellent functions for simulating artificial synaptic behavior, including excitatory post-synaptic current, paired-pulse facilitation, paired-pulse depression, spike-time-dependent plasticity, and short and long-term plasticity. Digits recognition ability of the memristor devices is evaluated though a single-layer perceptron model, in which recognition accuracy of digital can reach 86.78% after 20 training iterations. These results provide new way for epitaxial composite ferroelectric films as memristor medium with high temperature intolerance and better durability integrated on silicon.

A huge amount of data requires the non-volatile memory (NVM) technology to exhibit large-capacity storage and fast calculation speed. To further solve the bottleneck of storage capacity and speed, nano-memristors based on two-dimensional (2D) layered materials are expected to realize NVM. This study proposes the fabrication of an Ag/2D-TiO_x/Pt high-performance memristor device based on the 2D titania nanosheet material. The device demonstrates stable electrical characteristics under the direct current (DC) mode, including bipolar resistive switching (RS) behavior, multi-level memristive modes, and retention property. Also, it exhibits low switching voltage (0.42 V/–0.2 V), high R_OFF/R_ON resistance ratio (10⁵), low switching power (10^–9 W/10⁻⁵ W), and fast response speed. More importantly, the device realizes information encoding and decoding through a multi-level storage performed by different compliance currents. Multiple devices are connected to the actual circuit to realize a storage function with information processing and programmable characteristics. This work provides a powerful platform for the 2D titania nanosheet application in NVM and information processing.

Realization of functional flexible artificial synapse is a significant step toward neuromorphic computing. Herein, a flexible artificial synapse based on ferroelectric tunnel junctions (FTJs) is demonstrated, using BiFeO₃ (BFO) thin film as the functional layer. The inorganic single crystalline FTJs grown on rigid perovskite substrates at high temperatures are integrated with the flexible plastic substrates, by using the water-soluble Sr₃Al₂O₆ (SAO) as the sacrificial layer and the following transfer. The transferred freestanding BFO thin film exhibits excellent ferroelectric properties. Moreover, the memristive properties and the brain-like synaptic learning performance of the flexible FTJs are investigated. The results show that multilevel resistance states were maintained well of the flexible artificial synapse, together with their stable synaptic learning properties. Our work indicates the promising opportunity of ferroelectric thin film based flexible synapse used in the future neuromorphic computing system.

Flexible memristor devices based on plastic substrates have attracted considerable attention due to their applications in wearable computers and integrated circuits. However, most plastic-substrate memristors cannot function or be grown in high-temperature environments. In this study, scotch-tape-exfoliated mica was used as the flexible memristor substrate in order to resolve these high-temperature issues. Our TiN/ZHO/IGZO memristor, which was constructed using a thin (10 μm) mica substrate, has superior flexibility and thermostability. After bending it 103 times, the device continues to exhibit exceptional electrical characteristics. It can also be implemented for transitions between high and low resistance states, even in temperatures of up to 300 ℃. More importantly, the biological synaptic characteristics of paired-pulse facilitation/depression (PPF/PPD) and spike-timing-dependent plasticity (STDP) were observed through applying different pulse measurement modes. This work demonstrates that flexible memristor devices on mica substrates may potentially allow for the realization of high-temperature memristor applications for biologically-inspired computing systems.