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.