Neuromorphic devices are among the most emerging electronic components to realize artificial neural systems and replace traditional complementary metal-oxide semiconductor devices in recent times. In this work, tri-layer HfO 2 /BiFeO 3 (BFO)/HfO 2 memristors are designed by inserting traditional ferroelectric BFO layers measuring ≈4 nm after thickness optimization. The novel designed memristor shows excellent resistive switching (RS) performance such as a storage window of 10 4 and multi-level storage ability. Remarkably, essential synaptic functions can be successfully realized on the basis of the linearity of conductance modulation. The pattern recognition simulation based on neuromorphic network is conducted with 91.2% high recognition accuracy. To explore the RS performance enhancement and artificial synaptic behaviors, conductive filaments (CFs) composed of Hafnium (Hf ) single crystal with a hexaganal lattice structure are observed using high-resolution transmission electron microscopy. It is reasonable to believe that the sufficient oxygen vacancies in the inserting BFO thin film play a crucial role in adjusting the morphology and growth of Hf CFs, which leads to the promising synaptic and enhanced RS behavior, thus demonstrating the potential of this memristor for use in neuromorphic computing.

With the booming growth of artificial intelligence (AI), the traditional von Neumann computing architecture based on complementary metal oxide semiconductor devices are facing memory wall and power wall. Memristor based in-memory computing can potentially overcome the current bottleneck of computer and achieve hardware breakthrough. In this review, the recent progress of memory devices in material and structure design, device performance and applications are summarized. Various resistive switching materials, including electrodes, binary oxides, perovskites, organics, and two-dimensional materials, are presented and their role in the memristor are discussed. Subsequently, the construction of shaped electrodes, the design of functional layer and other factors influencing the device performance are analyzed. We focus on the modulation of the resistances and the effective methods to enhance the performance. Furthermore, synaptic plasticity, optical-electrical properties, the fashionable applications in logic operation and analog calculation are introduced. Finally, some critical issues such as the resistive switching mechanism, multi-sensory fusion, system-level optimization are discussed.

Memristor, processing data storage and logic operation all‐in‐one, is an advanced configuration for next generation computer. In this work, a bismuth doped tin oxide (Bi:SnO2) memristor with ITO/Bi:SnO2/TiN structure has been fabricated. Observing from transmission electron microscope (TEM) for the Bi:SnO2 device, it is found that the bismuth atoms surround the surface of SnO2 crystals to form the coaxial Bi conductive filament. The self‐compliance current, switching voltage and operating current of Bi:SnO2 memristor are remarkably smaller than that of ITO/SnO2/TiN device. With the content of 4.8% Bi doping, the SET operating power of doped device is 16 µW for ITO/Bi:SnO2/TiN memory cell of 0.4 × 0.4 µm2, which is cut down by two orders of magnitude. Hence, the findings in this study suggest that Bi:SnO2 memristors hold significant potential for application in low power memory and broadening the understanding of existing resistive switching (RS) mechanism.

The development of advanced microelectronics requires new device architecture and multi‐functionality. Low‐dimensional material is considered as a powerful candidate to construct new devices. In this work, a flexible memristor is fabricated utilizing 2D cadmium phosphorus trichalcogenide nanosheets as the functional layer. The memristor exhibits excellent resistive switching performance under different radius and over 103 bending times. The device mechanism is systematically investigated, and the synaptic plasticity including paired‐pulse facilitation and spiking timing‐dependent plasticity are further observed. Furthermore, based on the linearly conductance modulation capacity of the flexible memristor, the applications on decimal operation are explored, that the addition, subtraction, multiplication, and division of decimal calculation are successfully achieved. These results demonstrate the potential of metal phosphorus trichalcogenide in novel flexible neuromorphic devices, which accelerate the application process of neuromorphic computing.

No abstract