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With the advancement of artificial intelligence technology, memristors have aroused the interest of researchers because they can realize a variety of biological functions, good scalability, and high running speed. In this work, the amorphous semiconductor material silicon carbide (SiC) was used as the dielectric to fabricate the memristor with the Ag/SiC/n-Si structure. The device has a power consumption as low as 3.4 pJ, a switching ratio of up to 105, and a lower set voltage of 1.26 V, indicating excellent performance. Importantly, by adjusting the current compliance, the strength of the formed filaments changes, and the threshold characteristic and bipolar resistance switching phenomenon could be simultaneously realized in one device. On this basis, the biological long- and short-term memory process was simulated. Importantly, we have implemented leakage integration and fire models constructed based on structured Ag/SiC/n-Si memristor circuits. This low-power reconfigurable device opens up the possibilities for memristor-based applications combining artificial neurons and synapses.

Electronic synaptic devices with photoelectric sensing function are becoming increasingly important in the development of neuromorphic computing system. Here, we present a photoelectrical synaptic system based on high-quality epitaxial Ba0.6Sr0.4TiO3 (BST) films in which the resistance ramp characteristic of the device provides the possibility to simulate synaptic behavior. The memristor with the Pt/BST/Nb:SrTiO3 structure exhibits reliable I–V characteristics and adjustable resistance modulation characteristics. The device can faithfully demonstrate synaptic functions, such as potentiation and depression, spike time-dependent plasticity, and paired pulse facilitation, and the recognition accuracy of handwritten digits was as high as 92.2%. Interestingly, the functions of visual perception, visual memory, and color recognition of the human eyes have also been realized based on the device. This work will provide a strong candidate for the neuromorphic computing hardware system of photoelectric synaptic devices.

As a nanoscale semiconductor memory device, a ferroelectric memristor has promising prospects to break through the von Neumann framework in terms of artificial synaptic function, information processing, and integration. This study presents the fabrication of Li0.09Bi0.91FeO3 as the functional layer for a memristor device based on the Si substrate, enabling the integration of silicon complementary metal oxide semiconductor technology. In addition, it exhibits bipolar resistance switching characteristics in a direct current mode and can rapidly achieve stable conductance tunability at higher frequencies through the applied pulse for biosynapse simulation. More importantly, multiple devices are connected into electrical circuits to realize storage functions with information processing and programmable characteristics. This work paves the way for near-future applications of ferroelectric memristors in information processing.