Two‐dimensional (2D) transition metal dichalcogenide has attracted significant attention recently due to its unique electrical and optical properties. However, MoS2 nanosheets fabricated by traditional methods exhibit poor electrical performance due to the existence of the 1T metal phase. A solution‐processable pure 2H semiconductor phase MoS2 nanosheet for use as the functional layer of high‐performance memristors is presented. The resulting memristor based on a Ag/MoS2/Pt structure exhibits excellent resistive switching properties including high endurance and multilevel retention. Moreover, the power consumption and programming current of the SET operation can be as low as 7.35 nW and 100 nA, respectively. Interestingly, it is demonstrated that the resistance of the Ag/MoS2/Pt device can be bidirectionally modulated over a wide range (pulse number >500) with an approximately linear trend. Furthermore, the accuracy of pattern recognition with handwritten data can reach 90.37%. This work provides a simple and practical method for the realization of fabrication of pure 2H‐MoS2 and application in biological neural computing systems.

Designing suitable material systems to construct artificial synapses and exploring novel synaptic functions is a crucial step toward the realization of efficient large-scale bioinspired neuromorphic systems. In this work, flexible and insoluble bio-memristor devices are fabricated by precisely engineering the molecular structures of wool keratin. This flexible Ag/ keratin/indium tin oxide-polyethylene naphthalate synaptic device possesses enhanced mechanical resistance, which is achieved by photocross-linking keratin molecules, and can withstand a bending radius of up to 1.2 mm. This device is promising for implantable applications because it is water-resistant. When modulated by triangle-wave DC voltages and pulsed voltages, this flexible electronic device emulates typical memristor characteristics and synaptic functions, including potentiation/depression, spike timing dependent plasticity, and long-term/short-term plasticity. Simulation results indicate that a memristor network made by this woolkeratin based device has ≈95.8% memory learning accuracy and capability for pattern learning. Combined, these features prove that the cross-linked wool-keratin based device has potential in wearable and flexible neuron computing systems.

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.

Threshold switching (TS) devices are finding increasing use in the hardware implementation of neuromorphic network computing. Here, a simple structured Ag/amorphous Si/Pt TS device with a switching ratio of ∼105 is prepared, with turn-on and turn-off speeds as high as ∼20 ns and ∼16 ns, respectively. We use this TS device to construct a leaky integration-and-firing artificial neuron that emulates key biological neuron features like threshold-driven firing, all-or-nothing spiking, refractory period, intensity-modulated frequency response, and conductance-modulated frequency response. These results suggest that Si film-based TS device artificial neurons have significant potential for building high-speed artificial neural networks.