Neuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the von Neumann architecture. This computing is realized based on memristive hardware neural networks in which synaptic devices that mimic biological synapses of the brain are the primary units. Mimicking synaptic functions with these devices is critical in neuromorphic systems. In the last decade, electrical and optical signals have been incorporated into the synaptic devices and promoted the simulation of various synaptic functions. In this review, these devices are discussed by categorizing them into electrically stimulated, optically stimulated, and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals. The working mechanisms of the devices are analyzed in detail. This is followed by a discussion of the progress in mimicking synaptic functions. In addition, existing application scenarios of various synaptic devices are outlined. Furthermore, the performances and future development of the synaptic devices that could be significant for building efficient neuromorphic systems are prospected.

Silicon is vital for its high abundance, vast production, and perfect compatibility with the well-established CMOS processing industry. Recently, artificially stacked layered 2D structures have gained tremendous attention via fine-tuning properties for electronic devices. This article presents neuromorphic devices based on silicon nanosheets that are chemically exfoliated and surface-modified, enabling self-assembly into hierarchical stacking structures. The device functionality can be switched between a unipolar memristor and a feasibly reset-able synaptic device. The memory function of the device is based on the charge storage in the partially oxidized SiNS stacks followed by the discharge activated by the electric field at the Au-Si Schottky interface, as verified in both experimental and theoretical means. This work further inspired elegant neuromorphic computation models for digit recognition and noise filtration. Ultimately, it brings silicon - the most established semiconductor - back to the forefront for next-generation computations.

Currently, neuromorphic computing is regarded as the most efficient way to solve the von Neumann bottleneck. Transistor-based devices have been considered suitable for emulating synaptic functions in neuromorphic computing due to their synergistic control capabilities on synaptic weight changes. Various low-dimensional inorganic materials such as silicon nanomembranes, carbon nanotubes, nanoscale metal oxides, and two-dimensional materials are employed to fabricate transistor-based synaptic devices. Although these transistor-based synaptic devices have progressed in terms of mimicking synaptic functions, their application in neuromorphic computing is still in its early stage. In this review, transistor-based synaptic devices are analyzed by categorizing them into different working mechanisms, and the device fabrication processes and synaptic properties are discussed. Future efforts that could be beneficial to the development of transistor-based synaptic devices in neuromorphic computing are proposed.

Zinc Oxide (ZnO) has been regarded as a promising electron transport layer (ETL) in perovskite solar cells (PSCs) owing to its high electron mobility. However, the acid-nonresistance of ZnO could destroy organic-inorganic hybrid halide perovskite such as methylammonium lead triiodide (MAPbI₃) in PSCs, resulting in poor power conversion efficiency (PCE). It is demonstrated in this work that Nb₂O₅/ZnO films were deposited at room temperature with RF magnetron sputtering and were successfully used as double electron transport layers (DETL) in PSCs due to the energy band matching between Nb₂O₅ and MAPbI₃ as well as ZnO. In addition, the insertion of Nb₂O₅ between ZnO and MAPbI₃ facilitated the stability of the perovskite film. A systematic investigation of the ZnO deposition time on the PCE has been carried out. A deposition time of five minutes achieved a ZnO layer in the PSCs with the highest power conversion efficiency of up to 13.8%. This excellent photovoltaic property was caused by the excellent light absorption property of the high-quality perovskite film and a fast electron extraction at the perovskite/DETL interface.