Optoelectronic synapses are considered to be important cornerstones in the construction of neuromorphic computing systems because of their low power consumption, high operating speeds, and high scalability. In this work, we demonstrate an optoelectronic synaptic device based on a ZnO/HfO_x heterojunction in which optical potentiation/electrical depression behaviors and nonvolatile high current state can be implemented. The heterojunction device exhibits conductance evolution with high linearity. The excellent optoelectronic memristive behavior of the device can be attributed to the interface barrier between ZnO and HfO_x, which hinders the recombination of photo-excited electron–hole pairs to increase the carrier lifetime, and realizes the nonvolatile high current state. More importantly, the artificial vision system based on optoelectronic synaptic devices can achieved a high recognition accuracy of 96.1%. Our work provides a feasible pathway toward the development of optoelectronic synaptic devices for use in high-performance neuromorphic vision systems.

Metal oxide-based resistive switching memristors are emerging as promising nanodevices for the hardware implementation of neuromorphic computing. We focus on the development of a neuromorphic computing system based on metal oxide memristors.

The optoelectronic memristor integrates the multifunctionalities of image sensing, storage, and processing, which has been considered as the leading candidate to construct novel neuromorphic visual system. In particular, memristive materials with all-optical modulation and complementary metal oxide semiconductor (CMOS) compatibility are highly desired for energy-efficient image perception. As a p-type oxide material, Cu ₂ O exhibits outstanding theoretical photoelectric conversion efficiency and broadband photoresponse. In this work, an all-optically controlled memristor based on the Cu ₂ O/TiO ₂ /sodium alginate nanocomposite film is developed. Optical potentiation and depression behaviors have been implemented by utilizing visible (680 nm) and ultraviolet (350 nm) light. Furthermore, a 7 × 9 optoelectronic memristive array with satisfactory device variation and environment stability is constructed to emulate the image preprocessing function in biological retina. The random noise can be reduced effectively by utilizing bidirectional optical input. Beneficial from the image preprocessing function, the accuracy of handwritten digit classification increases more than 60%. Our work presents a pathway toward high-efficient neuromorphic visual system and promotes the development of artificial intelligence technology.

Emerging optoelectronic memristors are promising candidates to develop neuromorphic computing, owing to the combined advantages of photonics and electronics. However, the reversible modulation on device conductance usually requires complicated operations involving hybrid optical/electrical signals. Herein, we design a fully light-modulated memristor based on ZnO/MoOx heterojunction, which exhibits potentiation and depression behaviors under the irradiation of ultraviolet and visible light, respectively. Several basic synaptic functions have been emulated by utilizing optical signals, including short-term/long-term plasticity and spike-number-dependent plasticity. Based on the all-optical modulation characteristics, low-level image pre-processing (including contrast enhancement and noise reduction) is demonstrated. Furthermore, logic operations (“AND,” “NOTq,” and “NIMP”) can be performed by combining various optical signals in the same device. The memristive switching mechanism under optical stimulus can be attributed to barrier change at the heterojunction interface. This work proposes a fully light-modulated memristor based on ZnO/MoOx heterojunction that may promote the development of neuromorphic computing with high efficiency.

In article number 2000536 by Zhongqiang Wang, Haiyang Xu, Yichun Liu, and co‐workers, activation of silent synapse to functional synapse is demonstrated by plasma‐treatment in TiO2‐nanowire based memristor. The pristine device acts as the silent synapse without synaptic plasticity and the plasmatreated device mimicked functional synapse, which is beneficial for expanding the plasticity of artificial synapse.

Electrochemical metallization (ECM) memristors, with their advantages of high ON/OFF ratio, fast switching speeds, and low power consumption, have shown broad prospects in emerging applications such as neuromorphic computing, low-power logic computing, and artificial intelligence hardware. To fully realize the potential of ECM memristors, it is necessary to gain a deep understanding of the complexity of ion migration and redox reactions associated with resistive switching at the nanoscale. This review first systematically elucidates the resistive switching mechanism of ECM memristors, then focuses on discussing various novel physical effects emerging at the nanoscale, including the nanobattery effect, quantized conductance effect, diffusion effect, photo-induced resistive switching effect, and bio-voltage effect. A further review of the latest application advancements of these effects in cutting-edge fields such as artificial synapses, bioelectronic interfaces, in-memory computing, and neuromorphic perception. Finally, we explored the key challenges and potential opportunities facing ECM memristors in their future development, aiming to lay the groundwork for future neuromorphic computing research.