Exploration of optoelectronic memristors with the capability to combine sensing and processing functions is required to promote development of efficient neuromorphic vision. In this work, the authors develop a plasmonic optoelectronic memristor that relies on the effects of localized surface plasmon resonance (LSPR) and optical excitation in an Ag–TiO₂ nanocomposite film. Fully light‐induced synaptic plasticity (e.g., potentiation and depression) under visible and ultraviolet light stimulations is demonstrated, which enables the functional combination of visual sensing and low‐level image pre‐processing (including contrast enhancement and noise reduction) in a single device. Furthermore, the light‐gated and electrically‐driven synaptic plasticity can be performed in the same device, in which the spike‐timing‐dependent plasticity (STDP) learning functions can be reversibly modulated by visible and ultraviolet light illuminations. Thereby, the high‐level image processing function, i.e., image recognition, can also be performed in this memristor, whose recognition rate and accuracy are obviously enhanced as a result of image pre‐processing and light‐gated STDP enhancement. Experimental analysis shows that the memristive switching mechanism under optical stimulation can be attributed to the oxidation/reduction of Ag nanoparticles due to the effects of LSPR and optical excitation. The authors' work proposes a new type of plasmonic optoelectronic memristor with fully light‐modulated capability that may promote the future development of efficient neuromorphic vision.

Organic‐inorganic halide perovskites (OHPs) have been intensively studied for application in solar cells with high conversion efficiency exceeding 22%. The unique electrical and optical properties of OHPs have led to their use in optoelectronic device applications beyond photovoltaics, such as light‐emitting diodes, photodetectors, transistors. New information storage technologies and computing architectures are being researched extensively with the aim of addressing the growing challenge of approaching end of Moore's law and von Neumann bottleneck. As the fourth basic circuit element, memristor is a leading candidate with powerful capabilities in information storage and neuromorphic computing applications. Recently, OHPs have received growing attention as promising materials for memristors. In particular, their mixed ionic‐electronic conduction ability paired with light sensitivity provide OHPs with the opportunity to display novel functions such as optical‐erase memory, optogenetics‐inspired synaptic functions, and light‐accelerated learning capability. This review covers recent advances in OHP‐based memristors development including memristive mechanism and analytical models, universal memristive characteristics for memory and neuromorphic computing applications, and novel multi‐functionalization. Challenges and future prospects of OHP‐based memristors are also discussed.

Semitransparent flexible resistive-switching memory devices, using amorphous InGaZnO as the switching layer, are fabricated on plastic substrates at room temperature. The device shows high performance, excellent flexibility, and mechanical endurance in bending tests. No performance degradation occurs, and the stored information is not lost after bending the device to different angles and up to 10⁵ times. Studies on the temperature-dependent electrical properties reveal that the conducting channels of the low-resistance state are composed of oxygen-deficient defects, and partial oxidation of these defects switches the device to the high-resistance state. The unique electronic structure and flexible mechanical properties of amorphous InGaZnO ensure stable device performance in flexible applications.

By introducing Ag nanoclusters (NCs), ZnO-based resistive switching memory devices offer improved performance, including improved uniformity of switching parameters, and increased switching speed with excellent reliability. These Ag NCs are formed between the top-electrode (cathode) and the switching layer by an electromigration process in the initial several switching cycles. The electric field can be enhanced around Ag NCs due to their high surface curvature. The enhanced local-electric-field (LEF) results in (1) the localization of the switching site near Ag NCs, where oxygen-vacancy-based conducting filaments have a simple structure, and tend to connect Ag NCs along the LEF direction; (2) an increase in migration and recombination rates of oxygen ions and oxygen vacancies. These factors are responsible for the improvement in device performance.

In our brain, information is exchanged among neurons in the form of spikes where both the space (which neuron fires) and time (when the neuron fires) contain relevant information. Every neuron is connected to other neurons by synapses, which are continuously created, updated, and stimulated to enable information processing and learning. Realizing the brain-like neuron/synapse network in silicon would enable artificial autonomous agents capable of learning, adaptation, and interaction with the environment. Toward this aim, the conventional microelectronic technology, which is based on complementary metal–oxide–semiconductor transistors and the von Neumann computing architecture, does not provide the desired energy efficiency and scaling potential. A generation of emerging memory devices, including resistive switching random access memory (RRAM) also known as the memristor, can offer a wealth of physics-enabled processing capabilities, including multiplication, integration, potentiation, depression, and time-decaying stimulation, which are suitable to recreate some of the fundamental phenomena of the human brain in silico. This work provides an overview about the status and the most recent updates on brain-inspired neuromorphic computing devices. After introducing the RRAM device technologies, we discuss the main computing functionalities of the human brain, including neuron integration and fire, dendritic filtering, and short- and long-term synaptic plasticity. For each of these processing functions, we discuss their proposed implementation in terms of materials, device structure, and brain-like characteristics. The rich device physics, the nano-scale integration, the tolerance to stochastic variations, and the ability to process information in situ make the emerging memory devices a promising technology for future brain-like hardware intelligence.