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.

An energy-efficient memristive synapse is highly desired for the development of brain-like neurosynaptic chips. In this work, a ZnO-based memristive synapse with ultralow-power consumption was achieved by simple N-doping. The introduction of N atoms, as the acceptor, reduces the carrier concentration and greatly increases the resistance of the ZnO film. The low energy consumption, which is as low as 60 fJ per synaptic event, can be achieved in our device. Essential synaptic learning functions have been demonstrated, including excitatory postsynaptic current, paired-pulse facilitation, and experience-dependent learning behaviors. Furthermore, the device can still exhibit the synaptic performance in the bent state or even after 100 bending cycles. Our memristive synapse is not only promising for energy-efficient neuromorphic computing systems but also suitable for the development of wearable neuromorphic electronics.

Organic–inorganic hybrid halide perovskites (OIHPs) with inherent mixed ionic–electronic conduction ability have been proposed as promising candidates for memristors with unique optoelectronic characteristics. Despite the great achievements toward understanding the working mechanism and exploring their functionality as water-sensitive materials, the humidity effect on the resistive switching (RS) characteristics still remains to be studied. This study investigates the humidity effect on the RS characteristics of Au/CH3NH3PbI3/FTO memristor. The memristor works well at moderate relative humidity (RH, <75%) and degrades rapidly at higher RH of 90%. An obvious decrease in low resistance states on increasing the RH level is observed, which could be attributed to water-induced reduction of the iodide migration barrier. Raman and X-ray diffraction analyses indicate that the migration barrier reduction possibly originated from the weakening of the Pb–I bond caused by the intercalation of water molecules into the crystal lattice. The humidity-sensitive RS characteristics of the memristor could extend the scope of OIHP application for sensing and security applications and also prompt researchers to pay attention to the humidity effect on memristor devices with OIHPs.

Emulation of biological synapses by electronic devices will form a foundation for realizing brain‐inspired computational systems. In addition to mimicking functional synapses, it is also important to demonstrate activation functionality of silent synapses in memristors, to emulate the evolutionary processes of human brain development. Here, a silent synapse without synaptic plasticity is achieved in a single‐crystalline TiO2 nanowire‐based memristor. Importantly, the short‐term plasticity transforms to long‐term plasticity in plasma (O2, Ar, and Ar‐H2) treated devices, representing activation of a silent synapse to a functional synapse. The memristive mechanism is attributed to the accumulation and diffusion of oxygen vacancies at the Pt/TiO2 interface, which adjusts the Schottky barrier and conductance. The use of various plasma treatments also enables synaptic function modulation by changing the density of oxygen vacancies. The results provide a feasible method for activating silent synapses and modulating synaptic learning functions in a memristor‐based artificial synapse.

Auditory motion perception is one crucial capability to decode and discriminate the spatiotemporal information for neuromorphic auditory systems. Doppler frequency‐shift feature and interaural time difference (ITD) are two fundamental cues of auditory information processing. In this work, the functions of azimuth detection and velocity detection, as the typical auditory motion perception, are demonstrated in a WOx‐based memristive synapse. The WOx memristor presents both the volatile mode (M1) and semi‐nonvolatile mode (M2), which are capable of implementing the high‐pass filtering and processing the spike trains with a relative timing and frequency shift. In particular, the Doppler frequency‐shift information processing for velocity detection is emulated in the WOx memristor based auditory system for the first time, which relies on a scheme of triplet spike‐timing‐dependent‐plasticity in the memristor. These results provide new opportunities for the mimicry of auditory motion perception and enable the auditory sensory system to be applied in future neuromorphic sensing.

Optoelectronic resistive switching (RS) device is attracting interest for their promising potential in optical communication technology. In this work, photo-tunable organic memory device based on poly(4-vinylphenol) (PVP) and N-doped carbon...

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/HfOx 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 HfOx, 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.

Mimicking Pavlovian conditioning by memristive synapse is significant to implement neuromorphic computing at the hardware level. In this work, we demonstrated the Pavlovian conditioning based on the artificial synapse architecture of one-transistor/one-resistor (1T1R), which included an AgInSbTe/α-C-based memristor as a variable resistance and an N-MOS transistor. Thanks to stable resistance switching behavior of memristor and outstanding controllability of device conductance by transistor gating of 1T1R, the experimental demonstration of the acquisition and extinction of Pavlovian conditioning were realized. Moreover, the temporal relation between the conditioned and unconditioned stimuli was also established in which the memory time of associative learning decreased with the increase in the interval of two stimuli. This work provided an idea to biorealistically mimic the Pavlovian conditioning, paving the way for memristive neuromorphic computing.