Neuromorphic computing can potentially solve the von Neumann bottleneck of current mainstream computing because it excels at self‐adaptive learning and highly parallel computing and consumes much less energy. Synaptic devices that mimic biological synapses are critical building blocks for neuromorphic computing. Inspired by recent progress in optogenetics and visual sensing, light has been increasingly incorporated into synaptic devices. This paves the way to optoelectronic synaptic devices with a series of advantages such as wide bandwidth, negligible resistance–capacitance (RC) delay and power loss, and global regulation of multiple synaptic devices. Herein, the basic functionalities of synaptic devices are introduced. All kinds of optoelectronic synaptic devices are then discussed by categorizing them into optically stimulated synaptic devices, optically assisted synaptic devices, and synaptic devices with optical output. Existing practical scenarios for the application of optoelectronic synaptic devices are also presented. Finally, perspectives on the development of optoelectronic synaptic devices in the future are outlined.

Optoelectronic synaptic devices have been attracting increasing attention due to their critical role in the development of neuromorphic computing based on optoelectronic integration. Here we start with silicon nanomembrane (Si NM) to fabricate optoelectronic synaptic devices. Organolead halide perovskite (MAPbI 3 ) is exploited to form a hybrid structure with Si NM. We demonstrate that synaptic transistors based on the hybrid structure are very sensitive to optical stimulation with low energy consumption. Synaptic functionalities such as excitatory post-synaptic current (EPSC), paired-pulse facilitation, and transition from short-term memory to long-term memory (LTM) are all successfully mimicked by using these optically stimulated synaptic transistors. The backgate-enabled tunability of the EPSC of these devices further leads to the LTM-based mimicking of visual learning and memory processes under different mood states. This work contributes to the development of Si-based optoelectronic synaptic devices for neuromorphic computing.

An electronic skin (e-skin) that can detect both normal and tangential forces with a differentiable signals output is essential for wearable electronics. A flexible, stretchable, and highly sensitive tactile sensor is presented that enables the detection of both normal and tangential forces, with specific opposite and thus easily being differentiated resistance changing outputs. The e-skin, which is based on two-sublayered carbon nanotubes (CNTs)/ graphene oxide (GO) hybrid 3D conductive networks, that are anchored on a thin porous polydimethylsiloxane (PDMS) layer, is synthesized via a porogen (GO wrapped NaCl) assisted self-assembling process. The fabricated CNTs/ GO@PDMS-based e-skin shows superior sensitivity (gauge factor of 2.26 under a pressure loading of 1 kPa) to tangential force, moderate sensitivity (−0.31 kPa −1 at 0.05-3.8 kPa, and −0.03 kPa −1 at 3.8-6.3 kPa, respectively) to normal force, and a high-reproducible response over 5000 loading cycles including stretching, bending, and shearing. For applications, the e-skin can not only detect wrist pulsing, discriminating different roughness of surfaces, but also produce an obvious responding to an extremely slight ticking (<20 mg) from a feather, and even can real-timely monitor human's breath and music in rhythm.

No abstract

No abstract

A three-dimensional (3D) nitrogen-doped carbon nanotubes/graphene (NCNTs/G) composite was prepared by pyrolysis of pyridine over a graphene-sheet-supported Ni catalyst. The morphology and structure of the NCNTs/G composite was investigated by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, and Raman spectroscopy. Tangled NCNTs with lengths of several hundred nanometers are sparsely, but tighly, distributed on graphene sheets, forming quasi-aligned NCNT arrays. The N content in the NCNTs/G measured by X-ray photoelectron spectroscopy is about 6.6 at. %. The NCNTs/G shows a higher activity and selectivity to the oxygen reduction reaction in alkaline electrolyte compared with undoped CNTs/G, as demonstrated by cyclic voltammetry, rotating disk electrode, and rotating ring-disk electrode measurements. The results indicate that the 3D NCNTs/G composite has potential application in fuel cells.

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.

No abstract