Molybdenum Disulfide Nanosheet/Quantum Dot Dynamic Memristive Structure Driven by Photoinduced Phase Transition

One-Stage Process of Reduction, Fluorination, and Doping with Nitrogen of Graphene Oxide Films

The possibility of chemical modification of a graphene oxide film deposited on a Si/SiO2 substrate during a one-stage hydrothermal process in the presence of fluorine ions and reducing agents, such as ascorbic acid or hydrazine, is shown. The proposed technique makes it possible to obtain reduced fluorinated graphene nitride oxide (RGOFN) in the form of a thin film with a controlled composition of functional groups by changing the type and concentration of the reducing agent and then transferring the obtained films to any substrate. XPS and IR spectroscopy of the obtained films revealed controlled changes in the structure and composition of graphene oxide associated with the removal of oxygen groups and the incorporation of fluorine ions as well as the reduction of conjugated double bonds and the controlled incorporation of nitrogen into thin RGOFN films. The current-voltage characteristics of the fabricated RGOFN structures showed that their electrical properties are well controlled by doping with nitrogen during the proposed one-stage process.

Graphene/MoS2-xOx/graphene photomemristor with tunable non-volatile responsivities for neuromorphic vision processing

Conventional artificial intelligence (AI) machine vision technology, based on the von Neumann architecture, uses separate sensing, computing, and storage units to process huge amounts of vision data generated in sensory terminals. The frequent movement of redundant data between sensors, processors and memory, however, results in high-power consumption and latency. A more efficient approach is to offload some of the memory and computational tasks to sensor elements that can perceive and process the optical signal simultaneously. Here, we proposed a non-volatile photomemristor, in which the reconfigurable responsivity can be modulated by the charge and/or photon flux through it and further stored in the device. The non-volatile photomemristor has a simple two-terminal architecture, in which photoexcited carriers and oxygen-related ions are coupled, leading to a displaced and pinched hysteresis in the current-voltage characteristics. For the first time, non-volatile photomemristors implement computationally complete logic with photoresponse-stateful operations, for which the same photomemristor serves as both a logic gate and memory, using photoresponse as a physical state variable instead of light, voltage and memresistance. The polarity reversal of photomemristors shows great potential for in-memory sensing and computing with feature extraction and image recognition for neuromorphic vision.

Geometry-asymmetric photodetectors from metal-semiconductor-metal van der Waals heterostructures

The functional diversities of two-dimensional (2D) material devices with simple architectures are ultimately limited by immature doping techniques. An alternative strategy is to use geometry-asymmetric metal-semiconductor-metal (GA-MSM) structures, which enable the basic functions of semiconductor junctions such as rectification and photovoltaics. Here, the mixed-dimensional van der Waals heterostructures (MDvdWHs) based on the separation and self-assembly of p-type SnS layered nanosheets (NSs) and n-type SnS₂ nanoparticles (NPs) are obtained using an aqueous phase exfoliation (APE) method. Due to the surface charge transfer doping, the carrier transport mechanism of devices based on MDvdWHs turns from thermionic field emission (TFE) to thermionic emission (TE), with the rectification factor (I_forward/I_reverse) changing from 0.7 to 3. To further illustrate the experimental results, the generic current transport models of GA-MSM devices have been established based on the TE and TFE mechanisms in which the TE and TFE mechanisms lead to opposite rectification phenomena in good agreement with the experimental results. The GA-MSM devices show a photovoltaic effect with a high responsivity of 35 A W^-1 and detectivity of 3.4 × 10¹¹ cm Hz^1/2 W^-1. This study not only provides a novel strategy to design photovoltaic devices with MDvdWHs, but more importantly, we have established fundamental models for the rectification behavior of GA-MSM devices.

Low-Dimensional Layered Light-Sensitive Memristive Structures for Energy-Efficient Machine Vision

Layered two-dimensional (2D) and quasi-zero-dimensional (0D) materials effectively absorb radiation in the wide ultraviolet, visible, infrared, and terahertz ranges. Photomemristive structures made of such low-dimensional materials are of great interest for creating optoelectronic platforms for energy-efficient storage and processing of data and optical signals in real time. Here, photosensor and memristor structures based on graphene, graphene oxide, bismuth oxyselenide, and transition metal dichalcogenides are reviewed from the point of view of application in broadband image recognition in artificial intelligence systems for autonomous unmanned vehicles, as well as the compatibility of the formation of layered neuromorphic structures with CMOS technology.

Recent advances in optical and optoelectronic data storage based on luminescent nanomaterials

The substantial amount of data generated every second in the big data age creates a pressing requirement for new and advanced data storage techniques. Luminescent nanomaterials (LNMs) not only possess the same optical properties as their bulk materials but also have unique electronic and mechanical characteristics due to the strong constraints of photons and electrons at the nanoscale, enabling the development of revolutionary methods for data storage with superhigh storage capacity, ultra-long working lifetime, and ultra-low power consumption. In this review, we investigate the latest achievements in LNMs for constructing next-generation data storage systems, with a focus on optical data storage and optoelectronic data storage. We summarize the LNMs used in data storage, namely upconversion nanomaterials, long persistence luminescent nanomaterials, and downconversion nanomaterials, and their applications in optical data storage and optoelectronic data storage. We conclude by discussing the superiority of the two types of data storage and survey the prospects for the field.

Memristive Devices from CuO Nanoparticles

Memristive systems can provide a novel strategy to conquer the von Neumann bottleneck by evaluating information where data are located in situ. To meet the rising of artificial neural network (ANN) demand, the implementation of memristor arrays capable of performing matrix multiplication requires highly reproducible devices with low variability and high reliability. Hence, we present an Ag/CuO/SiO₂/p-Si heterostructure device that exhibits both resistive switching (RS) and negative differential resistance (NDR). The memristor device was fabricated on p-Si and Indium Tin Oxide (ITO) substrates via cost-effective ultra-spray pyrolysis (USP) method. The quality of CuO nanoparticles was recognized by studying Raman spectroscopy. The topology information was obtained by scanning electron microscopy. The resistive switching and negative differential resistance were measured from current–voltage characteristics. The results were then compared with the Ag/CuO/ITO device to understand the role of native oxide. The interface barrier and traps associated with the defects in the native silicon oxide limited the current in the negative cycle. The barrier confined the filament rupture and reduced the reset variability. Reset was primarily influenced by the filament rupture and detrapping in the native oxide that facilitated smooth reset and NDR in the device. The resistive switching originated from traps in the localized states of amorphous CuO. The set process was mainly dominated by the trap-controlled space-charge-limited; this led to a transition into a Poole–Frenkel conduction. This research opens up new possibilities to improve the switching parameters and promote the application of RS along with NDR.