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

Human somatosensory systems based on sensor-memory-integrated technology

As a representative artificial neural network (ANN) for incorporating sensing functions and memory functions into one system to achieve highly miniaturized and highly integrated devices or systems, artificial sensory systems (ASSs) can have a far-reaching influence on precise instrumentation, sensing, and automation engineering. Artificial sensory systems have enjoyed considerable progress in recent years, from low degree integrations to highly advanced sophisticated integrations, from single-modal perceptions to multimode-fused perceptions. However, there are issues around the large hardware area, power consumption, and communication bandwidth needed during the processes where multimodal sensing signals are converted into a digital mode before they can be processed by a digital processor. Therefore, deepening the research into sensory integration is of great importance. In this review, we briefly introduce fundamental knowledge about the memristor mechanism, describe some representative human somatosensory systems, and elucidate the relationship between the properties of memristor devices and the structure. The electronic character of the sensors, future prospects, and key challenges surrounding sensor-memory integrated technologies are also discussed.

Spectrometer-Less Remote Sensing Image Classification Based on Gate-Tunable van der Waals Heterostructures

Remote sensing technology, which conventionally employs spectrometers to capture hyperspectral images, allowing for the classification and unmixing based on the reflectance spectrum, has been extensively applied in diverse fields, including environmental monitoring, land resource management, and agriculture. However, miniaturization of remote sensing systems remains a challenge due to the complicated and dispersive optical components of spectrometers. Here, m-phase GaTe0.5Se0.5 with wide-spectral photoresponses (250-1064 nm) and stack it with WSe2 are utilizes to construct a two-dimensional van der Waals heterojunction (2D-vdWH), enabling the design of a gate-tunable wide-spectral photodetector. By utilizing the multi-photoresponses under varying gate voltages, high accuracy recognition can be achieved aided by deep learning algorithms without the original hyperspectral reflectance data. The proof-of-concept device, featuring dozens of tunable gate voltages, achieves an average classification accuracy of 87.00% on 6 prevalent hyperspectral datasets, which is competitive with the accuracy of 250-1000 nm hyperspectral data (88.72%) and far superior to the accuracy of non-tunable photoresponse (71.17%). Artificially designed gate-tunable wide-spectral 2D-vdWHs GaTe0.5Se0.5/WSe2-based photodetector present a promising pathway for the development of miniaturized and cost-effective remote sensing classification technology.

Nonlinear Nano-Imaging of Interlayer Coupling in 2D Graphene-Semiconductor Heterostructures

The emergent electronic, spin, and other quantum properties of 2D heterostructures of graphene and transition metal dichalcogenides are controlled by the underlying interlayer coupling and associated charge and energy transfer dynamics. However, these processes are sensitive to interlayer distance and crystallographic orientation, which are in turn affected by defects, grain boundaries, or other nanoscale heterogeneities. This obfuscates the distinction between interlayer charge and energy transfer. Here, nanoscale imaging in coherent four-wave mixing (FWM) and incoherent two-photon photoluminescence (2PPL) is combined with a tip distance-dependent coupled rate equation model to resolve the underlying intra- and inter-layer dynamics while avoiding the influence of structural heterogeneities in mono- to multi-layer graphene/WSe2 heterostructures. With selective insertion of hBN spacer layers, it is shown that energy, as opposed to charge transfer, dominates the interlayer-coupled optical response. From the distinct nano-FWM and -2PPL tip-sample distance-dependent modification of interlayer and intralayer relaxation by tip-induced enhancement and quenching, an interlayer energy transfer time ofτ ET ≈ ( 0 . 35 - 0.15 + 0.65 ) $\tau _{\rm ET} \approx (0.35^{+0.65}_{-0.15})$  ps consistent with recent reports is derived. As a local probe technique, this approach highlights the ability to determine intrinsic sample properties even in the presence of large sample heterogeneity.

Nonvolatile and reconfigurable two-terminal electro-optic duplex memristor based on III-nitride semiconductors

With the fast development of artificial intelligence (AI), Internet of things (IOT), etc, there is an urgent need for the technology that can efficiently recognize, store and process a staggering amount of information. The AlScN material has unique advantages including immense remnant polarization, superior temperature stability and good lattice-match to other III-nitrides, making it easy to integrate with the existing advanced III-nitrides material and device technologies. However, due to the large band-gap, strong coercive field, and low photo-generated carrier generation and separation efficiency, it is difficult for AlScN itself to accumulate enough photo-generated carriers at the surface/interface to induce polarization inversion, limiting its application in in-memory sensing and computing. In this work, an electro-optic duplex memristor on a GaN/AlScN hetero-structure based Schottky diode has been realized. This two-terminal memristor shows good electrical and opto-electrical nonvolatility and reconfigurability. For both electrical and opto-electrical modes, the current on/off ratio can reach the magnitude of 104, and the resistance states can be effectively reset, written and long-termly stored. Based on this device, the "IMP" truth table and the logic "False" can be successfully reproduced, indicating the huge potential of the device in the field of in-memory sensing and computing.

PbS/CsPbBr3 Heterojunction for Broadband Neuromorphic Vision Sensing

Neuromorphic light sensors with analogue-domain image processing capability hold promise for overcoming the energy efficiency limitations and latency of von Neumann architecture-based vision chips. Recently, metal halide perovskites, with strong light-matter interaction, long carrier diffusion length, and exceptional photoelectric conversion efficiencies, exhibit reconfigurable photoresponsivity due to their intrinsic ion migration effect, which is expected to advance the development of visual sensors. However, suffering from a large bandgap, it is challenging to achieve highly tunable responsivity simultaneously with a wide-spectrum response in perovskites, which will significantly enhance the image recognition accuracy through the machine learning algorithm. Herein, we demonstrate a broadband neuromorphic visual sensor from visible (Vis) to near-infrared (NIR) by coupling all-inorganic metal halide perovskites (CsPbBr3) with narrow-bandgap lead sulfide (PbS). The PbS/CsPbBr3 heterostructure is composed of high-quality single crystals of PbS and CsPbBr3. Interestingly, the ion migration of CsPbBr3 with the implementation of an electric field induces the energy band dynamic bending at the interface of the PbS/CsPbBr3 heterojunction, leading to reversible, multilevel, and linearly tunable photoresponsivity. Furthermore, the reconfigurable and broadband photoresponse in the PbS/CsPbBr3 heterojunction allows convolutional neuronal network processing for pattern recognition and edge enhancements from the Vis to the NIR waveband, suggesting the great potential of the PbS/CsPbBr3 heterostructure in artificial intelligent vision sensing.

Recent Advances in Functional Materials for Optical Data Storage

In the current data age, the fundamental research related to optical applications has been rapidly developed. Countless new-born materials equipped with distinct optical properties have been widely explored, exhibiting tremendous values in practical applications. The optical data storage technique is one of the most significant topics of the optical applications, which is considered as the prominent solution for conquering the challenge of the explosive increase in mass data, to achieve the long-life, low-energy, and super high-capacity data storage. On this basis, our review outlines the representative reports for mainly introducing the functional systems based on the newly established materials applied in the optical storage field. According to the material categories, the representative functional systems are divided into rare-earth doped nanoparticles, graphene, and diarylethene. In terms of the difference of structural features and delicate properties among the three materials, the application in optical storage is comprehensively illustrated in the review. Meanwhile, the potential opportunities and critical challenges of optical storage are also discussed in detail.

Resistive Switching in Bigraphene/Diamane Nanostructures Formed on a La3Ga5SiO14 Substrate Using Electron Beam Irradiation

Memristors, resistive switching memory devices, play a crucial role in the energy-efficient implementation of artificial intelligence. This study investigates resistive switching behavior in a lateral 2D composite structure composed of bilayer graphene and 2D diamond (diamane) nanostructures formed using electron beam irradiation. The resulting bigraphene/diamane structure exhibits nonlinear charge carrier transport behavior and a significant increase in resistance. It is shown that the resistive switching of the nanostructure is well controlled using bias voltage. The impact of an electrical field on the bonding of diamane-stabilizing functional groups is investigated. By subjecting the lateral bigraphene/diamane/bigraphene nanostructure to a sufficiently strong electric field, the migration of hydrogen ions and/or oxygen-related groups located on one or both sides of the nanostructure can occur. This process leads to the disruption of sp3 carbon bonds, restoring the high conductivity of bigraphene.

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.

Reconfigurable, non-volatile neuromorphic photovoltaics

The neural network image sensor-which mimics neurobiological functions of the human retina-has recently been demonstrated to simultaneously sense and process optical images. However, highly tunable responsivity concurrent with non-volatile storage of image data in the neural network would allow a transformative leap in compactness and function of these artificial neural networks. Here, we demonstrate a reconfigurable and non-volatile neuromorphic device based on two-dimensional semiconducting metal sulfides that is concurrently a photovoltaic detector. The device is based on a metal-semiconductor-metal (MSM) two-terminal structure with pulse-tunable sulfur vacancies at the M-S junctions. By modulating sulfur vacancy concentrations, the polarities of short-circuit photocurrent can be changed with multiple stable magnitudes. The bias-induced motion of sulfur vacancies leads to highly reconfigurable responsivities by dynamically modulating the Schottky barriers. A convolutional neuromorphic network is finally designed for image processing and object detection using the same device. The results demonstrated that neuromorphic photodetectors can be the key components of visual perception hardware.

Broadband sensory networks with locally stored responsivities for neuromorphic machine vision

As the most promising candidates for the implementation of in-sensor computing, retinomorphic vision sensors can constitute built-in neural networks and directly implement multiply-and-accumulation operations using responsivities as the weights. However, existing retinomorphic vision sensors mainly use a sustained gate bias to maintain the responsivity due to its volatile nature. Here, we propose an ion-induced localized-field strategy to develop retinomorphic vision sensors with nonvolatile tunable responsivity in both positive and negative regimes and construct a broadband and reconfigurable sensory network with locally stored weights to implement in-sensor convolutional processing in spectral range of 400 to 1800 nanometers. In addition to in-sensor computing, this retinomorphic device can implement in-memory computing benefiting from the nonvolatile tunable conductance, and a complete neuromorphic visual system involving front-end in-sensor computing and back-end in-memory computing architectures has been constructed, executing supervised and unsupervised learning tasks as demonstrations. This work paves the way for the development of high-speed and low-power neuromorphic machine vision for time-critical and data-intensive applications.