Broadband Van-der-Waals Photodetector Driven by Ferroelectric Polarization.

The potential for various future industrial applications has made broadband photodetectors beyond visible light an area of great interest. Although most 2D van-der-Waals (vdW) semiconductors have a relatively large energy bandgap (>1.2 eV), which limits their use in short-wave infrared detection, they have recently been considered as a replacement for ternary alloys in high-performance photodetectors due to their strong light-matter interaction. In this study, a ferroelectric gating ReS2 /WSe2 vdW heterojunction-channel photodetector is presented that successfully achieves broadband light detection (>1300 nm, expandable up to 2700 nm). The staggered type-II bandgap alignment creates an interlayer gap of 0.46 eV between the valence band maximum (VBMAX ) of WSe2 and the conduction band minimum (CBMIN ) of ReS2 . Especially, the control of poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)) ferroelectric dipole polarity for a specific wavelength allows a high photoresponsivity of up to 6.9 × 103 A W-1 and a low dark current below 0.26 nA under the laser illumination with a wavelength of 405 nm in P-up mode. The achieved high photoresponsivity, low dark current, and full-range near infrared (NIR) detection capability open the door for next-generation photodetectors beyond traditional ternary alloy photodetectors.

Probing Optical Multi-Level Memory Effects in Single Core-Shell Quantum Dots and Application Through 2D-0D Hybrid Inverters.

Challenges in the development of a multi-level memory (MM) device for multinary arithmetic computers have posed an obstacle to low-power, ultra-high-speed operation. For the effective transfer of a huge amount of data between arithmetic and storage devices, optical communication technology represents a compelling solution. Here, by replicating a floating gate architecture with CdSe/ZnS type-I core/shell quantum dots (QDs), a 2D-0D hybrid optical multi-level memory (OMM) device operated is demonstrated by laser pulses. In the device, laser pulses create linear optically trapped currents with MM characteristics, while conversely, voltage pulses reset all the trapped currents at once. Assuming electron transfer via the energy band alignment between MoS2 and CdSe, the study also establishes the mechanism of the OMM effect. Analysis of the designed device led to a new hypothesis that charge transfer is difficult for laterally adjacent QDs facing a double ZnS shell, which is tested by separately stimulating different positions on the 2D-0D hybrid structure with finely focused laser pulses. Results indicate that each laser pulse induced independent MM characteristics in the 2D-0D hybrid architecture. Based on this phenomenon, we propose a MM inverter to produce MM effects, such as programming and erasing, solely through the use of laser pulses. Finally, the feasibility of a fully optically-controlled intelligent system based on the proposed OMM inverters is evaluated through a CIFAR-10 pattern recognition task using a convolutional neural network.

An Optogenetics-Inspired Flexible van der Waals Optoelectronic Synapse and its Application to a Convolutional Neural Network.

Optogenetics refers to a technique that uses light to modulate neuronal activity with a high spatiotemporal resolution, which enables the manipulation of learning and memory functions in the human brain. This strategy of controlling neuronal activity using light can be applied for the development of intelligent systems, including neuromorphic and in-memory computing systems. Herein, a flexible van der Waals (vdW) optoelectronic synapse is reported, which is a core component of optogenetics-inspired intelligent systems. This synapse is fabricated on 2D vdW layered rhenium disulfide (ReS2 ) that features an inherent photosensitive memory nature derived from the persistent photoconductivity (PPC) effect, successfully mimicking the dynamics of biological synapses. Based on first-principles calculations, the PPC effect is identified to originate from sulfur vacancies in ReS2 that have an inherent tendency to form shallow defect states near the conduction band edges and under optical excitation lead to large lattice relaxation. Finally, the feasibility of applying the synapses in optogenetics-inspired intelligent systems is demonstrated via training and inference tasks for the CIFAR-10 dataset using a convolutional neural network composed of vdW optoelectronic synapse devices.

Two-Dimensional MXene Synapse for Brain-Inspired Neuromorphic Computing.

MXenes, an emerging class of two-dimensional (2D) transition metal carbides and nitrides, have attracted wide attention because of their fascinating properties required in functional electronics. Here, an atomic-switch-type artificial synapse fabricated on Ti3 C2 Tx MXene nanosheets with lots of surface functional groups, which successfully mimics the dynamics of biological synapses, is reported. Through in-depth analysis by X-ray photoelectron spectroscopy, transmission electron microscopy, and energy dispersive X-ray spectroscopy, it is found that the synaptic dynamics originated from the gradual formation and annihilation of the conductive metallic filaments on the MXene surface with distributed functional groups. Subsequently, via training and inference tasks using a convolutional neural network for the Canadian-Institute-For-Advanced-Research-10 dataset, the applicability of the artificial MXene synapse to hardware neural networks is demonstrated.

Artificial van der Waals hybrid synapse and its application to acoustic pattern recognition.

Brain-inspired parallel computing, which is typically performed using a hardware neural-network platform consisting of numerous artificial synapses, is a promising technology for effectively handling large amounts of informational data. However, the reported nonlinear and asymmetric conductance-update characteristics of artificial synapses prevent a hardware neural-network from delivering the same high-level training and inference accuracies as those delivered by a software neural-network. Here, we developed an artificial van-der-Waals hybrid synapse that features linear and symmetric conductance-update characteristics. Tungsten diselenide and molybdenum disulfide channels were used selectively to potentiate and depress conductance. Subsequently, via training and inference simulation, we demonstrated the feasibility of our hybrid synapse toward a hardware neural-network and also delivered high recognition rates that were comparable to those delivered using a software neural-network. This simulation involving the use of acoustic patterns was performed with a neural network that was theoretically formed with the characteristics of the hybrid synapses.