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.

Deep attentive spatio-temporal feature learning for automatic resting-state fMRI denoising.

Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive functional neuroimaging modality that has been widely used to investigate functional connectomes in the brain. Since noise and artifacts generated by non-neuronal physiological activities are predominant in raw rs-fMRI data, effective noise removal is one of the most important preprocessing steps prior to any subsequent analysis. For rs-fMRI denoising, a common trend is to decompose rs-fMRI data into multiple components and then regress out noise-related components. Therefore, various machine learning techniques have been used in such analyses with predefined procedures and manually engineered features. However, the lack of a universal definition of a noise-related source or artifact complicates manual feature engineering. Manual feature selection can result in the failure to capture unknown types of noise. Furthermore, the possibility that the hand-crafted features will only work for the broader population (e.g., healthy adults) but not for "outliers" (e.g., infants or subjects that belong to a disease cohort) is quite high. In practice, we have limited knowledge of which features should be extracted; thus, multi-classifier assembly must be implemented to improve performance, although this process is quite time-consuming. However, in real rs-fMRI applications, fast and accurate automatic identification of noise-related components on different datasets is critical. To solve this problem, we propose a novel, automatic, and end-to-end deep learning framework dedicated to noise-related component identification via a faster and more effective multi-layer feature extraction strategy that learns deeply embedded spatio-temporal features of the components. In this study, we achieved remarkable performance on various rs-fMRI datasets, including multiple adult rs-fMRI datasets from different rs-fMRI studies and an infant rs-fMRI dataset, which is quite heterogeneous and differs from that of adults. Our proposed framework also dramatically increases the noise detection speed owing to its inherent ability for deep learning (< 1s for single-component classification). It can be easily integrated into any preprocessing pipeline, even those that do not use standard procedures but depend on alternative toolboxes.

Performance Improvement of Residue-Free Graphene Field-Effect Transistor Using Au-Assisted Transfer Method.

We report a novel graphene transfer technique for fabricating graphene field-effect transistors (FETs) that avoids detrimental organic contamination on a graphene surface. Instead of using an organic supporting film like poly(methyl methacrylate) (PMMA) for graphene transfer, Au film is directly deposited on the as-grown graphene substrate. Graphene FETs fabricated using the established organic film transfer method are easily contaminated by organic residues, while Au film protects graphene channels from these contaminants. In addition, this method can also simplify the device fabrication process, as the Au film acts as an electrode. We successfully fabricated graphene FETs with a clean surface and improved electrical properties using this Au-assisted transfer method.

Self-Powered Broadband Photodetector Based on NiO/Si Heterojunction Incorporating Graphene Transparent Conducting Layer.

In this study, a self-powered broadband photodetector based on graphene/NiO/n-Si was fabricated by the direct spin-coating of nanostructured NiO on the Si substrate. The current-voltage measurement of the NiO/Si heterostructure exhibited rectifying characteristics with enhanced photocurrent under light illumination. Photodetection capability was measured in the range from 300 nm to 800 nm, and a higher photoresponse in the UV region was observed due to the wide bandgap of NiO. The presence of a top graphene transparent conducting electrode further enhanced the responsivity in the whole measured wavelength region from 350 to 800 nm. The photoresponse of the NiO/Si detector at 350 nm was found to increase from 0.0187 to 0.163 A/W at -1 V with the insertion of the graphene top layer. A high photo-to-dark current ratio (≃104) at the zero bias indicates that the device has advantageous application in energy-efficient high-performance broadband photodetectors.

A Unified Multi-Modality Fusion Framework for Deep Spatio-Temporal-Spectral Feature Learning in Resting-State fMRI Denoising.

Resting-state functional magnetic resonance imaging (rs-fMRI) is a commonly used functional neuroimaging technique to investigate the functional brain networks. However, rs-fMRI data are often contaminated with noise and artifacts that adversely affect the results of rs-fMRI studies. Several machine/deep learning methods have achieved impressive performance to automatically regress the noise-related components decomposed from rs-fMRI data, which are expressed as the pairs of a spatial map and its associated time series. However, most of the previous methods individually analyze each modality of the noise-related components and simply aggregate the decision-level information (or knowledge) extracted from each modality to make a final decision. Moreover, these approaches consider only the limited modalities making it difficult to explore class-discriminative spectral information of noise-related components. To overcome these limitations, we propose a unified deep attentive spatio-spectral-temporal feature fusion framework. We first adopt a learnable wavelet transform module at the input-level of the framework to elaborately explore the spectral information in subsequent processes. We then construct a feature-level multi-modality fusion module to efficiently exchange the information from multi-modality inputs in the feature space. Finally, we design confidence-based voting strategies for decision-level fusion at the end of the framework to make a robust final decision. In our experiments, the proposed method achieved remarkable performance for noise-related component detection on various rs-fMRI datasets.

Topological van der Waals Contact for Two-Dimensional Semiconductors.

The relentless miniaturization inherent in complementary metal-oxide semiconductor technology has created challenges at the interface of two-dimensional (2D) materials and metal electrodes. These challenges, predominantly stemming from metal-induced gap states (MIGS) and Schottky barrier heights (SBHs), critically impede device performance. This work introduces an innovative implementation of damage-free Sb2Te3 topological van der Waals (T-vdW) contacts, representing an ultimate contact electrode for 2D materials. We successfully fabricate p-type and n-type transistors using monolayer and multilayer WSe2, achieving ultralow SBH (∼24 meV) and contact resistance (∼0.71 kΩ·µm). Simulations highlight the role of topological surface states in Sb2Te3, which effectively mitigate the MIGS effect, thereby significantly elevating device efficiency. Our experimental insights revealed the semiohmic behavior of Sb2Te3 T-vdW contacts, with an exceptional photoresponsivity of 716 A/W and rapid response times of approximately 60 µs. The findings presented herein herald topological contacts as a superior alternative to traditional metal contacts, potentially revolutionizing the performance of miniaturized electronic and optoelectronic devices.

Junctionless Negative-Differential-Resistance Device Using 2D Van-Der-Waals Layered Materials for Ternary Parallel Computing.

Negative-differential-resistance (NDR) devices offer a promising pathway for developing future computing technologies characterized by exceptionally low energy consumption, especially multivalued logic computing. Nevertheless, conventional approaches aimed at attaining the NDR phenomenon involve intricate junction configurations and/or external doping processes in the channel region, impeding the progress of NDR devices to the circuit and system levels. Here, an NDR device is presented that incorporates a channel without junctions. The NDR phenomenon is achieved by introducing a metal-insulator-semiconductor capacitor to a portion of the channel area. This approach establishes partial potential barrier and well that effectively restrict the movement of hole and electron carriers within specific voltage ranges. Consequently, this facilitates the implementation of both a ternary inverter and a ternary static-random-access-memory, which are essential components in the development of multivalued logic computing technology.

Fabrication and RF characterization of a single nickel silicide nanowire for an interconnect.

We fabricated a nickel silicide nanowire (NiSi NW) device with a low thermal budget and characterized it by measuring the S-parameters in the radio-frequency (RF) regime. A single silicon nanowire (Si NW) was assembled on a substrate with a two-port coplanar waveguide structure using the dielectrophoresis method. Then, the Si NW on the device was perfectly transformed into a NiSi NW. The NiSi NW device was characterized by performing measurements in the DC and RF regimes. The transformation into the NiSi NW resulted in reducing about three-order more the resistance than before the transformation. Hence, the transmission of the NiSi NW device was 25 dB higher than that of the Si NW device up to gigahertz. We also discussed extracting the intrinsic properties of the NiSi NW by using de-embedding, circuit modeling, and simulation.

Large-scale fabrication of 2-D nanoporous graphene using a thin anodic aluminum oxide etching mask.

A large-scale nanoporous graphene (NPG) fabrication method via a thin anodic aluminum oxide (AAO) etching mask is presented in this paper. A thin AAO film is successfully transferred onto a hydrophobic graphene surface under no external force. The AAO film is completely stacked on the graphene due to the van der Waals force. The neck width of the NPG can be controlled ranging from 10 nm to 30 nm with different AAO pore widening times. Extension of the NPG structure is demonstrated on a centimeter scale up to 2 cm2. AAO and NPG structures are characterized using optical microscopy (OM), Raman spectroscopy and field-emission scanning electron microscopy (FE-SEM). A field effect transistor (FET) is realized by using NPG. Its electrical characteristics turn out to be different from that of pristine graphene, which is due to the periodic nanostructures. The proposed fabrication method could be adapted to a future graphene-based nano device.

A Van Der Waals Reconfigurable Multi-Valued Logic Device and Circuit Based on Tunable Negative-Differential-Resistance Phenomenon.

Multi-valued logic (MVL) technology that utilizes more than two logic states has recently been reconsidered because of the demand for greater power saving in current binary logic systems. Extensive efforts have been invested in developing MVL devices with multiple threshold voltages by adopting negative differential transconductance and resistance. In this study, a reconfigurable, multiple negative-differential-resistance (m-NDR) device with an electric-field-induced tunability of multiple threshold voltages is reported, which comprises a BP/ReS2 heterojunction and a ReS2 /h-BN/metal capacitor. Tunability for the m-NDR phenomenon is achieved via the resistance modulation of the ReS2 layer by electrical pulses applied to the capacitor region. Reconfigurability is verified in terms of the function of an MVL circuit composed of a reconfigurable m-NDR device and a load transistor, wherein staggered-type and broken-type double peak-NDR device operations are adopted for ternary inverter and latch circuits, respectively.

Recent Advances in Electrical Doping of 2D Semiconductor Materials: Methods, Analyses, and Applications.

Two-dimensional materials have garnered interest from the perspectives of physics, materials, and applied electronics owing to their outstanding physical and chemical properties. Advances in exfoliation and synthesis technologies have enabled preparation and electrical characterization of various atomically thin films of semiconductor transition metal dichalcogenides (TMDs). Their two-dimensional structures and electromagnetic spectra coupled to bandgaps in the visible region indicate their suitability for digital electronics and optoelectronics. To further expand the potential applications of these two-dimensional semiconductor materials, technologies capable of precisely controlling the electrical properties of the material are essential. Doping has been traditionally used to effectively change the electrical and electronic properties of materials through relatively simple processes. To change the electrical properties, substances that can donate or remove electrons are added. Doping of atomically thin two-dimensional semiconductor materials is similar to that used for silicon but has a slightly different mechanism. Three main methods with different characteristics and slightly different principles are generally used. This review presents an overview of various advanced doping techniques based on the substitutional, chemical, and charge transfer molecular doping strategies of graphene and TMDs, which are the representative 2D semiconductor materials.

Ferroelectric polymer-based artificial synapse for neuromorphic computing.

Recently, various efforts have been made to implement synaptic characteristics with a ferroelectric field-effect transistor (FeFET), but in-depth physical analyses have not been reported thus far. Here, we investigated the effects by (i) the formation temperature of the ferroelectric material, poly(vinylidene fluoride-trifluoroethylene) P(VDF-TrFE) and (ii) the nature of the contact metals (Ti, Cr, Pd) of the FeFET on the operating performance of a FeFET-based artificial synapse in terms of various synaptic performance indices. Excellent ferroelectric properties were induced by maximizing the size and coverage ratio of the ß-phase domains by annealing the P(VDF-TrFE) film at 140 °C. A metal that forms a relatively high barrier improved the dynamic range and nonlinearity by suppressing the contribution of the tunneling current to the post-synaptic current. Subsequently, we studied the influence of the synaptic characteristics on the training and recognition tasks by using two MNIST datasets (fashion and handwritten digits) and the multi-layer perceptron concept of neural networks.

Flexible artificial Si-In-Zn-O/ion gel synapse and its application to sensory-neuromorphic system for sign language translation.

We propose a flexible artificial synapse based on a silicon-indium-zinc-oxide (SIZO)/ion gel hybrid structure directly fabricated on a polyimide substrate, where the channel conductance is effectively modulated via ion movement in the ion gel. This synaptic operation is possible because of the low-temperature deposition process of the SIZO layer (<150°C) and the surface roughness improvement of the poly(4-vinylphenol) buffer layer (12.29→1.81 nm). The flexible synaptic device exhibits extremely stable synaptic performance under high mechanical (bending 1500 times with a radius of 5 mm) and electrical stress (application of voltage pulses 104 times) without any degradation. Last, a sensory-neuromorphic system for sign language translation, which consists of stretchable resistive sensors and flexible artificial synapses, is designed and successfully evaluated via training and recognition simulation using hand sign patterns obtained by stretchable sensors (maximum recognition rate, 99.4%).

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.

A multiple negative differential resistance heterojunction device and its circuit application to ternary static random access memory.

For increasing the restricted bit-density in the conventional binary logic system, extensive research efforts have been directed toward implementing single devices with a two threshold voltage (VTH) characteristic via the single negative differential resistance (NDR) phenomenon. In particular, recent advances in forming van der Waals (vdW) heterostructures with two-dimensional crystals have opened up new possibilities for realizing such NDR-based tunneling devices. However, it has been challenging to exhibit three VTH through the multiple-NDR (m-NDR) phenomenon in a single device even by using vdW heterostructures. Here, we show the m-NDR device formed on a BP/(ReS2 + HfS2) type-III double-heterostructure. This m-NDR device is then integrated with a vdW transistor to demonstrate a ternary vdW latch circuit capable of storing three logic states. Finally, the ternary latch is extended toward ternary SRAM, and its high-speed write and read operations are theoretically verified.

Double Negative Differential Resistance Device Based on Hafnium Disulfide/Pentacene Hybrid Structure.

Recently, combinations of 2D van der Waals (2D vdW) materials and organic materials have attracted attention because they facilitate the formation of various heterojunctions with excellent interface quality owing to the absence of dangling bonds on their surface. In this work, a double negative differential resistance (D-NDR) characteristic of a hybrid 2D vdW/organic tunneling device consisting of a hafnium disulfide/pentacene heterojunction and a 3D pentacene resistor is reported. This D-NDR phenomenon is achieved by precisely controlling an NDR peak voltage with the pentacene resistor and then integrating two distinct NDR devices in parallel. Then, the operation of a controllable-gain amplifier configured with the D-NDR device and an n-channel transistor is demonstrated using the Cadence Spectre simulation platform. The proposed D-NDR device technology based on a hybrid 2D vdW/organic heterostructure provides a scientific foundation for various circuit applications that require the NDR phenomenon.

Nitrogen-Induced Filament Confinement Technique for a Highly Reliable Hafnium-Based Electrochemical Metallization Threshold Switch and Its Application to Flexible Logic Circuits.

Electrochemical metallization (ECM) threshold switches are in great demand for various applications such as next-generation logic technology, future memory, and neuromorphic computing. However, the instability of operation due to inherent filamentary randomness is a severe problem that is yet to be solved. Here, we propose a specially treated hafnium oxide (HfO x:N)-based ECM threshold switch with high reliability, low-voltage operation (0.2 V), high ON/OFF ratio (5 × 108), great endurance (106), and fast switching speed (1.5 µs at 2 V). The nitrogen ions in the HfO x:N layer assist confining the path of the metallic filament, which significantly suppresses filament randomness as well as reduces power consumption and alternating current response time. The feasibility of ECM threshold switches to logic applications, AND and OR, is first introduced. The ECM threshold switch has great potential to be utilized in complementary logic circuits because of its ultralow operation power consumption, high integrability using an array structure (4 F2), and fast switching characteristics. Furthermore, we have successfully verified its applicability to flexible electronics on polyethylene naphthalate films that can retain stable operation under considerable mechanical stress. We believe that this research paves the way to fabricate highly reliable ECM threshold switches for flexible complementary logic circuits with ultralow power consumption.

Rhenium Diselenide (ReSe2) Near-Infrared Photodetector: Performance Enhancement by Selective p-Doping Technique.

In this study, a near-infrared photodetector featuring a high photoresponsivity and a short photoresponse time is demonstrated, which is fabricated on rhenium diselenide (ReSe2) with a relatively narrow bandgap (0.9-1.0 eV) compared to conventional transition-metal dichalcogenides (TMDs). The excellent photo and temporal responses, which generally show a trade-off relation, are achieved simultaneously by applying a p-doping technique based on hydrochloric acid (HCl) to a selected ReSe2 region. Because the p-doping of ReSe2 originates from the charge transfer from un-ionized Cl molecules in the HCl to the ReSe2 surface, by adjusting the concentration of the HCl solution from 0.1 to 10 m, the doping concentration of the ReSe2 is controlled between 3.64 × 1010 and 3.61 × 1011 cm-2. Especially, the application of the selective HCl doping technique to the ReSe2 photodetector increases the photoresponsivity from 79.99 to 1.93 × 103 A W-1, and it also enhances the rise and decay times from 10.5 to 1.4 ms and from 291 to 3.1 ms, respectively, compared with the undoped ReSe2 device. The proposed selective p-doping technique and its fundamental analysis will provide a scientific foundation for implementing high-performance TMD-based electronic and optoelectronic devices.

Ultrasensitive MoS2 photodetector by serial nano-bridge multi-heterojunction.

The recent reports of various photodetectors based on molybdenum disulfide (MoS2) field effect transistors showed that it was difficult to obtain optoelectronic performances in the broad detection range [visible-infrared (IR)] applicable to various fields. Here, by forming a mono-/multi-layer nano-bridge multi-heterojunction structure (more than > 300 junctions with 25 nm intervals) through the selective layer control of multi-layer MoS2, a photodetector with ultrasensitive optoelectronic performances in a broad spectral range (photoresponsivity of 2.67 × 106 A/W at λ = 520 nm and 1.65 × 104 A/W at λ = 1064 nm) superior to the previously reported MoS2-based photodetectors could be successfully fabricated. The nano-bridge multi-heterojunction is believed to be an important device technology that can be applied to broadband light sensing, highly sensitive fluorescence imaging, ultrasensitive biomedical diagnostics, and ultrafast optoelectronic integrated circuits through the formation of a nanoscale serial multi-heterojunction, just by adding a selective layer control process.

A Neuromorphic Device Implemented on a Salmon-DNA Electrolyte and its Application to Artificial Neural Networks.

A bioinspired neuromorphic device operating as synapse and neuron simultaneously, which is fabricated on an electrolyte based on Cu2+-doped salmon deoxyribonucleic acid (S-DNA) is reported. Owing to the slow Cu2+ diffusion through the base pairing sites in the S-DNA electrolyte, the synaptic operation of the S-DNA device features special long-term plasticity with negative and positive nonlinearity values for potentiation and depression (αp and αd), respectively, which consequently improves the learning/recognition efficiency of S-DNA-based neural networks. Furthermore, the representative neuronal operation, "integrate-and-fire," is successfully emulated in this device by adjusting the duration time of the input voltage stimulus. In particular, by applying a Cu2+ doping technique to the S-DNA neuromorphic device, the characteristics for synaptic weight updating are enhanced (|αp|: 31→20, |αd|: 11→18, weight update margin: 33→287 nS) and also the threshold conditions for neuronal firing (amplitude and number of stimulus pulses) are modulated. The improved synaptic characteristics consequently increase the Modified National Institute of Standards and Technology (MNIST) pattern recognition rate from 38% to 44% (single-layer perceptron model) and from 89.42% to 91.61% (multilayer perceptron model). This neuromorphic device technology based on S-DNA is expected to contribute to the successful implementation of a future neuromorphic system that simultaneously satisfies high integration density and remarkable recognition accuracy.