Abstract Organic–inorganic halide perovskites are widely investigated for various optoelectronics devices such as photodetectors and solar cells. However, Pb‐based perovskite photodetectors perform ...poorly because of insufficient mobility and absence of an effective photoconductive gain mechanism. Herein, ZnON/perovskite heterostructures are demonstrated to realize the fabrication of high‐performance hybrid phototransistors. Additionally, a guanidinium iodide (GuI), alternating‐cation‐interlayer (ACI)‐type organic spacers, are utilized as an interface passivation layer. It is shown that the GuI interface layer reduces the defect density of the ZnON surface as well as the bulk perovskite film and promotes the separation and transport of photo‐generated charge carriers. Devices fabricated with the GuI interface passivation layer have a response speed of 0.1 s, excellent responsivity of 5.97 × 10 6 A W −1 , and ultra‐high specific detectivity of 1.28 × 10 18 Jones, the highest value among previously reported perovskite‐based hybrid phototransistors. This work provides valuable insights into the design and fabrication of high‐performance metal oxide/perovskite heterostructures with promising applications in future optoelectronic devices.
Efficient current‐induced switching of perpendicular magnetization is an essential task in spintronics for realizing high‐performance information processing and for storage device application. ...However, the spin‐orbit torque (SOT) by injection of in‐plane polarized spins cannot deterministically switch the magnetization of ferromagnetic thin films with perpendicular magnetic anisotropy (PMA) without an additionally applied in‐plane external magnetic field to break the symmetry of the PMA. Considering the difficulties of applying the magnetic field to the localized area only within a device structure, it is essential to contrive a facile field‐free SOT switching mechanism. Here, deterministic field‐free SOT switching of perpendicular magnetization is achieved in amorphous and ferrimagnetic Gd/Co multilayers accompanied by a tilted magnetic anisotropy axis. This tilted anisotropy originates from the combined contributions of many internal anisotropies in different orientations from the multilayers and is shown to be controllable. It is expected that the introduction of controlled tilted anisotropy into Gd/Co multilayers over the entire film surface in the present study can be extended to the development of wafer‐scale technologies for the spintronics memory and logic devices.
The slightly tilted magnetic anisotropy axis of the perpendicular magnetic anisotropy film enables the field‐free magnetization switching of magnetization by spin currents. Stable switching operations can be repeatedly executed using pulsed spin currents.
The development of brain-inspired neuromorphic computing, including artificial intelligence (AI) and machine learning, is of considerable importance because of the rapid growth in hardware and ...software capacities, which allows for the efficient handling of big data. Devices for neuromorphic computing must satisfy basic requirements such as multilevel states, high operating speeds, low energy consumption, and sufficient endurance, retention and linearity. In this study, inorganic perovskite-type amorphous strontium vanadate (a-SrVOx: a-SVO) synthesized at room temperature is utilized to produce a high-performance memristor that demonstrates nonvolatile multilevel resistive switching and synaptic characteristics. Analysis of the electrical characteristics indicates that the a-SVO memristor illustrates typical bipolar resistive switching behavior. Multilevel resistance states are also observed in the off-to-on and on-to-off transition processes. The retention resistance of the a-SVO memristor is shown to not significantly change for a period of 2 × 104 s. The conduction mechanism operating within the Ag/a-SVO/Pt memristor is ascribed to the formation of Ag-based filaments. Nonlinear neural network simulations are also conducted to evaluate the synaptic behavior. These results demonstrate that a-SVO-based memristors hold great promise for use in high-performance neuromorphic computing devices.
One of the effective ways to minimize the spread of COVID‐19 infection is to diagnose it as early as possible before the onset of symptoms. In addition, if the infection can be simply diagnosed using ...a smartwatch, the effectiveness of preventing the spread will be greatly increased. In this study, we aimed to develop a deep learning model to diagnose COVID‐19 before the onset of symptoms using heart rate (HR) data obtained from a smartwatch. In the deep learning model for the diagnosis, we proposed a transformer model that learns HR variability patterns in presymptom by tracking relationships in sequential HR data. In the cross‐validation (CV) results from the COVID‐19 unvaccinated patients, our proposed deep learning model exhibited high accuracy metrics: sensitivity of 84.38%, specificity of 85.25%, accuracy of 84.85%, balanced accuracy of 84.81%, and area under the receiver operating characteristics (AUROC) of 0.8778. Furthermore, we validated our model using external multiple datasets including healthy subjects, COVID‐19 patients, as well as vaccinated patients. In the external healthy subject group, our model also achieved high specificity of 77.80%. In the external COVID‐19 unvaccinated patient group, our model also provided similar accuracy metrics to those from the CV: balanced accuracy of 87.23% and AUROC of 0.8897. In the COVID‐19 vaccinated patients, the balanced accuracy and AUROC dropped by 66.67% and 0.8072, respectively. The first finding in this study is that our proposed deep learning model can simply and accurately diagnose COVID‐19 patients using HRs obtained from a smartwatch before the onset of symptoms. The second finding is that the model trained from unvaccinated patients may provide less accurate diagnosis performance compared with the vaccinated patients. The last finding is that the model trained in a certain period of time may provide degraded diagnosis performances as the virus continues to mutate.
A scaling law elucidates the universality in nature, presiding over many physical phenomena which seem unrelated. Thus, exploring the universality class of scaling law in a particular system ...enlightens its physical nature in relevance to other systems and sometimes unearths an unprecedented new dynamic phase. Here, the dynamics of weakly driven magnetic skyrmions are investigated, and its scaling law is compared with the motion of a magnetic domain wall (DW) creep. This study finds that the skyrmion does not follow the scaling law of the DW creep in 2D space but instead shows a hopping behavior similar to that of the particle‐like DW in 1D confinement. In addition, the hopping law satisfies even when a topological charge of the skyrmion is removed. Therefore, the distinct scaling behavior between the magnetic skyrmion and the DW stems from a general principle beyond the topological charge. This study demonstrates that the hopping behavior of skyrmions originates from the bottleneck process induced by DW segments with diverging collective lengths, which is inevitable in any closed‐shape spin structure in 2D. This work reveals that the structural topology of magnetic texture determines the universality class of its weakly driven motion, which is distinguished from the universality class of magnetic DW creep.
Universal scaling behavior of hopping motion determined by the shape of the interface and its topology is discovered through the current‐driven motion of magnetic skyrmions. This provides insights for implementing the skyrmionic devices and, for understanding the dynamics of moving bodies with a closed interface, which appears in a wide range of physical systems.
Organic–inorganic halide perovskites have been widely investigated for the fabrication of photodetectors because of their excellent optoelectronic properties. However, the toxicity of Pb‐based ...perovskites and the lack of manufacturing technology in air limits the commercialization of perovskite‐based photodetectors. Tin halide perovskite (THP) is a promising candidate to replace Pb‐based perovskites due to its low toxicity and potential commercialization. However, THP suffers poor stability in air because Sn2+ is easily oxidized to Sn4+. This “self‐doping” effect not only degrades device performance rapidly, but also makes it difficult to fabricate THP‐based devices in air. Here, Pb‐free Sn‐based 2D perovskite PEA2SnI4 films with tin fluoride (SnF2) additive are prepared in air using a nitrogen quenching hot casting method. The photodetectors with an optimal SnF2 concentration of 20% exhibit a fast response speed of 0.56 ms and an excellent specific detectivity of 6.32 × 1013 Jones, the best combination of THP‐based photodetectors reported hitherto. The SnF2 additive is found to suppress the formation of Sn vacancies and improve the crystallinity of PEA2SnI4 films. The work provides an air‐processing technology of Pb‐free perovskite, which is easy to commercialize, and SnF2 additive engineering offering an effective way to demonstrate high performance photodetectors and image recognition applications.
Sn‐based perovskite PEA2SnI4 films are fabricated via nitrogen quenching hot casting method in air for photodetector applications. To adjust “self‐doping” effect of Sn‐based perovskites, tin fluoride additive is used. The effect of SnF2 doping on optoelectronic properties of PEA2SnI4 film and photodetector performance is investigated. The PEA2SnI4 photodetector with optimal SnF2 doping concentration shows high specific detectivity, fast response speed.
Wearable exoskeleton robots have become a promising technology for supporting human motions in multiple tasks. Activity recognition in real-time provides useful information to enhance the robot's ...control assistance for daily tasks. This work implements a real-time activity recognition system based on the activity signals of an inertial measurement unit (IMU) and a pair of rotary encoders integrated into the exoskeleton robot. Five deep learning models have been trained and evaluated for activity recognition. As a result, a subset of optimized deep learning models was transferred to an edge device for real-time evaluation in a continuous action environment using eight common human tasks: stand, bend, crouch, walk, sit-down, sit-up, and ascend and descend stairs. These eight robot wearer's activities are recognized with an average accuracy of 97.35% in real-time tests, with an inference time under 10 ms and an overall latency of 0.506 s per recognition using the selected edge device.
The recent discovery of room‐temperature ferromagnetism in 2D van der Waals (vdW) materials, such as Fe3GaTe2 (FGaT), has garnered significant interest in offering a robust platform for 2D spintronic ...applications. Various fundamental operations essential for the realization of 2D spintronics devices are experimentally confirmed using these materials at room temperature, such as current‐induced magnetization switching or tunneling magnetoresistance. Nevertheless, the potential applications of magnetic skyrmions in FGaT systems at room temperature remain unexplored. In this work, the current‐induced generation of magnetic skyrmions in FGaT flakes employing high‐resolution magnetic transmission soft X‐ray microscopy is introduced, supported by a feasible mechanism based on thermal effects. Furthermore, direct observation of the current‐induced magnetic skyrmion motion at room temperature in FGaT flakes is presented with ultra‐low threshold current density. This work highlights the potential of FGaT as a foundation for room‐temperature‐operating 2D skyrmion device applications.
Current‐induced magnetic skyrmion motion is directly visualized in 2D van der Waals (vdW) ferromagnet Fe3GaTe2 (FGaT) at room temperature. The electrical pulse can thermally generate the skyrmions under an external magnetic field. In addition, magnetic skyrmions are effectively driven by electrical current. The results demonstrate the feasibility of operating skyrmion devices at room temperature using 2D materials.
•A novel deep learning segmentation methodology of skin lesions is proposed.•The proposed FrCN method learns the full resolution features of each pixel of the input data.•The segmentation performance ...was evaluated using two publicly datasets: ISBI 2017 and PH2.•Results of FrCN overcomes the latest deep learning approaches such as FCN, U-Net, and SegNet.
Automatic segmentation of skin lesions in dermoscopy images is still a challenging task due to the large shape variations and indistinct boundaries of the lesions. Accurate segmentation of skin lesions is a key prerequisite step for any computer-aided diagnostic system to recognize skin melanoma.
In this paper, we propose a novel segmentation methodology via full resolution convolutional networks (FrCN). The proposed FrCN method directly learns the full resolution features of each individual pixel of the input data without the need for pre- or post-processing operations such as artifact removal, low contrast adjustment, or further enhancement of the segmented skin lesion boundaries. We evaluated the proposed method using two publicly available databases, the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 Challenge and PH2 datasets. To evaluate the proposed method, we compared the segmentation performance with the latest deep learning segmentation approaches such as the fully convolutional network (FCN), U-Net, and SegNet.
Our results showed that the proposed FrCN method segmented the skin lesions with an average Jaccard index of 77.11% and an overall segmentation accuracy of 94.03% for the ISBI 2017 test dataset and 84.79% and 95.08%, respectively, for the PH2 dataset. In comparison to FCN, U-Net, and SegNet, the proposed FrCN outperformed them by 4.94%, 15.47%, and 7.48% for the Jaccard index and 1.31%, 3.89%, and 2.27% for the segmentation accuracy, respectively. Furthermore, the proposed FrCN achieved a segmentation accuracy of 95.62% for some representative clinical benign cases, 90.78% for the melanoma cases, and 91.29% for the seborrheic keratosis cases in the ISBI 2017 test dataset, exhibiting better performance than those of FCN, U-Net, and SegNet.
We conclude that using the full spatial resolutions of the input image could enable to learn better specific and prominent features, leading to an improvement in the segmentation performance.
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Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer ...sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or dynamic, is recognized by means of statistical signal features and artificial-neural nets (ANNs). The upper level recognition uses the autoregressive (AR) modeling of the acceleration signals, thus, incorporating the derived AR-coefficients along with the signal-magnitude area and tilt angle to form an augmented-feature vector. The resulting feature vector is further processed by the linear-discriminant analysis and ANNs to recognize a particular human activity. Our proposed activity-recognition method recognizes three states and 15 activities with an average accuracy of 97.9% using only a single triaxial accelerometer attached to the subject's chest.
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