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Abstract: Defect engineering represents one degree of freedom to tune the physical and chemical properties of two-dimensional (2D) materials. Here, we demonstrate that the thermal and electronic properties of 2D Bi2O2Se can be optimized simultaneously by introducing oxygen defects. 2D Bi2O2Se and its oxygen-deficient counterpart (Bi2OxSe, x < 2) can be controllably synthesized by the chemical vapor deposition (CVD) method. By introducing oxygen defects, the thermal conductivity of 2D Bi2O2Se is reduced by nearly three times, achieving an extremely low thermal conductivity of 0.68 ± 0.06 W/mK at room temperature via the thermal bridge technique. This low thermal conductivity is enabled by the scattering of phonons by targeting of high-, mid-, and low-frequency phonons due to oxygen defects, strong anharmonicity, and nanostructure boundaries, respectively. Meanwhile, the mobility is also improved to 260–500 cm2·V−1·s−1 and the usual polar optical phonon scattering in 2D Bi2O2Se is weakened owing to the introduction of oxygen defects. Our results promise potential applications for thermoelectric design, nanoelectronics, and thermal barrier coating devices based on emerging 2D materials. PubDate: 2023-05-19
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Abstract: In recent years, researchers have increasingly directed their attention towards modulating light fields through the unique properties of two-dimensional materials and the free designability of meta-structures. Graphene, transition metal sulfides, transition metal nitrides, and other two-dimensional materials have emerged as star materials in recent years due to their extraordinary properties that are vastly different from those of traditional three-dimensional materials. As a result, these materials hold immense potential for further exploration and research. Taking advantage of the free designability of meta-structures can be an effective means of unlocking the full potential of 2D materials. Accordingly, this review presents an overview of recent research progress in the area of light field modulation achieved by combining 2D materials with meta-structures. The review initially covers the properties of 2D materials, followed by the concepts, principles, design, and preparation of meta-structures. Then the review delves into the concrete examples of the impact and effect of the combination on light field modulation. Lastly, the review concludes with a comprehensive summary and analysis of the current challenges and potential future developments of combining 2D materials with meta-structures. PubDate: 2023-05-19
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Abstract: For the emerging excellent two-dimensional semiconductor black phosphorus (BP), doping has been proven as an effective way to tune its intrinsic properties. For the further development and expansion of BP-based research and application, the direct growth of doped BP films is highly desirable but still remains a challenge. In this work, the direct growth of uniformly doped-BP films on silicon substrates is achieved by a simple one-step vapor growth. The proposed decoupled feeding strategy significantly improves the effectiveness of doping and enables uniform dopant distribution in the grown films. The substitutional doping nature and high crystal quality of the grown doped films are confirmed by microscopy and crystal structural determination. Electrical transport measurement results reveal that Se and Te dopants perform mild electron doping effect and enable improve the electron mobility relative to pristine BP, while As dopant performs mild hole doping effect. It is believed that the direct growth of doped BP films in this work will facilitate the research development of BP in electronics and optoelectronics. PubDate: 2023-05-18
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Abstract: In this paper, the spectral efficiency of a full-duplex (FD) densely distributed multi-input multi-output (MIMO) system with wireless backhaul is considered. In-band full-duplex (IBFD) backhauling, in which the backhaul and access transmissions take place on the same spectrum, is exploited for wireless backhauling to enable an efficient spectrum reuse. However, the severe cross-tier interference and cross-link interference reduce the gains of IBFD backhauling. To evaluate the achievable spectral efficiency with imperfect channel state information (CSI), we propose a two-phase channel estimation scheme to estimate the CSI for two wireless links, and the scheme estimates an effective interference CSI between access points (APs) based on beamforming training to perform interference cancelation at APs. Given the estimated CSI, the closed-form expressions of the uplink and downlink achievable rates with maximum ratio transmission beamforming and maximum ratio combining receivers, respectively, are derived with Gamma approximation. Numerical results verify the accuracy of the derived closed-form expressions and the effectiveness of the two-phase channel estimation scheme for interference cancelation. Moreover, compared with half-duplex densely distributed MIMO systems, FD systems with interference cancelation have a better performance. PubDate: 2023-05-18
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Abstract: Memristors are memory-capable electronic components that consist of two terminals and a switching layer, whose resistance can be adjusted by an applied bias voltage. Two-dimensional (2D) materials with ultrathin layered structures are used as switching layers to overcome the limitations of traditional resistive materials in reducing the memristor sizes, demonstrating their potential in memory, flexible electronics, neuromorphic computing, and other related fields. Particularly, MoS2 is widely used as a representative 2D semiconductor, and the MoS2-based memristors have been intensively studied. In this review article, we have summarized the recent progress of MoS2-based memristors, including the fabrication process, device structure, device performance, switching mechanism, and synaptic applications. In addition, we also discussed the prospects and challenges for their future development. PubDate: 2023-05-18
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Abstract: Various steep-slope devices based on novel structures and mechanisms garnered considerable attention for their potential in ultra-low power logic applications. In this work, a novel steep-slope negative quantum capacitance field-effect transistor (NQCFET) with molybdenum disulfide (MoS2)-integrated gate stack was realized by theoretical analysis and experimental evaluation. By combining the MoS2 equivalent capacitance model calibrated with experimental results, the NQCFET device model is further established. The results demonstrated that the optimized MoS2-integrated NQCFET can achieve a subthreshold swing (SS) of sub-60 mV/dec over a current range of 5 decades, with the minimum SS reaching 29 mV/dec, indicating the remarkable potential of MoS2-integrated NQCFETs for ultra-low power applications. PubDate: 2023-05-18
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Abstract: Edge devices play an increasingly important role in the convolutional neural network (CNN) inference. However, the large computation and storage requirements are challenging for resource- and power-constrained hardware. These limitations might be overcome by exploring the following: (a) error tolerance via approximate computing, such as stochastic computing (SC); (b) data sparsity, including the weight and activation sparsity. Although SC can perform complex calculations with compact and simple arithmetic circuits, traditional SC-based accelerators suffer from the low reconfigurability and long bitstream, further making it difficult to benefit from the data sparsity. In this paper, we propose spatial-parallel stochastic computing (SPSC), which improves the spatial parallelism of the SC-based multiplier to the full extent while consuming fewer logic gates than the fixed-point implementation. Moreover, we present SPA, a highly reconfigurable SPSC-based sparse CNN accelerator with the proposed hybrid zero-skipping scheme (HZSS), to efficiently take advantage of different zero-skipping strategies for different types of layers. Comprehensive experiments show that SPA with up to 2477.6 Gops/W outperforms existing several binary-weight accelerators, SC-based accelerators, and the sparse CNN accelerator considering energy efficiency. PubDate: 2023-05-17
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Abstract: Co-saliency detection within a single image is a common vision problem that has not yet been well addressed. Existing methods often used a bottom-up strategy to infer co-saliency in an image in which salient regions are firstly detected using visual primitives such as color and shape and then grouped and merged into a co-saliency map. However, co-saliency is intrinsically perceived complexly with bottom-up and top-down strategies combined in human vision. To address this problem, this study proposes a novel end-to-end trainable network comprising a backbone net and two branch nets. The backbone net uses ground-truth masks as top-down guidance for saliency prediction, whereas the two branch nets construct triplet proposals for regional feature mapping and clustering, which drives the network to be bottom-up sensitive to co-salient regions. We construct a new dataset of 2019 natural images with co-saliency in each image to evaluate the proposed method. Experimental results show that the proposed method achieves state-of-the-art accuracy with a running speed of 28 fps. PubDate: 2023-05-17
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Abstract: The extraction of atomic-level material features from electron microscope images is crucial for studying structure-property relationships and discovering new materials. However, traditional electron microscope analyses rely on time-consuming and complex human operations; thus, they are only applicable to images with a small number of atoms. In addition, the analysis results vary due to observers’ individual deviations. Although efforts to introduce automated methods have been performed previously, many of these methods lack sufficient labeled data or require various conditions in the detection process that can only be applied to the target material. Thus, in this study, we developed AtomGAN, which is a robust, unsupervised learning method, that segments defects in classical 2D material systems and the heterostructures of MoS2/WS2 automatically. To solve the data scarcity problem, the proposed model is trained on unpaired simulated data that contain point and line defects for MoS2/WS2. The proposed AtomGAN was evaluated on both simulated and real electron microscope images. The results demonstrate that the segmented point defects and line defects are presented perfectly in the resulting figures, with a measurement precision of 96.9%. In addition, the cycled structure of AtomGAN can quickly generate a large number of simulated electron microscope images. PubDate: 2023-05-17
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Abstract: The increase in mobile data traffic has imposed unprecedented pressure on wireless network management. KPI-based anomaly diagnosis can alleviate such pressure by automatically identifying the cause of abnormalities in the traffic and providing end-to-end monitoring and optimization. Previous approaches mainly focus on finding a subset of anomaly-inducing KPIs on the basis of supervised learning procedures. These studies have two possible limitations: (1) the inherent correlations between KPIs that are proven to be effective for the anomaly diagnosis, are still largely underexplored; (2) machine learning models heavily rely on human annotations, which are expensive and labor-intensive. Therefore, we propose random matrix theory-based KPI identification (RKI), a novel method that automatically mines rich interactions between KPIs for anomaly diagnosis without using any learnable parameters or human annotations. Specifically, RKI diagnoses the abnormal KPIs in two steps. First, we build a matrix for anomaly KPI detection to mine the spectrum of its covariances. Second, another new matrix is reconstructed to calculate the correlation difference. By doing so, the anomaly KPIs that have larger correlation difference scores can be efficiently identified in the wireless traffic without any trainable parameters. In extensive experiments on a public dataset, RKI yields a 6.5% higher true diagnostic rate and 11.36% lower false alarming rate than the statistical model, demonstrating its effectiveness. A 100 × larger scale synthetic dataset also demonstrates the capabilities of RKI to explore massive data traffic under real-word scenarios. Finally, we discuss RKI’s potential applications of our method in future 6G wireless networks. PubDate: 2023-05-10
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Abstract: Neuromorphic computing based on artificial optoelectronic synapses has attracted considerable attention owing to its high time/power efficiency and parallel processing capability. However, existing devices are mainly suitable for only the visible range. Here, high-performance near-infrared optoelectronic memories and synapses were demonstrated using Te/α-In2Se3 heterostructures. Owing to the entangled ferroelectricity-semiconducting properties of α-In2Se3, whose ferroelectric polarizations can be switched by photocarriers that migrated from the Te near-infrared light absorber, the device could be set into a non-volatile high-resistance/low-resistance state through the application of positive gate voltages/near-infrared light pulses. Hence, the device could function as a high-performance photodetector, with a photoresponsive on/off ratio of 5.25 × 104/8.3 × 103 and a specific detectivity of 2.6 × 1011/7.5 × 1010 Jones at 1550/1940 nm. In addition, the device could function as a multi-state optoelectronic synapse with good stability and high linearity; moreover, using the device, we developed an optoelectronic artificial neural network with high recognition accuracies of 100% and 89.9% for a database composed of 64-pixel letters with 10% and 70% noise levels, respectively. Our work provides a feasible avenue for developing neuromorphic networks applicable in the infrared range. PubDate: 2023-05-10
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Abstract: In this study, the event-triggered asymptotic tracking control problem is considered for a class of nonholonomic systems in chained form for the time-varying reference input. First, to eliminate the ripple phenomenon caused by the imprecise compensation of the time-varying reference input, a novel time-varying event-triggered piecewise continuous control law and a triggering mechanism with a time-varying triggering function are developed. Second, an explicit integral input-to-state stable Lyapunov function is constructed for the time-varying closed-loop system regarding the sampling error as the external input. The origin of the closed-loop system is shown to be uniformly globally asymptotically stable for any global exponential decaying threshold signals, which in turn rules out the Zeno behavior. Moreover, infinitely fast sampling can be avoided by appropriately tuning the exponential convergence rate of the threshold signal. A numerical simulation example is provided to illustrate the proposed control approach. PubDate: 2023-05-10
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Abstract: Strategies to modulate the exciton dynamics in ultrathin two-dimensional (2D) semiconductors have always been an integral component in the bid towards improved optoelectronics and quantum photonic devices. The capability to non-destructively tune the relaxation dynamics, valley polarization, binding energies, and population ratio of various excitonic species has been well-sought for advanced applications. Through the rationale design of a WS2-ZnO hybrid platform, we present a distinct increment in the trion-to-exciton ratio for WS2 emission across a patterned heterostructure. The shift in dominant excitonic species arose due to the efficient charge segregation at the spatially confined interface of the type-II heterostructure. Owing to the charge transfer process, the resultant emission profile presents up to four times amplification in the trion-to-exciton ratio, with temperature variable trion binding energies up to 59 meV. Since trions possess non-zero charge and spin degrees of freedom, the provision of a higher density of trions with increased binding stability would encourage new opportunities for reproducible optoelectronics and quantum emitters. PubDate: 2023-05-10
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Abstract: In recent years, two-dimensional (2D) materials-based fundamental preparing process such as high-quality wafer-level single crystal thin film synthesis technology and high-performance electrode preparing technology has developed rapidly. In addition, the integrated application prospect of 2D materials has been preliminarily verified, owing to the flat and clean interface between 2D materials and substrates. From the perspective of electronics and optoelectronics based on 2D materials, this paper will summarize the recent studies of integrated circuit hardware, integrated optoelectronic hardware, and hetero-integrated hardware, showing their advantages and potential application. PubDate: 2023-05-10
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Abstract: Libration points are vital for lunar and deep space explorations because of their unique positions and dynamics. This paper first traces the development of relevant missions since ISEE-3 and then presents the details of the trajectory design and implementation of four Chinese libration point missions in the lunar exploration project: the two Sun-Earth libration point missions by CHANG’E-2 and CHANG’E-5 and the two lunar libration point missions accomplished by CHANG’E-5T1 and Queqiao. The orbit technologies for these libration point missions are also elaborated on regarding trajectory design, maneuvering, and tracking, as well as orbit determination. This paper is expected to provide a reference for future cislunar and deep space exploration. PubDate: 2023-05-09
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Abstract: Fault detection and isolation (FDI) problems for linear parameter-varying (LPV) systems with state time-delays are studied in this paper. By defining the concept of unobservability subspace and designing its calculation algorithm, the geometric approach is introduced to the time-delay LPV systems. Utilizing Wirtinger-based integral inequality, we obtain a sufficient condition to solve the so-called H∞-based residual generation problem for the LPV systems. In this paper, we consider two cases: the time delay is known and the time delay is unknown but its estimated value can be obtained. Corresponding observers are proposed for both cases based on the geometric approach and H∞ techniques. Lyapunov-Krasovskii functional is utilized to handle the time-delays and Wirtinger’s inequality is employed to reduce conservatism. Numerical examples are presented to demonstrate the effectiveness of the proposed approach. PubDate: 2023-05-09
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Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Point cloud registration is a challenging problem in the condition of large initial misalignments and noises. A major problem encountered in the registration algorithms is the definition of correspondence between two point clouds. Point clouds contain rich geometric information and the same geometric structure implies the same feature even if they are in different poses, which motivates us to seek a rotation-invariant feature representation for calculating the correspondence. This work proposes a rotation-invariant neural network for point cloud registration. To acquire rotation-invariant features, we firstly propose a rotation-invariant point cloud representation (RIPR) at the input level. Instead of using the original coordinates, we propose to use point pair features (PPF) and the transformed coordinates in the local reference frame (LRF) to represent a point. Then, we design a new convolution operator named Cylinder-Conv which utilizes the symmetry of cylinder-shaped voxels and the hierarchical geometry information of the surface of 3D shapes. By specifying the cylinder-shaped structures and directions, Cylinder-Conv can better capture the local neighborhood geometry of each point and maintain rotation-invariance. Finally, we combine RIPR and Cylinder-Conv to extract normalized rotation-invariant features to generate the correspondence and perform a differentiable singular value decomposition (SVD) step to estimate the rigid transformation. The proposed network presents state-of-the-art performance on point cloud registration. Experiments show that our method is robust to initial misalignments and noises. PubDate: 2023-04-23
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