Search for: All records

Abstract GeSn photodetectors monolithically grown on Ge virtual substrates demonstrate mid‐wave infrared (MWIR) detection at room temperature. The lattice mismatch between GeSn and Ge causes dislocations and compressive strain, creating leakage pathways and unwanted indirect band transitions. Designed thin Ge_0.91Sn_0.09triple‐step buffer layers of ≈175 nm total thickness reduce dislocations and enable full relaxation, showing 100% lattice relaxation and smooth surface roughness of 0.83 nm with shorter auto‐correlation length in surface morphology compared to single‐step buffers. Ge_1‐xSn_xphotodetectors (x= 0.09, 0.12, and 0.15) on triple‐step buffers withn‐i‐pconfigurations achieve lattice strain relaxations of 99%, 88%, and 80%, respectively. Ge_0.91Sn_0.09and Ge_0.88Sn_0.12show gradual variation in auto‐correlation amplitude, while Ge_0.85Sn_0.15shows an increase due to lattice mismatch. Shockley–Read–Hall recombination current dominates at low reverse bias due to mismatch‐induced dislocations, while band‐to‐band tunneling current dominates at higher reverse bias due to narrowing bandgap under strong electric fields. The photodetectors show extended spectral response with increasing Sn composition ofi‐GeSn active layer sandwiched by barriers. Ge_0.88Sn_0.12and Ge_0.85Sn_0.15exhibit extended wavelength cut‐offs of 3.12 and 3.27 µm at room temperature, demonstrating significant potential for silicon‐based MWIR applications. 

We experimentally demonstrate a low-cost transfer process of GeSn ribbons to insulating substrates for short-wave infrared (SWIR) sensing/imaging applications. By releasing the original compressive GeSn layer to nearly fully relaxed state GeSn ribbons, the room-temperature spectral response of the photodetector is further extended to 3.2 μm, which can cover the entire SWIR range. Compared with the as-grown GeSn reference photodetectors, the fabricated GeSn ribbon photodetectors have a fivefold improvement in the light-to-dark current ratio, which can improve the detectivity for high-performance photodetection. The transient performance of a GeSn ribbon photodetector is investigated with a rise time of about 40 μs, which exceeds the response time of most GeSn (Ge)-related devices. In addition, this transfer process can be applied on various substrates, making it a versatile technology that can be used for various applications ranging from optoelectronics to large-area electronics. These results provide insightful guidance for the development of low-cost and high-speed SWIR photodetectors based on Sn-containing group IV low-dimensional structures. 

With the increase of multi-view graph data, multi-view graph clustering (MVGC) that can discover the hidden clusters without label supervision has attracted growing attention from researchers. Existing MVGC methods are often sensitive to the given graphs, especially influenced by the low quality graphs, i.e., they tend to be limited by the homophily assumption. However, the widespread real-world data hardly satisfy the homophily assumption. This gap limits the performance of existing MVGC methods on low homophilous graphs. To mitigate this limitation, our motivation is to extract high-level view-common information which is used to refine each view's graph, and reduce the influence of non-homophilous edges. To this end, we propose dual label-guided graph refinement for multi-view graph clustering (DuaLGR), to alleviate the vulnerability in facing low homophilous graphs. Specifically, DuaLGR consists of two modules named dual label-guided graph refinement module and graph encoder module. The first module is designed to extract the soft label from node features and graphs, and then learn a refinement matrix. In cooperation with the pseudo label from the second module, these graphs are refined and aggregated adaptively with different orders. Subsequently, a consensus graph can be generated in the guidance of the pseudo label. Finally, the graph encoder module encodes the consensus graph along with node features to produce the high-level pseudo label for iteratively clustering. The experimental results show the superior performance on coping with low homophilous graph data. The source code for DuaLGR is available at https://github.com/YwL-zhufeng/DuaLGR. 

In addition to the multiple sensors to measure parameters that can be used to improve both safety and efficiency, modern vehicles also gather information about external data (e.g., traffic conditions, weather) which, if properly used, could further improve the overall trip experience. Specifically, when it comes to navigation, one source that can provide increased context awareness, especially for autonomous driving, are the High Definition (HD) maps, which have recently witnessed a tremendous growth of popularity in vehicular technology and use. As they are limited to a particular geographic area, different portions need to be downloaded (and processed) on multiple occasions throughout a given trip, along with the other data from other internal and external sources. In this paper, we provide an effective deep learning approach for the recently introduced problem of Predicting Map Data Consumption (PMDC) in the future time instants for a given trip. We propose a novel methodology that integrates multiple data sources (road network, traffic, historic trips, HD maps) and, for a given trip, enables prediction of the map data consumption. Our experimental observations demonstrate the benefits of the proposed approach over the candidate baselines.