arXiv subjects

Zeyu Wan

Publications and source records attributed to Zeyu Wan.

8 recordsLinked to original sources

arXiv2025

Impact of Ge substrate Thicknesses and Epitaxy Growth Conditions on the Optical and Material Properties of Ge- and GaAs-based VCSELs

We present a comparative study of the optical and material property dependences of VCSELs on Ge or GaAs substrate thicknesses and epitaxy process conditions. It was found that adjusting the Ge substrate thickness and optimizing the epitaxy process can shift the stopband center and cavity resonance wavelength by several nanometers. Ge-based VCSELs exhibit improved epitaxial uniformity, smaller deviations from design specifications, reduced stoichiometry variations, and strain magnitudes comparable to those of GaAs-based counterparts. In the selected 46.92 square micron sample area, no defects were observed in the quantum well (QW) regions of Ge-based VCSELs, and the threading dislocation density (TDD) was measured to be below 2.13e6 per square cm. These results highlight the potential of Ge substrates as promising candidates for advanced VCSELs.

cond-mat.mtrl-sci↗

arXiv2024

Impacts of Backside Insulation on the Dynamic On-Resistance of Lateral p-GaN HEMTs-on-Si

We examined the effect of backside insulation on the dynamic on-resistance of lateral p-GaN HEMTs. To gain a comprehensive understanding of the dynamic onresistance difference between substrate grounded and substrate floating p-GaN HEMTs, we conducted in-circuit double pulse testing and long-term direct current (DC) bias stress. We have realized that while backside insulation can enhance the breakdown voltage of lateral p-GaN HEMTs, it also comes with a tradeoff in device reliability. Results through Sentaurus TCAD simulation suggest that the use of backside insulation in devices gradually disperses potential to the buffer barrier. As a result, the potential barrier at the buffer edge of the 2DEG channel decreases significantly, leading to considerable electron trappings at buffer traps. This breakdown voltage and reliability tradeoff also applies to HEMT technologies using insulating substrates.

physics.app-ph↗

arXiv2024

SLR: Learning Quadruped Locomotion without Privileged Information

The recent mainstream reinforcement learning control for quadruped robots often relies on privileged information, demanding meticulous selection and precise estimation, thereby imposing constraints on the development process. This work proposes a Self-learning Latent Representation (SLR) method, which achieves high-performance control policy learning without the need for privileged information. To enhance the credibility of the proposed method's evaluation, SLR was directly compared with state-of-the-art algorithms using their open-source code repositories and original configuration parameters. Remarkably, SLR surpasses the performance of previous methods using only limited proprioceptive data, demonstrating significant potential for future applications. Ultimately, the trained policy and encoder empower the quadruped robot to traverse various challenging terrains. Videos of our results can be found on our website: https://11chens.github.io/SLR/

cs.RO↗

arXiv2023

Fine-Grained Cross-View Geo-Localization Using a Correlation-Aware Homography Estimator

In this paper, we introduce a novel approach to fine-grained cross-view geo-localization. Our method aligns a warped ground image with a corresponding GPS-tagged satellite image covering the same area using homography estimation. We first employ a differentiable spherical transform, adhering to geometric principles, to accurately align the perspective of the ground image with the satellite map. This transformation effectively places ground and aerial images in the same view and on the same plane, reducing the task to an image alignment problem. To address challenges such as occlusion, small overlapping range, and seasonal variations, we propose a robust correlation-aware homography estimator to align similar parts of the transformed ground image with the satellite image. Our method achieves sub-pixel resolution and meter-level GPS accuracy by mapping the center point of the transformed ground image to the satellite image using a homography matrix and determining the orientation of the ground camera using a point above the central axis. Operating at a speed of 30 FPS, our method outperforms state-of-the-art techniques, reducing the mean metric localization error by 21.3% and 32.4% in same-area and cross-area generalization tasks on the VIGOR benchmark, respectively, and by 34.4% on the KITTI benchmark in same-area evaluation.

cs.CV↗

arXiv2023

LP-SLAM: Language-Perceptive RGB-D SLAM system based on Large Language Model

Simultaneous localization and mapping (SLAM) is a critical technology that enables autonomous robots to be aware of their surrounding environment. With the development of deep learning, SLAM systems can achieve a higher level of perception of the environment, including the semantic and text levels. However, current works are limited in their ability to achieve a natural-language level of perception of the world. To address this limitation, we propose LP-SLAM, the first language-perceptive SLAM system that leverages large language models (LLMs). LP-SLAM has two major features: (a) it can detect text in the scene and determine whether it represents a landmark to be stored during the tracking and mapping phase, and (b) it can understand natural language input from humans and provide guidance based on the generated map. We illustrated three usages of the LLM in the system including text cluster, landmark judgment, and natural language navigation. Our proposed system represents an advancement in the field of LLMs based SLAM and opens up new possibilities for autonomous robots to interact with their environment in a more natural and intuitive way.

cs.RO↗

arXiv2023

Tac2Structure: Object Surface Reconstruction Only through Multi Times Touch

Inspired by humans' ability to perceive the surface texture of unfamiliar objects without relying on vision, the sense of touch can play a crucial role in robots exploring the environment, particularly in scenes where vision is difficult to apply, or occlusion is inevitable. Existing tactile surface reconstruction methods rely on external sensors or have strong prior assumptions, making the operation complex and limiting their application scenarios. This paper presents a framework for low-drift surface reconstruction through multiple tactile measurements, Tac2Structure. Compared with existing algorithms, the proposed method uses only a new vision-based tactile sensor without relying on external devices. Aiming at the difficulty that reconstruction accuracy is easily affected by the pressure at contact, we propose a correction algorithm to adapt it. The proposed method also reduces the accumulative errors that occur easily during global object surface reconstruction. Multi-frame tactile measurements can accurately reconstruct object surfaces by jointly using the point cloud registration algorithm, loop-closure detection algorithm based on deep learning, and pose graph optimization algorithm. Experiments verify that Tac2Structure can achieve millimeter-level accuracy in reconstructing the surface of objects, providing accurate tactile information for the robot to perceive the surrounding environment.

cs.RO↗

arXiv2022

Enhance Accuracy: Sensitivity and Uncertainty Theory in LiDAR Odometry and Mapping

Currently, the improvement of LiDAR poses estimation accuracy is an urgent need for mobile robots. Research indicates that diverse LiDAR points have different influences on the accuracy of pose estimation. This study aimed to select a good point set to enhance accuracy. Accordingly, the sensitivity and uncertainty of LiDAR point residuals were formulated as a fundamental basis for derivation and analysis. High-sensitivity and low -uncertainty point residual terms are preferred to achieve higher pose estimation accuracy. The proposed selection method has been theoretically proven to be capable of achieving a global statistical optimum. It was tested on artificial data and compared with the KITTI benchmark. It was also implemented in LiDAR odometry (LO) and LiDAR inertial odometry (LIO), both indoors and outdoors. The experiments revealed that utilizing selected LiDAR point residuals simultaneously enhances optimization accuracy, decreases residual terms, and guarantees real-time performance.

cs.RO↗

arXiv2019

Gate Leakage Suppression and Breakdown Voltage Enhancement in p-GaN HEMTs using Metal/Graphene Gates

In this work, single-layer intrinsic and fluorinated graphene were investigated as gate insertion layers in normally-OFF p-GaN gate HEMTs, which wraps around the bottom of the gate forming Ti/graphene/p-GaN at the bottom and Ti/graphene/ SiNx on the two sides. Compared to the Au/Ti/p-GaN HEMTs without graphene, the insertion of graphene can increase the ION/IOFF ratios by a factor of 50, increase the VTH by 0.30 V and reduce the off-state gate leakage by 50 times. Additionally, this novel gate structure has better thermal stability. After thermal annealing at 350 °C, gate breakdown voltage holds at 12.1 V, which is first reported for Schottky gate p-GaN HEMTs. This is considered to be a result of the 0.24 eV increase in Schottky barrier height and the better quality of the Ti/graphene/p-GaN and Ti/graphene/SiNx interfaces. This approach is very effective in improving the Ion/Iff ratio and gate BV of normally-OFF GaN HEMTs.

physics.app-ph↗