arXiv subjects

Jinghan Gao

Publications and source records attributed to Jinghan Gao.

10 recordsLinked to original sources

arXiv2026

Surface-mediated frequency aging beyond quality-factor saturation in an AlScN-on-silicon resonator

Vacuum package integrity in micro- and nanoelectromechanical resonators is commonly assessed through the quality factor Q, although Q probes residual-gas damping rather than the surface-state evolution that can govern frequency aging. Here, we disentangle the pressure responses of Q and the resonance frequency f0 in a 64.21 MHz Al0.7Sc0.3N-on-silicon cross-sectional Lamé-mode resonator between 0.01 to 760 Torr. Measurements at 25 °C and at the 68.8°C frequency turnover, where first-order thermal sensitivity is suppressed, reveal widely separated equilibration timescales. Following each pressure step, Q reaches a reversible, history-independent steady value on the pressure-control timescale and approaches a fitted pressure-independent ceiling of 8.5e4 below approximately 1 Torr. By contrast, f0 responds measurably down to 1e-5 Torr and remains history-dependent, relaxing for hours at fixed pressure. The transients follow stretched-exponential kinetics, consistent with a broad distribution of surface relaxation rates, and individual pressure steps produce fractional frequency shifts as large as 1.9 ppm. Ten-hour phase-locked measurements show a common short-term time-deviation floor near 1e-10 s from 0.01 to 100 Torr, whereas residual deterministic relaxation dominates at long averaging times; at 760 Torr, gas damping degrades short-term tracking through the reduced Q. These results establish gas-damping equilibrium and frequency equilibrium as distinct states. Quality-factor saturation alone is therefore insufficient to qualify vacuum packaging for precision mechanical frequency references; package specifications must also constrain surface-mediated frequency aging.

cond-mat.mes-hall↗

arXiv2026

Coherent phononic frequency combs in ferroelectric CMOS oxides

Modern electronic systems require tens of clock and carrier frequencies, each synthesized by a dedicated phase-locked loop from a shared reference, imposing routing, power and synchronization burdens that grow with every domain. Optical frequency combs solved this problem in photonics, whereas electronics has lacked an equivalent source in its native radiofrequency domain. Here we report broadband phononic frequency combs in ferroelectric hafnia-zirconia nanoelectromechanical resonators built from complementary metal-oxide-semiconductor (CMOS) oxides. Lithographically defined detuning of a 2:1 internal resonance selects the generation mechanism: two-tone-seeded wave mixing yields more than 170 lines distributed over two octaves, with mutual coherence verified for representative pump and generated lines, whereas an autonomous Hopf route yields hierarchical combs of more than 200 lines through torus and period-doubling dynamics, in agreement with slow-flow bifurcation theory. Geometric scaling extends comb generation across 0.44 GHz to 2.1 GHz. Heterodyne measurements, analyzed using the modified Allan deviation (MDEV), establish a two-timescale law. The mechanism governs short-term stability: seeded combs inherit the white-phase-noise scaling of their pumps, whereas autonomous combs acquire the phase diffusion of a free-running oscillator, with the one-second MDEV increasing from 1e-11 to 1e-8. The material governs long-term stability: in air and without active thermal control, the temperature-compensated stack suppresses the random-walk drift that dominates uncompensated resonators. These results establish mechanism- and material-level design rules for operating a single resonator as chip-scale frequency infrastructure, from multi-clock generation to radiofrequency parallel processing.

cond-mat.mes-hall↗

arXiv2024

Aluminum Scandium Nitride as a Functional Material at 1000°C

Aluminum scandium nitride (AlScN) has emerged as a highly promising material for high-temperature applications due to its robust piezoelectric, ferroelectric, and dielectric properties. This study investigates the behavior of Al0.7Sc0.3N thin films in extreme thermal environments, demonstrating functional stability up to 1000°C, making it suitable for use in aerospace, hypersonics, deep-well, and nuclear reactor systems. Tantalum silicide (TaSi2)/Al0.7Sc0.3N/TaSi2 capacitors were fabricated and characterized across a wide temperature range, revealing robust ferroelectric and dielectric properties, along with significant enhancement in piezoelectric performance. At 1000°C, the ferroelectric hysteresis loops showed a substantial reduction in coercive field from 4.3 MV/cm to 1.2 MV/cm, while the longitudinal piezoelectric coefficient increased nearly tenfold, reaching 75.1 pm/V at 800°C. Structural analysis via scanning and transmission electron microscopy confirmed the integrity of the TaSi2/Al0.7Sc0.3N interfaces, even after exposure to extreme temperatures. Furthermore, the electromechanical coupling coefficient was calculated to increase by over 500%, from 12.9% at room temperature to 82% at 700°C. These findings establish AlScN as a versatile material for high-temperature ferroelectric, piezoelectric, and dielectric applications, offering unprecedented thermal stability and functional enhancement.

cond-mat.mtrl-sci↗

arXiv2024

RockTrack: A 3D Robust Multi-Camera-Ken Multi-Object Tracking Framework

3D Multi-Object Tracking (MOT) obtains significant performance improvements with the rapid advancements in 3D object detection, particularly in cost-effective multi-camera setups. However, the prevalent end-to-end training approach for multi-camera trackers results in detector-specific models, limiting their versatility. Moreover, current generic trackers overlook the unique features of multi-camera detectors, i.e., the unreliability of motion observations and the feasibility of visual information. To address these challenges, we propose RockTrack, a 3D MOT method for multi-camera detectors. Following the Tracking-By-Detection framework, RockTrack is compatible with various off-the-shelf detectors. RockTrack incorporates a confidence-guided preprocessing module to extract reliable motion and image observations from distinct representation spaces from a single detector. These observations are then fused in an association module that leverages geometric and appearance cues to minimize mismatches. The resulting matches are propagated through a staged estimation process, forming the basis for heuristic noise modeling. Additionally, we introduce a novel appearance similarity metric for explicitly characterizing object affinities in multi-camera settings. RockTrack achieves state-of-the-art performance on the nuScenes vision-only tracking leaderboard with 59.1% AMOTA while demonstrating impressive computational efficiency.

cs.CV↗

arXiv2024

Unprecedented Enhancement of Piezoelectricity in Wurtzite Nitride Semiconductors via Thermal Annealing

The incorporation of rare-earth elements in wurtzite nitride semiconductors, e.g., scandium alloyed aluminum nitride (ScAlN), promises dramatically enhanced piezoelectric responses, critical to a broad range of acoustic, electronic, photonic, and quantum devices and applications. Experimentally, however, the measured piezoelectric responses of nitride semiconductors are far below what theory has predicted. Here, we show that the use of a simple, scalable, post-growth thermal annealing process can dramatically boost the piezoelectric response of ScAlN thin films. We achieve a remarkable 3.5-fold increase in the piezoelectric modulus, d33 for 30% Sc content ScAlN, from 12.3 pC/N in the as-grown state to 45.5 pC/N, which is eight times larger than that of AlN. The enhancement in piezoelectricity has been unambiguously confirmed by three separate measurement techniques. Such a dramatic enhancement of d33 has been shown to impact the effective electromechanical coupling coefficient kt2 : increasing it from 13.8% to 76.2%, which matches the highest reported values in millimeter thick lithium niobate films but is achieved in a 100 nm ScAlN with a 10,000 fold reduction in thickness, thus promising extreme frequency scaling opportunities for bulk acoustic wave resonators for beyond 5G applications. By utilizing a range of material characterization techniques, we have elucidated the underlying mechanisms for the dramatically enhanced piezoelectric responses, including improved structural quality at the macroscopic scale, more homogeneous and ordered distribution of domain structures at the mesoscopic scale, and the reduction of lattice parameter ratio (c/a) for the wurtzite crystal structure at the atomic scale. Overall, the findings present a simple yet highly effective pathway that can be extended to other material families to further enhance their piezo responses.

cond-mat.mtrl-sci↗

arXiv2024

Fast-Poly: A Fast Polyhedral Framework For 3D Multi-Object Tracking

3D Multi-Object Tracking (MOT) captures stable and comprehensive motion states of surrounding obstacles, essential for robotic perception. However, current 3D trackers face issues with accuracy and latency consistency. In this paper, we propose Fast-Poly, a fast and effective filter-based method for 3D MOT. Building upon our previous work Poly-MOT, Fast-Poly addresses object rotational anisotropy in 3D space, enhances local computation densification, and leverages parallelization technique, improving inference speed and precision. Fast-Poly is extensively tested on two large-scale tracking benchmarks with Python implementation. On the nuScenes dataset, Fast-Poly achieves new state-of-the-art performance with 75.8% AMOTA among all methods and can run at 34.2 FPS on a personal CPU. On the Waymo dataset, Fast-Poly exhibits competitive accuracy with 63.6% MOTA and impressive inference speed (35.5 FPS). The source code is publicly available at https://github.com/lixiaoyu2000/FastPoly.

cs.CV↗

arXiv2023

OFVL-MS: Once for Visual Localization across Multiple Indoor Scenes

In this work, we seek to predict camera poses across scenes with a multi-task learning manner, where we view the localization of each scene as a new task. We propose OFVL-MS, a unified framework that dispenses with the traditional practice of training a model for each individual scene and relieves gradient conflict induced by optimizing multiple scenes collectively, enabling efficient storage yet precise visual localization for all scenes. Technically, in the forward pass of OFVL-MS, we design a layer-adaptive sharing policy with a learnable score for each layer to automatically determine whether the layer is shared or not. Such sharing policy empowers us to acquire task-shared parameters for a reduction of storage cost and task-specific parameters for learning scene-related features to alleviate gradient conflict. In the backward pass of OFVL-MS, we introduce a gradient normalization algorithm that homogenizes the gradient magnitude of the task-shared parameters so that all tasks converge at the same pace. Furthermore, a sparse penalty loss is applied on the learnable scores to facilitate parameter sharing for all tasks without performance degradation. We conduct comprehensive experiments on multiple benchmarks and our new released indoor dataset LIVL, showing that OFVL-MS families significantly outperform the state-of-the-arts with fewer parameters. We also verify that OFVL-MS can generalize to a new scene with much few parameters while gaining superior localization performance.

cs.CV↗

arXiv2023

Poly-MOT: A Polyhedral Framework For 3D Multi-Object Tracking

3D Multi-object tracking (MOT) empowers mobile robots to accomplish well-informed motion planning and navigation tasks by providing motion trajectories of surrounding objects. However, existing 3D MOT methods typically employ a single similarity metric and physical model to perform data association and state estimation for all objects. With large-scale modern datasets and real scenes, there are a variety of object categories that commonly exhibit distinctive geometric properties and motion patterns. In this way, such distinctions would enable various object categories to behave differently under the same standard, resulting in erroneous matches between trajectories and detections, and jeopardizing the reliability of downstream tasks (navigation, etc.). Towards this end, we propose Poly-MOT, an efficient 3D MOT method based on the Tracking-By-Detection framework that enables the tracker to choose the most appropriate tracking criteria for each object category. Specifically, Poly-MOT leverages different motion models for various object categories to characterize distinct types of motion accurately. We also introduce the constraint of the rigid structure of objects into a specific motion model to accurately describe the highly nonlinear motion of the object. Additionally, we introduce a two-stage data association strategy to ensure that objects can find the optimal similarity metric from three custom metrics for their categories and reduce missing matches. On the NuScenes dataset, our proposed method achieves state-of-the-art performance with 75.4\% AMOTA. The code is available at https://github.com/lixiaoyu2000/Poly-MOT

cs.RO↗

arXiv2022

Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning

Few-shot learning aims to fast adapt a deep model from a few examples. While pre-training and meta-training can create deep models powerful for few-shot generalization, we find that pre-training and meta-training focuses respectively on cross-domain transferability and cross-task transferability, which restricts their data efficiency in the entangled settings of domain shift and task shift. We thus propose the Omni-Training framework to seamlessly bridge pre-training and meta-training for data-efficient few-shot learning. Our first contribution is a tri-flow Omni-Net architecture. Besides the joint representation flow, Omni-Net introduces two parallel flows for pre-training and meta-training, responsible for improving domain transferability and task transferability respectively. Omni-Net further coordinates the parallel flows by routing their representations via the joint-flow, enabling knowledge transfer across flows. Our second contribution is the Omni-Loss, which introduces a self-distillation strategy separately on the pre-training and meta-training objectives for boosting knowledge transfer throughout different training stages. Omni-Training is a general framework to accommodate many existing algorithms. Evaluations justify that our single framework consistently and clearly outperforms the individual state-of-the-art methods on both cross-task and cross-domain settings in a variety of classification, regression and reinforcement learning problems.

cs.LG↗

arXiv2021

Self-Tuning for Data-Efficient Deep Learning

Deep learning has made revolutionary advances to diverse applications in the presence of large-scale labeled datasets. However, it is prohibitively time-costly and labor-expensive to collect sufficient labeled data in most realistic scenarios. To mitigate the requirement for labeled data, semi-supervised learning (SSL) focuses on simultaneously exploring both labeled and unlabeled data, while transfer learning (TL) popularizes a favorable practice of fine-tuning a pre-trained model to the target data. A dilemma is thus encountered: Without a decent pre-trained model to provide an implicit regularization, SSL through self-training from scratch will be easily misled by inaccurate pseudo-labels, especially in large-sized label space; Without exploring the intrinsic structure of unlabeled data, TL through fine-tuning from limited labeled data is at risk of under-transfer caused by model shift. To escape from this dilemma, we present Self-Tuning to enable data-efficient deep learning by unifying the exploration of labeled and unlabeled data and the transfer of a pre-trained model, as well as a Pseudo Group Contrast (PGC) mechanism to mitigate the reliance on pseudo-labels and boost the tolerance to false labels. Self-Tuning outperforms its SSL and TL counterparts on five tasks by sharp margins, e.g. it doubles the accuracy of fine-tuning on Cars with 15% labels.

cs.LG↗