arXiv subjects

Xiaohua Ma

Publications and source records attributed to Xiaohua Ma.

4 recordsLinked to original sources

arXiv2026

Drift-free characterization of electro-optic tuning efficiency in lithium niobate photonic nanocavities

Lithium niobate photonic crystal nanobeam cavity (PCNBC) represents a premier platform for integrated electro-optics, offering deep sub-wavelength mode confinement, enhanced light-matter interactions, and ultralow power consumption. However, accurate characterization of the electro-optic (EO) tuning efficiency in such high-Q devices is fundamentally impeded by DC drift, a time-dependent spectral instability arising from charge redistribution, surface screening, or buffer layer relaxation under sustained electric fields. Here, we report the systematic analysis of DC drift dynamics in lithium niobate nanocavities and demonstrate that conventional quasi-static DC voltage scanning yields highly unreliable characterization data. To circumvent this limitation, we introduce a drift-free, dynamic measurement methodology that employs high-frequency triangular-wave voltage sweeps to effectively decouple the instantaneous electronic Pockels response from slow charge-relaxation processes. Validated across 35 devices with varying electrode geometries, our method delivers reproducible tuning efficiency of 4.3-4.5 pm/V with a low coefficient of variation of 1.1%, showing excellent quantitative agreement with three-dimensional finite-element simulations. This robust, drift-free measurement technique establishes a rigorous standard for the characterization and optimization of resonant cavity electro-optics, accelerating the development of high-performance thin-film lithium niobate photonic integrated circuits.

physics.optics↗

arXiv2022

Galaxy Image Classification using Hierarchical Data Learning with Weighted Sampling and Label Smoothing

With the development of a series of Galaxy sky surveys in recent years, the observations increased rapidly, which makes the research of machine learning methods for galaxy image recognition a hot topic. Available automatic galaxy image recognition researches are plagued by the large differences in similarity between categories, the imbalance of data between different classes, and the discrepancy between the discrete representation of Galaxy classes and the essentially gradual changes from one morphological class to the adjacent class (DDRGC). These limitations have motivated several astronomers and machine learning experts to design projects with improved galaxy image recognition capabilities. Therefore, this paper proposes a novel learning method, ``Hierarchical Imbalanced data learning with Weighted sampling and Label smoothing" (HIWL). The HIWL consists of three key techniques respectively dealing with the above-mentioned three problems: (1) Designed a hierarchical galaxy classification model based on an efficient backbone network; (2) Utilized a weighted sampling scheme to deal with the imbalance problem; (3) Adopted a label smoothing technique to alleviate the DDRGC problem. We applied this method to galaxy photometric images from the Galaxy Zoo-The Galaxy Challenge, exploring the recognition of completely round smooth, in between smooth, cigar-shaped, edge-on and spiral. The overall classification accuracy is 96.32\%, and some superiorities of the HIWL are shown based on recall, precision, and F1-Score in comparing with some related works. In addition, we also explored the visualization of the galaxy image features and model attention to understand the foundations of the proposed scheme.

astro-ph.IM↗

arXiv2022

Hardware-algorithm collaborative computing with photonic spiking neuron chip based on integrated Fabry-Pérot laser with saturable absorber

Photonic neuromorphic computing has emerged as a promising avenue toward building a low-latency and energy-efficient non-von-Neuman computing system. Photonic spiking neural network (PSNN) exploits brain-like spatiotemporal processing to realize high-performance neuromorphic computing. However, the nonlinear computation of PSNN remains a significant challenging. Here, we proposed and fabricated a photonic spiking neuron chip based on an integrated Fabry-Pérot laser with a saturable absorber (FP-SA) for the first time. The nonlinear neuron-like dynamics including temporal integration, threshold and spike generation, refractory period, and cascadability were experimentally demonstrated, which offers an indispensable fundamental building block to construct the PSNN hardware. Furthermore, we proposed time-multiplexed spike encoding to realize functional PSNN far beyond the hardware integration scale limit. PSNNs with single/cascaded photonic spiking neurons were experimentally demonstrated to realize hardware-algorithm collaborative computing, showing capability in performing classification tasks with supervised learning algorithm, which paves the way for multi-layer PSNN for solving complex tasks.

cs.ET↗

arXiv2004

Influences of spin accumulation on the intrinsic spin Hall effect in two dimensional electron gases with Rashba spin-orbit coupling

In a two dimensional electron gas with Rashba spin-orbit coupling, the external electric field may cause a spin Hall current in the direction perpendicular to the electric field. This effect was called the intrinsic spin Hall effect. In this paper, we investigate the influences of spin accumulation on this intrinsic spin Hall effect. We show that due to the existence of boundaries in a real sample, the spin Hall current generated by the intrinsic spin Hall effect will cause spin accumulation near the edges of the sample, and in the presence of spin accumulation, the spin Hall conductivity will not have a universal value. The influences of spin accumulation on the intrinsic spin Hall effect in narrow strips of two dimensional electron gases with Rashba spin-orbit coupling are investigated in detail.

cond-mat.mes-hall↗