arXiv subjects

Yibing Zhou

Publications and source records attributed to Yibing Zhou.

4 recordsLinked to original sources

arXiv2026

Resonance-enhanced integrated acousto-optic beam steering

Optical beam steering is a key technology for free-space optical communication, sensing, and imaging. Mechanical beam steering systems suffer from limited scanning speed and bulky form factors, while existing solid-state solutions rely on pixelated synthetic aperture that requires complex fabrication and control architectures. Integrated acousto-optic beam steering (AOBS) is an emerging technology that enables continuous one-dimensional beam steering using integrated acoustic transducers and fixed-wavelength laser sources. Here, we integrate AOBS with an optical ring resonator on the same thin-film lithium niobate (TFLN) platform to significantly enhance beam steering efficiency and system functionality. The resulting device achieves a resonance-enhanced beam steering efficiency of up to $26\%$ and a field of view of $18^\circ$. Moreover, by leveraging integrated electro-optic control, we dynamically lock the ring-resonator's resonance to a chirped laser frequency, enabling frequency-modulated continuous-wave (FMCW) LiDAR operation. By combining lithium niobate's piezoelectric and electro-optic properties, this work establishes a compact, efficient, and scalable beam-steering platform with co-integrated acousto-optic modulation and electro-optic control for multifunctional applications.

physics.app-ph↗

arXiv2026

Temperature-dependent acoustic loss at microwave frequencies in thin-film lithium niobate

Thin-film lithium niobate (TFLN) has emerged as a versatile platform for phononic and photonic devices with applications ranging from classical signal processing to quantum technologies. However, acoustic loss fundamentally limits the performance of acoustic devices on TFLN platforms, yet its physical origin remains insufficiently understood. Here, we systematically investigate acoustic propagation loss in various TFLN platforms, including lithium niobate on insulator (LNOI), lithium niobate on sapphire (LNOS), suspended LN thin films, and bulk LN at gigahertz frequencies over temperatures ranging from 4 K to above room temperature. Using a delay-line method, we extract frequency- and temperature-dependent losses for Rayleigh, shear-horizontal, and Lamb modes. We observe an anomalous non-monotonic temperature dependence in LNOI that closely resembles acoustic loss in amorphous materials, indicating a dominant loss channel associated with the buried oxide layer at low temperatures. At elevated temperatures, the loss converges to the Akhiezer damping governed by phonon-phonon interactions. High-resolution electron microscopy further reveals nanoscale interfacial crystal impurities that may contribute to the increased acoustic loss in TFLN platforms relative to bulk LN. These results elucidate the acoustic loss mechanisms in TFLN and provide guidelines for designing low-loss acoustic devices.

physics.app-ph↗

arXiv2025

Unsupervised Candidate Ranking for Lexical Substitution via Holistic Sentence Semantics

A key subtask in lexical substitution is ranking the given candidate words. A common approach is to replace the target word with a candidate in the original sentence and feed the modified sentence into a model to capture semantic differences before and after substitution. However, effectively modeling the bidirectional influence of candidate substitution on both the target word and its context remains challenging. Existing methods often focus solely on semantic changes at the target position or rely on parameter tuning over multiple evaluation metrics, making it difficult to accurately characterize semantic variation. To address this, we investigate two approaches: one based on attention weights and another leveraging the more interpretable integrated gradients method, both designed to measure the influence of context tokens on the target token and to rank candidates by incorporating semantic similarity between the original and substituted sentences. Experiments on the LS07 and SWORDS datasets demonstrate that both approaches improve ranking performance.

cs.CL↗

arXiv2014

Application of Artificial Neural Networks in Predicting Abrasion Resistance of Solution Polymerized Styrene-Butadiene Rubber Based Composites

Abrasion resistance of solution polymerized styrene-butadiene rubber (SSBR) based composites is a typical and crucial property in practical applications. Previous studies show that the abrasion resistance can be calculated by the multiple linear regression model. In our study, considering this relationship can also be described into the non-linear conditions, a Multilayer Feed-forward Neural Networks model with 3 nodes (MLFN-3) was successfully established to describe the relationship between the abrasion resistance and other properties, using 23 groups of data, with the RMS error 0.07. Our studies have proved that Artificial Neural Networks (ANN) model can be used to predict the SSBR-based composites, which is an accurate and robust process.

cs.CE↗