Search arXiv⌕ Search

arXiv · 2610.18519

Continuous-wave 148.4 nm Generation in Quasi-phase Matched BaMgF4

Abstract

We report continuous-wave (CW) vacuum-ultraviolet (VUV) generation at 148.4 nm by second-harmonic generation of 296.8-nm fundamental light in quasi-phase matched (QPM) BaMgF4 (BMF). At the incident power of 170 mW, a VUV output power of approximately 31 pW can be obtained. The quadratic dependence of the signal on the incident power in the QPM region and its attenuation in the ambient environment support the identification of VUV generation. The polarization dependence shows a sine-function shape with a 15% output attenuation from the maximum to the minimum. These results highlight the potential of BMF as a nonlinear medium for CW VUV generation.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yuanzhuang Bu, Xiangting Wang, Ruiyin Gu, Chongtai Liu, Luxin Jiang, Xinqing Ding, Ke Zhang, Yongkai Li, Kemi Xu. 2026-10-13. Continuous-wave 148.4 nm Generation in Quasi-phase Matched BaMgF4. https://arxiv.org/abs/2610.18519

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Photonic reservoir computing with complex networks

Photonic reservoir computing has attracted increasing attention as a fast and low-cost approach for time-series prediction. Photonic reservoir computing utilizes the high speed, broad bandwidth, and spatial parallelism of light. However, the effect of the internal connection structure (network topology) on the computing performance has not been investigated for large-scale photonic reservoirs. In this study, we experimentally and numerically demonstrate photonic reservoir computing using a spatial light modulator to systematically evaluate the relationship between the network topology and the performance of reservoir computing. We introduce complex network structures such as small-world and scale-free network topologies of the internal nodes in the reservoir. We perform the memory capacity measurement and the one-step-ahead prediction task of the chaotic time series to compare the performance. We found that the small-world network exhibits the maximum memory capacity and the best prediction performance. Our numerical calculations reveal that the performance of the time-series prediction can be optimized by changing the rewiring probability of the network and the leak rate of the reservoir. We also implement photonic human brain network as a reservoir, which is designed by the connectomes of human brain activities. We found that the network topology strongly affects the performance of reservoir computing, and the small-world network structure outperforms the other configurations.

physics.optics↗

Approximate reservoir computing with a semiconductor laser for reducing energy consumption

Photonic reservoir computing is a promising physical machine-learning approach for time-series prediction. Quantization of the reservoir response signal is an important consideration in the implementation of photonic reservoir computing, and the number of quantization bits and sampling frequency need to be optimized to achieve high prediction performance and low energy consumption. However, few studies have investigated the effects of the number of quantization bits and sampling frequency on both prediction performance and energy consumption. In this study, we introduce the concept of approximate reservoir computing with a semiconductor laser by quantizing the reservoir node states and output weights. We numerically evaluate the performance of a chaotic time-series prediction task and estimate the energy consumption per sample using a component-level model. By optimizing the number of quantization bits, the sampling frequency, and the injection current of the semiconductor laser, we achieve a significant reduction in the estimated energy consumption per sample while maintaining prediction performance. We also obtain model-based estimates of the throughput, energy efficiency, and energy per operation of the proposed reservoir-computing system.

physics.optics↗

Fabrication-Aware Design of a Hybrid Metasurface-Bragg Mirror for Low-Noise Precision Optics

High-reflectivity coatings for precision interferometry must simultaneously minimize optical losses and thermally driven displacement noise. Dielectric Bragg mirrors provide robust high reflectance but rely on thick multilayer coatings, whereas metasurface mirrors provide high reflectance and low noise but are sensitive to fabrication-induced deviations. A fabrication-aware photonic design framework is introduced that combines a resonant single-layer metasurface, an etch-stop layer, an antiresonant spacer, and a reduced Bragg reflector. Fabrication effects, including geometric tolerances and line-edge roughness, are explicitly considered in the optical design. Full-wave electromagnetic simulations are combined with a truncated-Gaussian Monte Carlo analysis to determine the performance distribution under the assumed fabrication conditions. The ideal metasurface design achieves a modeled non-reflected power of $1-R<10^{-5}$. After roughness-aware reoptimization, the modeled non-reflected power remains below $1.04\cdot10^{-4}$ at $80\%$ fabrication yield. For the cryogenic ETpathfinder gravitational-wave testbed, two Bragg layer pairs reduce the non-reflected power of the complete stack to approximately $6.6\,\mathrm{ppm}$. The estimated thermal displacement-noise amplitude spectral density is $1.97\cdot10^{-20}\,\mathrm{m/\sqrt{Hz}}$ at $100\,\mathrm{Hz}$. The architecture connects fabrication robustness, optical performance, and thermal-noise reduction within a single design framework.

physics.optics↗