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Single-cycle optical pulses offer a strong carrier-envelope-offset (CEO) dependent electric field and the highest peak intensity for a given pulse energy. Absence of demonstrated GHz single-cycle lasers constrains exploration of single/sub-cycle dynamics at this repetition rate. By leveraging fiber soliton effects and suppressing higher-order dispersion, we achieve single-cycle pulse generation at a 1 GHz repetition rate in an all-fiber format. The laser produces 7.1 fs (1.1-cycle) pulses with 1.8 W average power, centered around 1970 nm. Temporal characterization shows 60% of the pulse energy is concentrated in the pulse center, yielding a peak power of 110 kW. The seed laser demonstrates a 43 dB signal-to-noise ratio for the CEO frequency, facilitating comb stabilization and CEO control. Our model, which matches experimental observations, identifies conditions for achieving single-cycle duration and predicts scalability to a 2 GHz repetition rate. This work presents the first GHz single-cycle source. We envision these advances will drive studies in single/sub-cycle light-matter interaction, spectroscopy, microscopy, and CEO-sensitive nonlinear optics.

Mid-infrared frequency comb spectroscopy enables measurement of molecular at megahertz spectral resolution, sub-hertz frequency accuracy and microsecond acquisition speed. However, the widespread adoption of this technique has been hindered by the complexity and alignment sensitivity of mid-infrared frequency comb sources. Leveraging the underexplored mid-infrared window of silica fibers presents a promising approach to address these challenges. In this study, we present the first experimental demonstration and quantitative numerical description of mid-infrared frequency comb generation in silica fibers. Our all-silica-fiber frequency comb spans over two octaves (0.8 $\mu$m to 3.5 $\mu$m) with a power output of 100 mW in the mid-infrared region. The amplified quantum noise is suppressed using four-cycle (25 fs) driving pulses, with the carrier-envelope offset frequency exhibiting a signal-to-noise ratio of 40 dB and a free-running bandwidth of 90 kHz. Our developed model provides quantitative guidelines for mid-infrared frequency comb generation in silica fibers, enabling all-fiber frequency comb spectroscopy in diverse fields such as organic synthesis, pharmacokinetics processes, and environmental monitoring.

We demonstrate the post-induction of high-quality microcavity on silicon photonic crystal (PC) waveguide by integrating few-layer GaSe crystal, which promises highly efficient on-chip optical frequency conversions. The integration of GaSe shifts the dispersion bands of the PC waveguide mode into the bandgap, resulting in localized modes confined by the bare PC waveguides. Thanks to the small contrast of refractive index at the boundaries of microcavity, it is reliably to obtain quality (Q) factors exceeding 10^4. With the enhanced light-GaSe interaction by the microcavity modes and high second-order nonlinearity of GaSe, remarkable second-harmonic generation (SHG) and sum-frequency generation (SFG) are achieved. A record-high on-chip SHG conversion efficiency of 131100% W^-1 is obtained, enabling the clear SHG imaging of the resonant modes with the pump of sub-milliwatts continuous-wave (CW) laser. Driven by a pump of on-resonance CW laser, strong SFGs are successfully carried out with the other pump of a CW laser spanning over the broad telecom-band. Broadband frequency conversion of an incoherent superluminescent light-emitting diode with low spectral power density is also realized in the integrated GaSe-PC waveguide. Our results are expected to provide new strategies for high-efficiency light-matter interactions, nonlinear photonics and light source generation in silicon photonic integrated circuits., Comment: 5 figures

We demonstrate an efficient nonlinear photodetector (NLPD) with quadratic response based on a few-layer InSe p-n homojunction, which is beneficial from the strong second harmonic generation (SHG) process in InSe and effective harvest of photocarriers actuated by the high-quality homojunction. The NLPD can sense light with photon energy smaller than InSe electronic bandgap because the SHG process in InSe doubles the frequency of incident light, extending InSe photodetection wavelength range to 1750 nm. The InSe p-n homojunction, which is electrostatically doped by two split back gates, presents a rectification ratio exceeding 106 with a dark current down to 2 pA and a high normalized responsivity of 0.534 A/W2 for the telecom-band pulsed light at 1550 nm. The photocurrents of the SHG-assisted photodetection have a quadratic dependence on the optical powers, making the NLPD highly sensitive to light intensity variation with improved spatial resolution. As examples, the NLPD is employed to precisely determine the localization point of a focused laser beam waist and implement spatial imaging with an improved resolution compared with the linear photodetector. These features highlight the potential of the proposed NLPD in developing advanced optical sensing and imaging systems.

We propose and demonstrate an on-chip 1*N power splitter based on topological photonic crystal (TPC) on a monolithic silicon photonic platform. Benefiting from the valley-locked propagation mode at the interface of TPCs with different topological phases, the proposed power splitter has negligible backscattering around the sharp bendings and good robustness to fabrication defects, which therefore enable lower insertion loss, better uniformity, and more compact footprint than the conventional designs. For the fabricated 1*2 (8) power splitter, the uniformity among the output ports is below 0.35 (0.65) dB and the maximum insertion loss is 0.38 (0.58) dB with compact footprint of 5*5 um2 (10*12 um2) within a bandwidth of 70 nm. In addition, the topological power splitter only requires simple configurations of TPCs with different topological phases, which is more reliable in design and fabrication compared with the conventional designs., Comment: 8 pages,4 figures

This study examines nonnegative solutions to the problem \begin{equation*}\left\{\arraycolsep=1.5pt \begin {array}{lll} \Delta u=\displaystyle\frac{\lambda|x|^{\alpha}}{u^p} \ \ &\hbox{ in} \,\ \R ^2\setminus \{0\},\\[2mm] u(0)=0 \ \text{and}\ u> 0 \ \ &\hbox{ in} \,\ \R ^2\setminus \{0\},\\ \end{array}\right. \label{eqn} \end{equation*} where $\lam >0,$ $\alp>-2$, and $p>0$ are constants. The possible asymptotic behaviors of $u(x)$ at $|x|=0$ and $|x|=\infty$ are classified according to $(\alpha,p)$. In particular, the results show that for some $(\alpha,p)$, $u(x)$ exhibits only ``isotropic" behavior at $|x|=0$ and $|x|=\infty$. However, in other cases, $u(x)$ may exhibit the "anisotropic" behavior at $|x|=0$ or $|x|=\infty$. Furthermore, the relation between the limit at $|x|=0$ and the limit at $|x|=\infty$ for a global solution is investigated., Comment: 26 pages, 2 figures, 2 tables

The increasing installation rate of wind power poses great challenges to the global power system. In order to ensure the reliable operation of the power system, it is necessary to accurately forecast the wind speed and power of the wind turbines. At present, deep learning is progressively applied to the wind speed prediction. Nevertheless, the recent deep learning methods still reflect the embarrassment for practical applications due to model interpretability and hardware limitation. To this end, a novel deep knowledge-based learning method is proposed in this paper. The proposed method hybridizes pre-training method and auto-encoder structure to improve data representation and modeling of the deep knowledge-based learning framework. In order to form knowledge and corresponding absorbers, the original data is preprocessed by an optimization model based on correlation to construct multi-layer networks (knowledge) which are absorbed by sequence to sequence (Seq2Seq) models. Specifically, new cognition and memory units (CMU) are designed to reinforce traditional deep learning framework. Finally, the effectiveness of the proposed method is verified by three wind prediction cases from a wind farm in Liaoning, China. Experimental results show that the proposed method increases the stability and training efficiency compared to the traditional LSTM method and LSTM/GRU-based Seq2Seq method for applications of wind speed forecasting.