skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Elucidating the origin of laser-induced nonlinearities in propagation inside transparent media: a comparative numerical study of silicon and fused silica
Abstract The creation of localized bulk modification using femtosecond pulses inside semiconductors like silicon (Si) is quite challenging, whereas it is not difficult to achieve it for dielectric materials like fused silica (FS). This report addresses the fundamental origin of this issue. By taking a simple numerical approach, it has been found that in FS we can deliver stronger fluence due to self-focusing at higher power levels compared to Si. The origin for the above lies in the spatio-temporal pulse-splitting behavior, which is dominant in the case of FS at the focus, whereas, for Si, it is only effective after focus. We have also considered the influence of plasma and Kerr terms to elucidate the reason behind these nonlinearities. For the FS case, omission of Kerr term dominates, whereas, for Si, the influence of each term does not significantly create self-focusing like FS under a similar focusing condition. This study could provide an important guideline for researchers to understand the complexity of laser-matter interaction in transparent materials specifically being studied by many laser-processing industries.  more » « less
Award ID(s):
2129006
PAR ID:
10655956
Author(s) / Creator(s):
;
Publisher / Repository:
IOP Publishing
Date Published:
Journal Name:
Journal of Physics: Photonics
Volume:
6
Issue:
4
ISSN:
2515-7647
Page Range / eLocation ID:
045016
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Stimulated Raman scattering is ubiquitous in many high-intensity laser environments. Parametric four-wave mixing between the pump and Raman sidebands can affect the Raman gain, but stringent phase matching requirements and strongly nonlinear dynamics obscure clear understanding of its effects at high laser powers. Here we investigate four-wave mixing in the presence of strong self-focusing and weak ionization at laser powers above the Kerr critical power. Theoretical analysis shows that the plasma generated at focus naturally leads to phase matching conditions suitable for enhanced Raman gain, almost without regard to the initial phase mismatch. Multidimensional nonlinear optical simulations with multiphoton and collisional ionization confirm the enhancement and suggest that it may lead to significantly higher Raman losses in some high-intensity laser environments. 
    more » « less
  2. While industrial-grade Yb-based amplifiers have become very prevalent, their limited gain bandwidth has created a large demand for robust spectral broadening techniques that allow for few-cycle pulse compression. In this work, we perform a comparative study between several atomic and molecular gases as media for spectral broadening in a hollow-core fiber geometry. Exploiting nonlinearities such as self-phase modulation, self-steepening, and stimulated Raman scattering, we explore the extent of spectral broadening and its dependence on gas pressure, the critical power for self-focusing, and the optimal regime for few-cycle pulse compression. Using a 3-mJ, 200-fs input laser pulses, we achieve 17 fs, few-cycle pulses with 80% fiber energy transmission efficiency. The optimal parameters can be scaled for higher or lower input pulse energies with appropriate gas parameters and fiber geometry. 
    more » « less
  3. The high power and variable repetition-rate of Yb femtosecond lasers makes them very attractive for ultrafast science. However, for capturing sub-200 fs dynamics, efficient, high-fidelity and high-stability pulse compression techniques are essential. Spectral broadening using an all-solid-state free-space geometry is particularly attractive, as it is simple, robust and low-cost. However, spatial and temporal losses caused by spatio-spectral inhomogeneities have been a major challenge to date, due to coupled space-time dynamics associated with unguided nonlinear propagation. In this work, we use all-solid-state free-space compressors to demonstrate compression of 170 fs pulses at a wavelength of 1030nm from a Yb:KGW laser to ∼9.2 fs, with a highly spatially homogeneous mode. This is achieved by ensuring that the nonlinear beam propagation in periodic layered Kerr media occurs in spatial soliton modes, and by confining the nonlinear phase through each material layer to less than 1.0 rad. A remarkable spatio-spectral homogeneity of ∼0.87 can be realized, which yields a high efficiency of >50% for few-cycle compression. The universality of the method is demonstrated by implementing high-quality pulse compression under a wide range of laser conditions. The high spatiotemporal quality and the exceptional stability of the compressed pulses are further verified by high-harmonic generation. Our predictive method offers a compact and cost-effective solution for high-quality few-cycle-pulse generation from Yb femtosecond lasers, and will enable broad applications in ultrafast science and extreme nonlinear optics. 
    more » « less
  4. Abstract The modifier field strength (FS) is believed to play an important role in determining the elastic–plastic responses of aluminoborosilicate (ABS) glasses, but its effect is not well understood. Three novel alkali and three alkaline earth (AE) ABS compositions were created for this study which is the first part of two studies that explored the elastoplastic responses of these glasses. Six compositions were designed using different network modifiers (NWMs) to cover a range of cation FS. The glasses were also designed such that the concentrations of NWM and Al2O3were similar, which maximized the three‐coordinated boron fraction in the network. It is well known that modifier FS can affect the coordination number (CN) of Al and B in an ABS glass structure, for example, a higher FS modifier can promote B3 → B4and higher [Al5,6], but the degree of this depends on network former (NWF) ratios. Previous work used solid‐state NMR spectroscopic analysis on the current glasses to find that there was variation between [B4] and [Al4] between the two glass series (alkali vs. AE) but that was attributed to synthesis factors and no trend with FS was associated with the varying NWF CN. Further,29Si ssNMR showed no evidence of NBOs which made sense based on composition. The conclusion, therefore, was that there was a far greater correlation with modifier FS for the increased mechanical and physical properties rather than the CN of Al and B. Part I of the current work focused on the elastic moduli, Poisson's ratio, the indentation size effect (ISE), and the bow‐in parameter. This part laid out the foundation to investigate the intersection of these elastoplastic properties with hardness and crack resistance as a function of NWM FS. Results showed that: (i) the Young's, bulk, and shear moduli increased with modifier FS, whereas Poisson's ratio did not trend with FS; (ii) the alkali glasses had a significantly higher magnitudes of ISE compared to the AE glasses; and (iii) the bow‐in parameter was load dependent and decreased with modifier FS at the highest indentation load. 
    more » « less
  5. Abstract This review paper examines the application and challenges of machine learning (ML) in intelligent welding processes within the automotive industry, focusing on resistance spot welding (RSW) and laser welding. RSW is predominant in body-in-white assembly, while laser welding is critical for electric vehicle battery packs due to its precision and compatibility with dissimilar materials. The paper categorizes ML applications into three key areas: sensing, in-process decision-making, and post-process optimization. It reviews supervised learning models for defect detection and weld quality prediction, unsupervised learning for feature extraction and data clustering, and emerging generalizable ML approaches like transfer learning and federated learning that enhance adaptability across different manufacturing conditions. Additionally, the paper highlights the limitations of current ML models, particularly regarding generalizability when moving from lab environments to real-world production, and discusses the importance of adaptive learning techniques to address dynamically changing conditions. Case studies like virtual sensing, defect detection in RSW, and optimization in laser welding illustrate practical applications. The paper concludes by identifying future research directions to improve ML adaptability and robustness in high-variability manufacturing environments, aiming to bridge the gap between experimental ML models and real-world implementation in automotive welding. 
    more » « less