Spectroscopy, especially for plasma spectroscopy, provides a powerful platform for biological and material analysis with its elemental and molecular fingerprinting capability. Artificial intelligence (AI) has the tremendous potential to build a universal quantitative framework covering all branches of plasma spectroscopy based on its unmatched representation and generalization ability. Herein, we introduce an AI-based unified method called self-supervised image-spectrum twin information fusion detection (SISTIFD) to collect twin co-occurrence signals of the plasma and to intelligently predict the physical parameters for improving the performances of all plasma spectroscopic techniques. It can fuse the spectra and plasma images in synchronization, derive the plasma parameters (total number density, plasma temperature, electron density, and other implicit factors), and provide accurate results. The experimental data demonstrate their excellent utility and capacity, with a reduction of 98% in evaluation indices (root mean square error, relative standard deviation, etc.) and an analysis frequency of 143 Hz (much faster than the mainstream detection frame rate of 1 Hz). In addition, as a completely end-to-end and self-supervised framework, the SISTIFD enables automatic detection without manual preprocessing or intervention. With these advantages, it has remarkably enhanced various plasma spectroscopic techniques with state-of-the-art performance and unsealed their possibility in industry, especially in the regions that require both capability and efficiency. This scheme brings new inspiration to the whole field of plasma spectroscopy and enables in situ analysis with a real-world scenario of high throughput, cross-interference, various analyte complexity, and diverse applications.
Neural networks have provided faster and more straightforward solutions for laser modulation. However, their effectiveness when facing diverse structured lights and various output resolutions remains vulnerable because of the specialized end-to-end training and static model. Here, we propose a redefinable neural network (RediNet), realizing customized modulation on diverse structured light arrays through a single general approach. The network input format features a redefinable dimension designation, which ensures RediNet wide applicability and removes the burden of processing pixel-wise light distributions. The prowess of originally generating arbitrary-resolution holograms with a fixed network is first demonstrated. The versatility is showcased in the generation of 2D/3D foci arrays, Bessel and Airy beam arrays, (perfect) vortex beam arrays, and even snowflake-intensity arrays with arbitrarily built phase functions. A standout application is producing multichannel compound vortex beams, where RediNet empowers a spatial light modulator (SLM) to offer comprehensive multiplexing functionalities for free-space optical communication. Moreover, RediNet has the hitherto highest efficiency, only consuming 12 ms (faster than the mainstream SLM framerate of 60 Hz) for a 10002-resolution holograph, which is critical in real-time required scenarios. Considering the fine resolution, high speed, and unprecedented universality, RediNet can serve extensive applications, such as next-generation optical communication, parallel laser direct writing, and optical traps.
In the framework of laser technology, blue-light pumped Pr-doped materials are ideal candidates for blue, green, orange, red and deep red laser emission due to abundant transitions in visible band of praseodymium ions (Pr3+). In this communication, for the first time to our knowledge, we report on direct generations of an orthogonally polarized continuous-wave (CW) laser with dual-wavelength at 639nm and 721nm by means of a diode-pumped Pr:YLF laser platform. Without any extra thermal control on the crystal, the adjustment of pumping power or the cavity length enables the shift of the bi-chromatic output from one wavelength to the other. The stability of the output dual-wavelength laser is significantly improved when the resonator is appropriately misaligned. This work provides a simple way for direct generation and convenient control of an orthogonally polarized dual-wavelength laser at red and deep red with compact structure, high conversion efficiency and high beam quality.
Aiming at tightly focusing vector polarized partially coherent vortex laser beams, this paper introduced a new kind of vortex beams, named radially polarized multi-Gaussian Schell-model (MGSM) power-exponent-phase vortex beam (PEPVB). Based on the vectorial diffraction theory, this work theoretically and numerically investigated the tight focusing properties of radially polarized MGSMPEPVB passing through a high numerical aperture objective lens. Thus, we analyzed the impact of topological charge, power exponent, beam index and coherence length on the intensity of focal zone. We discovered that by increasing beam index, the intensity distribution of focal plane gradually changed from Gaussian to flat-top. Especially, when the power exponent was a non-negative fraction close to 1, regardless of whether the topological charge was an integer or not, the circular symmetry of the focused spot at focal plane would be destroyed, showing a non-uniform and asymmetric central dark core optical intensity distribution. Besides, the value of the fractional part of the topological charge would make the hollow structure of the central dark core fully open due to the introduction of power-exponent phase, which is an improvement over the tight focusing properties of radially polarized MGSM vortex beams. This work has clearly demonstrated that by changing the values of topological charge, power exponent, beam index and coherence length, the special focal spot structures with different intensity distributions including flat-top beam and irregular hollow beam can be obtained, which have many potential applications in laser machining and particle capturing such as manipulation of certain irregular microparticles.
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