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In this article, using principles of automatic differentiation, we demonstrate a generic deep learning representation of group refractive index for photonic channel waveguides. It enables evaluation of group refractive indices in a split of second, without any traditional numerical calculations. Traditionally, the group refractive index is calculated by a repetition of the optical mode calculations via a parametric wavelength sweep of finite difference (or element) calculations. To the direct contrary, in this work, we show that the group refractive index can be quasi-instantaneously obtained from the auto-gradients of the neural networks that models the effective refractive index. We embed the wavelength dependence of the effective index in the deep learning model by applying the scaling property of the Maxwell's equations and this eliminates the problems caused by the curse of dimensionality. This work portrays a very clear illustration on how physics-based derived optical quantities can be calculated instantly from the underlying deep learning models of the parent quantities using automatic differentiation.
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Optical mode solving is of paramount importance in photonic design and discovery. In this paper we propose a deep deconvolutional neural network architecture for a meshless, and resolution scalable optical mode calculations. The solution is arbitrary in wavelengths and applicable for a wide range of photonic materials and dimensions. The deconvolutional model consists of two stages: the first stage projects the photonic geometrical parameters to a vector in a higher dimensional space, and the second stage deconvolves the vector into a mode image with the help of scaling blocks. Scaling block can be added or subtracted as per desired resolution in the final mode image, and it can be effectively trained using a transfer learning approach. Being a deep learning model, it is light, portable, and capable of rapidly disseminating edge computing ready solutions. Without the loss of generality, we illustrate the method for an optical channel waveguide, and readily generalizable for wide range photonic components including photonic crystals, optical cavities and metasurfaces.
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We use the beam model of Doppler backscattering (DBS), which was previously derived from beam tracing and the reciprocity theorem, to shed light on mismatch attenuation. This attenuation of the backscattered signal occurs when the wavevector of the probe beam's electric field is not in the plane perpendicular to the magnetic field. Correcting for this effect is important for determining the amplitude of the actual density fluctuations. Previous preliminary comparisons between the model and Mega-Ampere Spherical Tokamak (MAST) plasmas were promising. In this work, we quantitatively account for this effect on DIII-D, a conventional tokamak. We compare the predicted and measured mismatch attenuation in various DIII-D, MAST, and MAST-U plasmas, showing that the beam model is applicable in a wide variety of situations. Finally, we performed a preliminary parameter sweep and found that the mismatch tolerance can be improved by optimizing the probe beam's width and curvature at launch. This is potentially a design consideration for new DBS systems.
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This article describes a new two-dimensional physical topology-merged lattice, that allows dense number of wave localization states. Merged lattices are obtained as a result of merging two lattices of scatters of the same space group, but with slightly different spatial resonances. Such merging creates two-dimensional scattering "beats" which are perfectly periodic on the longer spatial scale. On the shorter spatial scale, the systematic breakage of the translational symmetry leads to strong wave scattering, and this causes the occurrences of wave localization states. Merged Lattices promises variety of localization states including tightly confined, and ring type annular modes. The longer scale perfect periodicity of the merged lattice, enables complete prediction and full control over the density of the localization states and its' quality factors. In addition, the longer scale periodicity, also allows design of integrated slow wave components. Merged lattices, thus, can be engineered easily to create technologically beneficial applications.
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A high permittivity dielectric gives the impression of outperforming plasmonic noble metal in visible light fluorescence enhancement primarily because of its small loss. Nonetheless, the performances of these two platforms in various situations remain obscure due to the different optical confinement mechanisms as well as the complexity in the fluorescence enhancement process. This study presents a comprehensive comparison between these two platforms based on nanoparticles (NPs) to evaluate their capability and applicability in fluorescence enhancement by taking into account the fluorescence excitation rate, the quantum yield, the fluorophore wavelengths and Stokes shifts as well as the far field intensity. In a low permittivity sensing medium (e.g. air), the dielectric NP can achieve comparable or higher fluorescence enhancement than the metal NP due to its decent NP-enhanced excitation rate and larger quantum yield. In a relatively high permittivity sensing medium (e.g. water), however, there is a significant decrement of the excitation rate of the dielectric NP as the permittivity contrast decreases, leading to a smaller fluorescence enhancement compared to the metallic counterpart. Combining the fluorescence enhancement and the far field intensity studies, we further conclude that for both dielectric and plasmonic NPs, the optimal situation occurs when the fluorescence excitation wavelength, the fluorescence emission wavelength and the electric-dipole-mode of the dielectric NP (or the plasmonic resonance of the metal NP) are the same and all fall in the low conductivity region of the NP material. We also find that the electric-dipole-mode of the dielectric NP performs better than the magnetic-dipole-mode for fluorescence enhancement applications because only the electric-dipole-mode can be strongly excited by the routinely used fluorescent dyes and quantum dots, which behave as electric dipoles by nature.
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Periodic structures are well known in various branches of physics for their ability to provide a stopband. In this article, using optical periodic structures we showed that, when a second periodicity--very closed to the original periodicity is introduced, large number of states appears in the stopband corresponding to the first periodicity. In the limit where the two periods matches, we have a continuum of states, and the original stopband completely disappears. This intriguing phenomena is uncovered by noticing that, regardless of the proximities of the two periodicities, there is an array of spatial points where the dielectric functions corresponding to the two periodicities interfere destructively. These spatial points mimic photonic atoms by satisfying the standards equations of quantum harmonic oscillators, and exhibit lossless, atom-like dispersions.
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Here, we demonstrate the broadband slow light effects in a new family of one dimensional photonic crystals, which are obtained by logically combining two photonic crystals of slightly different periods. The logical combination slowly destroys the original translational symmetries of the individual photonic crystals. Consequently, the Bloch modes of the individual photonic crystals with different wavevectors couple with each other, creating a vast number of slow modes. Specifically, we describe a photonic crystal architecture that results from a logical "OR" mixture of two one dimensional photonic crystals with a periods ratio of r = R/(R - 1), where R > 2 is an integer. Such a logically combined architecture, exhibits a broad region of frequencies in which a dense number of slow modes with varnishing group velocities, appear naturally as Bloch modes.