Nonlinear frequency conversion of light inside a microcavity

Changing the color of light in a small mode volume is essential for applications that require on-chip operation. Microcavities are profound structures given their property to confine light in a small mode volume. Here, I numerically investigate the frequency conversion of light in a microcavity and show that the frequency converted light intensity is distinguished from an open medium such as an optical fiber or a nonlinear crystal. I observe that the frequency converted light intensity increases with increased rate of change of the refractive index of the microcavity. Notably, this study shows that the intensity of the frequency converted light is maximized when the duration of the index perturbation is matched to the cavity storage time. The results provide a set of optimum parameters for increasing frequency conversion efficiency inside a microcavity

Hyper-spectral imaging through a multi-mode fiber

Multi-mode fibers provide an increased amount of data transfer rates given a large number of transmission modes. Unfortunately, the increased number of modes in a multi-mode fiber hinders the accurate transfer of information due to interference of these modes which results in a random speckle pattern. The complexity of the system impedes the analytical expression of the system thereby the information is lost. However, deep learning algorithms can be used to recover the information efficiently. In this study, we utilize deep learning architecture to reconstruct input colored images from the output speckle patterns at telecommunication wavelength (C-band). Our model successfully identifies hyper-spectral speckle patterns at twenty-six separate wavelengths and twenty-six distinct letters. Remarkably, we can reconstruct the complete input images only by analyzing a small portion of the output speckle pattern. Thereby, we manage to decrease the computational load without sacrificing the accuracy of the classification. We believe that this study will show a transformative impact in many fields: biomedical imaging, communication, sensing, and photonic computing.Comment: 5 pages, 8 figure

Effective bandwidth approach for spectral splitting of solar spectrum using diffractive optical elements

Spectral splitting of the sunlight using diffractive optical elements (DOEs) is an effective method to increase the efficiency of solar panels. Here, we design phase-only DOEs by using an iterative optimization algorithm to spectrally split and simultaneously concentrate solar spectrum. In our calculations, we take material dispersion into account as well as the normalized blackbody spectrum of the sunlight. The algorithm consists of the local search optimization and is strengthen with an outperforming logic operation called MEAN optimization. Using the MEAN optimization algorithm, we demonstrate spectral splitting of a dichromatic light source at 700 nm and 1100 nm with spectral splitting efficiencies of 92% and 94%, respectively. In this manuscript, we introduce an effective bandwidth approach, which reduces the computation time of DOEs from 89 days to 8 days, while preserving the spectral splitting efficiency. Using our effective bandwidth method we manage to spectrally split light into two separate bands between 400 nm - 700 nm and 701 nm - 1100 nm, with splitting efficiencies of 56% and 63%, respectively. Our outperforming and effective bandwidth design approach can be applied to DOE designs in color holography, spectroscopy, and imaging applications.Comment: 11 figures, 7 page

Spectral splitting and concentration of broadband light using neural networks

Compact photonic elements that control both the diffraction and interference of light offer superior performance at ultra-compact dimensions. Unlike conventional optical structures, these diffractive optical elements can provide simultaneous control of spectral and spatial profile of light. However, the inverse-design of such a diffractive optical element is time-consuming with current algorithms, and the designs generally lack experimental validation. Here, we develop a neural network model to experimentally design and validate SpliCons; a special type of diffractive optical element that can achieve spectral splitting and simultaneous concentration of broadband light. We use neural networks to exploit nonlinear operations that result from wavefront reconstruction through a phase plate. Our results show that the neural network model yields enhanced spectral splitting performance for phase plates with quantitative assessment compared to phase plates that are optimized via local search optimization algorithm. The capabilities of the phase plates optimized via neural network are experimentally validated by comparing the intensity distribution at the output plane. Once the neural networks are trained, we manage to design SpliCons with 96.6 $\pm$ 2.3% accuracy within 2 seconds, which is orders of magnitude faster than iterative search algorithms. We openly share the fast and efficient framework that we develop in order to contribute to the design and implementation of diffractive optical elements that can lead to transformative effects in microscopy, spectroscopy, and solar energy applications.Comment: 7 pages, 4 figure

Optimal all-optical switching of a microcavity resonance in the telecom range using the electronic Kerr effect

We have switched GaAs/AlAs and AlGaAs/AlAs planar microcavities that operate in the "Original" (O) telecom band by exploiting the instantaneous electronic Kerr effect. We observe that the resonance frequency reversibly shifts within one picosecond. We investigate experimentally and theoretically the role of several main parameters: the material backbone and its electronic bandgap, the pump power, the quality factor, and the duration of the switch pulse. The magnitude of the shift is reduced when the backbone of the central $\lambda-$layer has a greater electronic bandgap; pumping with photon energies near the bandgap resonantly enhances the switched magnitude. Our model shows that the magnitude of the resonance frequency shift depends on the pump pulse duration and is maximized when the duration matches the cavity storage time that is set by the quality factor. We provide the settings for the essential parameters so that the frequency shift of the cavity resonance can be increased to one linewidth

Differential ultrafast all-optical switching of the resonances of a micropillar cavity

We perform frequency- and time-resolved all-optical switching of a GaAs-AlAs micropillar cavity using an ultrafast pump-probe setup. The switching is achieved by two-photon excitation of free carriers. We track the cavity resonances in time with a high frequency resolution. The pillar modes exhibit simultaneous frequency shifts, albeit with markedly different maximum switching amplitudes and relaxation dynamics. These differences stem from the non-uniformity of the free carrier density in the micropillar, and are well understood by taking into account the spatial distribution of injected free carriers, their spatial diffusion and surface recombination at micropillar sidewalls.Comment: 4 pages, 3 figure

Tuning out disorder-induced localization in nanophotonic cavity arrays

Weakly coupled high-Q nanophotonic cavities are building blocks of slow-light waveguides and other nanophotonic devices. Their functionality critically depends on tuning as resonance frequencies should stay within the bandwidth of the device. Unavoidable disorder leads to random frequency shifts which cause localization of the light in single cavities. We present a new method to finely tune individual resonances of light in a system of coupled nanocavities. We use holographic laser-induced heating and address thermal crosstalk between nanocavities using a response matrix approach. As a main result we observe a simultaneous anticrossing of 3 nanophotonic resonances, which were initially split by disorder.Comment: 11 page