AlGaN-based light-emitting diodes (LEDs) operating in the deep-ultraviolet (UV-C) spectral range (210−280 nm) exhibit extremely low external quantum efficiency, primarily due to the presence of large threading dislocations and extremely low transverse magnetic (TM) light extraction efficiency.Here, we have demonstrated that such critical issues can be potentially addressed by using AlGaN quantum-well heterostructures grown on a hexagonal nanopatterned sapphire substrate (NPSS) and a flip-chip-bonded inclined Al mirror. Our finite-difference time domain-based numerical analysis confirms that the maximum achievable efficiency is limited by the poor light extraction efficiency due to the extremely low TM-polarized emission. In our experiment, with the usage of a meticulously designed hexagonal NPSS and an inclined Al side wall mirror (>90% reflective in the UV-C wavelength), the AlGaN quantum-well UV-C LEDs showed nearly 20% improvement in the light output power and efficiency compared to the conventional flat flip-chip LEDs. The UV-C LEDs operating at ∼275 nm exhibit a maximum output power of ∼25 mW at 150 mA, a peak external quantum efficiency of ∼4.7%, and a wall plug efficiency of ∼3.25% at 15 mA under continuous wave (CW) conditions. The presented approach opens up new opportunities to increase the extraction of UV light in the challenging spectral range by using properly designed patterned substrates and an engineered Al reflector.

In this work we propose and analyze techniques of in-plane directionality control of strongly localized resonant modes of light in random arrays of dielectric scatterers. Based on reported diameters and areal densities of epitaxially grown self-organized nanowires, two-dimensional (2D) arrays of dielectric scatterers have been analyzed where randomness is gradually increased along a preferred direction of directionality enhancement. In view of the multiple-scattering mediated wave dynamics and directionality enhancement of light in such arrays, a more conveniently realizable, practical structure is proposed where a 2D periodic array is juxtaposed with a uniform, random scattering medium. Far- and near-field emission characteristics of such arrays show that in spite of the utter lack of periodicity in the disordered regime of the structure, directionality of the high-Q resonant modes is modified such that on average more than 70% of the output power is emitted along the pre-defined direction of preference. Such directionality enhancement and strong localization are nonexistent when the 2D periodic array is replaced with a one-dimensional Bragg reflector, thereby confirming the governing role of in-plane multiple scattering in the process. The techniques presented herein offer novel means of realizing not only directionality tunable edge-emitting random lasers but also numerous other disordered media based photonic structures and systems with higher degrees of control and tunability.

In this work, we predict the most strongly confined resonant mode of light in strongly disordered systems of dielectric scatterers employing the data-driven approach of machine learning. For training, validation, and test purposes of the proposed regression architecture-based deep neural network (DNN), a dataset containing resonant characteristics of light in 8,400 random arrays of dielectric scatterers is generated employing finite difference time domain (FDTD) analysis technique. To enhance the convergence and accuracy of the overall model, an auto-encoder is utilized as the weight initializer of the regression model, which contains three convolutional layers and three fully connected layers. Given the refractive index profile of the disordered system, the trained model can instantaneously predict the Anderson localized resonant wavelength of light with a minimum error of 0.0037%. A correlation coefficient of 0.95 or higher is obtained between the FDTD simulation results and DNN predictions. Such a high level of accuracy is maintained in inhomogeneous disordered media containing Gaussian distribution of diameter of the scattering particles. Moreover, the prediction scheme is found to be robust against any combination of diameters and fill factors of the disordered medium. The proposed model thereby leverages the benefits of machine learning for predicting the complex behavior of light in strongly disordered systems.

By juxtaposing a 2D periodic array, Anderson localization of light is attained in a disordered medium of reduced area. Directionality control of the strongly localized mode of light is also attained with the proposed structure.

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