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Register a new userModal formulation for diffraction by absorbing photonic crystal slabs
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Added by
Bj\\\"orn C. P. Sturmberg
Publication date
2015
fields
Physics
and research's language is
English
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A finite element-based modal formulation of diffraction of a plane wave by an absorbing photonic crystal slab of arbitrary geometry is developed for photovoltaic applications. The semi-analytic approach allows efficient and accurate calculation of the absorption of an array with a complex unit cell. This approach gives direct physical insight into the absorption mechanism in such structures, which can be used to enhance the absorption. The verification and validation of this approach is applied to a silicon nanowire array and the efficiency and accuracy of the method is demonstrated. The method is ideally suited to studying the manner in which spectral properties (e.g., absorption) vary with the thickness of the array, and we demonstrate this with efficient calculations which can identify an optimal geometry.
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Photonic crystals with a finite size can support surface modes when appropriately terminated. We calculate the dispersion curves of surface modes for different terminations using the plane wave expansion method. These non-radiative surface modes can be excited with the help of attenuated total reflection technique. We did experiments and simulations to trace the surface band curve, both in good agreement with the numerical calculations.
Photonic components based on structured metallic elements show great potential for device applications where field enhancement and confinement of the radiation on a subwavelength scale is required. In this paper we report a detailed study of a prototypical metallo-dielectric photonic structure, where features well known in the world of dielectric photonic crystals, like band gaps and defect modes, are exported to the metallic counterpart, with interesting applications to infrared science and technology, as for instance in quantum well infrared photodetectors, narrow-band spectral filters, and tailorable thermal emitters.
A theoretical study of photonic bands for one-dimensional (1D) lattices embedded in planar waveguides with strong refractive index contrast is presented. The approach relies on expanding the electromagnetic field on the basis of guided modes of an effective waveguide, and on treating the coupling to radiative modes by perturbation theory. Photonic mode dispersion, gap maps, and intrinsic diffraction losses of quasi-guided modes are calculated for the case of self-standing membranes as well as for Silicon-on-Insulator structures. Photonic band gaps in a waveguide are found to depend strongly on the core thickness and on polarization, so that the gaps for transverse electric and transverse magnetic modes most often do not overlap. Radiative losses of quasi-guided modes above the light line depend in a nontrivial way on structure parameters, mode index and wavevector. The results of this study may be useful for the design of integrated 1D photonic structures with low radiative losses.
In this paper, we propose a method for tailoring the absorption in a photonic crystal membrane. For that purpose, we first applied Time Domain Coupled Mode Theory to such a subwavelength membrane and demonstrated that 100% resonant absorption can be reached even for a symmetric membrane, if degenerate modes are involved. Design rules were then derived from this model in order to tune the absorption. Subsequently, Finite Difference Time Domain simulations were used as a proof of concept and carried out on a low absorbing material (extinction coefficient=10-2) with a high refractive index corresponding to the optical indices of amorphous silicon at around 720 nm. In doing so, 85% resonant absorption was obtained, which is significantly higher than the commonly reported 50% maximum value. Those results were finally analyzed and confronted to theory so as to extend our method to other materials, configurations and applications.
A Deep Learning (DL) based forward modeling approach has been proposed to accurately characterize the relationship between design parameters and the optical properties of Photonic Crystal (PC) nanocavities. The proposed data-driven method using Deep Neural Networks (DNN) is set to replace conventional approaches manually performed in simulation software. The demonstrated DNN model makes predictions not only for the Q factor but also for the modal volume V for the first time, granting us precise control over both properties in the design process. Specifically, a three-channel convolutional neural network (CNN), which consists of two convolutional layers followed by two fully-connected layers, is trained on a large-scale dataset of 12,500 nanocavities. The experimental results show that the DNN has achieved a state-of-the-art performance in terms of prediction accuracy (up to 99.9999% for Q and 99.9890% for V ) and convergence speed (i.e., orders-of-magnitude speedup). The proposed approach overcomes shortcomings of existing methods and paves the way for DL-based on-demand and data-driven optimization of PC nanocavities applicable to the rapid prototyping of nanoscale lasers and integrated photonic devices of high Q and small V.
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