On-Demand Design of Tunable Complete Photonic Band Gaps based on Bloch Mode Analysis

Topological polarization selection concentrator

Topological polarization selection devices, which can separate topological photonic states of different polarizations into different positions, play a key role in the field of integrated photonics. However, there has been no effective method to realize such devices to date. Here, we have realized a topological polarization selection concentrator based on synthetic dimensions. The topological edge states of double polarization modes are constructed by introducing lattice translation as a synthetic dimension in a completed photonic bandgap photonic crystal with both TE and TM modes. The proposed device can work on multiple frequencies and is robust against disorders. This work provides a new,to the best of our knowledge, scheme to realize topological polarization selection devices, and it will enable practical applications such as topological polarization routers, optical storage, and optical buffers.

Biosensors based on novel nonlinear delta-function photonic crystals comprising weak nonlinearities

In this research, we propose a novel nonlinear delta-function photonic crystal for detecting sodium iodide (NaI) solution of different concentrations. The suggested structure comprises 50 delta stacks of GaP in an aqueous solution of NaI. These stacks are considered to have weak defocusing nonlinearity in the order of 10^-6 (V/m)^-2. Due to nonlinearity of the design, a defect-like resonance is formed within the photonic band gap. Thus, the detection of NaI with different concentrations can be easily investigated without the inclusion of a defect through the photonic crystal structure. The effects of both the linear part of the refractive index of GaP layers and nonlinear coefficient on the transmittance value are thoroughly discussed. The numerical findings investigate that the resonant peak begins to split at some critical nonlinearity. In our proposed structure, splitting occurs at about - 12 × 10^-6 (V/m)^-2. In this regard, the suggested sensor provides a high sensitivity of 409.7 nm/RIU and a wonderful detection limit of 0.0008.

Externally tunable multichannel filtering applications of organic material based 1D magnetic cold-plasma photonic crystals

In the present research work, we employed the transfer matrix method (TMM) in addition to MATLAB software to examine the transmission properties of various organic-based one-dimensional (1D) magnetic cold-plasma photonic crystals (MCPPhCs). The proposed structures were found to be made up of periodic layers of organic materials and magnetic cold-plasma (MCP) at normal incidence. An external magnetic field (B) polarized in right-hand (RH) and left-hand (LH) configurations was applied on 1D MCPPhCs. In this study, four organic materials, namely pentane, hexane, heptane, and octane, were chosen to design four 1D photonic crystals (PCs), named as PC₁ (pentane-MCP), PC₂ (hexane-MCP), PC₃ (heptane-MCP), and PC₄ (octane-MCP). Our results indicated that the central frequency of the resonant peaks of unit transmission inside the photonic band-gap (PBG) of the respective organic PCs could be tuned towards the higher or lower frequency side by applying B polarized in RH and LH configurations, respectively. We also studied the effect of the period number N to produce closely spaced N-1 transmission channels of unit transmission inside the PBG of all four organic PCs. By increasing the period number N we could increase the number of transmission channels inside the PBG as per our desire. These multiple resonant peaks of unit transmission inside PBG could be easily modulated inside the PBG to accommodate new frequencies by applying B polarized in either RH or LH configurations, respectively. Moreover, our results showed that under the RH configuration, increasing B resulted in a shifting of the resonant peak towards the higher frequency side with a reduction in its full width half maximum (FWHM), whereas the findings were the opposite in the case of increasing B under the LH configuration. These findings may be beneficial for designing externally tuneable organic chemical sensors in the microwave frequency region.

In the present research work, we employed the transfer matrix method (TMM) in addition to MATLAB software to examine the transmission properties of various organic-based one-dimensional (1D) magnetic cold-plasma photonic crystals (MCPPhCs).

Design and Performance Analysis of Reconfigurable 1D Photonic Crystal Biosensor employing Ge2Sb2Te5 (GST) for Detection of Women Reproductive Hormones

The present research demonstrates a novel 1D photonic crystal (PhC) based reconfigurable biosensor pertaining to label-free detection of different concentrations of progesterone and estradiol, which play a vital role in developing reproductive hormones in women. The proposed sensor is designed by an alternative arrangement of Na3AlF6 and CeO2, with a central defect layer. A thin layer of novel phase change chalcogenide material (Ge2Sb2Te5) is deposited along the two sides of the defect layer to improve the sensing performance. Numerical simulation of transmission spectrum for TE mode is carried out by using the transfer matrix method (TMM). The mainstay of this research is centered on the assay of shift in the defect mode position and intensity with respect to different concentrations of analyte, by changing the phase of the GST material from amorphous to crystalline. Interestingly, we observed a high tunability in defect mode wavelength, when the phase is changed from amorphous to crystalline, which leads to accomplishment of a high sensitivity of 1.75 nm/nmol/L for progesterone and 20.5 nm/nmol/L for estradiol. Aside from sensitivity, other significant parameters like figure of merit and detection limit are computed, which give a deep insight into the sensing performance. These encouraging sensing performances pave the path for efficient detection of different concentrations of progesterone and estradiol to monitor various gynecological problems in women.

Photonic crystal topological design for polarized and polarization-independent band gaps by gradient-free topology optimization

Photonic crystals can be adopted to control light propagation due to their superior band gap feature. It is well known the band gap feature of photonic crystals depends significantly on the topological design of the lattices, which is rather challenging due to the highly nonlinear objective function and multiple local minima feature of such design problems. To this end, this paper proposed a new band-gap topology optimization framework for photonic crystals considering different electromagnetic wave polarization modes. Based on the material-field series-expansion (MFSE) model and the dielectric permittivity interpolation scheme, the lattice topologies are represented by using a small number of design variables. Then, a sequential Kriging-based optimization algorithm, which shows strong global search capability and requires no sensitivity information, is employed to solve the band gap design problem as a series of sub-optimization problems with adaptive-adjusting design spaces. Numerical examples demonstrated the effectiveness of the proposed gradient-free method to maximize the band gap for transverse magnetic field (TM), transverse electric field (TE), and complete modes. Compared with previously reported designs, the present results exhibit less dependency on the guess of the initial design, larger band gaps and some interesting topology configurations.

Topology optimization of 3D photonic crystals with complete bandgaps

The design of photonic crystals with complete bandgaps has recently received considerable research focus for numerous reasons. This work leverages well-known nonlinear programming techniques to alleviate the non-smoothness caused by degenerate eigenvalues such that topology optimization problems can be solved with the open-source IPOPT software. A fully-vectorial plane wave expansion technique is used with an iterative eigensolver to efficiently predict dispersion properties of candidate structures. Nonlinear programming is employed to solve the inverse problem of designing three-dimensional periodic structures that exhibit complete two-dimensional (2D) and three-dimensional (3D) photonic bandgaps. Mesh refinement is performed to alleviate the large computational burden of designing and analyzing photonic crystals, and a periodic density filter is implemented to impose a minimum feature size for manufacturability considerations.

Wide-band self-collimation in a low-refractive-index hexagonal lattice

Wide-angle, broadband self-collimation (SC) is demonstrated in a hexagonal photonic crystal (PhC) fabricated in a low-refractive-index photopolymer by multiphoton lithography. The PhC can be described as a hexagonal array of cylindrical air holes in a block of dielectric material having a low-refractive index. Optical characterization shows that the device strongly self-collimates light at near-infrared wavelengths that span 1360 to 1610 nm. SC forces light to flow along the extrusion direction of the lattice without diffractive spreading, even when light couples into the device at high oblique angles. Numerical simulations corroborate the experimental findings.

Mapping the design space of photonic topological states via deep learning

Topological states in photonics offer novel prospects for guiding and manipulating photons and facilitate the development of modern optical components for a variety of applications. Over the past few years, photonic topology physics has evolved and unveiled various unconventional optical properties in these topological materials, such as silicon photonic crystals. However, the design of such topological states still poses a significant challenge. Conventional optimization schemes often fail to capture their complex high dimensional design space. In this manuscript, we develop a deep learning framework to map the design space of topological states in the photonic crystals. This framework overcomes the limitations of existing deep learning implementations. Specifically, it reconciles the dimension mismatch between the input (topological properties) and output (design parameters) vector spaces and the non-uniqueness that arises from one-to-many function mappings. We use a fully connected deep neural network (DNN) architecture for the forward model and a cyclic convolutional neural network (cCNN) for the inverse model. The inverse architecture contains the pre-trained forward model in tandem, thereby reducing the prediction error significantly.

Asymmetric transmission of light waves in a photonic crystal waveguide heterostructure with complete bandgaps

Here, we theoretically present an on-chip nanophotonic asymmetric transmission device (ATD) based on the photonic crystal (PhC) waveguide structure with complete photonic bandgaps (CPBGs). The ATD comprises two-dimensional silica and germanium PhCs with CPBGs, within which line defects are introduced to create highly efficient waveguides to achieve high forward transmittance. In the meantime, the total internal reflection principle is applied to block the backward incidence, achieving asymmetric transmission. We optimize the design of the PhCs and the waveguide structure by scanning different structure parameters. The optimized ATD shows a high forward transmittance of 0.581 and contrast ratio of 0.989 at the wavelength of 1582 nm for TE mode. The results deepen the understanding and open up the new possibility in designing novel ATDs. The on-chip ATD will find broad applications in optical communications and quantum computing.