Windows-based computational algorithms for 1D MPC and thin-film analysis: Comparison
Please note this is a comparison between Version 3 by Mohammad Nur E Alam and Version 2 by Mikhail Vasiliev.

The development of magnetic photonic crystals (MPC) has been a rapidly evolving research area since the late 1990s. Magneto-optic (MO) materials and the techniques for their characterization have also continually undergone functional and property-related improvements. MPC optimization is a feature-rich Windows software application designed to enable researchers to analyse the optical and magneto-optical spectral properties of multilayers containing gyrotropic constituents. A set of computational approaches, and a custom software package have been described, designed to enable the design and optimization of 1D magnetic photonic crystals in terms of the achievable combinations of Faraday rotation, transmission, and reflection spectra. 

  • 1D magnetic photonic crystals
  • multilayer film modeling
  • modeling of Faraday rotation spectra
  • MPC optimization
  • exhaustive computation
  • materials characterization

The one-dimensional MPCs have been reported to be applicable for various existing and emerging new nanotechnological device applications, due to the functionality of Faraday rotation enhancement and also their tunable properties. Many research groups have focused on optimizing the design and features of one-dimensional (1D) MPC structures using various software packages (most of them are quite complex to use and are not freely available online for wide range users).  Since working with garnet materials synthesis, the development of a new MPC optimization program was originated in 2005. After that till 2019, this new software was tested in-house, and it has finally been realised that a large group of material scientists, especially the young scientists who have recently started their research career, can benefit from this newly developed and freely available software package.  Here, we would like let the world’s researchers discover our MPC optimization software and also would like to open the doors for their valuable feedback.

The required solution code for this MPC optimization program has been written by using Microsoft Visual Studio 2003, by Dr Mikhail Vasiliev. NET Professional and multiple code changes and feature additions have been applied in between 2005–2019. During this time frame, the development of the algorithm and code features of the program have been discussed within our group (Prof Kamal Alameh and Dr Mohammad Nur-E-Alam, and greatly influenced and experimentally tested by through our work on the multiple materials-related projects. Figure 1 is reproduced from the published article in MDPI Technologies[1] which shows the graphical snapshot of the front-panel controls of MPC Optimization software. The details about the program code of this MPC software are outlined in Ref.[1] together with the installation directions. This is a very easy installation software, which is enabled by running the installer (.msi) file supplied within the .zip archived folder used for program redistribution. However there is a necessary prerequisite to install this program is the Microsoft .NET Framework 1.1, which must be installed on any Windows machine prior to running the MPC Optimization installer. The .NET 1.1 Framework installation file (dotnetfx.exe) is also supplied within the archived folder file used to redistribute MPC optimization.

Figure 1

Figure 1: A graphical snapshot of the Front-panel controls of MPC Optimization software and features implemented within MPC Optimization showing a sample optimization result.

 

This MPC software also provides the features of characterising the low absorptive semitransparent (single layer) thin-film materials. It is possible to fit the measured transmission spectrum of either new or previously known thin-film materials with the model predicted transmission spectrum, thus allowing to calculate the film thickness, and to derive the unknown optical parameter (absorption coefficient) for the thin-film materials. Figure 2 shows a practical demonstration of the use of this software to fit the transmission spectrum and also determine the optical absorption coefficient for a highly Bismuth substituted metal doped iron garnet thin film. Figure 2(a) shows the peak-to-peak fitting of measured transmission spectra of an as-deposited 684 nm garnet layer of composition type Bi0.9Lu1.85Y0.25Fe4.0Ga1O12, where zero absorption coefficient was considered for the modelled transmission spectrum. Figure 2(b) is the derived absorption coefficients of the garnet layer. And figure 2(c) shows the transmission spectrum fitting of a 1310 as-deposited garnet sample of same composition type where the derived absorption coefficients were considered during the simulation.

Figure 2

Figure 2. Magneto-photonic crystal (MPC) software fitted transmission spectra and the iterative (bisection algorithm-assisted) fitting of the absorption coefficient spectral dependency and the required pre-fitting of film thickness through matching transmission peak features. 

 

In order to understand the suitability and also to validate the calculation process using this free (available online, https://doi.org/10.3390/technologies7030049) and easy to install and use, several multi-defect multilayer MPC design, characterization and optimisation examples have been trailedused for illustration purposes. Figure 3 shows the result of an example run of MPC optimization software to reproduce the flat-top MPC transmission and Faraday rotation spectral properties for a four-defect design.

 

Figure 3

Figure 3. Result of reproduction of an optimized MPC design featuring “flat-top” response by using this new MPC optimization algorithm.

 

This MPC software package allows computational modelling of the optical spectral properties of various dielectrics-based generic single- and multilayer semitransparent thin films. Other additional program features of this software are the tools for fitting of the experimentally-measured transmission or reflection spectra to theoretical models, and the films physical thickness data recovery, if detailed refractive index information is available. Fitting of the absorption coefficient spectra in absorbing material layers is possible, using an automated algorithm reliant on the data for the measured transmission spectrum, refractive index spectrum, and physical thickness. A number of magneto-optic garnet material-related datasets are also available from the program installation directory once after the installation.

References

  1. Mikhail Vasiliev; Kamal Alameh; Mohammad Nur-E-Alam; Nur- E- Alam; Analysis, Optimization, and Characterization of Magnetic Photonic Crystal Structures and Thin-Film Material Layers. Technologies 2019, 7, 49, 10.3390/technologies7030049.
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