Query: photonic crystal machine learning

Candidate papers:

abstract: Using machine learning, we optimized an ultrasmall photonic crystal nanocavity to attain a high $Q$Q. Training data were collected via finite difference time domain simulation for models with randomly shifted holes, and a fully connected neural network (NN) was trained, resulting in a coefficient of determination between predicted and calculated values of 0.977. By repeating NN training and optimization of the $Q$Q value on the trained NN, the $Q$Q was roughly improved by a factor of 10 20 for various situations. Assuming a 180 nm thick semiconductor slab at a wavelength approximately 1550 nm, we obtained $Q={1}{011}{400}$Q=1,011,400 in air; 283,200 in a solution, which was suitable for biosensing; and 44,600 with a nanoslot for high sensitivity. Important hole positions were also identified using the linear Lasso regression algorithm.
author_names: Ryotaro Abe

Taichi Takeda

Ryo Shiratori

Shinichi Shirakawa

S Saito

Toshihiko Baba
title: Optimization of an H0 photonic crystal nanocavity using machine learning
year: 2020

abstract: By utilizing random localization patterns as training data for machine learning, we achieved a 0.2 nm wavelength resolution with a fabricated photonic crystal wavemeter, which greatly exceeds the limit imposed by the fabrication.
author_names: Jocelyn Hofs

Takumasa Kodama

Shengji Jin

Takasumi Tanabe
title: Breaking the Fabrication Determined Resolution Limit of Photonic Crystal Wavemeter by Machine Learning
venue: 2020 Conference on Lasers and Electro Optics (CLEO)
year: 2020

abstract: Abstract In this paper, a machine learning (ML) based approach is described to design a photonic crystal (PhC) gas sensor for identification and estimation of three greenhouse gases, namely S F 6 C H 4 and C O 2 Exploiting the refractive index dependence of the defect layer positions in the photonic bandgap (PBG) in the transmission spectrum of a PhC, structures are proposed to detect different types of gases. First, a pre trained classifier is used to identify the gases. Support Vector Machine (SVM) and Random Forest (RF) classifiers are used for performance evaluation with hyper parameter optimization. Random forest classifier yields the highest accuracy of 100% for the gas identification stage. In the next step, the densities of the gases are predicted using three pre trained artificial neural networks (ANNs) separately for each of the gases. The results show that the present approach helps design of a precision gas sensor even using a simple PhC structure.
author_names: Alekhya Ghosh

Arghadeep Pal

Nikhil Ranjan Das
title: An approach to design photonic crystal gas sensor using machine learning
year: 2020

abstract: We train a neural network to predict the optical properties (center wavelength l0, linewidth, sensitivity S) of photonic crystal slab structures. We are able to faithfully model the results to within 1% for l0 and S.
author_names: Eric Yi Zhu

Li Qian

Ofer Levi
title: Identifying Optimal Photonic Crystal Sensor Designs with Machine Learning
year: 2020

abstract: Photonic crystal fibers (PCFs) are the specialized optical waveguides that led to many interesting applications ranging from nonlinear optical signal processing to high power fiber amplifiers. In this paper, machine learning techniques are used to compute various optical properties including effective index, effective mode area, dispersion and confinement loss for a solid core PCF. These machine learning algorithms based on artificial neural networks are able to make accurate predictions of above mentioned optical properties for usual parameter space of wavelength ranging from 0.5 1.8 um, pitch from 0.8 2.0 um, diameter by pitch from 0.6 0.9 and number of rings as 4 or 5 in a silica solid core PCF. We demonstrate the use of simple and fast training feed forward artificial neural networks that predicts the output for unknown device parameters faster than conventional numerical simulation techniques. Computation runtimes required with neural networks (for training and testing) and Lumerical MODE solutions are also compared.
author_names: Sunny Chugh

Aamir Gulistan

Souvik Ghosh

B M Azizur Rahman
title: Machine learning approach for computing optical properties of a photonic crystal fiber.
venue: Optics express
year: 2019

abstract: A photonic crystal enhanced fluorescence imaging immunoassay biosensor is capable of detecting NT proBNP as a cardiovascular biomarker at various concentrations. Utilizing machine learning algorithms, we create a predictive model for the analyte quantification. (c) 2019 The Author(s)
author_names: Kenneth J Squire

Yaodong Zhao

Ai-ling Tan

Kundan Sivashanmugan

Joseph A Kraai

Gregory L Rorrer

Alan X Wang
title: Photonic Crystal Enhanced Fluorescence Imaging of Cardiovascular Biomarker with Machine Learning Analysis
year: 2019

abstract: A photonic crystal enhanced fluorescence imaging immunoassay biosensor is capable of detecting NT proBNP as a cardiovascular biomarker at various concentrations. Utilizing machine learning algorithms, we create a predictive model for the analyte quantification. (c) 2019 The Author(s)
author_names: Kenneth J Squire

Y A Zhao

Ai-ling Tan

Kundan Sivashanmugan

Joseph A Kraai

Gregory L Rorrer

Alan X Wang
title: Photonic Crystal Enhanced Fluorescence Imaging of Cardiovascular Biomarker with Machine Learning Analysis
venue: 2019 Conference on Lasers and Electro Optics (CLEO)
year: 2019

abstract: When myocardial walls experience stress due to cardiovascular diseases, like heart failure, hormone N terminal pro B type natriuretic peptide (NT proBNP) is secreted into the blood. Early detection of NT proBNP can assist diagnosis of heart failure and enable early medical intervention. A simple, cost effective detection technique such as the widely used fluorescence imaging immunoassay is yet to be developed to detect clinically relevant levels of NT proBNP. In this work, we demonstrate photonic crystal enhanced fluorescence imaging immunoassay using diatom biosilica, which is capable of detecting low levels of NT proBNP in solution with the concentration range of 0~100 pg/mL. By analyzing the fluorescence images in the spatial and spatial frequency domain with principle component analysis (PCA) and partial least squares regression (PLSR) algorithms, we create a predictive model that achieves great linearity with a validation R2 value of 0.86 and a predictive root mean square error of 14.47, allowing for good analyte quantification. To demonstrate the potential of the fluorescence immunoassay biosensor for clinical usage, we conducted qualitative screening of high and low concentrations of NT proBNP in human plasma. A more advanced machine learning algorithm, the support vector machine classification, was paired with the PCA and trained by 160 fluorescence images. In the 40 testing images, we achieved excellent specificity of 93% as well as decent accuracy and sensitivity of 78% and 65% respectively. Therefore, the photonic crystal enhanced fluorescence imaging immunoassay reported in this article is feasible to screen clinically relevant levels of NT proBNP in body fluid and evaluate the risk of heart failure.
author_names: Kenneth J Squire

Yong Zhao

Ai-ling Tan

Kundan Sivashanmugan

Joseph A Kraai

Gregory L Rorrer

Alan X Wang
title: Photonic Crystal Enhanced Fluorescence Imaging Immunoassay for Cardiovascular Disease Biomarker Screening with Machine Learning Analysis.
venue: Sensors and actuators. B, Chemical
year: 2019

author_names: Takashi Asano
title: Machine learning based simultaneous optimization of Q factors of two modes in a photonic crystal nanocavity aimed for the application to Raman silicon laser
year: 2019

abstract: Abstract The prediction and design of photonic features have traditionally been guided by theory driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in the field of machine learning, there has recently been a growing interest in developing complementary data driven methods for photonics. Here, we demonstrate several predictive and generative data driven approaches for the characterization and inverse design of photonic crystals. Concretely, we built a data set of 20,000 two dimensional photonic crystal unit cells and their associated band structures, enabling the training of supervised learning models. Using these data set, we demonstrate a high accuracy convolutional neural network for band structure prediction, with orders of magnitude speedup compared to conventional theory driven solvers. Separately, we demonstrate an approach to high throughput inverse design of photonic crystals via generative adversarial networks, with the design goal of substantial transverse magnetic band gaps. Our work highlights photonic crystals as a natural application domain and test bed for the development of data driven tools in photonics and the natural sciences.
author_names: Thomas Christensen

Charlotte Loh

Stjepan Picek

Domagoj Jakobovic

Li Jing

Sophie Fisher

Vladimir Ceperic

John D Joannopoulos

Marin Soljacic
title: Predictive and generative machine learning models for photonic crystals
year: 2020