An important goal in nanotechnology is to control and manipulate submicrometer objects in fluidic environments, for which optical traps based on strongly localized electromagnetic fields around plasmonic nanostructures can provide a promising solution. Conventional plasmonics based trapping occurs at predefined spots on the surface of a nanopatterned substrate and is severely speed-limited by the diffusion of colloidal objects into the trapping volume. As we demonstrate, these limitations can be overcome by integrating plasmonic nanostructures with magnetically driven helical microrobots and maneuvering the resultant mobile nanotweezers (MNTs) under optical illumination. These nanotweezers can be remotely maneuvered within the bulk fluid and temporarily stamped onto the microfluidic chamber surface. The working range of these MNTs matches that of state-of-the-art plasmonic tweezers and allows selective pickup, transport, release, and positioning of submicrometer objects with great speed and accuracy. The MNTs can be used in standard microfluidic chambers to manipulate one or many nano-objects in three dimensions and are applicable to a variety of materials, including bacteria and fluorescent nanodiamonds. MNTs may allow previously unknown capabilities in optical nanomanipulation by combining the strengths of two recent advances in nanotechnology.

Manipulation of colloidal objects with light is important in diverse fields. While performance of traditional optical tweezers is restricted by the diffraction-limit, recent approaches based on plasmonic tweezers allow higher trapping efficiency at lower optical powers but suffer from the disadvantage that plasmonic nanostructures are fixed in space, which limits the speed and versatility of the trapping process. As we show here, plasmonic nanodisks fabricated over dielectric microrods provide a promising approach toward optical nanomanipulation: these hybrid structures can be maneuvered by conventional optical tweezers and simultaneously generate strongly confined optical near-fields in their vicinity, functioning as near-field traps themselves for colloids as small as 40 nm. The colloidal tweezers can be used to transport nanoscale cargo even in ionic solutions at optical intensities lower than the damage threshold of living micro-organisms, and in addition, allow parallel and independently controlled manipulation of different types of colloids, including fluorescent nanodiamonds and magnetic nanoparticles.

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 μm, pitch from 0.8-2.0 μm, 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.

Machine learning is an application of artificial intelligence that focuses on the development of computer algorithms which learn automatically by extracting patterns from the data provided. Machine learning techniques can be efficiently used for a problem with a large number of parameters to be optimized and also where it is infeasible to develop an algorithm of specific instructions for performing the task. Here, we combine the finite element simulations and machine learning techniques for the prediction of mode effective indices, power confinement and coupling length of different integrated photonics devices. Initially, we prepare a dataset using COMSOL Multiphysics and then this data is used for training while optimizing various parameters of the machine learning model. Waveguide width, height, operating wavelength, and other device dimensions are varied to record different modal solution parameters. A detailed study has been carried out for a slot waveguide structure to evaluate different machine learning model parameters including number of layers, number of nodes, choice of activation functions, and others. After training, this model is used to predict the outputs for new input device specifications. This method predicts the output for different device parameters faster than direct numerical simulation techniques. Absolute percentage error of less than 5% in predicting an output has been obtained for slot, strip and directional waveguide coupler designs. This study pave the step towards using machine learning based optimization techniques for integrated silicon photonics devices. Index Terms-Machine learning, neural networks, regression, multilayer perceptron, silicon photonics. I. INTRODUCTION M ACHINE learning (ML) technology is being extensively used in many aspects of modern society: web searches, social networking, smartphones, bioinformatics, robotics, chatbots, and self-driving cars [1]. ML techniques are used to classify or detect objects in images, speech to text conversion, pattern recognition, natural language processing, sentiment analysis and recommendations of products/movies for users based on their search preferences. ML algorithms can be trained to perform exceptionally well when it is difficult to analyze the underlying physics and mathematics of the problem [2]. ML algorithms extract patterns from the raw data provided during the training without being explicitly programmed. The learned patterns can be used to make predictions on some other data of interest. ML systems can be trained more efficiently when massive amount of data is present [3], [4]. Recently, research on the application of ML techniques for optical communication systems and nanophotonic devices is

Articles you may be interested inMicrowave spectrum, dipole moment, barrier to internal rotation, and molecular structure of germylazide Four low-l rotational transitions of 03 16 have been measured in the region from 42 to llB kmc. The rotational spectroscopic constants found are A = 106,530 0±11 Mc, B= 13,349 06±0 06 Mc, C= 11,834.3± ~.1 Mc The o~ygen nuclei form an isosceles trIangle with an apex angle of 116°49'±30', and the two equal mternuc1~ar distances are 1 278±0.003A:. ~rom the. Stark :~litting of the 20• 2->1 1• 1 transition the dipole moment IS 0.53±0.02 debye Zeeman splittmgs of thiS transition show that ozone is not significantly paramagnetic.