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B.Sc. Mechatronics Specialization: Photonic Engineering
Study plan for: B.Sc. Mechatronics Specialization: Photonic Engineering Faculty of Mechatronics Study plan for reference only; may be subject to change. Semester 1 Course Lecture Tutorial Labs Pojects ECTS (hours) (hours) (hours) (hours) Physical Education and Sports 30 Patents and Intelectual Property 30 2 Optics and Photonics Applications 30 15 3 Calculus I 30 45 7 Algebra and Geometry 15 30 4 Engineering Graphics 15 30 2 Materials 30 2 Computer Science I 30 30 6 Engineering Physics 30 30 4 Total ECTS 30 Semester 2 Course Lecture Tutorial Labs Pojects ECTS (hours) (hours) (hours) (hours) Physical Education and Sports 30 Economics 30 2 Elective Lecture 1/Virtual and 30 3 Augmented Reality Calculus II 30 30 5 Engineering Graphics ‐ CAD 30 2 Computer Science II 15 15 5 Mechanics I i II 45 45 6 Mechanics of Structures I 30 15 4 Electric Circuits I 30 15 3 Total ECTS 30 1 Study Plan for B.Sc. Mechatronics (Spec. Photonic Engineering) Semester 3 Course Lecture Tutorial Labs Pojects ECTS (hours) (hours) (hours) (hours) Physical Education and Sports 30 0 Foreign Language 60 4 Elective Lecture 2/Introduction to 30 3 MEMS Calculus III 15 30 6 Mechanics of Structures II 15 15 4 Manufacturing Technology I 30 4 Fine Machine Design I 15 30 3 Electric Circuits II 30 3 Basics of Automation and Control I 30 15 4 Total ECTS 31 Semester 4 Course Lecture Tutorial Labs Pojects ECTS (hours) (hours) (hours) (hours) Physical Education and Sports 30 Foreign Language 60 4 Elective Lecture 3/Photographic 30 3 techniques in image acqusition Elective Lecture 4 30 3 /Enterpreneurship Optomechatronics 30 30 5 Electronics I 15 15 2 Electronics II 15 1 Fine Machine Design II 15 15 3 Manufacturing Technology 30 2 Metrology 30 30 4 Total ECTS 27 Semester 5 Course Lecture Tutorial Labs Pojects ECTS (hours) (hours) (hours) (hours) Physical Education and Sports 30 0 Foreign Language 60 4 Marketing 30 2 Elective Lecture 5/ Electric 30 2 2 Study Plan for B.Sc. -
Silicon Photonics
Silicon Photonics – Trends, Highlights and Challenges Overview Gnyan Ramakrishna, Chair, Technical Committee Photonics, EPS and Technical Leader, Cisco Systems Vipul Patel, Co-Chair, Technical Committee Photonics, EPS and Principal Engineer, Cisco Systems The Cisco Global Cloud Index estimates that total data center traffic (all traffic within or exiting a data center) will reach almost 20 zettabytes per year by 2021, up from 7 zettabytes in 2016. Data center traffic on a global scale will grow at a 25 percent CAGR, with cloud data center traffic growth rate at 27 percent CAGR or 3.3-fold growth from 2016 to 2021. The growth in internet traffic not only accelerates the need for next-generation technology to support higher port density and faster speed transitions but is also accompanied by large physical data center sizes as well as faster connectivity between the data centers. As the data rates and distances to carry high speed data are increasing, the limitations of traditional copper cable and multimode fiber-based solutions are becoming apparent and the industry focus is shifting towards adoption of single-mode fiber-optic solutions. Silicon Photonics is an emerging technology that is bringing a paradigm shift in the field of single mode fiber-optic communications. Silicon Photonics leverages mature CMOS wafer fabrication and packaging infrastructures to deliver high bandwidth, low power transceivers. Even though the current focus of the industry is to develop products for the pluggable market, it is generally accepted that Silicon Photonics will play a key role in the next generation of optics that is needed for co-packaging with ASICs. -
Merging Photonics and Artificial Intelligence at the Nanoscale
Intelligent Nanophotonics: Merging Photonics and Artificial Intelligence at the Nanoscale Kan Yao1,2, Rohit Unni2 and Yuebing Zheng1,2,* 1Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, USA 2Texas Materials Institute, The University of Texas at Austin, Austin, Texas 78712, USA *Corresponding author: [email protected] Abstract: Nanophotonics has been an active research field over the past two decades, triggered by the rising interests in exploring new physics and technologies with light at the nanoscale. As the demands of performance and integration level keep increasing, the design and optimization of nanophotonic devices become computationally expensive and time-inefficient. Advanced computational methods and artificial intelligence, especially its subfield of machine learning, have led to revolutionary development in many applications, such as web searches, computer vision, and speech/image recognition. The complex models and algorithms help to exploit the enormous parameter space in a highly efficient way. In this review, we summarize the recent advances on the emerging field where nanophotonics and machine learning blend. We provide an overview of different computational methods, with the focus on deep learning, for the nanophotonic inverse design. The implementation of deep neural networks with photonic platforms is also discussed. This review aims at sketching an illustration of the nanophotonic design with machine learning and giving a perspective on the future tasks. Keywords: deep learning; (nano)photonic neural networks; inverse design; optimization. 1. Introduction Nanophotonics studies light and its interactions with matters at the nanoscale [1]. Over the past decades, it has received rapidly growing interest and become an active research field that involves both fundamental studies and numerous applications [2,3]. -
Experimental Photonics Multiple Post-Doctoral Positions Experimental Expertise in Any One of the Following Topics/Areas Is Highly Desired
Experimental Photonics Multiple Post-Doctoral Positions Experimental Expertise in any one of the following topics/areas is highly desired . Single photon level measurements , quantum communications . Computational imaging, super-resolution imaging, biomedical imaging . Quantum dots, 2D materials, quantum devices, quantum transport . Single molecule spectroscopy/imaging . Fluorescence microscopy . Optical manipulation of spin , ODMR, Magnetometry, NV centers . Nanofabication (Metasurfaces, plasmonics,silicon photonics) . Streak camera or time-correlated single photon counting experiments . Ultrafast spectroscopy, pump-probe measurements . Single nanoparticle/nanoantenna experiments . Coupling of single quantum emitters to nanophotonic structures . Cold atoms and quantum optics . Infrared spectroscopy, thermal emission measurements Please send your full CV and three representative publications to: [email protected] Prof. Zubin Jacob Birck Nanotechnology Center School of Electrical and Computer Engineering Purdue University, U.S.A. www.electrodynamics.org Zubin Jacob Research Group: Purdue University www.electrodynamics.org About the group Google Scholar Page: https://scholar.google.ca/citations?user=8FXvN_EAAAAJ&hl=en Main Research Areas: Casimir forces, quantum nanophotonics, plasmonics, metamaterials, Vacuum fluctuations, open quantum systems Weblink: www.electrodynamics.org Theory and Experiment Twitter: twitter.com/zjacob_group • Opportunity to closely interact with theorists and experimentalists within the group • Opportunity to travel -
Photonics Engineer
Photonics Engineer Antelope company Antelope DX develops a point-of-need diagnostic platform that allows consumers and healthcare professionals to have on-the-spot access to key health parameters. The Antelope technology aims to offer clinical lab performance with the ease-of-use of a pregnancy test at a consumer price tag. The platform is based on an innovative lab-on-chip technology that can perform a sensitive test on any bodily fluid, without requiring complex user operations or sample preparation. Role The Antelope Photonics Engineer is responsible for the design & development of the silicon photonic chip, located inside the Antelope consumable. He/she will also contribute largely to the optics and photonics aspects of associated hardware such as the Antelope reader. He/she will need to perform these product developments in a way that is compatible to IVD industry standards, including the generation of associated documentation. Responsibilities and duties • Photonics design & optimization of the sensing circuits. • Set up an optical/photonic system model to better predict and understand deviations from the norm by e.g. manufacturing tolerances. • Setting up characterisation, verification and QC equipment and methodologies for the photonic wafers & chips. • Support the design of the optical components of the read-out system. • Support the developmentt of the algorithmic framework that processes the optical signals to a diagnostic answer. • Support the development of R&D tools & methodologies from a system perspective to increase R&D efficiency, throughput and data generation. • Support the improvement of the R&D experimental setups, used to generate assay results. • Setting up testing and verification planning. -
Roadmap on Silicon Photonics
Home Search Collections Journals About Contact us My IOPscience Roadmap on silicon photonics This content has been downloaded from IOPscience. Please scroll down to see the full text. View the table of contents for this issue, or go to the journal homepage for more Download details: IP Address: 152.78.236.131 This content was downloaded on 24/06/2016 at 13:44 Please note that terms and conditions apply. Journal of Optics J. Opt. 18 (2016) 073003 (20pp) doi:10.1088/2040-8978/18/7/073003 Roadmap Roadmap on silicon photonics David Thomson1,10,11, Aaron Zilkie2, John E Bowers3, Tin Komljenovic3, Graham T Reed1, Laurent Vivien4, Delphine Marris-Morini4, Eric Cassan4, Léopold Virot5,6, Jean-Marc Fédéli5,6, Jean-Michel Hartmann5,6, Jens H Schmid7, Dan-Xia Xu7, Frédéric Boeuf8, Peter O’Brien9, Goran Z Mashanovich1 and M Nedeljkovic1 1 Optoelectronics Research Centre, University of Southampton, Southampton, SO17 1BJ, UK 2 Rockley Photonics Inc., 234 E. Colorado Blvd, Suite 600, Pasadena, CA, 91101, USA 3 University of California, Santa Barbara, Electrical and Computer Engineering Department, Santa Barbara, CA 93106, USA 4 Centre for Nanoscience and Nanotechnology (C2N), CNRS, Université Paris Sud, Université Paris- Saclay, France 5 University of Grenoble Alpes, F-38000 Grenoble, France 6 CEA, LETI, Minatec Campus, 17 rue des Martyrs, F-38054 Grenoble, France 7 Information and Communications Technologies Portfolio, National Research Council of Canada, 1200 Montreal Road, Ottawa, Ontario K1A 0R6, Canada 8 STMicroelectronics (Crolles 2) SAS, 850 Rue Jean Monnet, BP 16, Crolles Cedex, France 9 Tyndall National Institute, University College Cork, Cork, Ireland E-mail: [email protected] Received 18 September 2015 Accepted for publication 24 February 2016 Published 24 June 2016 Abstract Silicon photonics research can be dated back to the 1980s. -
Inverse Design for Silicon Photonics: from Iterative Optimization Algorithms to Deep Neural Networks
applied sciences Review Inverse Design for Silicon Photonics: From Iterative Optimization Algorithms to Deep Neural Networks Simei Mao 1,2, Lirong Cheng 1,2 , Caiyue Zhao 1,2, Faisal Nadeem Khan 2, Qian Li 3 and H. Y. Fu 1,2,* 1 Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China; [email protected] (S.M.); [email protected] (L.C.); [email protected] (C.Z.) 2 Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; [email protected] 3 School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China; [email protected] * Correspondence: [email protected]; Tel.: +86-755-3688-1498 Abstract: Silicon photonics is a low-cost and versatile platform for various applications. For design of silicon photonic devices, the light-material interaction within its complex subwavelength geometry is difficult to investigate analytically and therefore numerical simulations are majorly adopted. To make the design process more time-efficient and to improve the device performance to its physical limits, various methods have been proposed over the past few years to manipulate the geometries of silicon platform for specific applications. In this review paper, we summarize the design methodologies for silicon photonics including iterative optimization algorithms and deep neural networks. In case of iterative optimization methods, we discuss them in different scenarios in the sequence of increased degrees of freedom: empirical structure, QR-code like structure and irregular structure. We also review inverse design approaches assisted by deep neural networks, which generate multiple devices Citation: Mao, S.; Cheng, L.; Zhao, with similar structure much faster than iterative optimization methods and are thus suitable in C.; Khan, F.N.; Li, Q.; Fu, H.Y. -
Recent Advances in Silicon Photonic Integrated Circuits John E
Invited Paper Recent Advances in Silicon Photonic Integrated Circuits John E. Bowers*, Tin Komljenovic, Michael Davenport, Jared Hulme, Alan Y. Liu, Christos T. Santis, Alexander Spott, Sudharsanan Srinivasan, Eric J. Stanton, Chong Zhang Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93106, USA *[email protected] ABSTRACT We review recent breakthroughs in silicon photonics technology and components and describe progress in silicon photonic integrated circuits. Heterogeneous silicon photonics has recently demonstrated performance that significantly outperforms native III-V components. The impact active silicon photonic integrated circuits could have on interconnects, telecommunications, sensors and silicon electronics is reviewed. Keywords: Heterogeneous silicon platform, integrated optoelectronics, optoelectronic devices, semiconductor lasers, silicon-on-insulator (SOI) technology, silicon photonics 1. INTRODUCTION Heterogeneous silicon photonics, due to its potential for medium- and large-scale integration, has been intensively researched. Recent developments have shown that heterogeneous integration not only allows for a reduced cost due to economy of scale, but also allows for same or even better performing devices than what has previously been demonstrated utilizing only III-V materials. Furthermore we believe that optical interconnects are the only way to solve the scaling limitation in modern processors, and that heterogeneous silicon photonics with on-chip sources is the best approach in the long term as it promises higher efficiency and lower cost. We address both beliefs in sections that follow. In this paper we plan to briefly address heterogeneous silicon approaches, and point-out that the heterogeneous silicon platform is more than just III-V on silicon but can have advantages for isolators, circulators and nonlinear devices (Section 2). -
Illuminating the History and Expanding Photonics Education
Illuminating the History and Expanding Photonics Education An Interactive Qualifying Project submitted to the Faculty of WORCESTER POLYTECHNIC INSTITUTE in partial fulfilment of the requirements for the degree of Bachelor of Science by Nicholas Marshall Brandon McLaughlin Date: 2nd June 2020 Report Submitted to: Worcester Polytechnic Institute Quinsigamond Community College Professor Douglas Petkie Worcester Polytechnic Institute This report represents work of WPI undergraduate students submitted to the faculty as evidence of a degree requirement. WPI routinely publishes these reports on its web site without editorial or peer review. For more information about the projects program at WPI, see http://www.wpi.edu/Academics/Projects. Abstract Photonics today is on the cusp of revolutionizing computing, just as it has already revolutionized communication, and becoming to this century what electricity was to the last (Sala, 2016). As the manifestation of mankind's millenia-spanning obsession with light, photonics evolved from optics, which itself developed over the long course of human history. That development has accelerated in the last several centuries, and today optics and photonics act as enablers for a variety of fields from biology to communication. Even so, most people don’t know just how essential optics and photonics are, and today those fields face a major staffing shortage. Most people don’t even know the basic principles of light’s behavior, with few formal education programs that focus on optics and photonics. In order to combat this, various initiatives have strived to drum up more interest in optics and photonics, with several focusing on pre-college age groups in order to get students involved sooner. -
Photonics and Optoelectronics
Photonics and Optoelectronics Effects of Line Edge Roughness on Photonic Device Performance through Virtual Fabrication ...................................... 43 Reprogrammable Electro-Chemo-Optical Devices ............................................................................................................... 44 On-chip Infrared Chemical Sensor Leveraging Supercontinuum Generation in GeSbSe Chalcogenide Glass Waveguide ............................................................................................................................... 45 Sensing Chemicals in the mid-Infrared using Chalcogenide Glass Waveguides and PbTe Detectors Monolithically Integrated On-chip ......................................................................................................... 46 Broadband Low-loss Nonvolatile Photonic Switches Based on Optical Phase Change Materials (O-PCMs) ............... 47 Chalcogenide Glass Waveguide-integrated Black Phosphorus mid-Infrared Photodetectors .......................................... 48 An Ultrasensitive Graphene-polymer Thermo-mechanical Bolometer ................................................................................ 49 Nanocavity Design for Reduced Spectral Diffusion of Solid-state Defects ......................................................................... 50 Two-dimensional Photonic Crystal Cavities in Bulk Single-crystal Diamond ....................................................................... 51 Quasi-Bessel-Beam Generation using Integrated Optical Phased Arrays .......................................................................... -
Silicon Photonics for Extreme Scale Systems
JOURNAL OF LIGHTWAVE TECHNOLOGY, VOL. 37, NO. 2, JANUARY 15, 2019 245 Silicon Photonics for Extreme Scale Systems Yiwen Shen , Student Member, IEEE, Xiang Meng, Member, IEEE, Qixiang Cheng , Member, IEEE, Sebastien´ Rumley , Nathan Abrams, Alexander Gazman , Evgeny Manzhosov, Madeleine Strom Glick, Senior Member, IEEE, Senior Member, OSA, and Keren Bergman , Fellow, IEEE, Fellow, OSA (Invited Tutorial) Abstract—High-performance systems are increasingly bottle- croprocessors with memory, accelerators, storage, and network necked by the growing energy and communications costs of in- components form the basis for realizing future massive parallel terconnecting numerous compute and memory resources. Recent supercomputers. While developments in parallelism have so far advances in integrated silicon photonics offer the opportunity of embedding optical connectivity that directly delivers high off-chip produced performance growth, the scalability of the system de- communication bandwidth densities with low power consumption. pends on the underlying interconnection technology to provide This paper reviews the design and integration of silicon photonic adequate bandwidth resources and minimal latency to memory interconnection networks that address the data-movement chal- and storage nodes to achieve the full computation potential of lenges in high-performance systems. Beyond alleviating the band- each processor. width/energy bottlenecks, embedded photonics can enable new dis- aggregated architectures that leverage the distance independence Over the last few years however, the growth of interconnect of optical transmission. This review paper presents some of the key bandwidth capacity has not been able to match the pace of the interconnect requirements to create a new generation of photonic increase in raw processing power gained through parallelism. -
The Role of Nanostructures in Integrated Photonics
The role of Nanostructures in Integrated Photonics James S. Harris Department of Electrical Engineering Stanford University 3rd U.S.-Korea Forum on Nanotechnology Seoul, Korea April 3 & 4, 2006 U.S.-Korea Forum on Nanotechnology-Korea-4/3/06 JSH 1 1993 Photonic Integrated Circuit Soref, Proc. IEEE, 1687 (1993) z Waveguide architecture with butt coupled fibers and edge emitting lasers z Hybrid bonding (non-monolithic) of different structures z Mostly III-V devices, very little electronics U.S.-Korea Forum on Nanotechnology-Korea-4/3/06 JSH 2 First Photonic Crystal Device DBR (Distributed Bragg Reflector z Single longitudinal mode emission, z 20-40 quarter wavelength independent of temperature and current injection different index layers (~70 nm) z Circular beam pattern z One-dimensional photonic crystal z Vertical emission--2-D array U.S.-Korea Forum on Nanotechnology-Korea-4/3/06 JSH 3 Dimensional Mismatch Between Optics and Electronics U.S.-Korea Forum on Nanotechnology-Korea-4/3/06 JSH 4 Unique Photonic Crystal Functionality Electric Field Strength U.S.-Korea Forum on Nanotechnology-Korea-4/3/06 JSH 5 Nanoscale Plasmonic Waveguides z 90° bends and splitters can be designed with 100% transmission from microwave to optical frequencies z Provides bridge between dimensions of electronics and photonics z Provides design flexibility for optoelectronic ICs U.S.-Korea Forum on Nanotechnology-Korea-4/3/06 JSH 6 A New Si-Based Optical Modulator Quantum-confined Stark effect (QCSE) z Strongest high-speed optical modulation mechanism z Used today for high-speed, low power telecommunications optical modulators but in III-V semiconductors z QCSE in germanium quantum wells on silicon substrates z Fully compatible with CMOS fabrication z Surprises z works in “indirect gap” semiconductor actually better than in III-V z higher speed (100 GHz) possible Y.