Search result: Catalogue data in Spring Semester 2020

Number Title Type ECTS Hours Lecturers Electrical Engineering and Information Technology Bachelor Bachelor Studies (Programme Regulations 2016) 4. Semester Examination Blocks Examination Block 3 401-0654-00L Numerical Methods O 4 credits 2V + 1U R. Käppeli Abstract The course introduces numerical methods according to the type of problem they tackle. The tutorials will include both theoretical exercises and practical tasks. Objective This course intends to introduce students to fundamental numerical methods that form the foundation of numerical simulation in engineering. Students are to understand the principles of numerical methods, and will be taught how to assess, implement, and apply them. The focus of this class is on the numerical solution of ordinary differential equations. During the course they will become familiar with basic techniques and concepts of numerical analysis. They should be enabled to select and adapt suitable numerical methods for a particular problem. Content Quadrature, Newton method, initial value problems for ordinary differential equations: explicit one step methods, step length control, stability analysis and implicit methods, structure preserving methods Literature M. Hanke Bourgeois: Grundlagen der Numerischen Mathematik und des Wissenschaftlichen Rechnens, BG Teubner, Stuttgart, 2002.W. Dahmen, A. Reusken: Numerik für Ingenieure und Naturwissenschaftler, Springer, 2008.Extensive study of the literature is not necessary for the understanding of the lectures. Prerequisites / Notice Prerequisite is familiarity with basic calculus and linear algebra. 227-0052-20L Electromagnetic Fields and Waves Only for Programme Regulations 2016. W 6 credits 2V + 2U L. Novotny Abstract This course is focused on the generation and propagation of electromagnetic fields. Based on Maxwell's equations we will derive the wave equation and its solutions. Specifically, we will discuss fields and waves in free space, refraction and reflection at plane interfaces, dipole radiation and Green functions, vector and scalar potentials, as well as gauge transformations. Objective Understanding of electromagnetic fields 227-0056-00L Semiconductor Devices O 4 credits 2V + 2U C. Bolognesi Abstract The course covers the basic principles of semiconductor devices in micro-, opto-, and power electronics. It imparts knowledge both of the basic physics and on the operation principles of pn-junctions, diodes, contacts, bipolar transistors, MOS devices, solar cells, photodetectors, LEDs and laser diodes. Objective Understanding of the basic principles of semiconductor devices in micro-, opto-, and power electronics. Content Brief survey of the history of microelectronics. Basic physics: Crystal structure of solids, properties of silicon and other semiconductors, principles of quantum mechanics, band model, conductivity, dispersion relation, equilibrium statistics, transport equations, generation-recombination (G-R), Quasi-Fermi levels. Physical and electrical properties of the pn-junction. pn-diode: Characteristics, small-signal behaviour, G-R currents, ideality factor, junction breakdown. Contacts: Schottky contact, rectifying barrier, Ohmic contact, Heterojunctions. Bipolar transistor: Operation principles, modes of operation, characteristics, models, simulation. MOS devices: Band diagram, MOSFET operation, CV- and IV characteristics, frequency limitations and non-ideal behaviour. Optoelectronic devices: Optical absorption, solar cells, photodetector, LED, laser diode. Lecture notes Lecture slides. Literature The lecture course follows the book Neamen, Semiconductor Physics and Devices, ISBN 978-007-108902-9, Fr. 89.00 Prerequisites / Notice Qualifications: Physics I+II 401-0604-00L Probability Theory and Statistics O 4 credits 2V + 1U V. Tassion Abstract Probability models and applications, introduction to statistical estimation and statistical tests. Objective Ability to understand the covered methods and models from probability theory and to apply them in other contexts. Ability to perform basic statistical tests and to interpret the results. Content The concept of probability space and some classical models: the axioms of Kolmogorov, easy consequences, discrete models, densities, product spaces, relations between various models, distribution functions, transformations of probability distributions. Conditional probabilities, definition and examples, calculation of absolute probabilities from conditional probabilities, Bayes' formula, conditional distribution. Expectation of a random variable,application to coding, variance, covariance and correlation, linear estimator, law of large numbers, central limit theorem. Introduction to statistics: estimation of parameters and tests Lecture notes yes Literature Textbuch: P. Brémaud: 'An Introduction to Probabilistic Modeling', Springer, 1988.
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