Search result: Catalogue data in Autumn Semester 2020

Number	Title	Type	ECTS	Hours	Lecturers
Quantum Engineering Master
Electives This is a selection of courses particularly suitable for the MSc QE. In agreement with the tutor, students may choose other courses from the ETH course catalogue.
227-0101-00L	Discrete-Time and Statistical Signal Processing	W	6 credits	4G	H.‑A. Loeliger
Abstract	The course introduces some fundamental topics of digital signal processing with a bias towards applications in communications: discrete-time linear filters, inverse filters and equalization, DFT, discrete-time stochastic processes, elements of detection theory and estimation theory, LMMSE estimation and LMMSE filtering, LMS algorithm, Viterbi algorithm.
Learning objective	The course introduces some fundamental topics of digital signal processing with a bias towards applications in communications. The two main themes are linearity and probability. In the first part of the course, we deepen our understanding of discrete-time linear filters. In the second part of the course, we review the basics of probability theory and discrete-time stochastic processes. We then discuss some basic concepts of detection theory and estimation theory, as well as some practical methods including LMMSE estimation and LMMSE filtering, the LMS algorithm, and the Viterbi algorithm. A recurrent theme throughout the course is the stable and robust "inversion" of a linear filter.
Content	1. Discrete-time linear systems and filters: state-space realizations, z-transform and spectrum, decimation and interpolation, digital filter design, stable realizations and robust inversion. 2. The discrete Fourier transform and its use for digital filtering. 3. The statistical perspective: probability, random variables, discrete-time stochastic processes; detection and estimation: MAP, ML, Bayesian MMSE, LMMSE; Wiener filter, LMS adaptive filter, Viterbi algorithm.
Lecture notes	Lecture Notes
227-0145-00L	Solid State Electronics and Optics	W	6 credits	4G	N. Yazdani, V. Wood
Abstract	"Solid State Electronics" is an introductory condensed matter physics course covering crystal structure, electron models, classification of metals, semiconductors, and insulators, band structure engineering, thermal and electronic transport in solids, magnetoresistance, and optical properties of solids.
Learning objective	Understand the fundamental physics behind the mechanical, thermal, electric, magnetic, and optical properties of materials.
Prerequisites / Notice	Recommended background: Undergraduate physics, mathematics, semiconductor devices
227-0146-00L	Analog-to-Digital Converters Does not take place this semester. Course will be moved to the autumn semester 2021.	W	6 credits	2V + 2U
Abstract	This course provides a thorough treatment of integrated data conversion systems from system level specifications and trade-offs, over architecture choice down to circuit implementation.
Learning objective	Data conversion systems are substantial sub-parts of many electronic systems, e.g. the audio conversion system of a home-cinema systems or the base-band front-end of a wireless modem. Data conversion systems usually determine the performance of the overall system in terms of dynamic range and linearity. The student will learn to understand the basic principles behind data conversion and be introduced to the different methods and circuit architectures to implement such a conversion. The conversion methods such as successive approximation or algorithmic conversion are explained with their principle of operation accompanied with the appropriate mathematical calculations, including the effects of non-idealties in some cases. After successful completion of the course the student should understand the concept of an ideal ADC, know all major converter architectures, their principle of operation and what governs their performance.
Content	- Introduction: information representation and communication; abstraction, categorization and symbolic representation; basic conversion algorithms; data converter application; tradeoffs among key parameters; ADC taxonomy. - Dual-slope & successive approximation register (SAR) converters: dual slope principle & converter; SAR ADC operating principle; SAR implementation with a capacitive array; range extension with segmented array. - Algorithmic & pipelined A/D converters: algorithmic conversion principle; sample & hold stage; pipe-lined converter; multiplying DAC; flash sub-ADC and n-bit MDAC; redundancy for correction of non-idealties, error correction. - Performance metrics and non-linearity: ideal ADC; offset, gain error, differential and integral non-linearities; capacitor mismatch; impact of capacitor mismatch on SAR ADC's performance. - Flash, folding an interpolating analog-to-digital converters: flash ADC principle, thermometer to binary coding, sparkle correction; limitations of flash converters; the folding principle, residue extraction; folding amplifiers; cascaded folding; interpolation for folding converters; cascaded folding and interpolation. - Noise in analog-to-digital converters: types of noise; noise calculation in electronic circuit, kT/C-noise, sampled noise; noise analysis in switched-capacitor circuits; aperture time uncertainty and sampling jitter. - Delta-sigma A/D-converters: linearity and resolution; from delta-modulation to delta-sigma modulation; first-oder delta-sigma modulation, circuit level implementation; clock-jitter & SNR in delta-sigma modulators; second-order delta-sigma modulation, higher-order modulation, design procedure for a single-loop modulator. - Digital-to-analog converters: introduction; current scaling D/A converter, current steering DAC, calibration for improved performance.
Lecture notes	Slides are available online under https://iis-students.ee.ethz.ch/lectures/analog-to-digital-converters/
Literature	- B. Razavi, Principles of Data Conversion System Design, IEEE Press, 1994 - M. Gustavsson et. al., CMOS Data Converters for Communications, Springer, 2010 - R.J. van de Plassche, CMOS Integrated Analog-to-Digital and Digital-to-Analog Converters, Springer, 2010
Prerequisites / Notice	It is highly recommended to attend the course "Analog Integrated Circuits" of Prof. Huang as a preparation for this course.
227-0157-00L	Semiconductor Devices: Physical Bases and Simulation	W	4 credits	3G	A. Schenk
Abstract	The course addresses the physical principles of modern semiconductor devices and the foundations of their modeling and numerical simulation. Necessary basic knowledge on quantum-mechanics, semiconductor physics and device physics is provided. Computer simulations of the most important devices and of interesting physical effects supplement the lectures.
Learning objective	The course aims at the understanding of the principle physics of modern semiconductor devices, of the foundations in the physical modeling of transport and its numerical simulation. During the course also basic knowledge on quantum-mechanics, semiconductor physics and device physics is provided.
Content	The main topics are: transport models for semiconductor devices (quantum transport, Boltzmann equation, drift-diffusion model, hydrodynamic model), physical characterization of silicon (intrinsic properties, scattering processes), mobility of cold and hot carriers, recombination (Shockley-Read-Hall statistics, Auger recombination), impact ionization, metal-semiconductor contact, metal-insulator-semiconductor structure, and heterojunctions. The exercises are focussed on the theory and the basic understanding of the operation of special devices, as single-electron transistor, resonant tunneling diode, pn-diode, bipolar transistor, MOSFET, and laser. Numerical simulations of such devices are performed with an advanced simulation package (Sentaurus-Synopsys). This enables to understand the physical effects by means of computer experiments.
Lecture notes	The script (in book style) can be downloaded from: https://iis-students.ee.ethz.ch/lectures/
Literature	The script (in book style) is sufficient. Further reading will be recommended in the lecture.
Prerequisites / Notice	Qualifications: Physics I+II, Semiconductor devices (4. semester).
227-0166-00L	Analog Integrated Circuits	W	6 credits	2V + 2U	T. Jang
Abstract	This course provides a foundation in analog integrated circuit design based on bipolar and CMOS technologies.
Learning objective	Integrated circuits are responsible for much of the progress in electronics in the last 50 years, particularly the revolutions in the Information and Communications Technologies we witnessed in recent years. Analog integrated circuits play a crucial part in the highly integrated systems that power the popular electronic devices we use daily. Understanding their design is beneficial to both future designers and users of such systems. The basic elements, design issues and techniques for analog integrated circuits will be taught in this course.
Content	Review of bipolar and MOS devices and their small-signal equivalent circuit models; Building blocks in analog circuits such as current sources, active load, current mirrors, supply independent biasing etc; Amplifiers: differential amplifiers, cascode amplifier, high gain structures, output stages, gain bandwidth product of op-amps; stability; comparators; second-order effects in analog circuits such as mismatch, noise and offset; data converters; frequency synthesizers; switched capacitors. The exercise sessions aim to reinforce the lecture material by well guided step-by-step design tasks. The circuit simulator SPECTRE is used to facilitate the tasks. There is also an experimental session on op-amp measurements.
Lecture notes	Handouts of presented slides. No script but an accompanying textbook is recommended.
Literature	Behzad Razavi, Design of Analog CMOS Integrated Circuits (Irwin Electronics & Computer Engineering) 1st or 2nd edition, McGraw-Hill Education
227-0225-00L	Linear System Theory	W	6 credits	5G	M. Colombino
Abstract	The class is intended to provide a comprehensive overview of the theory of linear dynamical systems, stability analysis, and their use in control and estimation. The focus is on the mathematics behind the physical properties of these systems and on understanding and constructing proofs of properties of linear control systems.
Learning objective	Students should be able to apply the fundamental results in linear system theory to analyze and control linear dynamical systems.
Content	- Proof techniques and practices. - Linear spaces, normed linear spaces and Hilbert spaces. - Ordinary differential equations, existence and uniqueness of solutions. - Continuous and discrete-time, time-varying linear systems. Time domain solutions. Time invariant systems treated as a special case. - Controllability and observability, duality. Time invariant systems treated as a special case. - Stability and stabilization, observers, state and output feedback, separation principle.
Lecture notes	Available on the course Moodle platform.
Prerequisites / Notice	Sufficient mathematical maturity, in particular in linear algebra, analysis.
227-0427-00L	Signal Analysis, Models, and Machine Learning Does not take place this semester. This course has been replaced by "Introduction to Estimation and Machine Learning" (autumn semester) and "Advanced Signal Analysis, Modeling, and Machine Learning" (spring semester).	W	6 credits	4G	H.‑A. Loeliger
Abstract	Mathematical methods in signal processing and machine learning. I. Linear signal representation and approximation: Hilbert spaces, LMMSE estimation, regularization and sparsity. II. Learning linear and nonlinear functions and filters: neural networks, kernel methods. III. Structured statistical models: hidden Markov models, factor graphs, Kalman filter, Gaussian models with sparse events.
Learning objective	The course is an introduction to some basic topics in signal processing and machine learning.
Content	Part I - Linear Signal Representation and Approximation: Hilbert spaces, least squares and LMMSE estimation, projection and estimation by linear filtering, learning linear functions and filters, L2 regularization, L1 regularization and sparsity, singular-value decomposition and pseudo-inverse, principal-components analysis. Part II - Learning Nonlinear Functions: fundamentals of learning, neural networks, kernel methods. Part III - Structured Statistical Models and Message Passing Algorithms: hidden Markov models, factor graphs, Gaussian message passing, Kalman filter and recursive least squares, Monte Carlo methods, parameter estimation, expectation maximization, linear Gaussian models with sparse events.
Lecture notes	Lecture notes.
Prerequisites / Notice	Prerequisites: - local bachelors: course "Discrete-Time and Statistical Signal Processing" (5. Sem.) - others: solid basics in linear algebra and probability theory
227-0468-00L	Analog Signal Processing and Filtering Suitable for Master Students as well as Doctoral Students.	W	6 credits	2V + 2U	H. Schmid
Abstract	This lecture provides a wide overview over analog filters (continuous-time and discrete-time), signal-processing systems, and sigma-delta conversion, and gives examples with sensor interfaces and class-D audio drivers. All systems and circuits are treated using a signal-flow view. The lecture is suitable for both analog and digital designers.
Learning objective	This lecture provides a wide overview over analog filters (continuous-time and discrete-time), signal-processing systems, and sigma-delta conversion, and gives examples with sensor interfaces and class-D audio drivers. All systems and circuits are treated using a signal-flow view. The lecture is suitable for both analog and digital designers. The way the exam is done allows for the different interests of the two groups. The learning goal is that the students can apply signal-flow graphs and can understand the signal flow in such circuits and systems (including non-ideal effects) well enough to gain an understanding of further circuits and systems by themselves.
Content	At the beginning, signal-flow graphs in general and driving-point signal-flow graphs in particular are introduced. We will use them during the whole term to analyze circuits on a system level (analog continuous-time, analog discrete-time, mixed-signal and digital) and understand how signals propagate through them. The theory and CMOS implementation of active Filters is then discussed in detail using the example of Gm-C filters and active-RC filters. The ideal and nonideal behaviour of opamps, current conveyors, and inductor simulators follows. The link to the practical design of circuits and systems is done with an overview over different quality measures and figures of merit used in scientific literature and datasheets. Finally, an introduction to discrete-time and mixed-domain filters and circuits is given, including sensor read-out amplifiers, correlated double sampling, and chopping, and an introduction to sigma-delta A/D and D/A conversion on a system level. This lecture does not go down to the details of transistor implementations. The lecture "227-0166-00L Analog Integrated Circuits" complements This lecture very well in that respect.
Lecture notes	The base for these lectures are lecture notes and two or three published scientific papers. From these papers we will together develop the technical content. Details: https://people.ee.ethz.ch/~haschmid/asfwiki/ The graph methods are also supported with teaching videos: https://tube.switch.ch/channels/d206c96c?order=episodes Some material is protected by password; students from ETHZ who are interested can write to haschmid@ethz.ch to ask for the password even if they do not attend the lecture.
Prerequisites / Notice	Live stream: due to Covids rules, the lecture will be streamed live. Join here: https://www.twitch.tv/hanspi42/ Prerequisites: Recommended (but not required): Stochastic models and signal processing, Communication Electronics, Analog Integrated Circuits, Transmission Lines and Filters. Knowledge of the Laplace transform and z transform and their interpretation (transfer functions, poles and zeros, bode diagrams, stability criteria ...) and of the main properties of linear systems is necessary.
227-0653-00L	Electromagnetic Precision Measurements and Opto-Mechanics Does not take place this semester.	W	4 credits	2V + 1U	M. Frimmer
Abstract	The measurement process is at the heart of both science and engineering. Electromagnetic fields have proven to be particularly powerful probes. This course provides the basic knowledge necessary to understand current state-of-the-art optomechanical measurement systems operating at the precision limits set by the laws of quantum mechanics.
Learning objective	The goal of this coarse is to understand the fundamental limitations of measurement systems relying on electromagnetic fields.
Content	The lecture starts with summarizing the relevant fundamentals of the treatment of noisy signals. Starting with the resolution limit of optical imaging systems, we familiarize ourselves with the concept of measurement imprecision in light-based measurement systems. We consider the process of photodetection and discuss the statistical fluctuations arising from the quantization of the electromagnetic fields into photons. We exemplify our insights at hand of concrete examples, such as homodyne and heterodyne photodetection. Furthermore, we focus on the process of measurement backaction, the inevitable result of the interaction of the probe with the system under investigation. The course emphasizes the connection between the taught concepts and current state-of-the-art research carried out in the field of optomechanics.
Prerequisites / Notice	1. Electrodynamics 2. Physics 1,2 3. Introduction to quantum mechanics
402-0465-58L	Intersubband Optoelectronics	W	6 credits	2V + 1U	G. Scalari
Abstract	Intersubband transitions in quantum wells are transitions between states created by quantum confinement in ultra-thin layers of semiconductors. Because of its inherent taylorability, this system can be seen as the "ultimate quantum designer's material".
Learning objective	The goal of this lecture is to explore both the rich physics as well as the application of these system for sources and detectors. In fact, devices based on intersubband transitions are now unlocking large area of the electromagnetic spectrum.
Content	The lecture will treat the following chapters: - Introduction: intersubband optoelectronics as an example of quantum engineering -Technological aspects - Electronic states in semiconductor quantum wells - Intersubband absorption and scattering processes - Mid-Ir and THz ISB Detectors -Mid-infrared and THz photonics: waveguides, resonators, metamaterials - Quantum Cascade lasers: -Mid-IR QCLs -THZ QCLs (direct and non-linear generation) -further electronic confinement: interlevel Qdot transitions and magnetic field effects -Strong light-matter coupling in Mid-IR and THz range
Lecture notes	The reference book for the lecture is "Quantum Cascade Lasers" by Jerome Faist , published by Oxford University Press.
Literature	Mostly the original articles, other useful reading can be found in: -E. Rosencher and B. Vinter, Optoelectronics , Cambridge Univ. Press -G. Bastard, Wave mechanics applied to semiconductor heterostructures, Halsted press
Prerequisites / Notice	Requirements: A basic knowledge of solid-state physics and of quantum electronics.
227-0655-00L	Nonlinear Optics	W	6 credits	2V + 2U	J. Leuthold
Abstract	Nonlinear Optics deals with the interaction of light with material, such as the response of material to light. We will introduce the framework to describe the phenomena based on a classical and quantum description. As an example we will cover fundamental phenomena such as the linear and nonlinear refractive index, the electro-optic effect, second harmonic generation, spontaneous four-wave mixing.
Learning objective	The important nonlinear optical phenomena are understood and can be classified. The effects can be described mathematical by means of the susceptibility.
Content	Chapter 1: The Wave Equations in Nonlinear Optics Chapter 2: Nonlinear Effects - An Overview Chapter 3: The Nonlinear Optical Susceptibility (Classical & Quantum) Chapter 4: Second Harmonic Generation Chapter 5: The Electro-Optic Effect and the Electro-Optic Modulator Chapter 6: Third Order Nonlinearities in Waveguides (Classical & Quantum) Chapter 7: Acousto-Optic Effect Chapter 8: Nonlinear Effects in Media with Gain The exercise focuses on phrasing the content of the lecture content from the perspective of an PhD (tutorial form). Furthermore, a journal club is offered to connect students with the current research, successful participation provides a bonus for the exam. Problem sets are also offered for independent learning of the students.
Literature	Lecture notes are distributed. For students enrolled in the course, additional information, lecture notes and exercises can be found on moodle (https://moodle-app2.let.ethz.ch/).
Prerequisites / Notice	Fundamentals of Electromagnetic Fields (Maxwell Equations) & Bachelor Lectures on Physics
227-0663-00L	Nano-Optics	W	6 credits	2V + 2U	M. Frimmer
Abstract	Nano-Optics is the study of light-matter interaction at the sub-wavelength scale. It is an flourishing field of fundamental and applied research enabled by the rapid advance of nanotechnology. Nano-optics embraces topics such as plasmonics, optical antennas, optical trapping and manipulation, and high/super-resolution imaging and spectroscopy.
Learning objective	Understanding concepts of light localization and light-matter interactions on the sub-wavelength scale.
Content	We start with the angular spectrum representation of fields to understand the classical resolution limit. We continue with the theory of strongly focused light, the point spread function, and resolution criteria of conventional microscopy, before turning to super-resolution techniques, based on near- and far-fields. We introduce the local density of states and approaches to control spontaneous emission rates in inhomogeneous environments, including optical antennas. Finally, we touch upon optical forces and their applications in optical tweezers.
Prerequisites / Notice	- Electromagnetic fields and waves (or equivalent) - Physics I+II
151-0563-01L	Dynamic Programming and Optimal Control	W	4 credits	2V + 1U	R. D'Andrea
Abstract	Introduction to Dynamic Programming and Optimal Control.
Learning objective	Covers the fundamental concepts of Dynamic Programming & Optimal Control.
Content	Dynamic Programming Algorithm; Deterministic Systems and Shortest Path Problems; Infinite Horizon Problems, Bellman Equation; Deterministic Continuous-Time Optimal Control.
Literature	Dynamic Programming and Optimal Control by Dimitri P. Bertsekas, Vol. I, 3rd edition, 2005, 558 pages, hardcover.
Prerequisites / Notice	Requirements: Knowledge of advanced calculus, introductory probability theory, and matrix-vector algebra.
252-0535-00L	Advanced Machine Learning	W	10 credits	3V + 2U + 4A	J. M. Buhmann, C. Cotrini Jimenez
Abstract	Machine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects.
Learning objective	Students will be familiarized with advanced concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. Machine learning projects will provide an opportunity to test the machine learning algorithms on real world data.
Content	The theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data. Topics covered in the lecture include: Fundamentals: What is data? Bayesian Learning Computational learning theory Supervised learning: Ensembles: Bagging and Boosting Max Margin methods Neural networks Unsupservised learning: Dimensionality reduction techniques Clustering Mixture Models Non-parametric density estimation Learning Dynamical Systems
Lecture notes	No lecture notes, but slides will be made available on the course webpage.
Literature	C. Bishop. Pattern Recognition and Machine Learning. Springer 2007. R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley & Sons, second edition, 2001. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2001. L. Wasserman. All of Statistics: A Concise Course in Statistical Inference. Springer, 2004.
Prerequisites / Notice	The course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments. Students should have followed at least "Introduction to Machine Learning" or an equivalent course offered by another institution. PhD students are required to obtain a passing grade in the course (4.0 or higher based on project and exam) to gain credit points.
252-0836-00L	Computer Science II	W	4 credits	2V + 1U	F. Mattern
Abstract	Introduction to basic problem solving methods, algorithms, and data structures. Topics: divide and conquer, recursion, sorting algorithms, backtracking, game tree search, data structures (lists, stacks, binary trees, etc.), discrete simulation, concurrency, complexity, verification. In the assignments and exercises, the programming language Java is used.
Learning objective	Introduction to the general methods of computer science for electrical engineers. Also provides basic skills for advanced exercises and projects later in the electrical engineering program.
Content	Part II of the lecture concentrates on the most common problem solving skills, algorithms, and data structures. It also teaches fundamental concepts and mechanisms of structured programming. Furthermore, working with formal systems, the necessity of abstraction, and the importance of modeling in computer science will be motivated. The emphasis of the lecture is on practical concepts of computer science. Specific topics are: complexity and correctness of algorithms, divide and conquer, recursion, algorithms for sorting, backtracking, game tree search, data structures (lists, stacks, inary trees, etc.), discrete simulation, concurrency, and verification. For the assignments and exercises, the programming language Java is used. Here, also modularization, abstraction, encapsulation, and object orientation will be considered. Occasionally, short remarks on the historical context of relevant concepts are given. In the practice groups, students program an automatic player for the game "Reversi"; at the end of the semester a tournament will take place.
Lecture notes	Copies of slides, extended with bonus slides that give hints to advanced concepts and present the historical context of selected concepts.
Literature	Textbook: Mark Allan Weiss: Data Structures and Problem Solving Using Java, Addison Wesley.
Prerequisites / Notice	Prerequisite: Part 1 of the course.
402-0209-00L	Quantum Physics for Non-Physicists	W	6 credits	3V + 2U	L. Pacheco Cañamero B. del Rio
Abstract	This course covers similar contents to Quantum Mechanics I, but through an information-theoretical approach, especially suited for students with backgrounds in computer science, mathematics or engineering. We start from the postulates of quantum theory and build up to the tools needed to study the behaviour of complex systems, from entangled spins to the hydrogen atom and nano heat engines.
Learning objective	This course teaches the formalism and physics of quantum mechanics. Students are equipped with tools to analyse complex settings such as the hydrogen atom, thermal engines and scattering. It covers similar contents to QM1 but from an information-theoretical perspective.
Content	1. Quantum formalism, from qubits to particles in space - Dirac notation - Postulates of quantum physics - Discrete systems: qubits, the Bloch sphere - Continuous variables: position and momentum, the wave function - Multiple systems: tensor product, entanglement - Application: internal degrees of freedom of photons and electrons 2. Time and dynamics for quantum systems - Unitary evolution and the Schrödinger equation - Hamiltonian evolution and functions of operators - Commutation relations and symmetries - Application: the double-slit experiment 3. Uncertainty and open systems - Modelling uncertainty: the density matrix - Example: thermal states - Open systems, irreversible evolution and Lindblad operators - Application: heat engines 4. Spin and oscillators - Spin and rotation - Orbital angular momentum - Ladder systems and the harmonic oscillator 5. Several particles, bosons and fermions - Relative coordinates - Identical particles and symmetry groups - Fermions and bosons - Second quantization 6. Problems in 1D - Dynamics of a free particle - Potential wells and stationary waves - Spin chains 7. Problems in 3D - Central potentials - The hydrogen atom 8. Perturbation theory - Assumptions and derivation - Application: scattering 9. Non-locality - Bell's theorem - Non-classicality of quantum theory (extra) - Modular momentum (extra) 10. Foundations of quantum theory - Paradoxes - Quantum reference frames - Deriving the postulates of quantum mechanics from first principles
Lecture notes	Lecture notes will be distributed through the semester.
Literature	Quantum Processes Systems, and Information, by Benjamin Schumacher and Michael Westmoreland, available at Link
Prerequisites / Notice	This course is an alternative to Quantum Mechanics I aimed primarily at non-physicists, and in particular at students with a background in computer science, mathematics or engineering. Basic linear algebra and calculus knowledge is required (equivalent to first-year courses). Basic physics knowledge (equivalent to first-year courses) is recommended but not strictly necessary. Note that while we follow an information-theoretical approach, this is not a course on quantum information theory or quantum computing. It therefore complements those courses offered at ETH in both semesters.
402-0257-00L	Advanced Solid State Physics	W	10 credits	3V + 2U	A. Zheludev, K. Povarov
Abstract	This course is an extension of the introductory course on solid state physics. The purpose of this course is to learn to navigate the complex collective quantum phases, excitations and phase transitions that are the dominant theme in modern solid state physics. The emphasis is on the main concepts and on specific experimental examples, both classic ones and those from recent research.
Learning objective	The goal is to study how novel phenomena emerge in the solid state.
Content	= Today's challenges and opportunities in Solid State Physics = Phase transitions and critical phenomena .Main concepts: coherence length, symmetry, order parameter, correlation functions, generalized susceptibility .Bragg-Williams mean field theory .Landau theory of phase transitions .Fluctuations in Landau theory .Critical exponents: significance, measurement, inequalities, equalities .Scaling and hyperscaling .Universality .Critical dynamics .Quantum phase transitions and quantum criticality =Fermi surface instabilities . The concept of the Landau Fermi liquid in metals . Kohn anomalies . Charge density waves . Metallic ferromagnets and half-metals . Spin density waves =Magnetism of insulators .Magnetic interactions in solids and the spin Hamiltonian .Magnetic structures and phase transitions .Spin waves .Quantum magnetism = Electron correlations in solids . Mott insulating state . Phases of the Hubbard model . Layered cuprates (non-superconducting properties)
Lecture notes	The printed material for this course involves: (1) a self-contained script, distributed electronically at semester start. (2) experimental examples (Power Point slide-style) selected from original publications, distributed at the start of every lecture.
Literature	A list of books will be distributed. Numerous references to useful published scientific papers will be provided.
Prerequisites / Notice	This course is for students who like to be engaged in active learning. The "exercise classes" are organized in a non-traditional way: following the idea of "less is more", we will work on only about half a dozen topics, and this gives students a chance to take a look at original literature (provided), and to get the grasp of a topic from a broader perspective. Students report back that this mode of "exercise class" is more satisfying than traditional modes, even if it does not mean less effort.
402-0317-00L	Semiconductor Materials: Fundamentals and Fabrication	W	6 credits	2V + 1U	S. Schön, W. Wegscheider
Abstract	This course gives an introduction into the fundamentals of semiconductor materials. The main focus is on state-of-the-art fabrication and characterization methods. The course will be continued in the spring term with a focus on applications.
Learning objective	Basic knowledge of semiconductor physics and technology. Application of this knowledge for state-of-the-art semiconductor device processing
Content	1. Fundamentals of Solid State Physics 1.1 Semiconductor materials 1.2 Band structures 1.3 Carrier statistics in intrinsic and doped semiconductors 1.4 p-n junctions 1.5 Low-dimensional structures 2. Bulk Material growth of Semiconductors 2.1 Czochralski method 2.2 Floating zone method 2.3 High pressure synthesis 3. Semiconductor Epitaxy 3.1 Fundamentals of Epitaxy 3.2 Molecular Beam Epitaxy (MBE) 3.3 Metal-Organic Chemical Vapor Deposition (MOCVD) 3.4 Liquid Phase Epitaxy (LPE) 4. In situ characterization 4.1 Pressure and temperature 4.2 Reflectometry 4.3 Ellipsometry and RAS 4.4 LEED, AES, XPS 4.5 STM, AFM 5. The invention of the transistor - Christmas lecture
Lecture notes	https://moodle-app2.let.ethz.ch/course/view.php?id=13428
Prerequisites / Notice	The "compulsory performance element" of this lecture is a short presentation of a research paper complementing the lecture topics. Several topics and corresponding papers will be offered on the moodle page of this lecture.
402-0402-00L	Ultrafast Laser Physics	W	10 credits	3V + 2U	L. Gallmann, S. Johnson, U. Keller
Abstract	Introduction to ultrafast laser physics with an outlook into cutting edge research topics such as attosecond science and coherent ultrafast sources from THz to X-rays.
Learning objective	Understanding of basic physics and technology for pursuing research in ultrafast laser science. How are ultrashort laser pulses generated, how do they interact with matter, how can we measure these shortest man-made events and how can we use them to time-resolve ultrafast processes in nature? Fundamental concepts and techniques will be linked to a selection of hot topics in current research and applications.
Content	The lecture covers the following topics: a) Linear pulse propagation: mathematical description of pulses and their propagation in linear optical systems, effect of dispersion on ultrashort pulses, concepts of pulse carrier and envelope, time-bandwidth product b) Dispersion compensation: technologies for controlling dispersion, pulse shaping, measurement of dispersion c) Nonlinear pulse propagation: intensity-dependent refractive index (Kerr effect), self-phase modulation, nonlinear pulse compression, self-focusing, filamentation, nonlinear Schrödinger equation, solitons, non-instantaneous nonlinear effects (Raman/Brillouin), self-steepening, saturable gain and absorption d) Second-order nonlinearities with ultrashort pulses: phase-matching with short pulses and real beams, quasi-phase matching, second-harmonic and sum-frequency generation, parametric amplification and generation e) Relaxation oscillations: dynamical behavior of rate equations after perturbation f) Q-switching: active Q-switching and its theory based on rate equations, active Q-switching technologies, passive Q-switching and theory g) Active modelocking: introduction to modelocking, frequency comb versus axial modes, theory for various regimes of laser operation, Haus master equation formalism h) Passive modelocking: slow, fast and ideally fast saturable absorbers, semiconductor saturable absorber mirror (SESAM), designs of and materials for SESAMs, modelocking with slow absorber and dynamic gain saturation, modelocking with ideally fast saturable absorber, Kerr-lens modelocking, soliton modelocking, Q-switching instabilities in modelocked lasers, inverse saturable absorption i) Pulse duration measurements: rf cables and electronics, fast photodiodes, linear system theory for microwave test systems, intensity and interferometric autocorrelations and their limitations, frequency-resolved optical gating, spectral phase interferometry for direct electric-field reconstruction and more j) Noise: microwave spectrum analyzer as laser diagnostics, amplitude noise and timing jitter of ultrafast lasers, lock-in detection k) Ultrafast measurements: pump-probe scheme, transient absorption/differential transmission spectroscopy, four-wave mixing, optical gating and more l) Frequency combs and carrier-envelope offset phase: measurement and stabilization of carrier-envelope offset phase (CEP), time and frequency domain applications of CEP-stabilized sources m) High-harmonic generation and attosecond science: non-perturbative nonlinear optics / strong-field phenomena, high-harmonic generation (HHG), phase-matching in HHG, attosecond pulse generation, attosecond technology: detectors and diagnostics, attosecond metrology (streaking, RABBITT, transient absorption, attoclock), example experiments n) Ultrafast THz science: generation and detection, physics in THz domain, weak-field and strong-field applications o) Brief introduction to other hot topics: relativistic and ultra-high intensity ultrafast science, ultrafast electron sources, free-electron lasers, etc.
Lecture notes	Class notes will be made available.
Prerequisites / Notice	Prerequisites: Basic knowledge of quantum electronics (e. g., 402-0275-00L Quantenelektronik).
402-0444-00L	Advanced Quantum Optics Does not take place this semester.	W	6 credits	2V + 1U	A. Imamoglu
Abstract	This course builds up on the material covered in the Quantum Optics course. The emphasis will be on quantum optics in condensed-matter systems.
Learning objective	The course aims to provide the knowledge necessary for pursuing advanced research in the field of Quantum Optics in condensed matter systems. Fundamental concepts and techniques of Quantum Optics will be linked to experimental research in systems such as quantum dots, exciton-polaritons, quantum Hall fluids and graphene-like materials.
Content	Description of open quantum systems using master equation and quantum trajectories. Decoherence and quantum measurements. Dicke superradiance. Dissipative phase transitions. Spin photonics. Signatures of electron-phonon and electron-electron interactions in optical response.
Lecture notes	Lecture notes will be provided
Literature	C. Cohen-Tannoudji et al., Atom-Photon-Interactions (recommended) Y. Yamamoto and A. Imamoglu, Mesoscopic Quantum Optics (recommended) A collection of review articles (will be pointed out during the lecture)
Prerequisites / Notice	Masters level quantum optics knowledge

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