Suchergebnis: Katalogdaten im Herbstsemester 2021
Biomedical Engineering Master  | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Bioelectronics | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kernfächer der Vertiefung Während des Studiums müssen mindestens 12 KP aus Kernfächern einer Vertiefung (Track) erreicht werden. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 151-0604-00L | Microrobotics | W | 4 KP | 3G | B. Nelson, N. Shamsudhin | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Microrobotics is an interdisciplinary field that combines aspects of robotics, micro and nanotechnology, biomedical engineering, and materials science. The aim of this course is to expose students to the fundamentals of this emerging field. Throughout the course, the students apply these concepts in assignments. The course concludes with an end-of-semester examination. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The objective of this course is to expose students to the fundamental aspects of the emerging field of microrobotics. This includes a focus on physical laws that predominate at the microscale, technologies for fabricating small devices, bio-inspired design, and applications of the field. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Main topics of the course include: - Scaling laws at micro/nano scales - Electrostatics - Electromagnetism - Low Reynolds number flows - Observation tools - Materials and fabrication methods - Applications of biomedical microrobots | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | The powerpoint slides presented in the lectures will be made available as pdf files. Several readings will also be made available electronically. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | The lecture will be taught in English. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-0605-00L | Nanosystems | W | 4 KP | 4G | A. Stemmer | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | From atoms to molecules to condensed matter: characteristic properties of simple nanosystems and how they evolve when moving towards complex ensembles. Intermolecular forces, their macroscopic manifestations, and ways to control such interactions. Self-assembly and directed assembly of 2D and 3D structures. Special emphasis on the emerging field of molecular electronic devices. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Familiarize students with basic science and engineering principles governing the nano domain. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The course addresses basic science and engineering principles ruling the nano domain. We particularly work out the links between topics that are traditionally taught separately. Familiarity with basic concepts of quantum mechanics is expected. Special emphasis is placed on the emerging field of molecular electronic devices, their working principles, applications, and how they may be assembled. Topics are treated in 2 blocks: (I) From Quantum to Continuum From atoms to molecules to condensed matter: characteristic properties of simple nanosystems and how they evolve when moving towards complex ensembles. (II) Interaction Forces on the Micro and Nano Scale Intermolecular forces, their macroscopic manifestations, and ways to control such interactions. Self-assembly and directed assembly of 2D and 3D structures. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | - Kuhn, Hans; Försterling, H.D.: Principles of Physical Chemistry. Understanding Molecules, Molecular Assemblies, Supramolecular Machines. 1999, Wiley, ISBN: 0-471-95902-2 - Chen, Gang: Nanoscale Energy Transport and Conversion. 2005, Oxford University Press, ISBN: 978-0-19-515942-4 - Ouisse, Thierry: Electron Transport in Nanostructures and Mesoscopic Devices. 2008, Wiley, ISBN: 978-1-84821-050-9 - Wolf, Edward L.: Nanophysics and Nanotechnology. 2004, Wiley-VCH, ISBN: 3-527-40407-4 - Israelachvili, Jacob N.: Intermolecular and Surface Forces. 2nd ed., 1992, Academic Press,ISBN: 0-12-375181-0 - Evans, D.F.; Wennerstrom, H.: The Colloidal Domain. Where Physics, Chemistry, Biology, and Technology Meet. Advances in Interfacial Engineering Series. 2nd ed., 1999, Wiley, ISBN: 0-471-24247-0 - Hunter, Robert J.: Foundations of Colloid Science. 2nd ed., 2001, Oxford, ISBN: 0-19-850502-7 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Course format: Lectures and Mini-Review presentations: Thursday 10-13 Homework: Mini-Review (compulsory continuous performance assessment) Each student selects a paper (list distributed in class) and expands the topic into a Mini-Review that illuminates the particular field beyond the immediate results reported in the paper. Each Mini-Review will be presented both orally and as a written paper. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-0621-00L | Microsystems I: Process Technology and Integration | W | 6 KP | 3V + 3U | M. Haluska, C. Hierold | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Die Stundenten werden in die Grundlagen der Mikrosystemtechnik, der Halbleiterphysik und der Halbleiterprozesstechnologie eingeführt und erfahren, wie die Herstellung von Mikrosystemen in einer Serie von genau definierten Prozessschritten erfolgt (Gesamtprozess und Prozessablauf). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Die Stundenten sind mit den Grundlagen der Mikrosystemtechnik und der Prozesstechnologie für Halbleiter vertraut und verstehen die Herstellung von Mikrosystemen durch die Kombination von Einzelprozesschritten ( = Gesamtprozess oder Prozessablauf). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | - Einführung in die Mikrosystemtechnik (MST) und in mikroelektromechanische Systeme (MEMS) - Grundlegende Siliziumtechnologie: thermische Oxidation, Fotolithografie und Ätztechnik, Diffusion und Ionenimplantation, Dünnschichttechnik. - Besondere Mikrosystemtechnologien: Volumen- und Oberflächenmikromechanik, Trocken- und Nassätzen, isotropisches und anisotropisches Ätzen, Herstellung von Balken und Membranen, Waferbonden, mechanische Eigenschaften von Dünnschichten. Die Anwendung ausgewählter Technologien wird anhand von Fallstudien nachgewiesen. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Handouts (online erhältlich) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | - S.M. Sze: Semiconductor Devices, Physics and Technology - W. Menz, J. Mohr, O.Paul: Microsystem Technology - Hong Xiao: Introduction to Semiconductor Manufacturing Technology - M. J. Madou: Fundamentals of Microfabrication and Nanotechnology, 3rd ed. - T. M. Adams, R. A. Layton: Introductory MEMS, Fabrication and Applications | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Voraussetzung: Physik I und II | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0105-00L | Introduction to Estimation and Machine Learning  | W | 6 KP | 4G | H.‑A. Loeliger | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Mathematical basics of estimation and machine learning, with a view towards applications in signal processing. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Students master the basic mathematical concepts and algorithms of estimation and machine learning. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Review of probability theory; basics of statistical estimation; least squares and linear learning; Hilbert spaces; Gaussian random variables; singular-value decomposition; kernel methods, neural networks, and more | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Lecture notes will be handed out as the course progresses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | solid basics in linear algebra and probability theory | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0311-00L | Qubits, Electrons, Photons | W | 6 KP | 3V + 2U | T. Zambelli | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | In-depth analysis of the quantum mechanics origin of nuclear magnetic resonance (qubits, two-level systems), of LASER (quantization of the electromagnetic field, photons), and of electron transfer (from electrochemistry to photosynthesis). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Beside electronics nanodevices, D-ITET is pushing its research in the fields of NMR (MRI), electrochemistry, bioelectronics, nano-optics, and quantum information, which are all rationalized in terms of quantum mechanics. Starting from the axioms of quantum mechanics, we will derive the fascinating theory describing spin and qubits, electron transitions and transfer, photons and LASER: quantum mechanics is different because it mocks our daily Euclidean intuition! In this way, students will work out a robust quantum mechanics (theoretical!) basis which will help them in their advanced studies of the following masters: EEIT (batteries), Biomedical Engineering (NMR, bioelectronics), Quantum Engineering, Micro- and Nanosystems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | • Lagrangian and Hamiltonian: Symmetries and Poisson Brackets • Postulates of QM: Hilbert Spaces and Operators • Heisenberg’s Matrix Mechanics: Hamiltonian and Time Evolution Operator • Spin: Qubits, Bloch Equations, and NMR • Entanglement • Symmetries and Corresponding Operators • Schrödinger's Wave Mechanics: Electrons in a Periodic Potential and Energy Bands • Harmonic Oscillator: Creation and Annihilation Operators • Identical Particles: Bosons and Fermions • Quantization of the Electromagnetic Field: Photons, Absorption and Emission, LASER • Electron Transfer: Marcus Theory via Born-Oppenheimer, Franck-Condon, Landau-Zener | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | No lecture notes because the proposed textbooks together with the provided supplementary material are more than exhaustive! !!!!! I am using OneNote. All lectures and exercises will be broadcast via ZOOM and correspondingly recorded (link in Moodle) !!!!! | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | • J.S. Townsend, "A Modern Approach to Quantum Mechanics", Second Edition, 2012, University Science Books • M. Le Bellac, "Quantum Physics", 2011, Cambridge University Press • (Lagrangian and Hamiltonian) L. Susskind, G. Hrabovsky, "Theoretical Minimum: What You Need to Know to Start Doing Physics", 2014, Hachette Book Group USA Supplementary material will be uploaded in Moodle. _ _ _ _ _ _ _ + (as rigorous and profound presentation of the mathematical framework) G. Dell'Antonio, "Lectures on the Mathematics of Quantum Mechanics I", 2015, Springer + (as account of those formidable years) G. Gamow, "Thirty Years that Shook Physics", 1985, Dover Publications Inc. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | The course has been intentionally conceived to be self-consistent with respect to QM for those master students not having encountered it in their track yet. Therefore, a presumably large overlapping has to be expected with a (welcome!) QM introduction course like the D-ITET "Physics II". A solid base of Analysis I & II as well as of Linear Algebra is really helpful. IMPORTANT: Wed 22.9, 29.9, and 22.12 are lectures (NOT exercises!). Please, look at the details in moodle! | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 227-0385-10L | Biomedical Imaging | W | 6 KP | 5G | S. Kozerke, K. P. Prüssmann | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Introduction and analysis of medical imaging technology including X-ray procedures, computed tomography, nuclear imaging techniques using single photon and positron emission tomography, magnetic resonance imaging and ultrasound imaging techniques. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | To understand the physical and technical principles underlying X-ray imaging, computed tomography, single photon and positron emission tomography, magnetic resonance imaging, ultrasound and Doppler imaging techniques. The mathematical framework is developed to describe image encoding/decoding, point-spread function/modular transfer function, signal-to-noise ratio, contrast behavior for each of the methods. Matlab exercises are used to implement and study basic concepts. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | - X-ray imaging - Computed tomography - Single photon emission tomography - Positron emission tomography - Magnetic resonance imaging - Ultrasound/Doppler imaging | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Lecture notes and handouts | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Webb A, Smith N.B. Introduction to Medical Imaging: Physics, Engineering and Clinical Applications; Cambridge University Press 2011 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Analysis, Linear Algebra, Physics, Basics of Signal Theory, Basic skills in Matlab programming | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0386-00L | Biomedical Engineering | W | 4 KP | 3G | J. Vörös, S. J. Ferguson, S. Kozerke, M. P. Wolf, M. Zenobi-Wong | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Introduction into selected topics of biomedical engineering as well as their relationship with physics and physiology. The focus is on learning the concepts that govern common medical instruments and the most important organs from an engineering point of view. In addition, the most recent achievements and trends of the field of biomedical engineering are also outlined. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Introduction into selected topics of biomedical engineering as well as their relationship with physics and physiology. The course provides an overview of the various topics of the different tracks of the biomedical engineering master course and helps orienting the students in selecting their specialized classes and project locations. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Introduction into neuro- and electrophysiology. Functional analysis of peripheral nerves, muscles, sensory organs and the central nervous system. Electrograms, evoked potentials. Audiometry, optometry. Functional electrostimulation: Cardiac pacemakers. Function of the heart and the circulatory system, transport and exchange of substances in the human body, pharmacokinetics. Endoscopy, medical television technology. Lithotripsy. Electrical Safety. Orthopaedic biomechanics. Lung function. Bioinformatics and Bioelectronics. Biomaterials. Biosensors. Microcirculation.Metabolism. Practical and theoretical exercises in small groups in the laboratory. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Introduction to Biomedical Engineering by Enderle, Banchard, and Bronzino AND https://lbb.ethz.ch/education/biomedical-engineering.html | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0393-10L | Bioelectronics and Biosensors | W | 6 KP | 2V + 2U | J. Vörös, M. F. Yanik | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The course introduces bioelectricity and the sensing concepts that enable obtaining information about neurons and their networks. The sources of electrical fields and currents in the context of biological systems are discussed. The fundamental concepts and challenges of measuring bioelectronic signals and the basic concepts to record optogenetically modified organisms are introduced. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | During this course the students will: - learn the basic concepts of bioelectronics - be able to solve typical problems in bioelectronics - learn about the remaining challenges in this field | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Lecture 1. Introduction to the field of bioelectronics and its challenges Sources of bioelectronic signals L2. Membrane and Transport L3. Action potential and Hodgkin-Huxley L4. Action potential and Hodgkin-Huxley 2 Measuring bioelectronic signals L5. Detection and Noise L6. Measuring currents in solutions, nanopore sensing and patch clamp pipettes L7. Measuring potentials in solution and core conductance L8. Measuring electronic signals with wearable electronics, ECG, EEG L9. Measuring mechanical signals with bioelectronics In vivo stimulation and recording L10. Functional electric stimulation L11. In vivo electrophysiology Optical recording and control of neurons (optogenetics) L12. Measuring neurons optically, fundamentals of optical microscopy L13. Fluorescent probes and scanning microscopy, optogenetics, in vivo microscopy L14. Measuring chemical signals | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | The course has its own script including the exercises. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | The course requires an open attitude to the interdisciplinary approach of bioelectronics. In addition, it requires undergraduate entry-level familiarity with electric & magnetic fields/forces, resistors, capacitors, electric circuits, differential equations, calculus, probability calculus, Fourier transformation & frequency domain, lenses / light propagation / refractive index, pressure, diffusion. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 227-0421-00L | Deep Learning in Artificial and Biological Neuronal Networks | W | 4 KP | 3G | B. Grewe | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Deep-Learning (DL) a brain-inspired weak for of AI allows training of large artificial neuronal networks (ANNs) that, like humans, can learn real-world tasks such as recognizing objects in images. However, DL is far from being understood and investigating learning in biological networks might serve again as a compelling inspiration to think differently about state-of-the-art ANN training methods. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The main goal of this lecture is to provide a comprehensive overview into the learning principles neuronal networks as well as to introduce a diverse skill set (e.g. simulating a spiking neuronal network) that is required to understand learning in large, hierarchical neuronal networks. To achieve this the lectures and exercises will merge ideas, concepts and methods from machine learning and neuroscience. These will include training basic ANNs, simulating spiking neuronal networks as well as being able to read and understand the main ideas presented in today’s neuroscience papers. After this course students will be able to: - read and understand the main ideas and methods that are presented in today’s neuroscience papers - explain the basic ideas and concepts of plasticity in the mammalian brain - implement alternative ANN learning algorithms to ‘error backpropagation’ in order to train deep neuronal networks. - use a diverse set of ANN regularization methods to improve learning - simulate spiking neuronal networks that learn simple (e.g. digit classification) tasks in a supervised manner. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Deep-learning a brain-inspired weak form of AI allows training of large artificial neuronal networks (ANNs) that, like humans, can learn real-world tasks such as recognizing objects in images. The origins of deep hierarchical learning can be traced back to early neuroscience research by Hubel and Wiesel in the 1960s, who first described the neuronal processing of visual inputs in the mammalian neocortex. Similar to their neocortical counterparts ANNs seem to learn by interpreting and structuring the data provided by the external world. However, while on specific tasks such as playing (video) games deep ANNs outperform humans (Minh et al, 2015, Silver et al., 2018), ANNs are still not performing on par when it comes to recognizing actions in movie data and their ability to act as generalizable problem solvers is still far behind of what the human brain seems to achieve effortlessly. Moreover, biological neuronal networks can learn far more effectively with fewer training examples, they achieve a much higher performance in recognizing complex patterns in time series data (e.g. recognizing actions in movies), they dynamically adapt to new tasks without losing performance and they achieve unmatched performance to detect and integrate out-of-domain data examples (data they have not been trained with). In other words, many of the big challenges and unknowns that have emerged in the field of deep learning over the last years are already mastered exceptionally well by biological neuronal networks in our brain. On the other hand, many facets of typical ANN design and training algorithms seem biologically implausible, such as the non-local weight updates, discrete processing of time, and scalar communication between neurons. Recent evidence suggests that learning in biological systems is the result of the complex interplay of diverse error feedback signaling processes acting at multiple scales, ranging from single synapses to entire networks. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | The lecture slides will be provided as a PDF after each lecture. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | This advanced level lecture requires some basic background in machine/deep learning. Thus, students are expected to have a basic mathematical foundation, including linear algebra, multivariate calculus, and probability. The course is not to be meant as an extended tutorial of how to train deep networks in PyTorch or Tensorflow, although these tools used. The participation in the course is subject to the following conditions: 1) The number of participants is limited to 120 students (MSc and PhDs). 2) Students must have taken the exam in Deep Learning (263-3210-00L) or have acquired equivalent knowledge. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0427-00L | Signal Analysis, Models, and Machine Learning Findet dieses Semester nicht statt. This course was replaced by "Introduction to Estimation and Machine Learning" and "Advanced Signal Analysis, Modeling, and Machine Learning". | W | 6 KP | 4G | H.‑A. Loeliger | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The course is an introduction to some basic topics in signal processing and machine learning. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Lecture notes. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Prerequisites: - local bachelors: course "Discrete-Time and Statistical Signal Processing" (5. Sem.) - others: solid basics in linear algebra and probability theory | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-1037-00L | Introduction to Neuroinformatics | W | 6 KP | 2V + 1U + 1A | V. Mante, M. Cook, B. Grewe, G. Indiveri, D. Kiper, W. von der Behrens | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The course provides an introduction to the functional properties of neurons. Particularly the description of membrane electrical properties (action potentials, channels), neuronal anatomy, synaptic structures, and neuronal networks. Simple models of computation, learning, and behavior will be explained. Some artificial systems (robot, chip) are presented. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Understanding computation by neurons and neuronal circuits is one of the great challenges of science. Many different disciplines can contribute their tools and concepts to solving mysteries of neural computation. The goal of this introductory course is to introduce the monocultures of physics, maths, computer science, engineering, biology, psychology, and even philosophy and history, to discover the enchantments and challenges that we all face in taking on this major 21st century problem and how each discipline can contribute to discovering solutions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | This course considers the structure and function of biological neural networks at different levels. The function of neural networks lies fundamentally in their wiring and in the electro-chemical properties of nerve cell membranes. Thus, the biological structure of the nerve cell needs to be understood if biologically-realistic models are to be constructed. These simpler models are used to estimate the electrical current flow through dendritic cables and explore how a more complex geometry of neurons influences this current flow. The active properties of nerves are studied to understand both sensory transduction and the generation and transmission of nerve impulses along axons. The concept of local neuronal circuits arises in the context of the rules governing the formation of nerve connections and topographic projections within the nervous system. Communication between neurons in the network can be thought of as information flow across synapses, which can be modified by experience. We need an understanding of the action of inhibitory and excitatory neurotransmitters and neuromodulators, so that the dynamics and logic of synapses can be interpreted. Finally, the neural architectures of feedforward and recurrent networks will be discussed in the context of co-ordination, control, and integration of sensory and motor information in neural networks. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 376-1714-00L | Biocompatible Materials | W | 4 KP | 3V | K. Maniura, M. Rottmar, M. Zenobi-Wong | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Introduction to molecules used for biomaterials, molecular interactions between different materials and biological systems (molecules, cells, tissues). The concept of biocompatibility is discussed and important techniques from biomaterials research and development are introduced. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The course covers the follwing topics: 1. Introdcution into molecular characteristics of molecules involved in the materials-to-biology interface. Molecular design of biomaterials. 2. The concept of biocompatibility. 3. Introduction into methodology used in biomaterials research and application. 4. Introduction to different material classes in use for medical applications. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Introduction into natural and polymeric biomaterials used for medical applications. The concepts of biocompatibility, biodegradation and the consequences of degradation products are discussed on the molecular level. Different classes of materials with respect to potential applications in tissue engineering, drug delivery and for medical devices are introduced. Strong focus lies on the molecular interactions between materials having very different bulk and/or surface chemistry with living cells, tissues and organs. In particular the interface between the materials surfaces and the eukaryotic cell surface and possible reactions of the cells with an implant material are elucidated. Techniques to design, produce and characterize materials in vitro as well as in vivo analysis of implanted and explanted materials are discussed. A link between academic research and industrial entrepreneurship is demonstrated by external guest speakers, who present their current research topics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Handouts are deposited online (moodle). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Literature: - Biomaterials Science: An Introduction to Materials in Medicine, Ratner B.D. et al, 3rd Edition, 2013 - Comprehensive Biomaterials, Ducheyne P. et al., 1st Edition, 2011 (available online via ETH library) Handouts and references therin. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 402-0674-00L | Physics in Medical Research: From Atoms to Cells | W | 6 KP | 2V + 1U | B. K. R. Müller | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Scanning probe and diffraction techniques allow studying activated atomic processes during early stages of epitaxial growth. For quantitative description, rate equation analysis, mean-field nucleation and scaling theories are applied on systems ranging from simple metallic to complex organic materials. The knowledge is expanded to optical and electronic properties as well as to proteins and cells. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The lecture series is motivated by an overview covering the skin of the crystals, roughness analysis, contact angle measurements, protein absorption/activity and monocyte behaviour. As the first step, real structures on clean surfaces including surface reconstructions and surface relaxations, defects in crystals are presented, before the preparation of clean metallic, semiconducting, oxidic and organic surfaces are introduced. The atomic processes on surfaces are activated by the increase of the substrate temperature. They can be studied using scanning tunneling microscopy (STM) and atomic force microscopy (AFM). The combination with molecular beam epitaxy (MBE) allows determining the sizes of the critical nuclei and the other activated processes in a hierarchical fashion. The evolution of the surface morphology is characterized by the density and size distribution of the nanostructures that could be quantified by means of the rate equation analysis, the mean-field nucleation theory, as well as the scaling theory. The surface morphology is further characterized by defects and nanostructure's shapes, which are based on the strain relieving mechanisms and kinetic growth processes. High-resolution electron diffraction is complementary to scanning probe techniques and provides exact mean values. Some phenomena are quantitatively described by the kinematic theory and perfectly understood by means of the Ewald construction. Other phenomena need to be described by the more complex dynamical theory. Electron diffraction is not only associated with elastic scattering but also inelastic excitation mechanisms that reflect the electronic structure of the surfaces studied. Low-energy electrons lead to phonon and high-energy electrons to plasmon excitations. Both effects are perfectly described by dipole and impact scattering. Thin-films of rather complex organic materials are often quantitatively characterized by photons with a broad range of wavelengths from ultra-violet to infra-red light. Asymmetries and preferential orientations of the (anisotropic) molecules are verified using the optical dichroism and second harmonic generation measurements. Recently, ellipsometry has been introduced to on-line monitor film thickness, and roughness with sub-nanometer precision. These characterisation techniques are vital for optimising the preparation of medical implants. Cell-surface interactions are related to the cell adhesion and the contractile cellular forces. Physical means have been developed to quantify these interactions. Other physical techniques are introduced in cell biology, namely to count and sort cells, to study cell proliferation and metabolism and to determine the relation between cell morphology and function. X rays are more and more often used to characterise the human tissues down to the nanometer level. The combination of highly intense beams only some micrometers in diameter with scanning enables spatially resolved measurements and the determination of tissue's anisotropies of biopsies. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Wahlfächer der Vertiefung Diese Fächer sind für die Vertiefung in Bioelectronics besonders empfohlen. Bei abweichender Fächerwahl konsultieren Sie bitte den Track Adviser. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-0509-00L | Microscale Acoustofluidics | W | 4 KP | 3G | J. Dual | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | In this lecture the basics as well as practical aspects (from modelling to design and fabrication ) are described from a solid and fluid mechanics perspective with applications to microsystems and lab on a chip devices. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Understanding acoustophoresis, the design of devices and potential applications | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Linear and nonlinear acoustics, foundations of fluid and solid mechanics and piezoelectricity, Gorkov potential, numerical modelling, acoustic streaming, applications from ultrasonic microrobotics to surface acoustic wave devices | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Yes, incl. Chapters from the Tutorial: Microscale Acoustofluidics, T. Laurell and A. Lenshof, Ed., Royal Society of Chemistry, 2015 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Microscale Acoustofluidics, T. Laurell and A. Lenshof, Ed., Royal Society of Chemistry, 2015 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Solid and fluid continuum mechanics. Notice: The exercise part is a mixture of presentation, lab sessions ( both compulsary) and hand in homework. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 151-0601-00L | Theory of Robotics and Mechatronics | W | 4 KP | 3G | P. Korba, S. Stoeter | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course provides an introduction and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Robotics is often viewed from three perspectives: perception (sensing), manipulation (affecting changes in the world), and cognition (intelligence). Robotic systems integrate aspects of all three of these areas. This course provides an introduction to the theory of robotics, and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | An introduction to the theory of robotics, and covers the fundamentals of the field, including rigid motions, homogeneous transformations, forward and inverse kinematics of multiple degree of freedom manipulators, velocity kinematics, motion planning, trajectory generation, sensing, vision, and control. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | available. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-0905-00L | Medical Technology Innovation - From Concept to Clinics | W | 4 KP | 3P | I. Herrmann | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Project-oriented learning on how to develop technological solutions to address unmet clinical needs. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | After completing the course, you will be able to effectively collaborate with medical doctors in order to identify important unmet clinical needs. You will be able to ideate and develop appropriate engineering solutions and implementation strategies for real-world clinical problems. This lecture aims to prepare you for typical engineering challenges in the real-world where - in addition to the development of an elegant solution -interdisciplinary team work and effective communication play a key role. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | will be available on the moodle. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 227-1033-00L | Neuromorphic Engineering I Registration in this class requires the permission of the instructors. Class size will be limited to available lab spots. Preference is given to students that require this class as part of their major. Information for UZH students: Enrolment to this course unit only possible at ETH. No enrolment to module INI404 at UZH. Please mind the ETH enrolment deadlines for UZH students: Link | W | 6 KP | 2V + 3U | T. Delbrück, G. Indiveri, S.‑C. Liu | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course covers analog circuits with emphasis on neuromorphic engineering: MOS transistors in CMOS technology, static circuits, dynamic circuits, systems (silicon neuron, silicon retina, silicon cochlea) with an introduction to multi-chip systems. The lectures are accompanied by weekly laboratory sessions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Understanding of the characteristics of neuromorphic circuit elements. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Neuromorphic circuits are inspired by the organizing principles of biological neural circuits. Their computational primitives are based on physics of semiconductor devices. Neuromorphic architectures often rely on collective computation in parallel networks. Adaptation, learning and memory are implemented locally within the individual computational elements. Transistors are often operated in weak inversion (below threshold), where they exhibit exponential I-V characteristics and low currents. These properties lead to the feasibility of high-density, low-power implementations of functions that are computationally intensive in other paradigms. Application domains of neuromorphic circuits include silicon retinas and cochleas for machine vision and audition, real-time emulations of networks of biological neurons, and the development of autonomous robotic systems. This course covers devices in CMOS technology (MOS transistor below and above threshold, floating-gate MOS transistor, phototransducers), static circuits (differential pair, current mirror, transconductance amplifiers, etc.), dynamic circuits (linear and nonlinear filters, adaptive circuits), systems (silicon neuron, silicon retina and cochlea) and an introduction to multi-chip systems that communicate events analogous to spikes. The lectures are accompanied by weekly laboratory sessions on the characterization of neuromorphic circuits, from elementary devices to systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | S.-C. Liu et al.: Analog VLSI Circuits and Principles; various publications. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Particular: The course is highly recommended for those who intend to take the spring semester course 'Neuromorphic Engineering II', that teaches the conception, simulation, and physical layout of such circuits with chip design tools. Prerequisites: Background in basics of semiconductor physics helpful, but not required. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0166-00L | Analog Integrated Circuits | W | 6 KP | 2V + 2U | T. Jang | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | This course provides a foundation in analog integrated circuit design based on bipolar and CMOS technologies. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Handouts of presented slides. No script but an accompanying textbook is recommended. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Behzad Razavi, Design of Analog CMOS Integrated Circuits (Irwin Electronics & Computer Engineering) 1st or 2nd edition, McGraw-Hill Education | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0447-00L | Image Analysis and Computer Vision | W | 6 KP | 3V + 1U | L. Van Gool, E. Konukoglu, F. Yu | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Light and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition. Deep learning and Convolutional Neural Networks. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | This course aims at offering a self-contained account of computer vision and its underlying concepts, including the recent use of deep learning. The first part starts with an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First the interaction of light with matter is considered. The most important hardware components such as cameras and illumination sources are also discussed. The course then turns to image discretization, necessary to process images by computer. The next part describes necessary pre-processing steps, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and 3D shape as two important examples. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed. A major part at the end is devoted to deep learning and AI-based approaches to image analysis. Its main focus is on object recognition, but also other examples of image processing using deep neural nets are given. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Course material Script, computer demonstrations, exercises and problem solutions | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Python and Linux. The course language is English. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0468-00L | Analog Signal Processing and Filtering Suitable for Master Students as well as Doctoral Students. | W | 6 KP | 2V + 2U | H. Schmid | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | 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 , and a Python-based open-source tool to manipulate graphs is available on https://github.com/hanspi42/signalflowgrapher 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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