Search result: Catalogue data in Spring Semester 2021

Computational Science and Engineering Master Information
Fields of Specialization
Robotics
NumberTitleTypeECTSHoursLecturers
151-0854-00LAutonomous Mobile Robots Information W5 credits4GR. Siegwart, M. Chli, N. Lawrance
AbstractThe objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, environment perception, and probabilistic environment modeling, localizatoin, mapping and navigation. Theory will be deepened by exercises with small mobile robots and discussed accross application examples.
ObjectiveThe objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, environment perception, and probabilistic environment modeling, localizatoin, mapping and navigation.
Lecture notesThis lecture is enhanced by around 30 small videos introducing the core topics, and multiple-choice questions for continuous self-evaluation. It is developed along the TORQUE (Tiny, Open-with-Restrictions courses focused on QUality and Effectiveness) concept, which is ETH's response to the popular MOOC (Massive Open Online Course) concept.
LiteratureThis lecture is based on the Textbook:
Introduction to Autonomous Mobile Robots
Roland Siegwart, Illah Nourbakhsh, Davide Scaramuzza, The MIT Press, Second Edition 2011, ISBN: 978-0262015356
151-0566-00LRecursive Estimation Information W4 credits2V + 1UR. D'Andrea
AbstractEstimation of the state of a dynamic system based on a model and observations in a computationally efficient way.
ObjectiveLearn the basic recursive estimation methods and their underlying principles.
ContentIntroduction to state estimation; probability review; Bayes' theorem; Bayesian tracking; extracting estimates from probability distributions; Kalman filter; extended Kalman filter; particle filter; observer-based control and the separation principle.
Lecture notesLecture notes available on course website: Link
Prerequisites / NoticeRequirements: Introductory probability theory and matrix-vector algebra.
151-0636-00LSoft and Biohybrid Robotics Information Restricted registration - show details W4 credits3GR. Katzschmann
AbstractSoft robotics takes inspiration from nature and produces systems that are inherently safer to interact with. The class teaches processes involved in creating the structures, actuators, sensors, mechanical models, controllers, and machine learning models exploiting the deformable structure of soft robots in challenging tasks.
(Class fully online via Zoom)
ObjectiveLearn about the processes involved in creating soft and biohybrid robotic structures, actuator, sensors, mechanical models, closed-loop controllers, and machine learning approaches. Understand how to exploit the structural impedance and the dynamics of soft and biohybrid robots in locomotion and object manipulation tasks. Demonstrated learned capabilities in either a simulation or a physical prototype built at home.
ContentStudents will gain experience on a range of soft technologies and a model-based approaches to design and simulation of soft continuum robots.
0) Semester-long take-home project requiring students to implement the skills and knowledge learned during the class by building their own soft robotic prototype or simulation
1) Functional and intelligent materials for use in soft and biohybrid robotic applications
2) Design and design morphologies of soft robotic actuators and sensors
3) Fabrication techniques including 3D printing, casting, roll-to-roll, tissue engineering
4) Mechanical modeling including minimal parameter models, finite-element models and ML-based models
5) Closed-loop controllers of soft robots that exploit the robot's impedance and dynamics for locomotion and manipulation tasks
6) Deep Learning approaches to soft robotics, for design synthesis, modeling, and control

(Class offered only online via Zoom, see Moodle for details)
Lecture notesAll class materials including slides, recordings, class challenges infos, pre-reads, and tutorial summaries can be found on Moodle: Link
Literature1) Wang, Liyu, Surya G. Nurzaman, and Fumiya Iida. "Soft-material robotics." (2017).
2) Polygerinos, Panagiotis, et al. "Soft robotics: Review of fluid‐driven intrinsically soft devices; manufacturing, sensing, control, and applications in human‐robot interaction." Advanced Engineering Materials 19.12 (2017): 1700016.
3) Verl, Alexander, et al. Soft Robotics. Berlin, Germany:: Springer, 2015.
4) Cianchetti, Matteo, et al. "Biomedical applications of soft robotics." Nature Reviews Materials 3.6 (2018): 143-153.
5) Ricotti, Leonardo, et al. "Biohybrid actuators for robotics: A review of devices actuated by living cells." Science Robotics 2.12 (2017).
6) Sun, Lingyu, et al. "Biohybrid robotics with living cell actuation." Chemical Society Reviews 49.12 (2020): 4043-4069.
Prerequisites / Notice(Robot) dynamics, control systems, introduction to robotics, materials for engineers.
Only for students at master or PhD level.
Class size limitation is at 40 students.
252-0579-00L3D Vision Information W5 credits3G + 1AM. Pollefeys, V. Larsson
AbstractThe course covers camera models and calibration, feature tracking and matching, camera motion estimation via simultaneous localization and mapping (SLAM) and visual odometry (VO), epipolar and mult-view geometry, structure-from-motion, (multi-view) stereo, augmented reality, and image-based (re-)localization.
ObjectiveAfter attending this course, students will:
1. understand the core concepts for recovering 3D shape of objects and scenes from images and video.
2. be able to implement basic systems for vision-based robotics and simple virtual/augmented reality applications.
3. have a good overview over the current state-of-the art in 3D vision.
4. be able to critically analyze and asses current research in this area.
ContentThe goal of this course is to teach the core techniques required for robotic and augmented reality applications: How to determine the motion of a camera and how to estimate the absolute position and orientation of a camera in the real world. This course will introduce the basic concepts of 3D Vision in the form of short lectures, followed by student presentations discussing the current state-of-the-art. The main focus of this course are student projects on 3D Vision topics, with an emphasis on robotic vision and virtual and augmented reality applications.
252-0220-00LIntroduction to Machine Learning Information Restricted registration - show details
Limited number of participants. Preference is given to students in programmes in which the course is being offered. All other students will be waitlisted. Please do not contact Prof. Krause for any questions in this regard. If necessary, please contact Link
W8 credits4V + 2U + 1AA. Krause, F. Yang
AbstractThe course introduces the foundations of learning and making predictions based on data.
ObjectiveThe course will introduce the foundations of learning and making predictions from data. We will study basic concepts such as trading goodness of fit and model complexitiy. We will discuss important machine learning algorithms used in practice, and provide hands-on experience in a course project.
Content- Linear regression (overfitting, cross-validation/bootstrap, model selection, regularization, [stochastic] gradient descent)
- Linear classification: Logistic regression (feature selection, sparsity, multi-class)
- Kernels and the kernel trick (Properties of kernels; applications to linear and logistic regression); k-nearest neighbor
- Neural networks (backpropagation, regularization, convolutional neural networks)
- Unsupervised learning (k-means, PCA, neural network autoencoders)
- The statistical perspective (regularization as prior; loss as likelihood; learning as MAP inference)
- Statistical decision theory (decision making based on statistical models and utility functions)
- Discriminative vs. generative modeling (benefits and challenges in modeling joint vy. conditional distributions)
- Bayes' classifiers (Naive Bayes, Gaussian Bayes; MLE)
- Bayesian approaches to unsupervised learning (Gaussian mixtures, EM)
LiteratureTextbook: Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press
Prerequisites / NoticeDesigned to provide a basis for following courses:
- Advanced Machine Learning
- Deep Learning
- Probabilistic Artificial Intelligence
- Seminar "Advanced Topics in Machine Learning"
401-5860-00LSeminar in Robotics for CSEW4 credits2SE. Konukoglu, R. Siegwart
AbstractThis course provides an opportunity to familiarize yourself with the advanced topics of robotics and mechatronics research. The seminar consists of a literature study, including a report and a presentation.
ObjectiveThe students are familiar with the challenges of the fascinating and interdisciplinary field of Robotics and Mechatronics. They are introduced in the basics of independent non-experimental scientific research and are able to summarize and to present the results efficiently.
ContentThis 4 ECTS course requires each student to discuss a study plan with the lecturer and select minimum 10 relevant scientific publications to read through. At the end of semester, the results should be presented in an oral presentation and summarized in a report.
Physics
For the field of specialization `Physics' basic knowledge in quantum mechanics is required.
NumberTitleTypeECTSHoursLecturers
402-0812-00LComputational Statistical Physics Information W8 credits2V + 2UM. Krstic Marinkovic
AbstractComputer simulation methods in statistical physics. Classical Monte-Carlo-simulations: finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Application to Boltzmann machines. Simulation of non-equilibrium systems.

Molecular dynamics simulations: long range interactions, Ewald summation, discrete elements, parallelization.
ObjectiveThe lecture will give a deeper insight into computer simulation methods in statistical physics. Thus, it is an ideal continuation of the lecture
"Introduction to Computational Physics" of the autumn semester. In the first part students learn to apply the following methods: Classical Monte Carlo-simulations, finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Moreover, students learn about the application of statistical physics methods to Boltzmann machines and how to simulate non-equilibrium systems.

In the second part, students apply molecular dynamics simulation methods. This part includes long range interactions, Ewald summation and discrete elements.
ContentComputer simulation methods in statistical physics. Classical Monte-Carlo-simulations: finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Application to Boltzmann machines. Simulation of non-equilibrium systems. Molecular dynamics simulations: long range interactions, Ewald summation, discrete elements, parallelization.
Lecture notesLecture notes and slides are available online and will be distributed if desired.
LiteratureLiterature recommendations and references are included in the lecture notes.
Prerequisites / NoticeSome basic knowledge about statistical physics, classical mechanics and computational methods is recommended.
402-0810-00LComputational Quantum Physics
Special Students UZH must book the module PHY522 directly at UZH.
W8 credits2V + 2UM. H. Fischer
AbstractThis course provides an introduction to simulation methods for quantum systems. Starting from the one-body problem, a special emphasis is on quantum many-body problems, where we cover both approximate methods (Hartree-Fock, density functional theory) and exact methods (exact diagonalization, matrix product states, and quantum Monte Carlo methods).
ObjectiveThrough lectures and practical programming exercises, after this course:
Students are able to describe the difficulties of quantum mechanical simulations.
Students are able to explain the strengths and weaknesses of the methods covered.
Students are able to select an appropriate method for a given problem.
Students are able to implement basic versions of all algorithms discussed.
Lecture notesA script for this lecture will be provided.
LiteratureA list of additional references will be provided in the script.
Prerequisites / NoticeA basic knowledge of quantum mechanics, numerical tools (numerical differentiation and integration, linear solvers, eigensolvers, root solvers, optimization), and a programming language (for the teaching assignments, you are free to choose your preferred one).
402-0448-01LQuantum Information Processing I: Concepts
This theory part QIP I together with the experimental part 402-0448-02L QIP II (both offered in the Spring Semester) combine to the core course in experimental physics "Quantum Information Processing" (totally 10 ECTS credits). This applies to the Master's degree programme in Physics.
W5 credits2V + 1UP. Kammerlander
AbstractThe course will cover the key concepts and ideas of quantum information processing, including descriptions of quantum algorithms which give the quantum computer the power to compute problems outside the reach of any classical supercomputer.
Key concepts such as quantum error correction will be described. These ideas provide fundamental insights into the nature of quantum states and measurement.
ObjectiveBy the end of the course students are able to explain the basic mathematical formalism of quantum mechanics and apply them to quantum information processing problems. They are able to adapt and apply these concepts and methods to analyse and discuss quantum algorithms and other quantum information-processing protocols.
ContentThe topics covered in the course will include quantum circuits, gate decomposition and universal sets of gates, efficiency of quantum circuits, quantum algorithms (Shor, Grover, Deutsch-Josza,..), error correction, fault-tolerant design, entanglement, teleportation and dense conding, teleportation of gates, and cryptography.
Lecture notesMore details to follow.
LiteratureQuantum Computation and Quantum Information
Michael Nielsen and Isaac Chuang
Cambridge University Press
Prerequisites / NoticeA good understanding of linear algebra is recommended.
227-0161-00LMolecular and Materials Modelling Information W4 credits2V + 2UD. Passerone, C. Pignedoli
AbstractThe course introduces the basic techniques to interpret experiments with contemporary atomistic simulation, including force fields or ab initio based molecular dynamics and Monte Carlo. Structural and electronic properties will be simulated hands-on for realistic systems.
The modern methods of "big data" analysis applied to the screening of chemical structures will be introduced with examples.
ObjectiveThe ability to select a suitable atomistic approach to model a nanoscale system, and to employ a simulation package to compute quantities providing a theoretically sound explanation of a given experiment. This includes knowledge of empirical force fields and insight in electronic structure theory, in particular density functional theory (DFT). Understanding the advantages of Monte Carlo and molecular dynamics (MD), and how these simulation methods can be used to compute various static and dynamic material properties. Basic understanding on how to simulate different spectroscopies (IR, X-ray, UV/VIS). Performing a basic computational experiment: interpreting the experimental input, choosing theory level and model approximations, performing the calculations, collecting and representing the results, discussing the comparison to the experiment.
Content-Classical force fields in molecular and condensed phase systems
-Methods for finding stationary states in a potential energy surface
-Monte Carlo techniques applied to nanoscience
-Classical molecular dynamics: extracting quantities and relating to experimentally accessible properties
-From molecular orbital theory to quantum chemistry: chemical reactions
-Condensed phase systems: from periodicity to band structure
-Larger scale systems and their electronic properties: density functional theory and its approximations
-Advanced molecular dynamics: Correlation functions and extracting free energies
-The use of Smooth Overlap of Atomic Positions (SOAP) descriptors in the evaluation of the (dis)similarity of crystalline, disordered and molecular compounds
Lecture notesA script will be made available and complemented by literature references.
LiteratureD. Frenkel and B. Smit, Understanding Molecular Simulations, Academic Press, 2002.

M. P. Allen and D.J. Tildesley, Computer Simulations of Liquids, Oxford University Press 1990.

C. J. Cramer, Essentials of Computational Chemistry. Theories and Models, Wiley 2004

G. L. Miessler, P. J. Fischer, and Donald A. Tarr, Inorganic Chemistry, Pearson 2014.

K. Huang, Statistical Mechanics, Wiley, 1987.

N. W. Ashcroft, N. D. Mermin, Solid State Physics, Saunders College 1976.

E. Kaxiras, Atomic and Electronic Structure of Solids, Cambridge University Press 2010.
529-0474-00LQuantum ChemistryW6 credits3GM. Reiher, T. Weymuth
AbstractIntroduction into the basic concepts of electronic structure theory and into numerical methods of quantum chemistry. Exercise classes are designed to deepen the theory; practical case studies using quantum chemical software to provide a 'hands-on' expertise in applying these methods.
ObjectiveNowadays, chemical research can be carried out in silico, an intellectual achievement for which Pople and Kohn have been awarded the Nobel prize of the year 1998. This lecture shows how that has been accomplished. It works out the many-particle theory of many-electron systems (atoms and molecules) and discusses its implementation into computer programs. A complete picture of quantum chemistry shall be provided that will allow students to carry out such calculations on molecules (for accompanying experimental work in the wet lab or as a basis for further study of the theory).
ContentBasic concepts of many-particle quantum mechanics. Derivation of the many-electron theory for atoms and molecules; starting with the harmonic approximation for the nuclear problem and with Hartree-Fock theory for the electronic problem to Moeller-Plesset perturbation theory and configuration interaction and to coupled cluster and multi-configurational approaches. Density functional theory. Case studies using quantum mechanical software.
Lecture notesHand-outs in German will be provided for each lecture (they are supplemented by (computer) examples that continuously illustrate how the theory works).

All information regarding this course, including links to the online streaming, will be available on this web page:
Link
LiteratureTextbooks on Quantum Chemistry:
F.L. Pilar, Elementary Quantum Chemistry, Dover Publications
I.N. Levine, Quantum Chemistry, Prentice Hall

Hartree-Fock in basis set representation:
A. Szabo and N. Ostlund, Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory, McGraw-Hill

Textbooks on Computational Chemistry:
F. Jensen, Introduction to Computational Chemistry, John Wiley & Sons
C.J. Cramer, Essentials of Computational Chemistry, John Wiley & Sons
Prerequisites / NoticeBasic knowledge in quantum mechanics (e.g. through course physical chemistry III - quantum mechanics) required
402-0778-00LParticle Accelerator Physics and Modeling IIW6 credits2V + 1UA. Adelmann
AbstractThe effect of nonlinearities on the beam dynamics of charged particles will be discussed. For the nonlinear beam transport, Lie-Methods in combination with differential algebra (DA) and truncated power series (TPS) will be introduced. In the second part we will discuss surrogate model construction for such non-linear dynamical systems using neural networks and polynomial chaos expansion.
ObjectiveModels for nonlinear beam dynamics can be applied to new or existing particle accelerators.
You create Python based surrogate models of dynamical systems, such as charged particle accelerators using Keras and Tensorflow.
Content- Symplectic Maps and Higher Order Beam Dynamics
- Taylor Modells and Differential Algebra
- Lie Methods
- Normal Forms
- Surrogate Models for dynamical systems
- Surrogate model based neural networks
- Surrogate model based polynomial chaos
- Uncertanty quantification of dynamical systems
Lecture notesLecture notes
Literature* Modern Map Methods in Particle Beam Physics
M. Berz (Link)
Prerequisites / NoticeIdeally Particle Accelerator Physics and Modelling 1 (PAM-1), however at the beginning of the semester, a crash course is offered introducing the minimum level of particle accelerator modeling needed to follow. This lecture is also suited for PhD. Students.
401-5810-00LSeminar in Physics for CSEW4 credits2SA. Adelmann
AbstractIn this seminar the students present a talk on an advanced topic in modern theoretical or computational physics.
Objective
Computational Finance
NumberTitleTypeECTSHoursLecturers
401-4658-00LComputational Methods for Quantitative Finance: PDE Methods Information Restricted registration - show details W6 credits3V + 1UC. Marcati, A. Stein
AbstractIntroduction to principal methods of option pricing. Emphasis on PDE-based methods. Prerequisite MATLAB and Python programming
and knowledge of numerical mathematics at ETH BSc level.
ObjectiveIntroduce the main methods for efficient numerical valuation of derivative contracts in a
Black Scholes as well as in incomplete markets due Levy processes or due to stochastic volatility
models. Develop implementation of pricing methods in MATLAB and Python.
Finite-Difference/ Finite Element based methods for the solution of the pricing integrodifferential equation.
Content1. Review of option pricing. Wiener and Levy price process models. Deterministic, local and stochastic
volatility models.
2. Finite Difference Methods for option pricing. Relation to bi- and multinomial trees.
European contracts.
3. Finite Difference methods for Asian, American and Barrier type contracts.
4. Finite element methods for European and American style contracts.
5. Pricing under local and stochastic volatility in Black-Scholes Markets.
6. Finite Element Methods for option pricing under Levy processes. Treatment of
integrodifferential operators.
7. Stochastic volatility models for Levy processes.
8. Techniques for multidimensional problems. Baskets in a Black-Scholes setting and
stochastic volatility models in Black Scholes and Levy markets.
9. Introduction to sparse grid option pricing techniques.
Lecture notesThere will be english lecture notes as well as MATLAB or Python software for registered participants in the course.
LiteratureMain reference (course text):
N. Hilber, O. Reichmann, Ch. Schwab and Ch. Winter: Computational Methods for Quantitative Finance, Springer Finance, Springer, 2013.

Supplementary texts:
R. Cont and P. Tankov : Financial Modelling with Jump Processes, Chapman and Hall Publ. 2004.

Y. Achdou and O. Pironneau : Computational Methods for Option Pricing, SIAM Frontiers in Applied Mathematics, SIAM Publishers, Philadelphia 2005.

D. Lamberton and B. Lapeyre : Introduction to stochastic calculus Applied to Finance (second edition), Chapman & Hall/CRC Financial Mathematics Series, Taylor & Francis Publ. Boca Raton, London, New York 2008.

J.-P. Fouque, G. Papanicolaou and K.-R. Sircar : Derivatives in financial markets with stochastic volatility, Cambridge Univeristy Press, Cambridge, 2000.
Prerequisites / NoticeKnowledge of Numerical Analysis/ Scientific Computing Techniques
corresponding roughly to BSc MATH or BSc RW/CSE at ETH is expected.
Basic programming skills in MATLAB or Python are required for the exercises,
and are _not_ taught in this course.
401-3932-19LMachine Learning in FinanceW6 credits3V + 1UJ. Teichmann
AbstractThe course will deal with the following topics with rigorous proofs and many coding excursions: Universal approximation theorems, Stochastic gradient Descent, Deep
networks and wavelet analysis, Deep Hedging, Deep calibration,
Different network architectures, Reservoir Computing, Time series analysis by machine learning, Reinforcement learning, generative adversersial networks, Economic games.
Objective
Prerequisites / NoticeBachelor in mathematics, physics, economics or computer science.
401-8908-00LContinuous Time Quantitative Finance (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: MFOEC204

Mind the enrolment deadlines at UZH:
Link
W3 credits3VUniversity lecturers
AbstractAmerican Options, Stochastic Volatility, Lévy Processes and Option Pricing, Exotic Options, Transaction Costs and Real Options.
ObjectiveThe course focuses on the theoretical foundations of modern derivative pricing. It aims at deriving and explaining important option pricing models by relying on some mathematical tools of continuous time finance.
A particular focus on jump processes is given. The introduction of possible financial crashes is now essential in some models and a clear understanding of Poisson processes is therefore important. A standard background in stochastic calculus is required.
ContentStochastic volatility models
Itô's formula and Girsanov theorem for jump-diffusion processes
The pricing of options in presence of possible discontinuities
Exotic options
Transaction costs
Lecture notesSee: Link
LiteratureSee: Link
Prerequisites / NoticeThis course replaces "Continuous Time Quantitative Finance" (MFOEC108), which will be discontinued. Students who have taken "Continuous Time Quantitative Finance" (MFOEC108) in the past, are not allowed to book this course "Continuous Time Quantitative Finance" (MFOEC204).
401-5820-00LSeminar in Computational Finance for CSEW4 credits2SJ. Teichmann
Abstract
Objective
Electromagnetics
recognition of 227-0662-00L and 227-0662-10L requires the successful completion of both course units
NumberTitleTypeECTSHoursLecturers
227-0536-00LMultiphysics Simulations for Power Systems Information
This course is defined so and planned to be an addition to the module "227-0537-00L Technology of Electric Power System Components".
However, the students who are familiar with the fundamentals of electromagnetic fields could attend only this course without its 227-0537-00-complement.
W4 credits2V + 2UJ. Smajic
AbstractThe goals of this course are a) understanding the fundamentals of the electromagnetic, thermal, mechanical, and coupled field simulations and b) performing effective simulations of primary equipment of electric power systems. The course is understood complementary to 227-0537-00L "Technology of Electric Power System Components", but can also be taken separately.
ObjectiveThe student should learn the fundamentals of the electromagnetic, thermal, mechanical, and coupled fields simulations necessary for modern product development and research based on virtual prototyping. She / he should also learn the theoretical background of the finite element method (FEM) and its application to low- and high-frequency electromagnetic field simulation problems. The practical exercises of the course should be done by using one of the commercially available field simulation software (Infolytica, ANSYS, and / or COMSOL). After completing the course the student should be able to properly and efficiently use the software to simulate practical design problems and to understand and interpret the obtained results.
Content1. Elektromagnetic Fields and Waves: Simulation Aspects (1 lecture, 2 hours)
a. Short review of the governing equations
b. Boundary conditions
c. Initial conditions
d. Linear and nonlinear material properties
e. Coupled fields (electro-mechanical and electro-thermal coupling)

2. Finite Element Method for elektromagnetic simulations (5 lectures and 3 exercises, 16 hours)
a. Scalar-FEM in 2-D (electrostatic, magnetostatic, eddy-currents, etc.)
b. Vector-FEM in 3-D (3-D eddy-currents, wave propagation, etc.)
c. Numerical aspects of the analysis (convergence, linear solvers, preconditioning, mesh quality, etc.)
d. Matlab code for 2-D FEM for learning and experimenting

3. Practical applications (5 lectures and 5 exercises, 20 hours)
a. Dielectric analysis of high-voltage equipment
b. Nonlinear quasi-electrostatic analysis of surge arresters
c. Eddy-currents analysis of power transformers
d. Electromagnetic analysis of electric machines
e. Very fast transients in gas insulated switchgears (GIS)
f. Electromagnetic compatibility (EMC)
227-0662-00LOrganic and Nanostructured Optics and Electronics (Course)
Does not take place this semester.
W3 credits2GV. Wood
AbstractThis course examines the optical and electronic properties of excitonic materials that can be leveraged to create thin-film light emitting devices and solar cells. Laboratory sessions provide students with experience in synthesis and optical characterization of nanomaterials as well as fabrication and characterization of thin film devices.
ObjectiveGain the knowledge and practical experience to begin research with organic or nanostructured materials and understand the key challenges in this rapidly emerging field.
Content0-Dimensional Excitonic Materials (organic molecules and colloidal quantum dots)

Energy Levels and Excited States (singlet and triplet states, optical absorption and luminescence).

Excitonic and Polaronic Processes (charge transport, Dexter and Förster energy transfer, and exciton diffusion).

Devices (photodetectors, solar cells, and light emitting devices).
LiteratureLecture notes and reading assignments from current literature to be posted on website.
227-0662-10LOrganic and Nanostructured Optics and Electronics (Project) Information Restricted registration - show details
Does not take place this semester.
W3 credits2AV. Wood
AbstractThis course examines the optical and electronic properties of excitonic materials that can be leveraged to create thin-film light emitting devices and solar cells. Laboratory sessions provide students with experience in synthesis and optical characterization of nanomaterials as well as fabrication and characterization of thin film devices.
ObjectiveGain the knowledge and practical experience to begin research with organic or nanostructured materials and understand the key challenges in this rapidly emerging field.
Content0-Dimensional Excitonic Materials (organic molecules and colloidal quantum dots)

Energy Levels and Excited States (singlet and triplet states, optical absorption and luminescence).

Excitonic and Polaronic Processes (charge transport, Dexter and Förster energy transfer, and exciton diffusion).

Devices (photodetectors, solar cells, and light emitting devices).
LiteratureLecture notes and reading assignments from current literature to be posted on website.
Prerequisites / NoticeAdmission is conditional to passing 227-0662-00L Organic and Nanostructured Optics and Electronics (Course)
  • First page Previous page Page  2  of  3 Next page Last page     All