00EINEU006 21S 5SWS VI Simulation of Semiconductor Properties   Hilfe Logo

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Simulation of Semiconductor Properties 
lecture with integrated exercises
Summer semester 2021
... alle LV-Personen
Associate Professorship of Simulation of Nanosystems for Energy Conversion (Prof. Gagliardi)
(Contact information)
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Investigation of the properties of semiconducting materials is crucial for designing state of the art semiconductor devices. Advanced simulation techniques can be very useful in assisting experimental synthesis of novel semiconducting materials. The course focuses on accurate simulation of semiconducting materials of all types. This includes pure elements like silicon to compounds like gallium nitride to organic semiconductors that are being researched for their unique properties. Various properties of semiconductors like density of state, band gap, band structure, electronic transfer integral and charge mobility will be explored using state of the art computational techniques. Ab initio simulation techniques like density functional theory (DFT) will be introduced for the accurate calculations starting from atomic structures. More recent empirical methods like machine learning will be introduced. Following a thorough theoretical understanding of machine learning algorithms, they will be applied to predict electronic properties of semiconductors.
(Recommended) requirements: Focused on grad students (Open to Physics, Chemistry and Materials Science students also) Basic programming skills Basic quantum mechanics
Study goals: After successful completion of the module, students
- Understand the concepts behind density functional theory (DFT)
- Know how to perform DFT calculations using Quantum Espresso
- Know how to simulate electronic properties of Silicon using DFT
- Understand the fundamentals of machine learning algorithms
- Know how to use python to perform end to end data analysis
- are able to apply machine learning for calculating charge transfer in organic semiconductor

This is a very interactive course in which the students will be actively involved in discussions, sharing concepts and question/answers. The course consists of weekly lectures and tutorials. In the lecture the module contents will be presented by the teacher, supported by an electronic presentation. During the tutorials, students will do hands-on calculations with numerical simulation programs/machine learning tools. In addition, there will be projects during which the students will have the freedom to work on interesting semiconducting materials.
Für die Anmeldung zur Teilnahme müssen Sie sich in TUMonline als Studierende*r identifizieren.
Note: Module level: Master
Literature: Some small readings and videos will be suggested during the course and will be announced in class
- V. Brazdova, D. R. Bowler: Atomistic Computer Simulations
Online information
e-learning course (moodle)
Media formats:
e-Learning material on TUM-Moodle will be shared when the course is ongoing. ----
- presentation
- Solutions to problems in tutorial