Semiconductor
Semiconductor devices are solid state bodies, whose electrical conductivity strongly depends on the temperature and other internal properties like the so-called doping. Depending on the temperature or other internal settigns, they can be regarded as insulator or conductor. (Physically speaken: Semiconductor materials have a band gap between.. and .. electron Volt)
This property makes them extremely useful in electronics, since this property can be easily employed to use them as switches. On nowadays computerchips and prozessors, millions of semiconductor devices (especially transistors) are included in an electronic circuit. In order to use common circuit simulation tools to simualte circuits containing those devices, semiconductor devices are often reflected by compact models - subcircuits of basic elements like resistors, capacitors, inductors and current/voltage sources. Those compact models shoul rebuild the input/output behaviour of the semiconductor device.
Ongoing miniaturization and the step from miro- to nanotechnology, however, leads to more powerful prozessors and chips, since higher packing density can be achieved. On the other hand, this higher packing density and miniaturization of the devices makes parasitic effects like heating predominant. Incorporation of those effects into compact models results in large compact models to describe a single semiconductor device. This makes it desireable to include more exact distributed device models - device models based on partial differential equations - into circuit simulation.
Moreover, smaller devices are driven by smaller signals, what makes them more energy efficient. On the other hand this results in a larger noise/signal ratio, what makes inclusion of non-deterministic effects into device models interesting. All in all, this leads to the following recent question in semiconductor/circuit modelling and simulation:
- Thermal effects in semiconductor devices
- Noise in semiconductor devices (SDEs)
- Quantum Effects in semiconductor devices
- Electro-thermal coupling of optoelectronic semiconductor devices with electric circuits
- Efficient Co-Simulation of circuit/semiconductor problems (Dynamic Iteration schemes)
Former and ongoing projects
Cooperations
- Vittorio Romano, Università degli studi di Catania, Italy
- Giuseppe Ali, Universitá della Calabria, Italy
- Ansgar Jüngel, TU Vienna, Austria
- Pina Milisic, University of Zagreb, Croatia
Open subjects for theses
- Master Thesis: Two-dimensional thermal-electric simulation of semiconductor MOSFET-devices (M.Brunk)
Publications
- 2023
5168.
Kossaczká, Tatiana; Ehrhardt, Matthias; Günther, Michael
Deep finite difference method for solving Asian option pricing problems
Preprint IMACM
2023
Herausgeber: Bergische Universität Wuppertal5167.
Kapllani, Lorenc; Teng, Long
Deep Learning algorithms for solving high-dimensional nonlinear Backward Stochastic Differential Equations
Discrete Contin. Dyn. Syst. - B
2023
ISSN: 1531-34925166.
Kapllani, Lorenc; Teng, Long
Deep Learning algorithms for solving high-dimensional nonlinear backward stochastic differential equations
Discrete Contin. Dyn. Syst. - B
20235165.
Ackermann, Julia; Jentzen, Arnulf; Kruse, Thomas; Kuckuck, Benno; Padgett, Joshua Lee
Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense
20235164.
Ackermann, Julia; Jentzen, Arnulf; Kruse, Thomas; Kuckuck, Benno; Padgett, Joshua Lee
Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the Lp-sense
Preprint
20235163.
Ackermann, Julia; Jentzen, Arnulf; Kruse, Thomas; Kuckuck, Benno; Padgett, Joshua Lee
Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the Lp-sense
Preprint
20235162.
Abdul Halim, Adila; others
Deep-Learning-Based Cosmic-Ray Mass Reconstruction Using the Water-Cherenkov and Scintillation Detectors of AugerPrime
PoS, ICRC2023 :371
20235161.
Kowol, Philipp; Bargmann, Swantje; Görrn, Patrick; Wilmers, Jana
Delamination Behavior of Highly Stretchable Soft Islands Multi-Layer Materials
Applied Mechanics, 4 (2) :514--527
2023
ISSN: 2673-31615160.
Khosrawi-Rad, Bijan; Borchers, Arne; Grogorick, Linda; Robra-Bissantz, Susanne
Design Principles for Gamified Pedagogical Conversational Agents
Seite 67 - 82
Academic MindTrek Conference (MindTrek)
Tampere, Finnland
20235159.
Ehrhardt, Matthias; Matyokubov, Kh Sh
Driven transparent quantum graphs
Preprint
20235158.
Ehrhardt, Matthias; Matyokubov, Kh Sh
Driven transparent quantum graphs
Preprint
20235157.
Felpel, Mike; Kienitz, Jörg; McWalter, Thomas
Effective stochastic local volatility models
Quantitative Finance, 23 (12) :1731–1750
2023
Herausgeber: Routledge5156.
Klamroth, Kathrin; Lang, Bruno; Stiglmayr, Michael
Efficient Dominance Filtering for Unions and Minkowski Sums of Non-Dominated Sets
Computers and Operations Research
2023
Herausgeber: Elsevier {BV}5155.
Di Persio, Luca; Ehrhardt, Matthias
Electricity price forecasting via statistical and deep learning approaches: The German case
AppliedMath, 3 (2) :316–342
2023
Herausgeber: MDPI5154.
Di Persio, Luca; Ehrhardt, Matthias
Electricity price forecasting via statistical and deep learning approaches: The German case
AppliedMath, 3 (2) :316–342
2023
Herausgeber: MDPI5153.
Di Persio, Luca; Ehrhardt, Matthias
Electricity Price Forecasting via Statistical and Deep Learning Approaches: The German Case
AppliedMath, 3 (2) :316--342
2023
Herausgeber: Multidisciplinary Digital Publishing Institute5152.
Glück, Jochen; Hölz, Julian
Eventual cone invariance revisited
Linear Algebra and its Applications, 675 :274 - 293
20235151.
Janssen, Nils; Fetzer, Jana R; Grewing, Jannis; Burgmann, Sebastian; Janoske, Uwe
Experimental investigation of particle--droplet--substrate interaction
Experiments in Fluids, 64 (3) :44
2023
Herausgeber: Springer Berlin Heidelberg Berlin/Heidelberg5150.
Ehrhardt, Matthias
Experimental observation and theoretical analysis of the low-frequency source interferogram and hologram in shallow water
Journal of Sound and Vibration, 544 :117388
2023
Herausgeber: Academic Press5149.
Ehrhardt, Matthias
Experimental observation and theoretical analysis of the low-frequency source interferogram and hologram in shallow water
Journal of Sound and Vibration, 544 :117388
2023
Herausgeber: Academic Press5148.
Grogorick, Linda; Rohde, Moritz; Khosrawi-Rad, Bijan; Robra-Bissantz, Susanne
Fiction or Reality - Which Game Story Promotes Learning Outcome More?
Seite 441-456
36th Bled eConference Digital Economy and Society
Bled, Slowenien
20235147.
Kääpä, Alex; Kampert, Karl-Heinz; Becker Tjus, Julia
Flux predictions in the transition region incorporating the effects from propagation of cosmic rays in the Galactic magnetic field
EPJ Web Conf., 283 :03006
20235146.
Khosrawi-Rad, Bijan; Grogorick, Linda; Robra-Bissantz, Susanne
Game-Inspired Pedagogical Conversational Agents – A Systematic Literature Review
AIS Transactions on Human-Computer Interaction, 15 (2) :146-192
20235145.
Grogorick, Linda; Mavrin, Max; Robra-Bissantz, Susanne
Gamification im Informatikunterricht
Seite 231-245
Gemeinschaft neuer Medien (GeNeMe)
Dresden
20235144.
Haussmann, Norman; Stroka, Steven; Schmuelling, Benedikt; Clemens, Markus
GPU-accelerated body-internal electric field exposure simulation using low-frequency magnetic field sampling points
COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, 42 (5) :982-992
Januar 2023
Herausgeber: Emerald Publishing Limited
ISSN: 0332-1649