Applied and Computational Mathematics (ACM)

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:

Former and ongoing projects

Cooperations

Open subjects for theses

  • Master Thesis: Two-dimensional thermal-electric simulation of semiconductor MOSFET-devices (M.Brunk)

Publications



2023

5205.

[german] Zeller, Diana
App des Monats: Monatliche Steckbriefe für einen abwechslungsreichen Einsatz digitaler Medien im Chemieunterricht
In Wilke, T.; Rubner, I., Editor, Band DiCE-Tagung 2023 - Digitalisation in Chemistry Education
Herausgeber: Friedrich-Schiller-Universität Jena, Institut für Anorganische und Analytische Chemie, Jena
2023

5204.

[en] Börger, Kristian; Ellingham, Jennifer; Belt, Alexander; Schultze, Thorsten; Bieder, Stefan; Weckman, Elizabeth; Arnold, Lukas
Assessing performance of LEDSA and Radiance method for measuring extinction coefficients in real-scale fire environments
Fire Safety Journal, 141
Dezember 2023
ISSN: 03797112

5203.

Dehne, Tobias
Assessment of horizontal flame spread with solid pyrolysis modelling in the Fire Dynamics Simulator
Bergische Universität Wuppertal
2023

5202.

Schäfers, Torben; Teng, Long
Asymmetry in stochastic volatility models with threshold and time-dependent correlation
Studies in Nonlinear Dynamics & Econometrics, 27 (2) :131–146
2023
Herausgeber: De Gruyter

5201.

Hastir, Anthony; Hosfeld, René; Schwenninger, Felix L.; Wierzba, Alexander A.
BIBO stability for funnel control: semilinear internal dynamics with unbounded input and output operators
2023

5200.

Gorski, Jochen; Klamroth, Kathrin; Sudhoff, Julia
Biobjective optimization problems on matroids with binary costs
Optimization, 72 (7) :1931-1960
2023
Herausgeber: Taylor & Francis

5199.

[german] Grandrath, Rebecca
CapCut – intuitive und vollständige Videobearbeitung
:1-2
2023
Herausgeber: Friedrich-Schiller-Universität Jena, Institut für Anorganische und Analytische Chemie

5198.

Hosfeld, René; Jacob, Birgit; Schwenninger, Felix L.
Characterization of Orlicz admissibility
Semigroup Forum, 106 :633–661
2023

5197.

Bohrmann-Linde, Claudia; Siehr, Ilona
Chemie Qualifikationsphase Nordrhein-Westfalen
Herausgeber: C.C.Buchner Verlag, Bamberg
2023

ISBN: 978-3-661-06002-6

5196.

Albrecht, Johannes; others
Comparison and efficiency of GPU accelerated optical light propagation in CORSIKA\textasciitilde{}8
PoS, ICRC2023 :417
2023

5195.

Carrillo, Jose Antonio; Totzeck, Claudia; Vaes, Urbain
Consensus-based Optimization and Ensemble Kalman Inversion for Global Optimization Problems with Constraints
, Modeling and Simulation for Collective Dynamics,Lecture Notes Series, Institute for Mathematical Sciences, NUS Band 40
2023

5194.

Halim, A. Abdul; others
Constraining the sources of ultra-high-energy cosmic rays across and above the ankle with the spectrum and composition data measured at the Pierre Auger Observatory
JCAP, 05 :024
2023

5193.

Yue, Baobiao; others
Constraints on BSM particles from the absence of upward-going air showers in the Pierre Auger Observatory
PoS, ICRC2023 :1095
2023

5192.

Abdul Halim, Adila; others
Constraints on UHECR characteristics from cosmogenic neutrino limits with the measurements of the Pierre Auger Observatory
PoS, ICRC2023 :1520
2023

5191.

Abdul Halim, Adila; others
Constraints on upward-going air showers using the Pierre Auger Observatory data
PoS, ICRC2023 :1099
2023

5190.

Acu, Ana-Maria; Heilmann, Margareta; Raşa, Ioan; Seserman, Andra
Convergence of linking Durrmeyer type modifications of generalized Baskakov operators
Bulletin of the Malaysian Math. Sciences Society, 46 (3)
2023

5189.

Jacob, Birgit; Mironchenko, Andrii; Partington, Jonathan R.; Wirth, Fabian
Corrigendum: Noncoercive Lyapunov functions for input-to-state stability of infinite-dimensional systems
SIAM J. Control Optim., 61 (2) :723-724
2023

5188.

Aerdker, S.; others
CRPropa 3.2: a public framework for high-energy astroparticle simulations
PoS, ICRC2023 :1471
2023

5187.

Günther, Michael; Jacob, Birgit; Totzeck, Claudia
Data-driven adjoint-based calibration of port-Hamiltonian systems in time domain
arXiv preprint arXiv:2301.03924
2023

5186.

Kossaczká, Tatiana; Ehrhardt, Matthias; Günther, Michael
Deep FDM: Enhanced finite difference methods by deep learning
Franklin Open, 4 :100039
2023
Herausgeber: Elsevier

5185.

Kossaczká, Tatiana; Ehrhardt, Matthias; Günther, Michael
Deep FDM: Enhanced finite difference methods by deep learning
Franklin Open, 4 :100039
2023
Herausgeber: Elsevier

5184.

Kossaczká, Tatiana; Ehrhardt, Matthias; Günther, Michael
Deep FDM: Enhanced finite difference methods by deep learning
Franklin Open, 4 :100039
2023
Herausgeber: Elsevier

5183.

Kossaczká, Tatiana; Ehrhardt, Matthias; Günther, Michael
Deep finite difference method for solving Asian option pricing problems
Preprint IMACM
2023
Herausgeber: Bergische Universität Wuppertal

5182.

Kossaczká, Tatiana; Ehrhardt, Matthias; Günther, Michael
Deep finite difference method for solving Asian option pricing problems
Preprint IMACM
2023
Herausgeber: Bergische Universität Wuppertal

5181.

Kapllani, Lorenc; Teng, Long
Deep Learning algorithms for solving high-dimensional nonlinear Backward Stochastic Differential Equations
Discrete Contin. Dyn. Syst. - B
2023
ISSN: 1531-3492