Efficient simulation of charge transport in deep-trap media

Brereton, Tim J., Kroese, Dirk P., Stenzel, Ole, Schmidt, Volker and Baumeier, Bjorn (2012). Efficient simulation of charge transport in deep-trap media. In: C. Laroque, J. Himmelspach, R. Pasupathy, O. Rose and A. M. Uhrmacher, Proceedings of the 2012 Winter Simulation Conference. Winter Simulation Conference, Berlin, Germany, (1-12). 9-12 December 2012. doi:10.1109/WSC.2012.6465003

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Author Brereton, Tim J.
Kroese, Dirk P.
Stenzel, Ole
Schmidt, Volker
Baumeier, Bjorn
Title of paper Efficient simulation of charge transport in deep-trap media
Conference name Winter Simulation Conference
Conference location Berlin, Germany
Conference dates 9-12 December 2012
Proceedings title Proceedings of the 2012 Winter Simulation Conference   Check publisher's open access policy
Journal name 2012 Winter Simulation Conference (Wsc)   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Publication Year 2012
Year available 2012
Sub-type Fully published paper
DOI 10.1109/WSC.2012.6465003
ISBN 9781467347792
ISSN 0891-7736
Editor C. Laroque
J. Himmelspach
R. Pasupathy
O. Rose
A. M. Uhrmacher
Start page 1
End page 12
Total pages 12
Collection year 2013
Language eng
Abstract/Summary This paper introduces a new approach to Monte Carlo estimation of the velocity of charge carriers drift-diffusing in a random medium. The random medium is modeled by a 1-dimensional lattice and the position of the charge carrier is modeled by a Markov jump process, whose state space is the set of lattice points. The transition rates of the Markov jump process are determined by the underlying energy landscape of the random medium. This energy landscape is modeled by a Gaussian process and contains regions of relatively low energy, in which charge carriers quickly become stuck. As a result, the state space is not adequately explored by the standard algorithms and the velocity of the charge carrier is poorly estimated. In addition, the conventional Monte Carlo estimators have very high variances. Our approach aims to reduce the number of simulation steps that are spent in the low energy problem regions. We do this by identifying the problem regions via a stochastic watershed algorithm. We then use a coarsened state space model, where the problem regions are treated as single states. In this way, we are able to simulate a semi-Markov process on the coarsened state space. This results in estimators that are unbiased and have considerably lower variance than the crude Monte Carlo alternatives.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Conference Paper
Collections: School of Mathematics and Physics
Official 2013 Collection
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Created: Fri, 17 May 2013, 12:13:37 EST by Prof Dirk P. Kroese on behalf of School of Mathematics & Physics