Semiconductor parameter extraction via current-voltage characterization and Bayesian inference methods

Kurchin, Rachel C. and Poindexter, Jeremy R. and Kitchaev, Daniil and Vähänissi, Ville and Cañizo Nadal, Carlos del and Zhe, Liu and Laine, Hannu S. and Roat, Chris and Levcenco, S. and Ceder, Gerbrand and Buonassisi, Tonio (2018). Semiconductor parameter extraction via current-voltage characterization and Bayesian inference methods. In: "Proceedings of IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC)", 10/06/2018 - 15/06/2018, Waikoloa (USA). ISBN 978-1-5386-8529-7. pp. 3271-3275. https://doi.org/10.1109/PVSC.2018.8547288.

Description

Title: Semiconductor parameter extraction via current-voltage characterization and Bayesian inference methods
Author/s:
  • Kurchin, Rachel C.
  • Poindexter, Jeremy R.
  • Kitchaev, Daniil
  • Vähänissi, Ville
  • Cañizo Nadal, Carlos del
  • Zhe, Liu
  • Laine, Hannu S.
  • Roat, Chris
  • Levcenco, S.
  • Ceder, Gerbrand
  • Buonassisi, Tonio
Item Type: Presentation at Congress or Conference (Article)
Event Title: Proceedings of IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC)
Event Dates: 10/06/2018 - 15/06/2018
Event Location: Waikoloa (USA)
Title of Book: Proceedings of 7th World Conference on Photovoltaic Energy Conversion (WCPEC) IEEE 2018
Date: June 2018
ISBN: 978-1-5386-8529-7
Subjects:
Freetext Keywords: Bayes methods; charge carrier lifetime; charge carrier mobility; parameter estimation; photovoltaic cells; silicon
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Electrónica Física
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Defects in semiconductors, although atomistic in scale and often scarce in concentration, frequently represent the performance-limiting factor in optoelectronic devices such as solar cells. However, due to this scale and scarcity, direct experimental characterization of defects is technically challenging, timeconsuming, and expensive. Even so, the fact that defects can limit device performance suggests that device-level characterization should be able to lend insight into their properties. In this work, we use Bayesian inference to demonstrate a way to relate experimental device measurements with defect properties (as well as other materials properties affected by the presence of defects, such as minority-carrier lifetime). We apply this method to solve the “inverse problem” to a forward device model – namely, determining which input parameters to the model produce the measured electrical output. This approach has distinct advantages over direct characterization. First, a single set of measurements can be used to determine many parameters (the number of which, in principle, is limited only by the computing resources available), saving time and cost of facilities and equipment. Second, since measurements are performed on materials and interfaces in their relevant device geometries (vs. separately prepared samples), the determined parameters are guaranteed to be physically relevant. We demonstrate application of this method to both tin monosulfide and silicon solar cells and discuss potential for future application in a broader array of systems.

More information

Item ID: 55929
DC Identifier: http://oa.upm.es/55929/
OAI Identifier: oai:oa.upm.es:55929
DOI: 10.1109/PVSC.2018.8547288
Official URL: https://ieeexplore.ieee.org/document/8547288
Deposited by: Memoria Investigacion
Deposited on: 10 Sep 2019 17:32
Last Modified: 10 Sep 2019 17:32
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