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

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


  • Rachel C. Kurchin
  • Jeremy R. Poindexter
  • Daniil Kitchaev
  • Ville Vähänissi

  • Carlos Del Cañizo
  • Liu Zhe
  • Hannu S. Laine
  • Chris Roat
  • Sergiu Levcenco
  • Gerbrand Ceder
  • Tonio Buonassisi

Research units

  • Massachusetts Institute of Technology
  • Technical University of Madrid
  • Helmholtz Centre Berlin for Materials and Energy
  • Alphabet Inc.
  • University of California at Berkeley


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 defectsis 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 beused to determine many parameters (the number of which, in principle, is limited only by the computingresources available), saving time and cost of facilities and equipment. Second, sincemeasurements 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 cellsand discuss potential for future application in a broader array of systems.


Original languageEnglish
Title of host publication2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC)
Publication statusPublished - 26 Nov 2018
MoE publication typeA4 Article in a conference publication
EventWorld Conference on Photovoltaic Energy Conversion - Waikoloa Village, United States
Duration: 10 Jun 201815 Jun 2018

Publication series

NameWorld Conference on Photovoltaic Energy Conversion
ISSN (Print)0160-8371


ConferenceWorld Conference on Photovoltaic Energy Conversion
Abbreviated titleWCPEC
CountryUnited States
CityWaikoloa Village
Internet address

    Research areas

  • Bayes methods, charge carrier lifetime, charge carrier mobility, parameter estimation, photovoltaic cells, silicon

Download statistics

No data available

ID: 31393525