Google Research

Rapid Photovoltaic Device Characterization through Bayesian Parameter Estimation

  • R.E. Brandt
  • Rachel Kurchin
  • Vera Steinmann
  • Daniil Kitchaev
  • Chris Roat
  • Sergiu Levcenco
  • Gerbrand Ceder
  • Thomas Unold
  • Tonio Buonassisi
Joule, vol. 1 (2017), pp. 843-856


In photovoltaic (PV) materials development, the complex relationship between device performance and underlying materials parameters obfuscates experimental feedback from current-voltage (J-V) characteristics alone. Here, we address this complexity by adding temperature and injection dependence and applying a Bayesian inference approach to extract multiple device-relevant materials parameters simultaneously. Our approach is an order of magnitude faster than the cumulative time of multiple individual spectroscopy techniques, with added advantages of using device-relevant materials stacks and interface conditions. We posit that this approach could be broadly applied to other semiconductor- and energy-device problems of similar complexity, accelerating the pace of experimental research.

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