Rapid Photovoltaic Device Characterization through Bayesian Parameter Estimation
Joule, vol. 1 (2017), pp.
R.E. Brandt, Rachel Kurchin, Vera Steinmann, Daniil Kitchaev, Chris Roat, Sergiu
Levcenco, Gerbrand Ceder, Thomas Unold, Tonio Buonassisi
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.