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Title: Rapid Photovoltaic Device Characterization through Bayesian Parameter Estimation

Abstract

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. Photovoltaic (PV) research has historically taken decades to bring new materials to market, as the pace of development is often limited by our ability to identify the causes of underperformance. There are many ways to make an underperforming PV cell, and their signatures on device efficiency alone are not unique. This uncertainty plagues other energy-storage or -conversion devices with similarly complex combinations of materials but may be used to our advantage. By probing or biasing a PV cell in different operating conditions, the signature of underlying material properties becomes more unique. This connection between the operating conditions, the material properties, andmore » the PV cell output may be solved through Bayesian inference algorithms. While computationally expensive, high-performance computing can enable such inference as a tool for experimentalists, a tool that could become increasingly valuable for accelerating the pace of materials research in PV and related fields. High-performance computing can greatly improve the workflow of experimentalists in energy materials, through the use of Bayesian inference. This allows us to solve the inverse problem of extracting underlying materials properties through the measurement of the electrical behavior of completed devices. Cheaper, faster measurements can be substituted for longer direct measurements of individual properties, without sacrificing accuracy or precision. Here, we provide a general framework to apply this to other materials systems and devices.« less

Authors:
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Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1548997
Alternate Identifier(s):
OSTI ID: 1476586
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Published Article
Journal Name:
Joule
Additional Journal Information:
Journal Name: Joule Journal Volume: 1 Journal Issue: 4; Journal ID: ISSN 2542-4351
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
30 DIRECT ENERGY CONVERSION; thin-film photovoltaics; device modeling; Bayesian inference; photovoltaics; Bayesian parameter estimation; inference; big data; high-throughput device modeling; temperature dependence; HPC; tin; sulfide

Citation Formats

Brandt, Riley E., Kurchin, Rachel C., Steinmann, Vera, Kitchaev, Daniil, Roat, Chris, Levcenco, Sergiu, Ceder, Gerbrand, Unold, Thomas, and Buonassisi, Tonio. Rapid Photovoltaic Device Characterization through Bayesian Parameter Estimation. United States: N. p., 2017. Web. doi:10.1016/j.joule.2017.10.001.
Brandt, Riley E., Kurchin, Rachel C., Steinmann, Vera, Kitchaev, Daniil, Roat, Chris, Levcenco, Sergiu, Ceder, Gerbrand, Unold, Thomas, & Buonassisi, Tonio. Rapid Photovoltaic Device Characterization through Bayesian Parameter Estimation. United States. https://doi.org/10.1016/j.joule.2017.10.001
Brandt, Riley E., Kurchin, Rachel C., Steinmann, Vera, Kitchaev, Daniil, Roat, Chris, Levcenco, Sergiu, Ceder, Gerbrand, Unold, Thomas, and Buonassisi, Tonio. Fri . "Rapid Photovoltaic Device Characterization through Bayesian Parameter Estimation". United States. https://doi.org/10.1016/j.joule.2017.10.001.
@article{osti_1548997,
title = {Rapid Photovoltaic Device Characterization through Bayesian Parameter Estimation},
author = {Brandt, Riley E. and Kurchin, Rachel C. and Steinmann, Vera and Kitchaev, Daniil and Roat, Chris and Levcenco, Sergiu and Ceder, Gerbrand and Unold, Thomas and Buonassisi, Tonio},
abstractNote = {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. Photovoltaic (PV) research has historically taken decades to bring new materials to market, as the pace of development is often limited by our ability to identify the causes of underperformance. There are many ways to make an underperforming PV cell, and their signatures on device efficiency alone are not unique. This uncertainty plagues other energy-storage or -conversion devices with similarly complex combinations of materials but may be used to our advantage. By probing or biasing a PV cell in different operating conditions, the signature of underlying material properties becomes more unique. This connection between the operating conditions, the material properties, and the PV cell output may be solved through Bayesian inference algorithms. While computationally expensive, high-performance computing can enable such inference as a tool for experimentalists, a tool that could become increasingly valuable for accelerating the pace of materials research in PV and related fields. High-performance computing can greatly improve the workflow of experimentalists in energy materials, through the use of Bayesian inference. This allows us to solve the inverse problem of extracting underlying materials properties through the measurement of the electrical behavior of completed devices. Cheaper, faster measurements can be substituted for longer direct measurements of individual properties, without sacrificing accuracy or precision. Here, we provide a general framework to apply this to other materials systems and devices.},
doi = {10.1016/j.joule.2017.10.001},
journal = {Joule},
number = 4,
volume = 1,
place = {United States},
year = {2017},
month = {12}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1016/j.joule.2017.10.001

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