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%0 Thesis
%A Das, Basita
%T Defect tolerant device geometries for lead-halide perovskite solar cells
%I Rheinisch-Westfälische Technische Hochschule Aachen
%V Dissertation
%C Aachen
%M RWTH-2023-11522
%P 1 Online-Ressource : Illustrationen
%D 2022
%Z Veröffentlicht auf dem Publikationsserver der RWTH Aachen University 2024
%Z Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2022
%X Motivation, Goal and Task of the Dissertation: Traditional photovoltaic device optimization efforts rely on reducing defect density by passivation of surfaces as well as improved material processing or usage of defect tolerant materials. However, recombination activity of a defect is not only a function of defect kinetics but also depends on the electrostatics and the design of the layer stack of a photovoltaic device. In this thesis we aim to propose, develop and prove an alternative approach to solar cell device optimization, by combining our knowledge of defects in a material with the impact of device geometry on recombination via those defects. However, to develop such guiding principles, we must first understand the recombination kinetics of defect mediated recombination. Hence, the first task we undertake is to develop an analytical model that estimates the electron and hole capture coefficients of defects. Defect capture coefficients are difficult to be determined experimentally and in the absence of better information, they are often heuristically assumed in device optimization studies. The information on defect capture coefficients is critical to explore strategies for modifying device geometry that will deliver a better performing PV device. So, in our second task, we use the model of defect capture coefficient in combination of the photovoltaic device simulator to study the impact of device geometry on the recombination activity through the defects inside the device. Device geometry affects the electrostatics of a device which controls the electron and hole concentration inside a device. From our efforts to device optimization in the first and second task it is apparent that besides the knowledge of defect kinetics, one also needs the knowledge of material properties of the different layers as well as their interfaces to pinpoint the root cause of underperformance to successfully improve a real device. The electron and hole concentration along with the defect capture coefficients determine the amount of defect mediated recombination inside a device. So, in our third task we take a step further and perform root cause analysis of underperformance as well as parameter estimation in perovskite solar cells to make informed decision in our device optimization efforts.Major Scientific Contributions: In the course of my doctoral studies, I have achieved all the three goals outlined above. In my first task I developed a microscopic model to estimate defect capture coefficients within the limits of harmonic oscillator approximation. The model developed in this thesis is a step beyond the state of the art in the sense that it predicts asymmetric capture coefficients, which is agreement to the reported values of capture coefficients in literature for well studies materials like GaAs. For my second task I performed extensive device simulation to study the impact of device geometry both by changing the properties of the charge transport layers as well as the absorber layer of in a perovskite solar cell. In this task, I also managed to achieve agreement with experimental findings in methylammonium lead halide perovskite solar cells with high open-circuit voltage. Through this task, I was able to propose, develop and prove the effectiveness of the new concept of "Defect tolerant solar cell geometries" and provide definite guiding principles for future device optimization efforts. In my third task, I was able to implement and apply Bayesian inference for root cause analysis and parameter estimation of perovskite solar cells. Usage of Bayesian inference techniques on perovskite solar cells have not been reported before in literature. This task introduces the perovskite solar cell community to Bayesian inference as well as machine learning methods for faster and non-invasive determination of material properties as well as root cause analysis for underperformance. The three tasks undertaken in the thesis involves three different kinds of theory and simulation approaches all equally complex. The first task involved pure analytical modeling, whereas the second task is based on device modeling by solution of coupled differential equation. The third task involved implementation of a system with Bayesian inference algorithms in combination of with deep neural networks. Major Publications: In this thesis I have made three first author publication covering the studies of the first and second task. A fourth first author journal article introducing the open source code developed as a part of the third task is submitted and a fifth first author journal article is being written discussing parameter estimation in perovskite solar cells using Bayesian inference.Published:1. Das, B., Aguilera, I., Rau, U. Kirchartz, T. What is a deep defect? Combining Shockley-Read-Hall statistics with multiphonon recombination theory. Phys. Rev. Mater. 4, 024602 (2020). https://doi.org/10.1103/PhysRevMaterials.4.0246022.Das, B., Liu, Z., Aguilera, I., Rau, U. Kirchartz, T. Defect tolerant device geometries for lead-halide perovskites. Mater. Adv. 2, 3655–3670 (2021). https://doi.org/10.1039/D0MA00902D3.Das, B., Aguilera, I., Rau, U. Kirchartz, T. Effect of Doping, Photodoping, and Bandgap Variation on the Performance of Perovskite Solar Cells. Adv. Opt. Mater. 2101947 (2022). https://doi.org/10.1002/adom.202101947Unpublished:4. Das, B., Rau, U., Kirchartz, T. Buonassisi, T. BayesMC: Python package for doing parameter estimation using Bayesian inference with Markov Chain Monte Carlo.5. Das, B., Rau, U., Buonassisi, T. Kirchartz, T. , Parameter estimation for perovskite solar cells using Bayesian inference.
%F PUB:(DE-HGF)11
%9 Dissertation / PhD Thesis
%R 10.18154/RWTH-2023-11522
%U https://publications.rwth-aachen.de/record/974664