Matching Items (2)
Description
In polycrystalline thin-film cadmium telluride (CdTe) solar cells, atomic defects (dopants: copper (Cu), arsenic (As); and selenium (Se) alloy) have significantly enhanced hole density and minority carrier lifetime. Density functional theory (DFT) has predicted the atomic configurations of relevant defects and their electronic structures. Yet, experimental evidence of the defects, especially their spatial distribution across the absorber, is still lacking. Herein, since it can probe local atomic structure of elements of interest with trace-elemental sensitivity, nanoprobe X-ray absorption near edge structure (XANES) spectroscopy was used to elucidate atomic structures of Cu, As, and Se. After XANES spectra were measured from CdTe devices, the atomic information was extracted from the measured spectra by fitting them with reference spectra, which were simulated from 1) point defects and grain boundaries (GBs) predicted by DFT; 2) secondary phases which could form under processing conditions. XANES analysis of various device architectures revealed structural inhomogeneities across the absorbers from point defects to secondary phases. The majority of the Cu dopant atoms form secondary phases with surrounding atoms even inside the absorbers, explaining the low dopant activation. When entering the target lattice site (Cd), Cu forms a complex with chlorine (Cl) and becomes a donor defect, compensating hole density. Compared to Cu, As dopant tends to enter the target site (Te) more frequently, explaining higher hole density in As-doped CdTe. Notably, As on the Te site forms neutral charged complexes with Cl. Although they are not as detrimental as the Cu-Cl complex, the As-Cl complexes may be responsible for low dopant activation and compensation observed in As-doped CdTe devices. Complementary to the DFT prediction, this work provided the distribution of Se local structures across the absorber, specifically the variation of Se-Cd bond lengths in differently performing areas. Under environmental stressors (heat and light), it showed atomic reconfiguration of Se and Cl at GBs, and Se diffusion into the bulk, co-occurring with device degradation. This framework was also extended to study defect evolution in other thin-film solar cells (CIGS and emerging perovskite). XANES analysis has shed light on atomic defects governing solar cell performance and stability, which are crucial in pushing the efficiency toward the theoretical efficiency limit.
ContributorsRojsatien, Srisuda (Author) / Bertoni, Mariana I. (Thesis advisor) / Mannodi-Kanakkithodi, Arun (Committee member) / Mu, Linqin (Committee member) / Holman, Zachary (Committee member) / Arizona State University (Publisher)
Created2024
Description
It is well known that the overall performance of a solar cell is limited by the worst performing areas of the device. These areas are usually micro and nano-scale defects inhomogenously distributed throughout the material. Mitigating and/or engineering these effects is necessary to provide a path towards increasing the efficiency of state-of-the-art solar cells. The first big challenge is to identify the nature, origin and impact of such defects across length scales that span multiple orders of magnitude, and dimensions (time, temperature etc.). In this work, I present a framework based on correlative X-ray microscopy and big data analytics to identify micro and nanoscale defects and their impact on material properties in CuIn1-xGaxSe2 (CIGS) solar cells.
Synchrotron based X-ray Fluorescence (XRF) and X-ray Beam Induced Current (XBIC) are used to study the effect that compositional variations, between grains and at grain boundaries, have on CIGS device properties. An experimental approach is presented to correcting XRF and XBIC quantification of CIGS thin film solar cells. When applying XRF and XBIC to study low and high gallium CIGS devices, it was determined that increased copper and gallium at grain boundaries leads to increased collection efficiency at grain boundaries in low gallium absorbers. However, composition variations were not correlated with changes in collection efficiency in high gallium absorbers, despite the decreased collection efficiency observed at grain boundaries.
Understanding the nature and impact of these defects is only half the battle; controlling or mitigating their impact is the next challenge. This requires a thorough understanding of the origin of these defects and their kinetics. For such a study, a temperature and atmosphere controlled in situ stage was developed. The stage was utilized to study CIGS films during a rapid thermal growth process. Comparing composition variations across different acquisition times and growth temperatures required the implementation of machine learning techniques, including clustering and classification algorithms. From the analysis, copper was determined to segregate the faster than indium and gallium, and clustering techniques showed consistent elemental segregation into copper rich and copper poor regions. Ways to improve the current framework and new applications are also discussed.
Synchrotron based X-ray Fluorescence (XRF) and X-ray Beam Induced Current (XBIC) are used to study the effect that compositional variations, between grains and at grain boundaries, have on CIGS device properties. An experimental approach is presented to correcting XRF and XBIC quantification of CIGS thin film solar cells. When applying XRF and XBIC to study low and high gallium CIGS devices, it was determined that increased copper and gallium at grain boundaries leads to increased collection efficiency at grain boundaries in low gallium absorbers. However, composition variations were not correlated with changes in collection efficiency in high gallium absorbers, despite the decreased collection efficiency observed at grain boundaries.
Understanding the nature and impact of these defects is only half the battle; controlling or mitigating their impact is the next challenge. This requires a thorough understanding of the origin of these defects and their kinetics. For such a study, a temperature and atmosphere controlled in situ stage was developed. The stage was utilized to study CIGS films during a rapid thermal growth process. Comparing composition variations across different acquisition times and growth temperatures required the implementation of machine learning techniques, including clustering and classification algorithms. From the analysis, copper was determined to segregate the faster than indium and gallium, and clustering techniques showed consistent elemental segregation into copper rich and copper poor regions. Ways to improve the current framework and new applications are also discussed.
ContributorsWest, Bradley (Author) / Bertoni, Mariana I (Thesis advisor) / Verebelyi, Darren (Committee member) / Holman, Zachary (Committee member) / Rose, Volker (Committee member) / Arizona State University (Publisher)
Created2018