Large Scale Light Trapping Nanostructures for Thin c-Si Solar Cells

Download files
Access & Terms of Use
open access
Embargoed until 2020-04-01
Copyright: Chang, Yuan-Chih
Altmetric
Abstract
The photovoltaic market has maintained rapid growth over the last two decades and is strongly dominated by Si with record cell efficiencies over 25% reported. These devices are approaching their theoretical efficiency limits and therefore the focus research on reducing the energy and material cost in fabrication becomes increasingly important. Cheaper solar cells could be achieved by reducing the absorber thickness as long as device efficiency is not negatively impacted, and this requires both good light-trapping (LT) and surface passivation. Conventional surface texturing can provide significant diffuse scattering for short wavelengths but the rough surface also potentially leads to increased surface recombination. This is also an issue for emerging tandem cell devices that would require a planar front for subsequent top cell depositions. Alternatively, plasmonic and diffraction gratings allow the wavelength of peak scattering to be tuned close to the bandgap without intrinsically affecting the surface passivation. In the last decade, numerous possible designs relying on plasmonics or/and diffraction gratings for enhancing light-trapping in thin solar cells have been reported. However, the conflict between efficiency gain, fabrication cost and controllability has prevented the commercial use. In this work, nanosphere lithography (NSL) has been investigated for large-area low-cost controllable fabrication of nanostructures suitable for incorporation in thin solar cells. A variety of periodic plasmonic nanostructures have been fabricated in order to demonstrate the wide usability of NSL fabrication techniques. These selected designs were first simulated using finite difference time domain methods to optimize the initial testbed fabrication efforts. Increased absorption has been observed from most testbed devices. Further investigation of the cost-effective incorporation of LT nanostructures into ultrathin c-Si solar cells was focused on the embedded nanosphere back-reflector structure. As part of this study, a novel Si wafer thinning technique was successfully developed to allow high quality and polished ultrathin c-Si wafers to be reliably produced in batches with an effective control of the resulting wafer thickness down to a few tens of micron. Comprehensive optical characterization was carried out on the final optical samples and the result show good agreement with the simulation indicating the fine controllability of the fabrication process. Furthermore, the test structures were found to yield an increased average absorption up to 11.1% (300-1200nm) which could potentially lead to a relative increase in photocurrent density of up to 4.33mA/cm2) in comparison to a planar 30?m-thick device. This work provides a promising approach to light-trapping in thin silicon solar cells but in order for this to be a competitive technology, more work is required to find a large-scale and cost-effective procedure for the production of thin wafers and solar cells, while further optimization of the light-trapping is still required in order to maintain efficiencies at necessary levels. Throughout this work, different nanoscale LT mechanisms, economical and scalable fabrication methods, and related in-line characterization techniques have been systematically studied that could be applied on a board range of thin solar cells.
Persistent link to this record
Link to Publisher Version
Link to Open Access Version
Additional Link
Author(s)
Chang, Yuan-Chih
Supervisor(s)
Pilla, Supriya
Bagnall, Darren
Payne, David
Pollard, Michael
Creator(s)
Editor(s)
Translator(s)
Curator(s)
Designer(s)
Arranger(s)
Composer(s)
Recordist(s)
Conference Proceedings Editor(s)
Other Contributor(s)
Corporate/Industry Contributor(s)
Publication Year
2018
Resource Type
Thesis
Degree Type
PhD Doctorate
UNSW Faculty
Files
download public version.pdf 17 MB Adobe Portable Document Format
Related dataset(s)