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Recent advances in machine learning (ML) have enabled predictive programs for photovoltaic characterization, optimization, and materials discovery. Despite these advances, the standard photovoltaic materials development workflow still involves manually performing multiple characterization techniques on every new device, requiring significant time and expenditures. One barrier to ML implementation is that most models reported to date are trained on computer simulated data, due to the difficulty in experimentally collecting the massive data sets needed for model training, limiting the ability to assess the limitations and validity of these methods, as well as to access new potential physical mechanisms absent in simulations. Herein, several neural networks trained on experimental data from PbS colloidal quantum dot thin‐film solar cells are introduced. These models predict multiple, complex materials properties, including carrier mobility, relative photoluminescence intensity, and electronic trap‐state density, from a single, simple measurement: illuminated current–voltage curves. The measurement system considers the spatial distribution of the materials parameters to gather and predict large amounts of data by treating an inhomogeneous device as a series of thousands of micro‐devices, a novel feature compared to existing solutions. This model can be extended to other materials and devices, accelerating development times for new optoelectronic technologies.
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Photoluminescence Excitation Spectroscopy Characterization of Surface and Bulk Quality for Early-Stage Potential of Material Systems
Abstract — Photoluminescence Excitation Spectroscopy (PLE) is a contactless characterization technique to quantify Shockley-Reed-Hall (SRH) lifetimes and recombination velocities in direct band gap experimental semiconductor materials and devices. It is also useful as to evaluate surface passivation and intermediate fabrication processes, since it can be implemented without the need for development of effective contact technologies. In this paper, we present a novel experimental PLE system for precision-based quantification of the aforementioned parameters as well as a system for which absolute PLE characterization may occur. Absolute PLE measurements can be used to directly calculate VOC for new photovoltaic (PV) material systems and devices. Key system capabilities include a continuous excitation spectrum from 300 nm –1.1 μm, automated characterization, up to 1 nm wavelength resolution (up to 60x higher than prior work), and a reduced ellipsometry requirement for post-processing of data. We utilize a GaAs double heterostructure (DH) and an InP crystalline wafer as calibration standards in comparison with data from an LED-based PLE to demonstrate the validity of the results obtained from this new system. Index Terms – photovoltaic cells, photoluminescence, charge carrier lifetime, gallium arsenide, indium phosphide.
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- Award ID(s):
- 1735282
- PAR ID:
- 10184483
- Date Published:
- Journal Name:
- Photoluminescence Excitation Spectroscopy Characterization of Surface and Bulk Quality for Early-Stage Potential of Material Systems
- Page Range / eLocation ID:
- 0377 to 0381
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/PVSC40753.2019.8980637
