Regression analysis ofr characterization of materials for photovoltaic cells
Optoelectronic devices using solution processing semiconductor materials requires extensive experimental and data analysis in order to tune several interdependent material and process optimization parameters. Photo induced charge extraction by linearly increasing voltage is one of the popular exp...
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| Format: | Theses and Dissertations |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/78863 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Optoelectronic devices using solution processing semiconductor materials requires extensive
experimental and data analysis in order to tune several interdependent material and process
optimization parameters. Photo induced charge extraction by linearly increasing voltage is
one of the popular experimental methods to obtain optoelectronic material parameters in an
operational device. The traditional Design-of-Experiments (DoEs) adapted so far, are slow
and the information obtained sometimes bears little relevance to optimizing the key
performance parameters of merit. Thus this project aims to study machine learning
algorithms incorporated with Photo CELIV transients which can provide statistical
understanding of the device physics at varying operating conditions in order to expedite the
device development.
In this project the main optoelectronic property considered is effect of oxygen induced traps
on charge mobility in bulk hetero junction solar cells. This project studies mainly mobility of
solar cells at various output voltage characteristics variations at different input voltages and
different temperatures conditions using machine learning algorithms. |
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