Regression analysis ofr characterization of materials for photovoltaic cells

• Main Author: Vundavalli, Bala Satya Manikanta
• Other Authors: Arokiaswami Alphones
• Format: Theses and Dissertations
• Language: English
• Published: 2019
• Subjects: Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics
• Online Access: http://hdl.handle.net/10356/78863
• Institution: Nanyang Technological University

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.