Charge carrier transport and recombination in semiconductors: Insights from statistical analysis and machine learning techniques

This thesis pioneers techniques to understand charge carrier dynamics in semiconductor materials, with the goal of advancing next–generation optoelectronic devices, with three main thrusts explored. In Chapter 4, efficient exciton energy transport in thin Ruddlesden-Popper perovskite flakes is visualised and understood using time-resolved photoluminescence microscopy. Excitons traverse through static disorder landscapes at low temperatures and diffusively hop between traps at higher temperatures, offering insights for designing efficient optoelectronic devices based on Ruddlesden-Popper perovskites. Chapter 5 introduces a versatile method for extracting crucial information from time– resolved photoluminescence data in semiconductor materials. This physically motivated model based on Hamiltonian Monte Carlo and Bayesian Inference outperforms existing fitting techniques, providing accurate parameter estimation and error estimates, thus advancing time–resolved photoluminescence analysis for optoelectronics and beyond. In Chapter 6, focused and widefield excitations are compared for obtaining local TRPL data and photoluminescence intensity maps in different semiconductor materials. Widefield excitation with confocal collection emerges as the preferred approach for materials with longer diffusion lengths, offering superior performance by suppressing diffusive effects and enabling better signal acquisition. This research contributes to the advancement of optoelectronics by enhancing our understanding of charge carrier dynamics in semiconductor materials. The proposed techniques provide valuable insights into exciton behaviour and offer improved analysis of TRPL data. The findings have practical implications for optoelectronic device design and could pave the way for further developments in this field.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2023

Author dc:creator

• Baldwin, Alan

Advisor dc:contributor.advisor

• Stranks, Samuel

• Perovskite

Licence

• https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/a1db7b60-48e6-490b-a6d9-660e41aa55d4/download
• https://www.rioxx.net/licenses/all-rights-reserved/

Language dc:language

eng

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.109348

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/369627