Machine learning enabled characterization of charge transport in organic semiconducting devices
While organic semiconducting materials have found immense potential in device applications such as field-effect transistors and photovoltaics in the past few decades, the mechanism of charge transport in these devices has not been fully understood. A scrutiny of the existing models suggests that the...
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sg-ntu-dr.10356-1618782022-10-04T01:04:35Z Machine learning enabled characterization of charge transport in organic semiconducting devices Lakshminarayanan, Madhavkrishnan Leong Wei Lin School of Electrical and Electronic Engineering wlleong@ntu.edu.sg Engineering::Electrical and electronic engineering::Semiconductors Engineering::Materials::Organic/Polymer electronics While organic semiconducting materials have found immense potential in device applications such as field-effect transistors and photovoltaics in the past few decades, the mechanism of charge transport in these devices has not been fully understood. A scrutiny of the existing models suggests that there is no unified approach to characterize transport in these materials. With machine learning algorithms that learn input-output relationships, device data can aid in the determination of transport parameters, offering physical insights about transport and material design. Furthermore, these techniques can aid in improving device figure-of-merit by screening for novel materials. In this thesis, we examine the extent of applicability of conventional theories on transport in semiconducting devices. We demonstrate different data-driven and physics-inspired approaches, independent of conventional theories, to characterise transport using fewer parameters. We also study structure at the molecular level by employing machine learning methods to predict thermoelectric properties of single molecular junctions. Doctor of Philosophy 2022-09-23T00:55:10Z 2022-09-23T00:55:10Z 2022 Thesis-Doctor of Philosophy Lakshminarayanan, M. (2022). Machine learning enabled characterization of charge transport in organic semiconducting devices. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/161878 https://hdl.handle.net/10356/161878 10.32657/10356/161878 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University |
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Engineering::Electrical and electronic engineering::Semiconductors Engineering::Materials::Organic/Polymer electronics Lakshminarayanan, Madhavkrishnan Machine learning enabled characterization of charge transport in organic semiconducting devices |
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While organic semiconducting materials have found immense potential in device applications such as field-effect transistors and photovoltaics in the past few decades, the mechanism of charge transport in these devices has not been fully understood. A scrutiny of the existing models suggests that there is no unified approach to characterize transport in these materials. With machine learning algorithms that learn input-output relationships, device data can aid in the determination of transport parameters, offering physical insights about transport and material design. Furthermore, these techniques can aid in improving device figure-of-merit by screening for novel materials. In this thesis, we examine the extent of applicability of conventional theories on transport in semiconducting devices. We demonstrate different data-driven and physics-inspired approaches, independent of conventional theories, to characterise transport using fewer parameters. We also study structure at the molecular level by employing machine learning methods to predict thermoelectric properties of single molecular junctions. |
author2 |
Leong Wei Lin |
author_facet |
Leong Wei Lin Lakshminarayanan, Madhavkrishnan |
format |
Thesis-Doctor of Philosophy |
author |
Lakshminarayanan, Madhavkrishnan |
author_sort |
Lakshminarayanan, Madhavkrishnan |
title |
Machine learning enabled characterization of charge transport in organic semiconducting devices |
title_short |
Machine learning enabled characterization of charge transport in organic semiconducting devices |
title_full |
Machine learning enabled characterization of charge transport in organic semiconducting devices |
title_fullStr |
Machine learning enabled characterization of charge transport in organic semiconducting devices |
title_full_unstemmed |
Machine learning enabled characterization of charge transport in organic semiconducting devices |
title_sort |
machine learning enabled characterization of charge transport in organic semiconducting devices |
publisher |
Nanyang Technological University |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/161878 |
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1746219645269966848 |