Path integral quantum Monte Carlo for semiconductor nanostructures
Path integral quantum Monte Carlo (PI-QMC) is a powerful technique, which can be used to model the properties of multiple interacting particles at finite temperatures. In this work path integral quantum Monte Carlo has been applied to the problem of few particle interactions in quantum dots and othe...
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ndltd-bl.uk-oai-ethos.bl.uk-4913222015-03-20T03:52:51ZPath integral quantum Monte Carlo for semiconductor nanostructuresGillies, Patrick R.2007Path integral quantum Monte Carlo (PI-QMC) is a powerful technique, which can be used to model the properties of multiple interacting particles at finite temperatures. In this work path integral quantum Monte Carlo has been applied to the problem of few particle interactions in quantum dots and other semiconductor nanostructures. Quantum dots are currently the subject of much research and in order to further understand their properties it is necessary to perform theoretical modelling. In this work, the method by which the problem of the attractive Coulomb potential was overcome is detailed. Following that, comparisons are made between . experimental data and PI-QMC results for excitonic complexes in 111-V dots. Both the energies and voltage extents were found to show good agreement between experiment and theory. Comparisons are also between theory and experiment of II-VI, with experimental data using a harmonic potential to model the dot. Again, good agreement is seen. Finally, as an example of the power of PI-QMC, the behaviour of electrons and holes is modelled for alternative nanostructures, such as coupled quantum dots, quantum rings and core-shell structures. With some simple modifications, the same PI-QMC method could be used.537.622Heriot-Watt Universityhttp://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.491322http://hdl.handle.net/10399/2033Electronic Thesis or Dissertation |
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537.622 Gillies, Patrick R. Path integral quantum Monte Carlo for semiconductor nanostructures |
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Path integral quantum Monte Carlo (PI-QMC) is a powerful technique, which can be used to model the properties of multiple interacting particles at finite temperatures. In this work path integral quantum Monte Carlo has been applied to the problem of few particle interactions in quantum dots and other semiconductor nanostructures. Quantum dots are currently the subject of much research and in order to further understand their properties it is necessary to perform theoretical modelling. In this work, the method by which the problem of the attractive Coulomb potential was overcome is detailed. Following that, comparisons are made between . experimental data and PI-QMC results for excitonic complexes in 111-V dots. Both the energies and voltage extents were found to show good agreement between experiment and theory. Comparisons are also between theory and experiment of II-VI, with experimental data using a harmonic potential to model the dot. Again, good agreement is seen. Finally, as an example of the power of PI-QMC, the behaviour of electrons and holes is modelled for alternative nanostructures, such as coupled quantum dots, quantum rings and core-shell structures. With some simple modifications, the same PI-QMC method could be used. |
author |
Gillies, Patrick R. |
author_facet |
Gillies, Patrick R. |
author_sort |
Gillies, Patrick R. |
title |
Path integral quantum Monte Carlo for semiconductor nanostructures |
title_short |
Path integral quantum Monte Carlo for semiconductor nanostructures |
title_full |
Path integral quantum Monte Carlo for semiconductor nanostructures |
title_fullStr |
Path integral quantum Monte Carlo for semiconductor nanostructures |
title_full_unstemmed |
Path integral quantum Monte Carlo for semiconductor nanostructures |
title_sort |
path integral quantum monte carlo for semiconductor nanostructures |
publisher |
Heriot-Watt University |
publishDate |
2007 |
url |
http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.491322 |
work_keys_str_mv |
AT gilliespatrickr pathintegralquantummontecarloforsemiconductornanostructures |
_version_ |
1716782764668223488 |