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A statistical method to optimize the chemical etching process of zinc oxide thin films

Zinc oxide (ZnO) is an attractive material for microscale and nanoscale devices. Its desirable semiconductor, piezoelectric and optical properties make it useful in applications ranging from microphones to missile warning systems to biometric sensors. This work introduces a demonstration of blending...

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Autores principales: Lynes, David D., Chandrahalim, Hengky, Brown, Justin M., Singh, Karanvir, Bodily, Kyle T., Leedy, Kevin D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9346367/
https://www.ncbi.nlm.nih.gov/pubmed/35950193
http://dx.doi.org/10.1098/rsos.211560
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author Lynes, David D.
Chandrahalim, Hengky
Brown, Justin M.
Singh, Karanvir
Bodily, Kyle T.
Leedy, Kevin D.
author_facet Lynes, David D.
Chandrahalim, Hengky
Brown, Justin M.
Singh, Karanvir
Bodily, Kyle T.
Leedy, Kevin D.
author_sort Lynes, David D.
collection PubMed
description Zinc oxide (ZnO) is an attractive material for microscale and nanoscale devices. Its desirable semiconductor, piezoelectric and optical properties make it useful in applications ranging from microphones to missile warning systems to biometric sensors. This work introduces a demonstration of blending statistics and chemical etching of thin films to identify the dominant factors and interaction between factors, and develop statistically enhanced models on etch rate and selectivity of c-axis-oriented nanocrystalline ZnO thin films. Over other mineral acids, ammonium chloride (NH(4)Cl) solutions have commonly been used to wet etch microscale ZnO devices because of their controllable etch rate and near-linear behaviour. Etchant concentration and temperature were found to have a significant effect on etch rate. Moreover, this is the first demonstration that has identified multi-factor interactions between temperature and concentration, and between temperature and agitation. A linear model was developed relating etch rate and its variance against these significant factors and multi-factor interactions. An average selectivity of 73 : 1 was measured with none of the experimental factors having a significant effect on the selectivity. This statistical study captures the significant variance observed by other researchers. Furthermore, it enables statistically enhanced microfabrication processes for other materials.
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spelling pubmed-93463672022-08-09 A statistical method to optimize the chemical etching process of zinc oxide thin films Lynes, David D. Chandrahalim, Hengky Brown, Justin M. Singh, Karanvir Bodily, Kyle T. Leedy, Kevin D. R Soc Open Sci Chemistry Zinc oxide (ZnO) is an attractive material for microscale and nanoscale devices. Its desirable semiconductor, piezoelectric and optical properties make it useful in applications ranging from microphones to missile warning systems to biometric sensors. This work introduces a demonstration of blending statistics and chemical etching of thin films to identify the dominant factors and interaction between factors, and develop statistically enhanced models on etch rate and selectivity of c-axis-oriented nanocrystalline ZnO thin films. Over other mineral acids, ammonium chloride (NH(4)Cl) solutions have commonly been used to wet etch microscale ZnO devices because of their controllable etch rate and near-linear behaviour. Etchant concentration and temperature were found to have a significant effect on etch rate. Moreover, this is the first demonstration that has identified multi-factor interactions between temperature and concentration, and between temperature and agitation. A linear model was developed relating etch rate and its variance against these significant factors and multi-factor interactions. An average selectivity of 73 : 1 was measured with none of the experimental factors having a significant effect on the selectivity. This statistical study captures the significant variance observed by other researchers. Furthermore, it enables statistically enhanced microfabrication processes for other materials. The Royal Society 2022-08-03 /pmc/articles/PMC9346367/ /pubmed/35950193 http://dx.doi.org/10.1098/rsos.211560 Text en © 2022 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Chemistry
Lynes, David D.
Chandrahalim, Hengky
Brown, Justin M.
Singh, Karanvir
Bodily, Kyle T.
Leedy, Kevin D.
A statistical method to optimize the chemical etching process of zinc oxide thin films
title A statistical method to optimize the chemical etching process of zinc oxide thin films
title_full A statistical method to optimize the chemical etching process of zinc oxide thin films
title_fullStr A statistical method to optimize the chemical etching process of zinc oxide thin films
title_full_unstemmed A statistical method to optimize the chemical etching process of zinc oxide thin films
title_short A statistical method to optimize the chemical etching process of zinc oxide thin films
title_sort statistical method to optimize the chemical etching process of zinc oxide thin films
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9346367/
https://www.ncbi.nlm.nih.gov/pubmed/35950193
http://dx.doi.org/10.1098/rsos.211560
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