A Statistical Method to Optimize the Chemical Etching Process of Zinc Oxide Thin Films

Authors

David D. Lynes, Air Force Institute of Technology
Hengky Chandrahalim, Air Force Institute of TechnologyFollow
Justin M. Brown, Air Force Institute of Technology
Karanvir Singh, Air Force Institute of Technology
Kyle T. Bodily, Air Force Institute of Technology
Kevin D. Leedy, Air Force Institute of Technology

Document Type

Article

Publication Date

8-3-2022

Abstract

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 (NH₄Cl) 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.

Comments

Copyright statement: © 2022 The Authors.

Published by the Royal Society, licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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DOI

10.1098/rsos.211560

Source Publication

Royal Society Open Science

Recommended Citation

David D. Lynes, Hengky Chandrahalim, Justin M. Brown, Karanvir Singh, Kyle T. Bodily, and Kevin D. Leedy, "A statistical method to optimize the chemical etching process of zinc oxide thin films," R. Soc. Open Sci., 9 (8), 2022, pp. 211560.