Reflectance anisotropy spectroscopy (RAS) for III/V semiconductor crystal dry-etching (RIE) for in-situ identification of self-organized roughness (roughness-RIE-RAS)
Reflectance anisotropy spectroscopy (RAS) for III/V semiconductor crystal dry-etching (RIE) for in-situ identification of self-organized roughness (roughness-RIE-RAS)
批准号:
423491951
负责人:
Professor Dr. Henning Fouckhardt
金额:
$0.0万
依托单位国家:
德国
项目类别:
Research Grants
财政年份:
2019
资助国家:
德国
项目状态:
已结题
起止时间:
2018-12-31 至 2022-12-31
中文摘要
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英文摘要
In certain parameter ranges maskless (maybe reactive) ion (beam) dry-etching (IE, IBE, RIE, RIBE) e.g. of semiconductor surfaces can result in characteristic morphologies like cones or wavelike features with typical characteristic lengths of 10-100 nm. The reason for these findings is self-organization. The characteristic structures are irregularly arranged on the surface, but lie dense in the plane. After a short etch time the morphologies stay stable and further on the surface is only etched as a whole.Reflection anisotropy spectroscopy (RAS) has already been developed to a powerful surface-sensitive optical measurement technique for the in-situ monitoring of epitaxial semiconductor growth. But in the applicant’s research group also already many successful attempts have been pursued to use RAS for the monitoring of dry-etch processes, so far mainly for etch parameters, which result in smooth etch fronts (so far to control etch-depth precisely), but rudimentarily also already in parameter ranges, where certain self-organized surface roughness morphologies evolve.Depending on device applications self-organized surface-structuring is disadvantageous or advantageous. Ideally its occurrence can be suppressed or promoted in real time during the etch process. RAS could provide for in-situ monitoring and control of the surface, allowing to make appropriate etch parameter changes. This is the main idea of this proposal.In particular investigations on the question are to be performed, whether and how RAS is suitable as an in-situ tool for identification and classification of these self-organized surface roughness morphologies.
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