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Atom Probe Tomography Method and Algorithm

Technology Benefits: - Eliminates the need to “choose” clustering parameters - Results are much more statistically reliable and more representative of actual data - User-selected parameters by statistical hypothesis test (by desired confidence level) - Add-on to current APT analysis packages

Technology Application: - Atom Probe Tomography

Detailed Technology Description: None

Others: Tech ID/UC Case
25706/2016-121-0

Related Cases
2016-121-0

*Abstract: Most cluster analysis parameters in atom probe tomography (APT) are selected ad hoc. This can often lead to data misinterpretation and misleading results by instrument technicians and researchers. Moreover, arbitrary cluster parameters can have suboptimal consequences on data quality and integrity, leading to inefficiencies for downstream data users. To address these problems, researchers at the University of California, Berkeley, have developed a framework and specific cluster analysis methods to efficiently extract knowledge from better APT data. By using parameter selection protocols with theoretical explanations, this technology allows for a more optimized and robust multivariate statistical analysis technique from the start, thus improving the quality of analysis and outcomes for both upstream and downstream data users.

*Principal Investigator: Name: Nathan Alexande Bailey
Department:

Name: Peter Martin Hosemann
Department:

Country/Region: USA

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