This paper proposes a high-precision characterization method for step height of micro-structured surfaces based on the Kmeans algorithm. First, the original measured surface data obtained by the three-dimensional surface measuring instrument is dimensionally reconstructed. Secondly, use the K-means-based clustering algorithm and data mapping to identify and remove outliers, and the centroid distance of the reconstructed data in the three-dimensional space is mapped to the required step height value. Finally, through the iterative convergence design, the accuracy and robustness of the algorithm characterization results are further improved. Experiment on simulated data shows that this method is robust against outliers. It can effectively and accurately characterize step heights for measurement data of big size. Also, the method can simultaneously ignore outliers during parameterization.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.