Three-dimensional visualization of large-area, nanoscale topography measurements

High-resolution metrology is a critical area of development for nanoscale manufacturing, especially as it affects production throughput and fabrication quality. Atomic force microscopy (AFM) is one of the most popular tools for nanometrology, and high-resolution AFM often requires a significant time commitment and produces datasets of several million points. It is therefore critical for the development of data processing techniques to keep pace with the requirements of analyzing this type of data, and for these techniques to be portable as miniaturization in AFM is becoming more common. This work presents a data fitting algorithm designed for reducing the parameters of large-area data sets which utilizes well-established spline fitting techniques. In this paper we show that basis-spline fitting can be used to accurately represent large AFM data sets, including data sets with noisy data and sharp features, while achieving at least 90% parameter reduction in all test cases.