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

Abstract

High-resolution nanometrology is a critical area of development for nanoscale manufacturing, especially as it affects production throughput and fabrication quality. Similarly, large area nanometrology is an emerging topic of interest particularly as demand rises for flexible electronics and large area microdevices. Atomic force microscopy (AFM) is one of the most popular tools for nanometrology, and high-resolution AFM scanning often requires a significant time commitment and often produces datasets of several million points. In large area AFM there is additional error introduced by long-range stage movements, and for larger datasets this presents challenges during analysis. 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, written in python, and utilizing well-established spline fitting techniques. This algorithm can reduce the number of parameters for a dataset by more than 90%, smooths vibrational noise, and simplifies visual analysis. The results of this work augment the current nanometrology analysis programs and will potentially drive forward the expansion of very large-area AFM to broader usage.