Fabrication imperfection analysis and statistics generation using precision and reliability optimization method

Abstract

This paper applies a recently proposed dominant point detection method - precision and reliability optimization (PRO) - for representing shapes in the microscopy images of fabricated structures. This method uses both the local and the global nature of fit for dominant point detection. A smaller value of its control parameter better represents the local curvature properties of the shape while a larger value better indicates the global curvature properties. The applicability of this method to a wide range of microscopy images is demonstrated using four microscopy examples of brightness enhancement films, electromagnetic and photonic band gap materials, and aspherical mirror alignments. It is shown that PRO can clearly highlight several image effects and imperfections which may not be easily identifiable by human eye or may be difficult to analyze and assess. Further, for large scale arrays, it can be used to generate useful fabrication accuracy statistics and detect features with low fidelity or more imperfections.