Accuracy-enhanced diffraction image profilometry using foreign aberration for resolving image ambiguity

A new optical surface measuring method based on artificial neural network (ANN) is developed for accuracy enhancement by introducing external optical aberration to a microscope. According to the diffraction theory, the diffractive images formed in the microscope can mainly depend on the microscopic optical system and the surface features of the tested object. Up to now, the most critical issue affecting the measurement accuracy of the diffractive image profilometry (DIP) is that the uniqueness of the diffractive images corresponding to various surface geometric parameters such as different heights and orientations cannot be always guaranteed. This situation can bring undesired uncertainties in surface measurement since undesired ambiguity in image correlation or model estimation may be introduced. To resolve this, a designed foreign aberration is introduced into the microscopic optical system to develop the feature variance of diffractive images for significantly increasing the degree of the image variance, therefore the risk of ambiguity is effectively avoided. The significance of the developed approach would be its capability in truly three-dimensional surface reconstruction.

Automated summary

A new optical surface measuring method based on artificial neural network is developed to enhance accuracy by introducing external optical aberration to a microscope, effectively avoiding the risk of ambiguity and achieving true three-dimensional surface reconstruction.