Precise 3-D microscopic profilometry using diffractive image microscopy and artificial neural network in single-exposure manner

Abstract

A single-exposure microscopic profilometry using artificial neural network (ANN) was developed for 3-D profile reconstruction of precise surface geometry. Optical profilometry is an important technique widely applied in many fields. However, in addition to optical aberration of the microscope itself, tilting of a local testing surface can affect the direction of the reflected light, thus causing undesired measurement errors. To overcome these problems, various methods or optical strategies have been developed, but most of them suffer from undue measurement errors attributed to the local surface gradient formed by the measured point and its neighboring surface geometry. This study proposed a novel method that combined diffractive image microscopy (DIM) with ANN. To train the ANN model, diffractive images of various surface orientations were collected for learning the complex mapping relationships between diffractive images and their corresponding surface orientations. The proposed method could simultaneously measure the height, tilting angle and tilting direction of a local surface. 3-D surface reconstruction by the proposed method required no prior knowledge of neighboring geometric information adjacent to the measured point. Using the developed approach achieved a surface height repeatability of 0.257 μm and a surface inclined angle repeatability of 0.037°, evidencing the realization of precise 3-D microscopic profilometry.