3D Surface Reconstruction and 2D Segmentation of Scanning Electron Microscopy Images

Abstract

Scanning Electron Microscopy (SEM) used in a wide range of industrial and research applications. SEM produces greyscale images of a specimen by bombarding it with high-energy electrons, causing secondary and backscattered electrons and x-ray photons to be produced by several different mechanisms. Images obtained from SEM have information about the topography and composition of the sample. Because the electron beam must scan the sample in a serial, rostered fashion, SEM imaging is time consuming and expensive. In this thesis we give two methods of using mathematical optimization to extract extra information from SEM images. In the first part, we reconstruct three-dimensional images from two-dimensional SEM images acquired with different types of electron contrasts and in different orientations, reducing the need for costly Atomic Force Microscopy (AFM) probes or other 3-D metrology tools. In the second part, we accelerate the SEM process of imaging by reconstructing full images from images with only a fraction of the rows scanned, and apply this to the problem of segmenting images used in circuit analysis for reverse engineering. In the example images, we show that by simultaneously acquiring two images from different types of electron detectors, and skipping three out of four scan lines, we can segment the example images with partial images acquired in one quarter of the time. To use two sparse, simultaneously-acquired images to build the segmentation model, we construct a second optimization problem which optimally combines the two images taking into account the different signal and noise characteristics of the two images.