Neural Network Based Image Modification for Shape from Observed SEM Images

Abstract

A new approach to recover 3-D shape from a Scanning Electron Microscope (SEM) image is described. With an ideal SEM image, 3-D shape can be recovered using the Fast Marching Method (FMM) applied to the Eikonal equation. However, when the light source direction is oblique, the correct shape cannot be obtained by the usual one-pass FMM. The new approach modifies the intensities in the original SEM image using an additional SEM image of a sphere and Neural Network (NN) training. Image modification is a two degree-of-freedom (DOF) rotation. No assumption is made about the specific functional form for intensity in an SEM image. The correct 3-D shape can be obtained using the FMM and NN learning, without iteration. The approach is demonstrated through computer simulation and validated through real experiment.