What is it about?
SEM images provide valuable information about patterning capability. Geometrical properties such as Critical Dimension (CD) can be extracted from them and are used to calibrate OPC models, thus making OPC more robust and reliable. However, there is currently a shortage of appropriate metrology tools to inspect complex two-dimensional patterns in the same way as one would work with simple one-dimensional patterns. In this article we present a full framework for the analysis of SEM images. It has been proven to be fast, reliable and robust for every type of structure, and particularly for two-dimensional structures.
Why is it important?
To achieve this result, several innovative solutions have been developed and are presented in the article. Firstly, we will present a new noise filter which is used to reduce noise on SEM images, followed by an efficient topography identifier, and finally we will describe the use of a topological skeleton as a measurement tool that can extend CD measurements on all kinds of patterns. Also we present a clever use of phylogenetic trees to sort SEM images according to their geometrical proximities without any need of target or reference. Moreover, it does not need any input from user to obtain a classification of images.
The following have contributed to this page: Mr Loïc Schneider