Presentation Title

Supervised STM Image Segmentation of Self-Assembled Molecule Layers

Presentation Type

Poster

Major

Mathematics

Abstract

Self-Assembled monolayers (SAMs) of cage molecules are useful in the construction of nanostructures and nanodevices. For the purposes of nanofabrication, it is useful to monitor the formation of defects in these SAMs and differences in molecular lattice orientation. Through autonomous and supervised image processing, this project aims to segment STM images of SAMs of cage molecules into domains of similar lattice orientation. Due to the translation invariance of the power spectra of the Fourier domain, it is possible to obtain a measure of the similarity or difference between the lattice orientations in two regions in an image and implement traditional clustering methods such as K mean and spectral clustering or more advanced methods such as Markov random fields. The results from the application of these methodologies are promising and could possibly replace manual segmentation as the industry standard.

Faculty Mentor

Timothy Lucas

Funding Source or Research Program

Summer Undergraduate Research Program, Undergraduate Research Fellowship

Location

Waves Cafeteria, Tyler Campus Center

Start Date

21-3-2014 2:00 PM

End Date

21-3-2014 3:00 PM

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Mar 21st, 2:00 PM Mar 21st, 3:00 PM

Supervised STM Image Segmentation of Self-Assembled Molecule Layers

Waves Cafeteria, Tyler Campus Center

Self-Assembled monolayers (SAMs) of cage molecules are useful in the construction of nanostructures and nanodevices. For the purposes of nanofabrication, it is useful to monitor the formation of defects in these SAMs and differences in molecular lattice orientation. Through autonomous and supervised image processing, this project aims to segment STM images of SAMs of cage molecules into domains of similar lattice orientation. Due to the translation invariance of the power spectra of the Fourier domain, it is possible to obtain a measure of the similarity or difference between the lattice orientations in two regions in an image and implement traditional clustering methods such as K mean and spectral clustering or more advanced methods such as Markov random fields. The results from the application of these methodologies are promising and could possibly replace manual segmentation as the industry standard.