Advanced image analysis of semiconductor structures
Lead Research Organisation:
University of Strathclyde
Department Name: Physics
Abstract
Recent advances in algorithms and computing power have produced new methods which, for some problems, can outperform human expertise in data analysis, image processing and pattern recognition. These techniques have been used to examine and analyse features in single and multiple images, as well as video footage. These machine learning approaches are beginning to revolutionise areas including astronomy, medicine and machine vision. This project aims to apply some of these new techniques to rapidly and accurately examine experimental data generated by the Semiconductor and Spectroscopy (SSD) Group using nanoscale scanning electron probe and microscopy measurements of semiconductor nano-structures.
This project will consist of developing new standard techniques for image processing of single and multi-image data sets. This will be coupled with existing hyperspectral analysis tools and modern machine learning to extract meaningful information on the morphology and spectral behaviour of nanoscale features of advanced materials. Specifically analysis of the the structural and correlated spectral properties of nitride semiconductor nanostructures will be accelerated by reducing the level of human intervention in finding the significant image features and extracting meaningful structure-property correlations and causative features.
Primary aims of the project are to:
1) Standardise the alignment and processing of images in the SSD group, developing a consistent pipeline for use on multi-image and multi-technique data that can be integrated with the CHIMP hyperspectral tools developed in the group.
2) Develop student expertise in applying supervised and un-supervised machine learning to extract the significant features from measured data on nitride structures from UK and international partners.
3) Apply recently developed statistical tests that distinguish cause and effect in measured data to discover what leads to efficient light production in short wavelength light emitting structures.
This project will consist of developing new standard techniques for image processing of single and multi-image data sets. This will be coupled with existing hyperspectral analysis tools and modern machine learning to extract meaningful information on the morphology and spectral behaviour of nanoscale features of advanced materials. Specifically analysis of the the structural and correlated spectral properties of nitride semiconductor nanostructures will be accelerated by reducing the level of human intervention in finding the significant image features and extracting meaningful structure-property correlations and causative features.
Primary aims of the project are to:
1) Standardise the alignment and processing of images in the SSD group, developing a consistent pipeline for use on multi-image and multi-technique data that can be integrated with the CHIMP hyperspectral tools developed in the group.
2) Develop student expertise in applying supervised and un-supervised machine learning to extract the significant features from measured data on nitride structures from UK and international partners.
3) Apply recently developed statistical tests that distinguish cause and effect in measured data to discover what leads to efficient light production in short wavelength light emitting structures.
