Optical imaging with machine learning for the automated characterization of micro- and nanoscale devices

This dissertation presents techniques and applications associated with enhancing the sensitivity of the optical images in nanotechnology. First, the dissertation presents a novel interpretable machine learning technique for defect detection and classification in noisy optical images of semiconductor wafer die. This solution is designed to solve the imbalanced data-set classification problem for noisy images with some feature similarities in the different classes. A baseline comparison is performed by using a standard technique for defect detection and classification. Secondly, a standard loss function is modified and combined with some portions of the new technique to implement defect detection and classification in optical images of semiconductor wafer die. The performances of both the techniques are compared. An optimization technique is presented for a specific category of functions encountered in this dissertation and the algorithm's performance is compared with standard optimization algorithm(s). Two phase retrieval techniques are compared: diffraction phase microscopy (DPM) and transport of intensity (TIE). TIE is modified for low intensity and zero intensity images. Modified TIE is optimized for theoretical analysis of Gaussian mode phase retrieval. Experimental trials are conducted and optimized hyperparameters are used for phase retrieval.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2019

Author dc:creator

• Purandare, Sanyogita

Contributors dc:contributor

• Goddard, Lynford
• Choquette, Kent
• Koyejo, Oluwasanmi
• Popescu, Gabriel
• Schwing, Alexander

• Machine Learning
• Optical Imaging
• Optical Image Processing

Statement dc:rights

• Copyright 2019 Sanyogita Purandare

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/105225

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/105225