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Title:Optical imaging with machine learning for the automated characterization of micro- and nanoscale devices
Author(s):Purandare, Sanyogita
Director of Research:Goddard, Lynford
Doctoral Committee Chair(s):Goddard, Lynford
Doctoral Committee Member(s):Choquette, Kent; Koyejo, Oluwasanmi; Popescu, Gabriel; Schwing, Alexander
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Machine Learning
Optical Imaging
Optical Image Processing
Abstract: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.
Issue Date:2019-04-18
Rights Information:Copyright 2019 Sanyogita Purandare
Date Available in IDEALS:2019-08-23
Date Deposited:2019-05

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