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

Purandare, Sanyogita

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https://hdl.handle.net/2142/105225 Copy

Description

Title

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

Author(s)

Purandare, Sanyogita

Issue Date

2019-04-18

Director of Research (if dissertation) or Advisor (if thesis)

Goddard, Lynford

Doctoral Committee Chair(s)

Goddard, Lynford

Committee Member(s)

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

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(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.

Graduation Semester

2019-05

Type of Resource

text

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

Copyright and License Information

Copyright 2019 Sanyogita Purandare

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