Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session A36: Undergraduate Research I: AMO and Optical Techniques
8:00 AM–10:36 AM,
Monday, March 15, 2021
Sponsoring
Unit:
APS/SPS
Chair: Chih Kuan Tung
Abstract: A36.00003 : Enhancing Optics-Based Detection of Semiconductor Patterning Defects Using Convolutional Neural Networks*
8:24 AM–8:36 AM
Live
Presenter:
Abdul Qadeer Rehan
(University of Richmond)
Authors:
Bryan M Barnes
(National Institute of Standards and Technology)
Martin Y. Sohn
(National Institute of Standards and Technology)
Abdul Qadeer Rehan
(University of Richmond)
Optical methods offer fast, non-destructive defect identification, but separating signals from measurement noise is daunting. An asymmetry exists between the impact of misclassifying a defect as pristine and vice versa. Data-driven machine learning is a key approach for the classification of patterning defect images, even if unresolved.
We have optimized a dataset of experimental images collected at 193 nm wavelength for enhanced processing using supervised neural networks. 30,000 images of scattering from bridges and line breaks had been collected using a scatterfield microscope using orthogonal linear polarizations aligned to an intentional defect array.
Large datasets are desirable to resolve overfitting in neural networks; data augmentation techniques and filtering are applied to images of pristine and defect patterning, increasing the training set size by about an order of magnitude. We compare the effects of wavelet compressions including the 2-D Haar wavelet transform on the training of the neural network. Initial results show the successful binary classification of the defect and no-defect samples for specific defect types, enabling future work.
*SPS Summer Internship
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