Investigation of temporally varying fringe pattern defects using machine learning for optical metrology

2020 ◽  
Author(s):  
Aditya Madipadaga ◽  
Rajshekhar Gannavarpu
Keyword(s):  
Machine Learning ◽  
Fringe Pattern ◽  
Optical Metrology

Advanced machine learning eco-system to address HVM optical metrology requirements

2020 ◽  
Author(s):  
Padraig R. Timoney ◽  
Roma Luthra ◽  
Alex Elia ◽  
Haibo Liu ◽  
Paul K. Isbester ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Metrology

scholarly journals A Fringe Phase Extraction Method Based on Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1664
Author(s):  
Wenxin Hu ◽  
Hong Miao ◽  
Keyu Yan ◽  
Yu Fu
Keyword(s):  
Neural Network ◽  
Fringe Pattern ◽  
Phase Shifting ◽  
Optical Metrology ◽  
Phase Extraction ◽  
Transform Method ◽  
The Neural Network ◽  
Phase Map ◽  
Phase Shifting Method ◽  
Fringe Patterns

In optical metrology, the output is usually in the form of a fringe pattern, from which a phase map can be generated and phase information can be converted into the desired parameters. This paper proposes an end-to-end method of fringe phase extraction based on the neural network. This method uses the U-net neural network to directly learn the correspondence between the gray level of a fringe pattern and the wrapped phase map, which is simpler than the exist deep learning methods. The results of simulation and experimental fringe patterns verify the accuracy and the robustness of this method. While it yields the same accuracy, the proposed method features easier operation and a simpler principle than the traditional phase-shifting method and has a faster speed than wavelet transform method.

Adaptive automatic data analysis in full-field fringe-pattern-based optical metrology

2016 ◽  
Author(s):  
Maciej Trusiak ◽  
Krzysztof Patorski ◽  
Lukasz Sluzewski ◽  
Krzysztof Pokorski ◽  
Zofia Sunderland
Keyword(s):  
Data Analysis ◽  
Fringe Pattern ◽  
Optical Metrology ◽  
Automatic Data ◽  
Full Field ◽  
Automatic Data Analysis

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining
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