EUV mask-blank defect avoidance solutions assessment

2012 ◽  
Author(s):  
Ahmad Elayat ◽  
Peter Thwaite ◽  
Steffen Schulze
Keyword(s):  
Defect Avoidance

scholarly journals Real-Time Defect Recognition and Optimized Decision Making for Structural Timber Jointing

2021 ◽  
pp. 36-45
Author(s):  
Dan Luo ◽  
Joseph M. Gattas ◽  
Poah Shiun Shawn Tan
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Structural Timber ◽  
Image Capture ◽  
Clear Wood ◽  
Single Piece ◽  
Common Strategy ◽  
Defect Avoidance ◽  
Defect Recognition ◽  
Real Time Optimization

AbstractNon-structural or out-of-grade timber framing material contains a large proportion of visual and natural defects. A common strategy to recover usable material from these timbers is the marking and removing of defects, with the generated intermediate lengths of clear wood then joined into a single piece of full-length structural timber. This paper presents a novel workflow that uses machine learning based image recognition and a computational decision-making algorithm to enhance the automation and efficiency of current defect identification and re-joining processes. The proposed workflow allows the knowledge of worker to be translated into a classifier that automatically recognizes and removes areas of defects based on image capture. In addition, a real-time optimization algorithm in decision making is developed to assign a joining sequence of fragmented timber from a dynamic inventory, creating a single piece of targeted length with a significant reduction in material waste. In addition to an industrial application, this workflow also allows for future inventory-constrained customizable fabrication, for example in production of non-standard architectural components or adaptive reuse or defect-avoidance in out-of-grade timber construction.

Using laser defect avoidance to build large-area FPGAs

1998 ◽  
Vol 15 (4) ◽  
pp. 75-81 ◽  
Author(s):  
G.H. Chapman ◽  
B. Dufort
Keyword(s):  
Large Area ◽  
Defect Avoidance
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