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Proceedings Paper

Neural network inspection of periodic structures from optical diffraction
Author(s): Alastair D. McAulay; Junqing Wang; Shawn Justice
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Paper Abstract

An optical diffraction method is described for inspecting periodic structures such as combs or semiconductor leads. Coherent light passing between the prongs of the structure self interfere at the fractional Talbot plane to provide a simple method of inspection. Computer simulation and laboratory experiments show the viability of this approach. The theory assumes infinite structures. In practice, large and effect signals arise due to the finiteness of the periodic structure. A neural network is demonstrated that learns to distinguish and effect signals from prong damage signals. The variability of the measuring process in a production environment makes neural networks an appropriate approach for this task.

Paper Details

Date Published: 28 July 1997
PDF: 5 pages
Proc. SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, (28 July 1997); doi: 10.1117/12.280817
Show Author Affiliations
Alastair D. McAulay, Lehigh Univ. (United States)
Junqing Wang, Lehigh Univ. (United States)
Shawn Justice, Lehigh Univ. (United States)


Published in SPIE Proceedings Vol. 3068:
Signal Processing, Sensor Fusion, and Target Recognition VI
Ivan Kadar, Editor(s)

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