Neural network inspection of periodic structures from optical diffraction

Alastair D. McAulay; Junqing Wang; Shawn Justice

doi:10.1117/12.280817

28 July 1997 Neural network inspection of periodic structures from optical diffraction

Alastair D. McAulay, Junqing Wang, Shawn Justice

Proceedings Volume 3068, Signal Processing, Sensor Fusion, and Target Recognition VI; (1997) https://doi.org/10.1117/12.280817
Event: AeroSense '97, 1997, Orlando, FL, United States

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.

Citation Download Citation

Alastair D. McAulay, Junqing Wang, and Shawn Justice "Neural network inspection of periodic structures from optical diffraction", Proc. SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, (28 July 1997); https://doi.org/10.1117/12.280817

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