Optical diffraction inspection of periodic structures using neural networks

An optical diffraction method is described for inspecting peri- odic structures such as combs or semiconductor leads. Coherent light passing between the prongs of the structure self-interfere at the frac- tional Talbot plane to provide a simple method of inspection. Computer simulation and laboratory experiments show the viability of this ap- proach. The theory assumes infinite structures. In practice, large end effect signals arise due to the finiteness of the periodic structure. A neu- ral network is demonstrated that learns to distinguish end 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.