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We describe a setup for optical quality assurance of silicon microstrip sensors.Pattern recognition algorithms were developed to analyze microscopic scans of the sensors for defects. It is shown that the software has a recognition and classification rate of > 90% for defects like scratches, shorts, broken metal lines etc.We have demonstrated that advanced image processing based on neural network techniques is able to further improve the recognition and defect classification rate.

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We describe a setup and procedures for contactless optical 3D-metrology of silicon micro-strip sensors. Space points are obtained by video microscopy and a high precision XY-table. The XY-dimensions are obtained from the movement of the table and pattern recognition, while the Z-dimension results from a Fast Fourier Transformation analyses of microscopic images taken at various distances of the optical system from the object under investigation. The setup is employed to measure the position of silicon sensors mounted onto a carbon fibre structure with a precision of a few microns.