Advanced Methods for the Optical Quality Assurance of Silicon Sensors

TL;DR

This paper presents an optical quality assurance system for silicon sensors using pattern recognition and neural networks, achieving over 90% accuracy in defect detection and classification.

Contribution

It introduces a novel combination of image processing and neural network techniques for improved defect recognition in silicon sensors.

Findings

Recognition and classification rate exceeds 90%

Neural networks enhance defect detection accuracy

Effective analysis of microscopic sensor scans

Abstract

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.