Optical inspection of 1191 silicon micro-strip sensors was performed using a custom made optical inspection setup, employing a machine-learning based approach for the defect analysis and subsequent quality assurance. Furthermore, metrological control of the sensors surface was performed. In this manuscript, we present the analysis of various sensor surface defects. Among these are implant breaks, p-stop breaks, aluminium strip opens, aluminium strip shorts, surface scratches, double metallization layer defects, passivation layer defects, bias resistor defects as well as dust particle identification. The defect detection was done using the application of Convolutional Deep Neural Networks (CDNNs). From this, defective strips and defect clusters were identified, as well as a 2D map of the defects using their geometrical positions on the sensor was performed. Based on the total number of defects found on the sensors surface, a method for the estimation of sensors overall quality grade and quality score was proposed.
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