Optical Inspection of the Silicon Micro-strip Sensors for the CBM Experiment employing Artificial Intelligence

TL;DR

This paper presents an AI-based optical inspection method for silicon micro-strip sensors, utilizing deep neural networks to detect various surface defects and assess overall sensor quality for the CBM experiment.

Contribution

It introduces a novel machine learning approach for defect detection and quality assessment in silicon sensors, enhancing inspection accuracy and efficiency.

Findings

Successful detection of multiple defect types using CDNNs

Creation of 2D defect maps for sensor analysis

Proposed method for sensor quality grading

Abstract

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 sensor's 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 sensor's surface, a method for the estimation of sensor's overall quality grade and quality score was proposed.