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

arXiv: https://arxiv.org/abs/2107.07714 · HTML: https://mlsanity.com/paper/2107.07714/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 16 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Activation Functions: Comparison of trends in Practice and Research for Deep Learning](https://mlsanity.com/paper/1811.03378/) (2018)
- [Advanced Methods for the Optical Quality Assurance of Silicon Sensors](https://mlsanity.com/paper/1807.00211/) (2018)

## Built on by (most cited first, 1 of 1)

- [Automated visual inspection of CMS HGCAL silicon sensor surface using an ensemble of a deep convolutional autoencoder and classifier](https://mlsanity.com/paper/2303.15319/) (2023)

## Abstract (arXiv)

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.