首页> 外文期刊>Nuclear Instruments & Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment >Advanced optical quality assurance of silicon micro-strip sensors for the CBM Silicon Tracking System
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Advanced optical quality assurance of silicon micro-strip sensors for the CBM Silicon Tracking System

机译:用于CBM硅跟踪系统的硅微带传感器的先进光学质量保证

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The Compressed Baryonic Matter (CBM) experiment at the Facility for Anti-proton and Ion Research (FAIR) in Darmstadt, Germany, aims to study strongly interacting matter under extreme conditions. The Silicon Tracking System (STS) is the key detector to reconstruct the charged particle tracks produced in heavy-ion collisions. This paper describes a setup for optical quality assurance of silicon micro-strip sensors used in the STS. Machine Vision Algorithms (MVA) were used to analyze microscopic scans of the sensors for the presence of defects with a resolution of about 1 mu m and to perform metrology tasks. The software developed has a recognition and classification rate of 87% for defects like scratches, shorts, broken metal lines etc. The use of advanced image processing employing neural networks allows to further improve the identification rate to 96%.
机译:在德国达姆施塔特的反质子和离子研究(公平)设施的压缩式放静电物质(CBM)实验旨在在极端条件下研究强烈互动的物质。硅跟踪系统(STS)是重建在重离子碰撞中产生的带电粒径的关键检测器。本文介绍了STS中使用的硅微带传感器的光学质量保证的设置。机器视觉算法(MVA)用于分析传感器的显微扫描,以存在具有约1μm的分辨率并进行计量任务。该软件的识别和分类率为87%,划痕,短裤,破碎的金属线等。采用神经网络的高级图像处理的使用允许进一步将识别率提高到96%。

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