Wafer particle inspection technique using computer vision based on a color space transform model
Wafer particle inspection technique using computer vision based on a color space transform model
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DOI：
10.1007/s00170-023-11888-y
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
Heebum Chun;Jingyan Wang;Jungsub Kim;Chabum Lee
中科院分区：
文献类型：
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作者：
Heebum Chun;Jingyan Wang;Jungsub Kim;Chabum Lee
无缺陷晶片的制备作为制造器件或芯片之前的关键阶段，因为不可能在有缺陷的晶片上图案化任何器件或芯片。在整个半导体工艺中，会引入各种缺陷，包括需要准确识别和控制的随机颗粒。为了有效地检测硅片上的颗粒，提出了一种基于HSV(色调-饱和度-值)颜色空间变换模型的硅片颗粒检测技术，该技术利用计算机视觉对不同类型的颗粒进行检测和分类。使用人工生成的基于粒子颜色特性的粒子图像来验证每个粒子的HSV颜色空间模型，并演示了该方法如何在最小串扰的情况下有效地根据粒子类型对粒子进行分类。为了进行实验验证，研制了一台由成像系统、照明系统和光谱仪组成的高分辨率显微镜。将三种不同类型的微米级颗粒随机放置在晶片上，在曝光的白光照明下采集图像。基于为每种粒子类型指定的预先开发的HSV颜色空间模型，根据粒子类型对获得的图像进行分析和分割。通过使用该方法，可以准确地检测和分类晶片上的颗粒存在。它有望在缺陷入库过程中对各种晶片颗粒进行检测和分类。
The preparation of defect-free wafers serves as a critical stage prior to fabrication of devices or chips as it is not possible to pattern any devices or chips on a defected wafer. Throughout the semiconductor process, various defects are introduced, including random particles that necessitate accurate identification and control. In order to effectively inspect particles on wafers, this study introduces a wafer particle inspection technique that utilizes computer vision based on HSV (hue-saturation-value) color space transformation models to detect and to classify different particles by types. Artificially generated particle images based on their color properties were used to verify HSV color space models of each particle and to demonstrate how the proposed method efficiently classifies particles by their types with minimum crosstalk. A high-resolution microscope consisting of an imaging system, illumination system, and spectrometer was developed for the experimental validation. Micrometer-scale particles of three different types were randomly placed on the wafers, and the images were collected under the exposed white light illumination. The obtained images were analyzed and segmented by particle types based on pre-developed HSV color space models specified for each particle type. By employing the proposed method, the presence of particles on wafers can be accurately detected and classified. It is expected to inspect and classify various wafer particles in the defect binning process.