Wafer particle inspection technique using computer vision based on a color space transform model

• Published in: International journal of advanced manufacturing technology Vol. 127; no. 11-12; pp. 5063 - 5071
• Main Authors: Chun, Heebum, Wang, Jingyan, Kim, Jungsub, Lee, ChaBum
• Format: Journal Article
• Language: English
• Published: London Springer London 01-08-2023; Springer Nature B.V
• Subjects: CAE) and Design; Classification; Color; Computer vision; Computer-Aided Engineering (CAD; Defects; Engineering; Illumination; Industrial and Production Engineering; Inspection; Mechanical Engineering; Media Management; Original Article; Saturation (color); Semiconductors; Wafers; White light; Wafer metrology and inspection; Particle inspection; Color space transform; Hue-saturation-value (HSV)
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-023-11888-y

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.