Paper
28 January 2009 An automatic detection system for flatness of integrated circuit pins
Author Affiliations +
Abstract
The flatness of pins is an important quality indicator for integrated circuit packaging. Almost all of the detection methods which are currently used can't be successful on efficiency and precision. In this system, the image of IC pins was captured by an properly optical systems and corresponding CCD sensor. To detect the edge of each pin, traditional algorithmic, such as Sobel operator and Roberts operator, have some disadvantages: the edge is too thick for system to accurately measure and the edge show directional character. An image segmentation and border extracting algorithm focus on the extreme of neighborhood image intensity change was adopted. The advantage of this algorithm was each pixel's neighborhood image intensity information was considered, so the algorithm is more suitable for accurately measure. After edge was extracted, how to identify the useful spots is cast as a binary classification task. The support vector machine (SVM) would be used to identify pin's spots. After proper image characteristics are obtained and a certain amount of training, SVM provides higher discrimination ratio to distinguish spots of the IC pins. To measure the flatness of pin, a particular line which can be identified easily should be put in the image as a baseline. Through calculating the distance between the pins spot and baseline, the flatness of pins is obtained accurately. In this system, the flatness of IC pins can be accurately and quickly measured, which is worthy of broad application prospect in IC packaging.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shichao Deng, Tiegen Liu, Zexin Xiao, and Xiuyan Li "An automatic detection system for flatness of integrated circuit pins", Proc. SPIE 7156, 2008 International Conference on Optical Instruments and Technology: Optical Systems and Optoelectronic Instruments, 715639 (28 January 2009); https://doi.org/10.1117/12.811973
Lens.org Logo
CITATIONS
Cited by 3 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Image segmentation

Detection and tracking algorithms

Image processing algorithms and systems

Imaging systems

Integrated circuits

Binary data

Edge detection

RELATED CONTENT


Back to Top