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|Title: ||Artificial intelligence techniques in IC chip marking|
|Authors: ||Muthukaruppan, Kartigayan|
Mohamed Rizon, Mohamed Juhari
|Keywords: ||Integrated circuits;Artificial intelligence;Optical Character Recognition (OCR);Optical character recognition devices;Integrated circuits -- Inspection;Integrated circuits -- Design and construction|
|Issue Date: ||2005|
|Publisher: ||Kolej Universiti Kejuruteraan Utara Malaysia|
|Citation: ||Journal of Engineering Research and Education, vol. 2, 2005, pages 17-29.|
|Abstract: ||In this paper, an industrial machine vision system incorporating Optical Character Recognition (OCR) is employed to inspect the marking on the Integrated Circuit (IC) Chips. This inspection is carried out while the ICs are coming out from the manufacturing line. A TSSOP-DGG type of IC package from Texas Instrument is used in this investigation. The IC chips markings are laser printed. This inspection system tests are laser printed marking on IC chips and are according to the specifications. Artificial intelligence (AI) techniques are used in this inspection. AI techniques utilized are neural network and fuzzy logic. The inspection is earned out to find the print errors; such as illegible character, upside down print and missing characters. The vision inspection of the printed markings on the IC chip is carried out in three phases, namely, image preprocessing, feature extraction and classification. MATLAB platform and its toolboxes are used for designing the inspection processing technique. The percentage of accuracy of the classification is found to be between 97% -100%.|
|Description: ||Link to publisher's homepage at http://www.unimap.edu.my/|
|Appears in Collections:||Journal of Engineering Research and Education|
Mohd. Rizon Mohamed Juhari, Prof. Ir. Dr.
Ramachandran, Nagarajan, Prof. Dr.
Sazali Yaacob, Prof. Dr.
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