Classification of the marking on integrated circuit chips based on moments and projection profile - a comparison

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 the investigation. The IC chips are laser printed. This inspection system ensures whether the laser printed marking on IC chips are proper. One of the artificial intelligence components the neural network, is used for inspection. The inspections are carried out to find the print errors such as illegible character, missing characters and up side down printing. The vision inspection of the printed markings on the IC chip are 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. Neural network is used as a classifier to detect the defectively marked IC chips coming from the manufacturing line. In neural network, feature extracted from moments and projection profile are used for inspection. Both feature extraction methods are compared in terms of marking inspection time.