Feature extraction methods for ic chip marking : inspection a comparison

الملخص EN

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 1C chips are laser printed.

This inspection system ensures that the laser printed marking on IC chips is proper, The inspection has to identify the print errors such as illegible character, missing characters and up side down printing.

The vision inspection of the printed markings on the 1C 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.

Projection profile and Moments are employed for feature extraction.

A neural network is used as a classifier to detect the defectively marked IC chips coming from the manufacturing line.

Four different neural network input structures arc considered for optimizing the training speed in projection profile extraction method and two different moments is used for moments feature extraction method.

The performance of projection profile is compared with that of few feature extraction methodologies, hi neural network, feature extracted from moments and projection profile are used for inspection, Both feature extraction methods arc compared in terms of marking inspection time