DEFECT DETECTION IN ELECTRONIC SURFACES USING TEMPLATE-BASED FOURIER IMAGE RECONSTRUCTION

Abstract

For defect detection in non-periodical pattern images, such as printed-circuit boards (PCBs) or integrated circuit (IC) dies found in the electronic industry, template matching could be the only applicable method to tackle the problem. The traditional template matching techniques work in the spatial domain and rely on local pixel information. They are sensitive to geometric and lighting changes, and random product variations. The currently available Fourier-based methods mainly work for plain and periodical texture surfaces. In this paper, we propose a global Fourier image reconstruction method to detect and localize small defects in non-periodical pattern images. It is based on the comparison of the whole Fourier spectra between the template and the inspection image. It retains only the frequency components associated with the local spatial anomaly. The inverse Fourier transform is then applied to reconstruct the test image, where the local anomaly will be restored and the common pattern will be removed as a uniform surface. The proposed method is invariant to translation and illumination, and can detect subtle defects as small as 1-pixel wide in a wide variety of non-periodical patterns found in the electronic industry.

Existing System

Support Vector Machine, Principal Component Analysis, color features and manifold models.

Proposed System

This system proposes a Fourier image reconstruction scheme to detect various local defects in non-periodical pattern images. The proposed algorithms are especially applied to subtle defect inspection in printed circuit boards and IC dies. The Fourier image reconstruction has been successfully used to detect local defects that break the regularity in homogeneously and periodically textured surfaces. It has not been applied to defect detection in non-periodical pattern images. The proposed method is based on the comparison of the whole Fourier spectra between the template and the test image. It retains only the suspicious frequency components in the Fourier spectrum of the test image, and discards the common frequency components found in both Fourier spectra. The inverse Fourier transform is then applied to restore the test image, where the local anomalies will be reconstructed and the common background pattern will be removed as a uniform surface. A simple statistical control limit is finally used as the adaptive threshold to segment the local defect. The proposed spectral method is invariant to translation and illumination, and can detect subtle defects as small as 1-pixel wide.

Architecture

BLOCK DIAGRAM