Detection Algorithm of Particle Contamination in Reticle Images with Continuous Wavelet Transform

This paper presents an inspection method of particle contamination for semiconductor reticles using continuous wavelet transform. Particle defect is considered as a singularity in the reticle image, and wavelet transform is applied to detect such an event. By taking the local maxima of wavelet transform as suspected defects, the candidate pixels under inspection reduce to a small fraction of the whole image. From the evolution of wavelet coefficients across scales, two features are extracted to identify detects among the suspected defects, Lipschitz Exponent (L.E.) and smoothing factor. With the rules of LE and smoothing factor trained from the image database, the defects can be detected with high accuracy. From the test of synthetic reticle images with defects at different size, it is concluded that the maximum scale of wavelet under which the defect is still visible can not be over about 8 times as much as the defect size. The simulation of a real reticle image and synthetic defects shows the effectiveness of the present method.