Markov Random Fields in Pattern Recognition for Semiconductor Manufacturing

Abstract

Under the most general conditions of an anisotropic Markov random field, we model the two-dimensional spatial distribution of microchips on a silicon wafer. The proposed model improves on its predecessors as it stipulates the spatial correlation of different strengths in all eight directions. Its canonical parameters represent the intensity of failures, main effects, and interactions of neighboring chips. Explicit forms of conditional distributions are derived, and maximum pseudo-likelihood estimates of canonical parameters are obtained. This numerical characteristic summarizes general patterns of clusters of failing chips on a wafer, capturing their size, shape, direction, density, and thickness. It is used to classify incoming wafers to known root-cause categories by matching them to the closest pattern.

KEY WORDS :

• Artificial neural network
• Clique
• Exponential family
• Gibbs sampler
• Maximum pseudo-likelihood
• Neighborhood