Data Mining for Significance in Yield-Defect Correlation Analysis

A yield analysis method using basic yield and in-line defect information in a statistical model to determine root-causes of yield loss in semiconductor manufacturing is presented. The goal of this analysis method is to provide the fab process line management yield loss accounting for defects identified at inspected process layers. Quantifying these losses, in terms of yield loss percent and statistical confidence allows the fab to set priorities for defect reduction work to achieve maximum yield enhancement. Separation of killer defects from nuisance defects and inspection or pattern related noise is a constant challenge. This tool provides statistical techniques for identifying the most effective inspection tool or recipe for a given inspection layer. Enhanced statistical resolution can be achieved through data mining by defect size, classification, or electrical failure bin information. These die level analysis techniques may be combined with memory bit level correlation analysis and physical failure analysis to provide a comprehensive yield accounting assessment.