Yield analysis is one of the most important subjects in IC companies. During the initial stage of new process development, several factors can greatly impact the yield simultaneously. Traditionally, several learning cycle iterations are required to solve yield loss issues. This paper describes a novel way to diagnose yield loss issues in less iteration. First, the failure classification of bitmap data is transferred to a new basis using principal component analysis. Second, the defective rates are calculated and the original bitmap data is reconstructed in the principal basis, allowing the yield loss space to be generated by Cluster Analysis. Third, physical failure analysis samples can be selected to solve yield loss issues. Furthermore, the new yield loss basis can be used to monitor the progress of yield improvement as a discriminate analysis measure for reducing failure patterns (bitmap failures).
- cluster analysis
- discriminate analysis
- principal component analysis (PCA)
- yield analysis
- yield loss space