Dimensionality Reduction and Clustering by Yield Signatures to Identify Candidates for Failure Analysis

Abstract The job of yield and failure analysis (YA and FA) engineers is to identify the root cause of low-yielding wafers. While physical FA is the most definitive method for determining root cause, resource limitations require YA engineers to search for root cause by identifying other wafers with similar yield signatures. The immense number of yield parameters, or features, collected in modern semiconductor processes makes this a difficult task. This paper presents a workflow employing multiple AI techniques to separate groups of wafers by their distinct yield signatures and determine the parameters most important to defining each group. This aids in the disposition of new low-yield wafers, maximizes the learning from previously collected FA wafers, and allows FA resources to be allocated more effectively, prioritizing them for the highest-impact, unknown fail modes.