Application of Data Mining Based Scan Diagnosis Yield Analysis in a Foundry and Fabless Working Environment

For advanced technology nodes, the ability to improve the yield of a product in a short time dictates the success for both fabless and foundry. Identification of failure mechanisms in digital logic is frequently done using scan diagnosis driven yield analysis (DDYA) [1][2][3]. Recent works on root cause deconvolution (RCD) [4][5] discussed a Bayes net model based diagnosis data learning algorithm. Combining RCD and diagnosis suspect location analysis address key considerations in applying DDYA in a production environment.