Analyzing Volume Diagnosis Results with Statistical Learning for Yield Improvement

A novel statistical learning algorithm is proposed to accurately analyze volume diagnosis results. This algorithm effectively overcomes the inherent ambiguities in logic diagnosis, to produce accurate feature failure probabilities, which are critical in understanding systematic yield limiters. The results of Monte-Carlo simulation are presented, which demonstrate the feasibility and impacts of various factors on this approach. Additional experiments based on injected defects are performed, which confirm the ability of this approach to generate accurate feature failure probabilities for an industrial design using actual diagnosis results.