A supervised machine learning application in volume diagnosis

Volume diagnosis has been used effectively to identify systematic defects for yield learning. Root cause deconvolution (RCD), an unsupervised machine learning technique which uses volume diagnosis data, has proven very effective for identifying root causes. As we march towards more advanced technology nodes, defects have more complicated behaviors rendering some model parameters used in RCD are not precise enough to be effective. In this paper we use a supervised machine learning technique to accurately learn these model parameters from training data. Controlled experiments using simulation data on several industrial designs show that our approach improves RCD accuracy. We also demonstrate that the approach correctly predicts 71% of the systematic defects in 21 cases validated by physical failure analysis of real silicon, which is a significantly better result compared to using the original parameters.