Improving PFA Accuracy and Defect Localization with Volume Scan Diagnosis

摘要

Advanced technology nodes with small feature sizes and increased design complexity have made identifying the root cause of yield loss increasingly time consuming. Several of the defects also occur inside a cell making physical failure analysis (PFA) and electrical failure analysis (EFA) much more challenging. Layout-aware and cell-aware scan diagnosis results with root cause deconvolution (ReD) machine learning can be leveraged to improve the accuracy of the choice of die for physical failure analysis and localize the search area during failure analysis (FA).