Using test data to improve IC quality and yield

The complexity of interactions in todaypsilas manufacturing processes makes test structures and experiments inadequate as sole drivers of yield-learning and design-for-manufacturing [DfM]. They must be driven by product impact. Product-impact-oriented test-based learning provides insight into the nature of model-hardware mismatches and variability that exist on and impact real products. That insight can be used to drive both parametric and defect-oriented process actions and DfM.