To achieve progressively lower defective parts per million (PPM) levels in silicon, we need to target a wide variety of fault and defect models, such as stuck-at, at-speed and bridging faults and IDDQ/IDD failures, and apply tests targeting each such model. This paper describes a novel MATLAB®-based methodology for quantifying PPM improvements based on fallout data during manufacturing test application. It is seen that tests that target a range of defect models, each with moderately high levels of coverage, may be better in terms of lowering PPM than those that target a single fault model with high levels of coverage. This analysis is explained using regression models for PPM yield versus fault/defect coverage. This approach is beneficial to semiconductor companies for calibrating their fault coverage goals to meet PPM requirements from automotive and other customers.