Estimating defect-type distributions through volume diagnosis and defect behavior attribution

We propose a methodology that effectively estimates the defect-type distribution that affects a design fabricated in a given manufacturing process. Understanding the distribution can improve design quality, test quality, and the manufacturing process itself. The methodology is composed of i) an improved approach for identifying the signal lines relevant to defect activation at each site reported by diagnosis, ii) a new behavior attribution method, and iii) a novel approach to estimate the defect-type distribution. The efficacy of this methodology is validated using circuit-level simulation experiments. The results show that the method achieves an average accuracy of 94% in identifying signal lines that are relevant to the activation of a defect. When estimating defect-type distribution for a population affected by a variety of defects, the average estimation accuracy is 92% with ideal diagnosis. With a realistic diagnosis (i.e., the inherent ambiguity of diagnosis is accounted for), the estimated defect-type distribution is 85% accurate, on average.