The Impact of Clustered Defect Distributions in IC Fabrication

Abstract

Whenever defect data is encountered in industrial quality control applications, the Poisson distribution is generally assumed to be the underlying distribution. It has been widely reported that the defect distributions in integrated circuit fabrication exhibit clustering behavior, a condition which invalidates the Poisson distribution assumption. In this paper we employ an alternative model for defect data that exhibits clustering. To demonstrate the impact of the proposed model, we use it to design acceptance sampling plans and show a dramatic difference between these plans and those determined under the Poisson distribution assumption. Although advances in quality control and techniques such as design for manufacture have eliminated the need for acceptance sampling in many areas, integrated circuit fabrication still involves processes with high variability (even using state of the art equipment). Thus acceptance sampling is widely used at various stages of manufacture to insure specified quality levels when 100 percent inspection is infeasible.