Analysis of the die test optimization algorithm for negative binomial yield statistics

摘要

Introduces a new adaptive testing algorithm that uses spatial defect clustering information and available test from neighbouring dies to optimize test lengths during wafer-probe testing. When applied to the defect distribution data for 12 sample wafers collected by Saji and Armstrong, the new approach showed potential for providing improvement in overall product quality. In this paper, the authors conduct a more general study to evaluate the proposed new test optimization algorithm based on the widely accepted negative binomial model for defect distributions on a wafer. The objective is to obtain a more accurate measure of the magnitude of the defect-level improvements that can be expected under various yield and defect-clustering conditions.