Hard to find, easy to find systematics; just find them

Abstract

In a manufacturing organization, every morning starts with the question: what is the yield today? The cost of wafer manufacturing being fairly constant, product yield is one of the most significant variables for profitability. With the yield paretos increasingly dominated by systematic defects, yield learning based on product test is fast becoming a fundamental requirement. For an integrated device manufacturer like IBM, product-based yield learning is even more critical as this drives technology learning as well. In this paper, we will present some of IBM's yield learning techniques and several case studies from high-volume manufacturing. These techniques extend from test data analysis, to analysis of scan-based product diagnosis results, to detailed layout analysis in conjunction with test, diagnosis and inline defect inspection data. We will discuss the increasing levels of complexity associated with the various techniques and argue that an effective yield learning strategy must comprise all of the above.