Theory of actionable data mining with application to semiconductor manufacturing control

Abstract

Accurate and timely prediction of a manufacturing process yield and flow times is often desired as a means of reducing overall production costs. To this end, this paper develops a new decision-theoretic classification framework and applies it to a real-world semiconductor wafer manufacturing line that suffers from constant variations in the characteristics of the chip-manufacturing process. The decisiontheoretic framework is based on a model for evaluating classifiers in terms of their value in decision-making. Recognizing that in many practical applications the values of the class probabilities as well as payoffs are neither static nor known exactly, a precise condition under which one classifier 'dominates' another classifier (i.e. achieves higher payoff ), regardless of payoff or class distribution information, is presented. Building on the decision-theoretic model, two robust ensemble classification methods are proposed that construct composite classifiers that are at least as good as any of the existing component classifiers for all possible payoff functions and class distributions. It is shown how these two robust ensemble classifiers are put into practice by developing decision rules for effectively monitoring and controlling the real-world semiconductor wafer fabrication line under study.

From data to big data in production research: the past and future trends

“…• detection of churn activities by a functional mixture model (Qian, Jiang, and Tsui 2006) or with clustering and fuzzy algorithms (Abbasimehr, Setak, and Soroor 2013); • manufacturing control by actionable data mining (Braha, Elovici, and Last 2007);…”

Visualizing the Yield Pattern Outcome for Automatic Data Exploration

“…Through the implementation, they were able to solve the yield problem 10 times faster than conventional method and yield increases 3% to 15%. [6] stated that the manufacturing data collected by semiconductor industries is constantly growing but it is still difficult to locate important data. Without the automated yield management system, the collected data does not able the manufacturing to effectively control the process.…”

A Review of Data Mining Applications in Semiconductor Manufacturing

“…In Table 5 the literature where data mining is used to support the decision-making process in semiconductors' manufacturing is presented. Analyzing this table, one can see that most contributions address yield management and failure detection issues (see [135][136][137][138][139][140][141][142][143][144][145]). The authors from [146] aim at the same problem, but focus on the development of a computer integrated manufacturing (CIM) system to improve product yield.…”