Condition Monitoring and Operational Decision-Making in Modern Semiconductor Manufacturing Systems

Conference paper
Part of the ICSA Book Series in Statistics book series (ICSABSS)

Abstract

Modern semiconductor manufacturing tools are often complex systems of numerous interacting subsystems that operate in multiple physical domains and often follow highly nonlinear distributed dynamics. In such systems, traditional condition monitoring methods, which rely on a direct link between sensor readings and the underlying condition of the system, cannot be used. Rather, one must acknowledge that the available sensor readings are only stochastically related to the condition of the monitored system, which therefore must be probabilistically inferred from the sensors. This manuscript describes a recently proposed condition monitoring method, based on characterizing the degradation process via a mixture of operation-specific hidden Markov models (HMMs), with hidden states representing the unobservable degradation states of the monitored system, while its observable variables represent the available sensor readings. The new monitoring paradigm was applied to monitoring of several tools operating in major semiconductor fabs over many months, with orders of magnitude better performance than traditional, purely signature-based approaches. The remainder of the paper focuses on describing how Markovian models of degradation of flexible manufacturing equipment, such as those utilized in modern semiconductor manufacturing, can be employed to concurrently optimize the sequence of production operations and schedule preventive maintenance for that machine. It will be shown that integrated decision-making in terms of product sequencing and maintenance operations carries significant potential benefits compared to the more traditional, fragmented decision-making. The manuscript ends with a brief summary of possible future research directions in process monitoring and maintenance decision-making in semiconductor manufacturing.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringUniversity of Texas at AustinAustinUSA

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