Hybrid machine learning system for integrated yield management in semiconductor manufacturing

Kang, Bo Gyeong; Lee, Janghee; Shin, Chung-Kwan; Yu, Song-Jin; Park, Sang C.

doi:10.1016/s0957-4174(98)00017-7

Cited by 25 publications

(11 citation statements)

References 8 publications

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“…4. 8 In Fig. 4, S represents the current set of instances, and represents the set of instances for which the attribute receives the value .…”

Section: Discussionmentioning

confidence: 99%

“…In [7], comprehensive applications of data mining within semiconductor manufacturing environments are described. In [8], an architecture is proposed for a generic integrated yield management system, without an actual application of it. A composite architecture that combine several data mining methods has been presented in [9], and has been applied to the refinement of a new dry cleaning technology that utilizes a laser beam for the removal of micro-contaminants.…”

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confidence: 99%

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On the use of decision tree induction for discovery of interactions in a photolithographic process

Braha

Shmilovici

2003

IEEE Trans. Semicond. Manufact.

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Abstract-This paper delineates a comprehensive and successful application of decision tree induction to 1054 records of production lots taken from a lithographic process with 45 processing steps. Complex interaction effects among manufacturing equipment that lead to increased product variability have been detected. The extracted information has been confirmed by the process engineers, and used to improve the lithographic process.The paper suggests that decision tree induction may be particularly useful when data is multidimensional, and the various process parameters and machinery exhibit highly complex interactions. Another implication is that on-line monitoring of the manufacturing process (e.g., closed-loop critical dimensions control) using data mining may be highly effective.

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“…4. 8 In Fig. 4, S represents the current set of instances, and represents the set of instances for which the attribute receives the value .…”

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

On the use of decision tree induction for discovery of interactions in a photolithographic process

Braha

Shmilovici

2003

IEEE Trans. Semicond. Manufact.

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“…Kang et al [19] integrated inductive decision trees and NNs with back-propagation and SOM algorithms to manage yields over major semiconductor manufacturing processes. Shin and Park [31] integrated neural networks and memory based reasoning to develop a wafer yield prediction system for semiconductor manufacturing.…”

Section: Research Methods Applied To Semiconductor Manufacturingmentioning

confidence: 99%

Recognizing yield patterns through hybrid applications of machine learning techniques

Lee

2009

Information Sciences

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“…Rough set theory has been proved to be a powerful tool for discovering new knowledge and autonomous decision making. Kusiak [119] Kang et al [122]. It uses inductive decision trees and neural network to manage yields over major manufacturing processes and provides functions to identify causal relationships between them such as yield prediction, process capability monitoring, feature selection, and wafer map stability monitoring.…”

Section: Prediction In Manufacturingmentioning

confidence: 99%

Data mining in manufacturing: a review based on the kind of knowledge

2008

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In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, such as product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection and so on. Data mining has emerged as an important tool for knowledge acquisition in manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with an special emphasis on the type of functions to be performed on data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been applied to the abstracts and keywords of 150 identified literatures to identify the research gaps and find the linkages between knowledge area, knowledge type and data mining tools and techniques applied.

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Hybrid machine learning system for integrated yield management in semiconductor manufacturing

Cited by 25 publications

References 8 publications

On the use of decision tree induction for discovery of interactions in a photolithographic process

On the use of decision tree induction for discovery of interactions in a photolithographic process

Recognizing yield patterns through hybrid applications of machine learning techniques

Data mining in manufacturing: a review based on the kind of knowledge

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