Fault isolation during semiconductor manufacturing using automated discovery from wafer tracking databases

Saxena, Sharad

doi:10.1109/caia.1993.366594

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…The amounts of data involved make the data analysis task extremely time-consuming and difficult. Several authors proposed procedures for using machinelearning techniques in semiconductor manufacturing [141][142][143][144][145][146][147]. Research results showed that machinelearning techniques can be powerful tools for continuous quality improvement in a large and complex process such as semiconductor manufacturing.…”

Section: Applications Of Machine-learning Techniques In Manufacturingmentioning

confidence: 99%

Machine-learning techniques and their applications in manufacturing

Pham

Afify

2005

Proceedings of the Institution of Mechanical Engineers, Part B:

136

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Machine learning is concerned with enabling computer programs automatically to improve their performance at some tasks through experience. Manufacturing is an area where the application of machine learning can be very fruitful. However, little has been published about the use of machine-learning techniques in the manufacturing domain. This paper evaluates several machine-learning techniques and examines applications in which they have been successfully deployed. Special attention is given to inductive learning, which is among the most mature of the machine-learning approaches currently available. Current trends and recent developments in machine-learning research are also discussed. The paper concludes with a summary of some of the key research issues in machine learning.

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Section: Applications Of Machine-learning Techniques In Manufacturingmentioning

confidence: 99%

Machine-learning techniques and their applications in manufacturing

Pham

Afify

2005

Proceedings of the Institution of Mechanical Engineers, Part B:

136

View full text Add to dashboard Cite

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Diagnosis of semiconductor manufacturing equipment and processes

Saxena

Unruh

1994

IEEE Trans. Semicond. Manufact.

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Enterprise Data Mining: A Review and Research Directions

Liao

2008

Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications

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Manufacturing enterprise systems and service enterprise systems carry out the bulk of economic activities in any country and in the increasingly connected world. Enterprise data are necessary to ensure that each manufacturing or service enterprise system is run efficiently and effectively. As it becomes easier to capture and fairly inexpensive to store, digitized data gradually overwhelms our ability to analyze in order to turn them into useful information for decision making. The rise of data mining and knowledge discovery as an interdisciplinary field for uncovering hidden and useful knowledge from large volumes of data stored in a database or data warehouse is very promising in many areas, including enterprise systems. Over the last decade, numerous studies have been carried out to investigate how enterprise data could be mined to generate useful models and knowledge for running the business more efficiently and effectively. This chapter intends to provide a comprehensive overview of previous studies on enterprise data mining. To give some idea about where the research is heading, some ongoing research programs and future research directions are also highlighted at the end of the chapter.

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Fault isolation during semiconductor manufacturing using automated discovery from wafer tracking databases

Cited by 4 publications

References 14 publications

Machine-learning techniques and their applications in manufacturing

Machine-learning techniques and their applications in manufacturing

Diagnosis of semiconductor manufacturing equipment and processes

Enterprise Data Mining: A Review and Research Directions

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