Getting More From the Semiconductor Test: Data Mining With Defect-Cluster Extraction

Ooi, Melanie Po-Leen; Joo, Eric Kwang Joo; Kuang, Ye Chow; Demidenko, Serge; Kleeman, Lindsay; Chan, Chris

doi:10.1109/tim.2011.2122430

Cited by 32 publications

(14 citation statements)

References 25 publications

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“…The process control chart, as a widely used approach to SPC [93,94], has been used to diagnose and identify the variability of the fab process. The statistical process system can help detect defects that might originate from the process steps to improve quality and eliminate the need for expensive post inspections [94,96]. With increasing the demand for high-quality products and reliable processes, multivariate statistical process control (MSPC) has been developed to ensure that equipment is "statistically controlled" by monitoring two or more related quality characteristics simultaneously [105].…”

Section: Application Of Dm and Big Data For Productionmentioning

confidence: 99%

A Review of Data Mining with Big Data towards Its Applications in the Electronics Industry

Kim

Zheng

et al. 2018

Applied Sciences

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Data mining (DM) with Big Data has been widely used in the lifecycle of electronic products that range from the design and production stages to the service stage. A comprehensive analysis of DM with Big Data and a review of its application in the stages of its lifecycle will not only benefit researchers to develop strong research themes and identify gaps in the field but also help practitioners for DM application system development. In this paper, a brief clarification of DM-related topics is presented first. A flowchart of DM and the main content of the flowchart steps are given in which commonly used data preparation and preprocessing approaches, DM functions and techniques, and performances indicators are summarized. Then, a comprehensive review covering 105 articles from 2007 to 2017 on DM or Big Data applications in the electronics industry is provided according to the flowchart from various points of view such as data handling, applications of DM, or Big Data at different lifecycle stages, and the software used in the applications. On this basis, a diagram of data content for different knowledge areas and a framework for DM and Big Data applications in the electronics industry are established. Finally, conclusions and future research directions are given.

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Section: Application Of Dm and Big Data For Productionmentioning

confidence: 99%

A Review of Data Mining with Big Data towards Its Applications in the Electronics Industry

Kim

Zheng

et al. 2018

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Many papers afford quality issues using DM techniques [50][51][52][53][54][55][56][57][58][59][60][61][62][63] . Among them, semiconductor and electronics manufacturing can be considered a hot topic [51,53,58,59,61,63]. Quality problems together with new capabilities usually influence the design process of new products.…”

Section: Kdd Techniques Applied To Manufacturing Processesmentioning

confidence: 99%

A knowledge discovery in databases approach for industrial microgrid planning

Gamarra

Guerrero

Montero

2016

Renewable and Sustainable Energy Reviews

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“…First, the segmentation, detection, and cluster extraction (SDC) algorithm was introduced to improve the SNR [2]. Second, a defectcluster recognition system was developed to overcome other challenges such as insufficient training samples and variations in cluster shapes [3].…”

Section: Algorithms For Semiconductor Cluster Recognitionmentioning

confidence: 99%

“…Modern semiconductor fabrication is a process in which each individual IC (die) is produced along with hundreds to thousands of other dies on a single silicon wafer. If failed dies on the wafer form a unique and systematic pattern, a defect cluster can be physically observed [1], [2]. It is challenging to develop an automatic, intelligent system that can perform accurate observation and report on the clustering phenomena on semiconductor production test data sets.…”

mentioning

confidence: 99%