A Virtual Metrology System for Predicting End-of-Line Electrical Properties Using a MANCOVA Model With Tools Clustering

Pan, Tianhong; Sheng, Bi-Qi; Wong, David Shan Hill; Jang, Shi-Shang

doi:10.1109/tii.2010.2098416

Cited by 26 publications

(10 citation statements)

References 11 publications

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“…Zeng and Spanos [8] employed various statistic techniques to create the VM model of the plasma etching process. Pan et al [9] utilized a MANCOVA model and the tools clustering technique to create a VM model for predicting end-of-line electrical properties of wafers. Fan and Chang [10] applied VM to develop an advanced process control system for the MIMO (Multiple-Input MultipleOutput) semiconductor processes.…”

Section: Related Workmentioning

confidence: 99%

“…Most VM systems in the existing VM-related literature (such as Refs. [6][7][8][9][10][11][12][13]) did not address the plant-wide VM deployment issue.…”

Section: Related Workmentioning

confidence: 99%

“…[6][7][8][9][10][11][12][13]. Most of them mainly focused on the development of different algorithms or mathematic methods to build VM models of production processes, as well as demonstrating the fault detection capability and prediction accuracy of the VM models.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of cloud-based automatic virtual metrology system for semiconductor industry

Huang

Lin

Hung

et al. 2015

Robotics and Computer-Integrated Manufacturing

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Section: Related Workmentioning

confidence: 99%

“…Most VM systems in the existing VM-related literature (such as Refs. [6][7][8][9][10][11][12][13]) did not address the plant-wide VM deployment issue.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Development of cloud-based automatic virtual metrology system for semiconductor industry

Huang

Lin

Hung

et al. 2015

Robotics and Computer-Integrated Manufacturing

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“…ETROLOGY is a critical activity in industry [21], [23] and, in particular in semiconductor manufacturing [13], [27], where it is increasingly becoming the focus of attention as feature sizes continue to shrink and wafer diameters increase from the current industrial standards of 200 mm and 300 mm to 450 mm [11]. In Chemical Vapor Deposition (CVD), for example, the feature of interest is the depth of the layer/film of material deposited on the wafer [22], while in plasma etching it is the dimensions (depth and width) of the trenches etched into the wafer surface [18].…”

mentioning

confidence: 99%

A Methodology for Efficient Dynamic Spatial Sampling and Reconstruction of Wafer Profiles

McLoone¹,

Johnston

Susto

2018

IEEE Trans. Automat. Sci. Eng.

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“…The proposed robust state-awareness architecture is based on the previously proposed anomaly detection methodology for advanced control systems [7]. The proposed FN-DFE architecture uses computational intelligence (CI) algorithms such as artificial neural networks (ANN) and fuzzy logic systems (FLS) for system behavior modeling, prediction, and anomaly detection [8]- [12].…”

mentioning

confidence: 99%

FN-DFE: Fuzzy-Neural Data Fusion Engine for Enhanced Resilient State-Awareness of Hybrid Energy Systems

Wijayasekara

Linda

Manic

et al. 2014

IEEE Trans. Cybern.

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Resiliency and improved state-awareness of modern critical infrastructures, such as energy production and industrial systems, is becoming increasingly important. As control systems become increasingly complex, the number of inputs and outputs increase. Therefore, in order to maintain sufficient levels of state-awareness, a robust system state monitoring must be implemented that correctly identifies system behavior even when one or more sensors are faulty. Furthermore, as intelligent cyber adversaries become more capable, incorrect values may be fed to the operators. To address these needs, this paper proposes a fuzzy-neural data fusion engine (FN-DFE) for resilient state-awareness of control systems. The designed FN-DFE is composed of a three-layered system consisting of: 1) traditional threshold based alarms; 2) anomalous behavior detector using self-organizing fuzzy logic system; and 3) artificial neural network-based system modeling and prediction. The improved control system state-awareness is achieved via fusing input data from multiple sources and combining them into robust anomaly indicators. In addition, the neural network-based signal predictions are used to augment the resiliency of the system and provide coherent state-awareness despite temporary unavailability of sensory data. The proposed system was integrated and tested with a model of the Idaho National Laboratory's hybrid energy system facility known as HYTEST. Experiment results demonstrate that the proposed FN-DFE provides timely plant performance monitoring and anomaly detection capabilities. It was shown that the system is capable of identifying intrusive behavior significantly earlier than conventional threshold-based alarm systems.

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A Virtual Metrology System for Predicting End-of-Line Electrical Properties Using a MANCOVA Model With Tools Clustering

Cited by 26 publications

References 11 publications

Development of cloud-based automatic virtual metrology system for semiconductor industry

Development of cloud-based automatic virtual metrology system for semiconductor industry

A Methodology for Efficient Dynamic Spatial Sampling and Reconstruction of Wafer Profiles

FN-DFE: Fuzzy-Neural Data Fusion Engine for Enhanced Resilient State-Awareness of Hybrid Energy Systems

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