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IET International Conference on Developments in Power System Protection

作者： Jun Han Won-Ki Kim Jae-Won Lee Chul-Hwan Kim Sungkyunkwan University

ISBN: (纸本)9781622761050

Modern power supplies must provide reliable, high-quality power. Therefore, it is important that a transmission line fault be identified accurately, reliably and corrected as soon as possible. A method for classifying fault type based on short time Fourier transform (STFT) is provided in this paper. STFT is a means of signal processing in which a Fourier transform (FT) is performed by setting a window function with a constant size. This transformation is used to analyze transients in the power system. In order to verify the suggested algorithm, the transmission system is modeled by EMTP-RV and line faults are simulated in the transmission line. This paper analyzes characteristics of STFT by using simulation results from MATLAB and classifies line faults by fault type. The simulation results show that the suggested algorithm is effective and valid for detecting fault types during line faults.

关键词： EMTP fault detection and classification Short Time Fourier Transform Transmission Line

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Computational Intelligence Techniques to fault detection in the Semiconductor Manufacturing Process

Computational Intelligence Techniques to Fault Detection in ...

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1st International Conference on Mechanical Engineering

作者： Kerdprasop, Kittisak Kerdprasop, Nittaya Suranaree Univ Technol Sch Comp Engn Data Engn & Knowledge Discovery Res Unit Nakhon Ratchasima 30000 Thailand

ISBN: (纸本)9783037850770

The semiconductor industry deals with the production at a scale of nanometer, thus resulting in the process control with little margin of error. Timely detection of faults during the manufacturing process is critical to the improvement in product yields. Difficulty of detecting accurately faulty processes and products is due to the abundant of data obtained from hundreds of tool-state and process-state sensors. We thus analyze this problem through the computational intelligence techniques. The analysis results reveal the minimal set of features for fault detection as well as the high precision classification model of faults.

关键词： fault detection and classification Feature selection Semiconductor manufacturing

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Sheath fault detection and classification based on wavelet analysis

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EUROPEAN TRANSACTIONS ON ELECTRICAL POWER 2006年第4期16卷 327-344页

作者： Wang, X. H. Song, Y. H. Brunel Univ Sch Engn & Design Uxbridge UB8 3PH Middx England Kingston Univ Sch Comp & Informat Syst Kingston upon Thames KT1 2EE Surrey England

This paper presents a wavelet based algorithm to detect and classify sheath insulation faults on underground transmission cables. This algorithm can detect a high impedance sheath to earth fault, classify it, and discriminate it from other phenomena, such as load change, harmonics and surges on the cores. For the development of the algorithm, a simplified fault model is introduced to simulate a sheath to earth fault on a power transmission cable. Before applying wavelet analysis, an 'adaptive noise cancelling' (ANC) technique is used to extract the fault signal. Simulation studies are reported on a 300 mm(2) concentric copper PVC insulated bonding lead. Copyright (C) 2006 John Wiley & Sons, Ltd.

关键词： sheath fault fault modeling adaptive noise canceling wavelet analysis fault detection and classification

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Lithography Light Source fault detection

Lithography Light Source Fault Detection

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Conference on Optical Microlithography XXIII

作者： Graham, Matthew Pantel, Erica Nelissen, Patrick Moen, Jeffrey Tincu, Eduard Dunstan, Wayne Brown, Daniel Cymer Inc San Diego CA USA

ISBN: (纸本)9780819480545

High productivity is a key requirement for today's advanced lithography exposure tools. Achieving targets for wafers per day output requires consistently high throughput and availability. One of the keys to high availability is minimizing unscheduled downtime of the litho cell, including the scanner, track and light source. From the earliest eximer laser light sources, Cymer has collected extensive performance data during operation of the source, and this data has been used to identify the root causes of downtime and failures on the system. Recently, new techniques have been developed for more extensive analysis of this data to characterize the onset of typical end-of-life behavior of components within the light source and allow greater predictive capability for identifying both the type of upcoming service that will be required and when it will be required. The new techniques described in this paper are based on two core elements of Cymer's light source data management architecture. The first is enhanced performance logging features added to newer-generation light source software that captures detailed performance data;and the second is Cymer OnLine (COL) which facilitates collection and transmission of light source data. Extensive analysis of the performance data collected using this architecture has demonstrated that many light source issues exhibit recognizable patterns in their symptoms. These patterns are amenable to automated identification using a Cymer-developed model-based fault detection system, thereby alleviating the need for detailed manual review of all light source performance information. Automated recognition of these patterns also augments our ability to predict the performance trending of light sources. Such automated analysis provides several efficiency improvements for light source troubleshooting by providing more content-rich standardized summaries of light source performance, along with reduced time-to-identification for previously classifie

关键词： light source fault detection and classification availability

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Automated fault detection and classification of etch systems using modular neural networks

Automated fault detection and classification of etch systems...

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Conference on Data Analysis and Modeling for Process Control

作者： Hong, SJ May, GS Yamartino, J Skumanich, A Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

ISBN: (纸本)0819452912

Modular neural networks (MNNs) are investigated as a tool for modeling process behavior and fault detection and classification (FDC) using tool data in plasma etching. Principal component analysis (PCA) is initially employed to reduce the dimensionality of the voluminous multivariate tool data and to establish relationships between the acquired data and the process state. MNNs are subsequently used to identify anomalous process behavior. A gradient-based fuzzy C-means clustering algorithm is implemented to enhance MNN performance. MNNs for eleven individual steps of etch runs are trained with data acquired from baseline, control (acceptable), and perturbed (unacceptable) runs, and then tested with data not used for training. In the fault identification phase, a 0% of false alarm rate for the control runs is achieved.

关键词： advanced process control fault detection and classification principal component analysis modular neural networks fuzzy C-means algorithm

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Semiconductor manufacturing process control and monitoring: A fab-wide framework

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JOURNAL OF PROCESS CONTROL 2006年第3期16卷 179-191页

作者： Qin, SJ Cherry, G Good, R Wang, J Harrison, CA Univ Texas Dept Chem Engn Austin TX 78712 USA

The semiconductor industry has started the technology transition from 200 min to 300 min wafers to improve manufacturing efficiency and reduce manufacturing cost. These technological changes present a unique opportunity to optimally design the process control systems for the next generation fabs. In this paper we first propose a hierarchical fab-wide control framework with the integration of 300 min equipment and metrology tools and highly automated material handling system. Relevant existing run-to-run technology is reviewed and analyzed in the fab-wide control context. Process and metrology data monitoring are discussed with an example. Missing components are pointed out as opportunities for future research and development. Concluding remarks are given at the end of the paper. (c) 2005 Published by Elsevier Ltd.

关键词： semiconductor manufacturing fab-wide control electrical parameter control run-to-run control fault detection and classification metrology data monitoring

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Information processing in reactive navigation and fault detection of walking robot

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8th International Conference on Climbing and Walking Robots (CLAWAR 2005)

作者： Vitko, A. Savel, M. Kameniar, D. Jurisica, L. Slovak Univ Technol Bratislava Fac Mech Engn Bratislava Slovakia Slovak Univ Technol Bratislava Fac Elect Engn & Informat Technol Bratislava Slovakia

ISBN: (纸本)3540264132

The paper first summarizes main approaches to the sensor integration for the purposes of reactive navigation and the fault detection. Then the topic is focused on the results obtained by the originally developed fusing and classification algorithm for detection and classification of abnormal behaviorus appearing in a walking robot.

关键词： sensor integration navigation fault detection and classification robot

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Manufacturability of multivariate applications in the semiconductor industry

Manufacturability of multivariate applications in the semico...

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IEEE International Conference on Automation Science and Engineering

作者： Jonathan Chang Yung-Cheng Cheng, Fan-Tien Natl Cheng Kung Univ Inst Mfg Engn Tainan 70101 Taiwan

ISBN: (纸本)9781424403103

When advanced automation technologies are applied in the semiconductor industry, advanced equipment control has become increasingly feasible;hence it is now easier to improve overall manufacturing performance, including cycle time, productivity, and product quality. Currently, fault detection and classification (FDC) is a newly deployed system that supports tool control in the semiconductor industry. Based on comprehensive and real time tool sensor data of an FDC infrastructure, numerous novel multivariate applications have been developed to further enhance tool and manufacturing productivity. However, when most multivariate applications were designed and pilot run in production environment, they all encountered a similar challenge - manufacturability. Because of insufficiency of manufacturing know-how and manufacturability, most multivariate applications cannot be widely deployed to an entire FAB for all types of tools and processes. This work investigates various multivariate applications and presents a novel methodology for improving manufacturability design of multivariate applications in the semiconductor industry.

关键词： multivariate fault detection and classification manufacturability

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Semiconductor manufacturing process control and monitoring: A fab-wide framework

Semiconductor manufacturing process control and monitoring: ...

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Symposium on Dynamics and Control of Process Systems

作者： Qin, SJ Cherry, G Good, R Wang, J Harrison, CA Univ Texas Dept Chem Engn Austin TX 78712 USA

关键词： semiconductor manufacturing fab-wide control electrical parameter control run-to-run control fault detection and classification metrology data monitoring

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Wafer-level fault detection and classification on a photo track in a high volume fab

Wafer-level fault detection and classification on a photo tr...

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Conference on Design, Process Integration, and Characterization for Microelectronics

作者： Jackson, TL Markle, RJ Miller, CW Stewart, EC Crowell, R Adv Micro Devices Inc Austin TX 78741 USA

ISBN: (纸本)0819444391

We will discuss Advanced Micro Devices's (AMD) fault detection and classification (FDC) program strategy and our six-step project template that we have identified that must be addressed for any successful FDC effort. We will discuss the recent development and implementation of a wafer-level FDC system on a TEL CLEAN TRACK ACT 8 (ACT 8) photo track system in AMD's Fab25, a high volume microprocessor factory. We will present our approach to designing and implementing this FDC system and demonstrate its ability to automatically identify specific wafers within a lot that require manual review. Upon manual review, the decision can be made to rework the specific wafers or the lot.

关键词： fault detection and classification FDC photolithography track scrap wafers reworked wafers

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