He et al., 2017 - Google Patents
Statistical process monitoring in the era of smart manufacturingHe et al., 2017
- Document ID
- 14188565292017102528
- Author
- He Q
- Wang J
- Publication year
- Publication venue
- 2017 American Control Conference (ACC)
External Links
Snippet
One of the focuses of smart manufacturing is to create manufacturing intelligence from real- time data to support accurate and timely decision-making. Therefore, data-driven statistical process monitoring is expected to contribute significantly to the advancement of smart …
- 238000000034 method 0 title abstract description 90
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
- G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by quality surveillance of production
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/45—Nc applications
- G05B2219/45031—Manufacturing semiconductor wafers
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B17/00—Systems involving the use of models or simulators of said systems
- G05B17/02—Systems involving the use of models or simulators of said systems electric
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B15/00—Systems controlled by a computer
- G05B15/02—Systems controlled by a computer electric
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
Similar Documents
Publication | Publication Date | Title |
---|---|---|
He et al. | Statistical process monitoring as a big data analytics tool for smart manufacturing | |
Fan et al. | Data-driven approach for fault detection and diagnostic in semiconductor manufacturing | |
Susto et al. | Anomaly detection through on-line isolation forest: An application to plasma etching | |
TWI729211B (en) | Method and non-transitory computerreadable storage medium for time-series fault detection, fault classification, and transition analysis using a k-nearestneighbor and logistic regression approach | |
Rato et al. | Translation-invariant multiscale energy-based PCA for monitoring batch processes in semiconductor manufacturing | |
TWI459487B (en) | Metrics independent and recipe independent fault classes | |
Gajjar et al. | A data-driven multidimensional visualization technique for process fault detection and diagnosis | |
Liu et al. | Similarity based method for manufacturing process performance prediction and diagnosis | |
Zeng et al. | Virtual metrology modeling for plasma etch operations | |
AU2012284497A1 (en) | Monitoring system using kernel regression modeling with pattern sequences | |
AU2012284459A1 (en) | Method of sequential kernel regression modeling for forecasting and prognostics | |
Van den Kerkhof et al. | Contribution plots for statistical process control: Analysis of the smearing-out effect | |
Baklouti et al. | Iterated robust kernel fuzzy principal component analysis and application to fault detection | |
US20200184373A1 (en) | Recurrent Gaussian Mixture Model For Sensor State Estimation In Condition Monitoring | |
Zhang et al. | Phase I analysis of multivariate profiles based on regression adjustment | |
Alaei et al. | A new integrated on-line fuzzy clustering and segmentation methodology with adaptive PCA approach for process monitoring and fault detection and diagnosis | |
Rendall et al. | A unifying and integrated framework for feature oriented analysis of batch processes | |
Hu et al. | Recursive-CPLS-based quality-relevant and process-relevant fault monitoring with application to the Tennessee Eastman process | |
He et al. | Statistical process monitoring in the era of smart manufacturing | |
Chouichi et al. | Chamber-to-chamber discrepancy detection in semiconductor manufacturing | |
Ayech et al. | New adaptive moving window PCA for process monitoring | |
Lv et al. | Interpretable fault detection using projections of mutual information matrix | |
Thieullen et al. | Application of Principal Components Analysis to improve fault detection and diagnosis on semiconductor manufacturing equipment | |
Susto | A dynamic sampling strategy based on confidence level of virtual metrology predictions | |
Yu et al. | Using minimum quantization error chart for the monitoring of process states in multivariate manufacturing processes |