Data processing: measuring – calibrating – or testing – Measurement system – Performance or efficiency evaluation
Reexamination Certificate
2005-10-28
2008-10-14
Raymond, Edward (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system
Performance or efficiency evaluation
C702S057000, C702S108000, C324S765010, C700S015000, C700S121000, C703S002000
Reexamination Certificate
active
07437271
ABSTRACT:
A method and apparatus for testing semiconductors according to various aspects of the present invention comprises a test system comprising composite data analysis element configured to analyze data from more than one dataset. The test system may be configured to provide the data in an output report. The composite data analysis element suitably performs a spatial analysis to identify patterns and irregularities in the composite data set. The composite data analysis element may also operate in conjunction with a various other analysis systems, such as a cluster detection system and an exclusion system, to refine the composite data analysis. The composite may also be merged into other data.
REFERENCES:
patent: 5240866 (1993-08-01), Friedman et al.
patent: 5787190 (1998-07-01), Peng et al.
patent: 6477685 (2002-11-01), Lovelace
patent: 6516309 (2003-02-01), Eberhart
patent: 6787379 (2004-09-01), Madge et al.
patent: 6807655 (2004-10-01), Rehani et al.
patent: 6939727 (2005-09-01), Allen, III et al.
patent: 6943042 (2005-09-01), Madge et al.
patent: 7167811 (2007-01-01), Tabor
Naumovich et al. , ‘Efficient Composite Data Flow Analysis Applied to Concurrent Programs’, 1998, ACM Publication, pp. 51-58.
Langford et al. , ‘The Identification and Analysis of Systematic Yield Loss’, 2000, IEEE Publication, pp. 92-95.
Singh et al., Screening for Known Good Die (KGD) Based on Defect Clustering: An Experimental Study, IEEE.
Singh, Position Statement: Good Die in Bad Neighborhoods, IEEE.
Miller, Position Statement: Good Die in Bad Neighborhoods, IEEE.
Roehr, Position Statement: Good Die in Bad Neighborhoods, IEEE.
Mann, “Leading Edge” of Wafer Level Testing.
Michelson, Statistically Calculating Reject Limits at Parametric Test, IEEE.
Sabade et al., Immediate Neighbor Difference IDDQ Test (INDIT) for Outlier Detection, Dept. of Computer Science—Texas A&M University.
Huang, et al., Image Processing Techniques for Wafer Defect Cluster Identification, IEEE.
Sabade, et al., Use of Multiple IDDQ Test Metrics for Outlier Identification, Dept. of Computer Science—Texas A&M University.
Chen, et al., A Neural-Network Approach to Recognize Defect Spatial Pattern in Semiconductor Fabrication, IEEE.
Sapozhnikova, et al., The Use of dARTMAP and Fuzzy Artmap to Solve the Quality Testing Task in Semiconductor Industry, IEEE.
Sikka, Automated Feature Detection & Characterization in Sort Wafer Maps.
Hansen, et al., Process Improvement Through the Analysis of Spatially Clustered Defects on Wafer Maps.
Denby, et al., A Graphical User Interface for Spatial Data Analysis in Integrated Circuit Manufacturing, AT&T Bell Laboratories.
Friedman, et al., Model-Free Estimation of Defect Clustering in Integrated Circuit Fabrication.
Hansen, et al., Monitoring Wafer Map Data From Integrated Circuit Fabrication Processes for Spatially Clustered Defects.
Xu, Statistical Problems in Semiconductor Manufacturing.
Thomas Gnieting, Measurement Related and Model Parameter Related Statistics.
Agilent PDQ-WLR(tm) Test and Analysis Software Environment—Product Note, Agilent Technologies, 2000.
Advance Parametric Tester with HP Specs, Hewlett-Packard Company, 1999.
Jeff Chappell, LSI Applies Statistics to Defectivity, Apr. 14, 2003 (http://www.reed-electronics.com/electronicnews/index.asp?layout=articlePrint&articleID=CA292185).
Erik Jan Marinissen, et al., Creating Value Through Test, IEEE 2003, 1530-1591.
Russell B. Miller, et al., Unit Level Predicted Yield: a Method of Identifying High Defect Density Die at Wafer Sort, IEEE 2001, 1118-1127.
Philippe Lejeune, et al., Optimizing Yield vs. DPPM for Parts Average Testing, www.galaxysemi.com.
Emilio Miguelanez, et al., Advanced Automatic Parametric Outlier Detection Technologies for the Production Environment.
Guidelines for Part Average Testing, Automotive Electronics Council, AEC-Q001-Rev-C Jul. 18, 2003.
Zinke, Kevin, et al. Yield Enhancement Techniques Using Neural Network Pattern Detection, IEEE 1997, 211-215.
Lejeune, Philippe et al., Minimizing Yield Loss with Parts Average Testing (PAT) and Other DPM Reduction Techniques, Tetradyne Users Group, 2006.
Lejeune, Philippe et al., Minimizing Yield Loss with Parts Average Testing (PAT) and Other DPM Reduction Techniques (Presentation), Tetradyne Users Group, 2006.
Defecter(tm)II—Defect Data Analysis Software, Semicon.
Stanley, James, Spatial Outlier Methods for Detecting Defects in Semiconductor Devices at Electrical Probe, Motorola.
Ratcliffe, Jeff, Setting a Nearest Neighbor IDDQ Threshold.
Daasch, Robert, Variance Reduction Using Wafer Patterns in Iddq Data, Proceeding of International Test Conference Oct. 2000, pp. 189-198.
Daasch, Robert, Neighbor Selection for Variance Reduction in Iddq and Other Parametric Data, ITC International Test Conference, IEEE 2001.
Pitts, John. A KGD Enabler: Full Wafer Contact Technology. 11th Annual Int'l KGD Packaging and Test Workshop, Sep. 12-15, 2004.
Secrest, Jerry. Reducing KGD Test Cost Via Prediction Testing. Secrest Research, KGD Workshop, Sep. 2002.
Madge, Robert, et al. Statistical Post-Processing at Wafersort—An Alternative to Burn-In and a Manufacturable Solution to Test Limit Setting for Sub-Micron . . . , IEEE 2002.
Motorola, Process Average Testing (PAT), Statistical Yield Analysis (SYA) and Junction Verification Test (JVT), Aug. 3, 1998.
Desta Elias
Raymond Edward
Test Advantage, Inc.
The Noblitt Group PLLC
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