Improved process monitoring with Independent Components

Shannon, T.T.; Abercrombie, David; McNames, James; Whitefield, Bruce

doi:10.1109/asmc.2004.1309559

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Xia and Howell (2003) developed a spectral ICA approach to transform the process measurements from the time domain to the frequency domain and to identify major oscillations. Shannon et al (2003Shannon et al ( , 2004 used ICA in monitoring a semiconductor manufacturing process. They applied ICA on microelectronic parametric test (E-test) data to generate independent components for isolating the sources of variation in the test data.…”

Section: No Training Phasementioning

confidence: 99%

An independent component analysis-based disturbance separation scheme for statistical process monitoring

2010

J Intell Manuf

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In this paper, an independent component analysis (ICA)-based disturbance separation scheme is proposed for statistical process monitoring. ICA is a novel statistical signal processing technique and has been widely applied in medical signal processing, audio signal processing, feature extraction and face recognition. However, there are still few applications of using ICA in process monitoring. In the proposed scheme, ICA is first applied to in-control training process data to determine the de-mixing matrix and the corresponding independent components (ICs). The IC representing the white noise information of the training data is then identified and the associated row vector of the IC in the de-mixing matrix is preserved. The preserved row vector is then used to generate the monitoring IC of the process data under monitoring. The disturbances in the monitoring process can be effectively enhanced in the monitoring IC. Finally, the traditional exponentially weighted moving average control chart is used to the monitoring IC for process control. For evaluating the effectiveness of the proposed scheme, simulated manufacturing process datasets with step-change disturbance are evaluated. Experiments reveal that the proposed monitoring scheme outperforms the traditional control charts in most instances and thus is effective for statistical process monitoring.

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Section: No Training Phasementioning

confidence: 99%

An independent component analysis-based disturbance separation scheme for statistical process monitoring

2010

J Intell Manuf

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ICA Based Disturbance Specific Control Charts

Shannon

McNames

2007

2007 IEEE International Conference on Information Reuse and Integration

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Concurrent process model and specification cause-effect monitoring using alternate diagnostic signatures

Devarakond

Sen

Bhattacharya

et al. 2010

2010 28th VLSI Test Symposium (VTS)

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With technology scaling, the impact of intra and inter-die process variations on the performance of mixed-signal/RF circuits has increased, making process monitoring a critical task in the overall silicon manufacturing flow. We propose a novel processspecification cause-effect monitoring scheme that allows the effects of process variations and shifts on device specifications to be monitored on a per-IC basis as opposed to existing techniques that rely only on electrical test data gathered across lots of wafers. The method relies on the use of alternate diagnostic tests under which the DUT response (alternate diagnostic signature) exhibits strong simultaneous correlation with its specifications as well as the critical process or circuit parameters with virtually zero extra test-time or testhardware cost. Simulation results indicate that critical process parameters can be diagnosed accurately from the applied tests.

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Improved process monitoring with Independent Components

Cited by 3 publications

References 11 publications

An independent component analysis-based disturbance separation scheme for statistical process monitoring

An independent component analysis-based disturbance separation scheme for statistical process monitoring

ICA Based Disturbance Specific Control Charts

Concurrent process model and specification cause-effect monitoring using alternate diagnostic signatures

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