Device Level Maverick Screening - Application of Independent Component Analysis in Semiconductor Industry

Anja Zernig, Olivia Bluder, Andre Kästner, Jürgen Pilz

2014

Abstract

To guarantee the reliability and functionality of semiconductor devices, risk chips, so-called Mavericks, have to be identified and eliminated. In this context, the most sensitive period is the early life stage, where failures are likely to occur and can lead to fatal consequences. Consequently, to identify potential early life failures, a pre-defined stress e.g. operation at elevated temperature, is applied to sort out weak devices during their initial lifetime. This method is commonly known as Burn-In process. Nevertheless, this method is expensive and therefore a reduction of the number of devices to be Burn-In tested is desirable. More cost- and also time-efficient are methods based on statistical analysis, e.g. Screening methods, which are applied to distinguish between good chips and Mavericks. Unfortunately, due to miniaturization of devices, the efficiency of currently applied Screening methods, like Part Average Testing (PAT) and Good Die in Bad Neighborhood (GDBN) techniques decreases. To compensate for this, advanced Screening methods are required. One promising strategy is an algorithm which combines the method of Independent Component Analysis (ICA) and Nearest Neighbor Residuals (NNR) and is therefore investigated. First evaluations confirm that this approach is highly promising to provide a clearer distinction between good chips and Mavericks, implying reduced Burn-In effort and therewith a relevant reduction of time and money.

References

  1. Bell, A. J. and Sejnowski, T. J. (1995). An informationmaximization approach to blind separation and blind deconvolution. Neural computation, 7:1129-1159.
  2. Friedman, J. H. (1987). Exploratory projection pursuit. Journal of the American Statistical Association, 82:249-266.
  3. Hyvärinen, A., Karhunen, J., and Oja, E. (2001). Independent Component Analysis. John Wiley & Sons.
  4. Miller, A. C. (1999). Iddq testing in deep submicron integrated circuits. In ITC International Test Conference.
  5. Naik, G. R. and Kumar, D. K. (2011). An overview of independent component analysis and its applications. Informatica, 35:63-81.
  6. Riordan, W. C., Miller, R., and St. Pierre, E. R. (2005). Reliability improvement and burn in optimization through the use of die level predictive modeling. In Annual IRPS. 43rd IEEE Annual IRPS.
  7. Turakhia, R. P., Benware, B., Madge, R., Shannon, T. T., and Daasch, W. R. (2005). Defect screening using independent component analysis on iddq. In VTS'05. 23rd IEEE VLSI Test Symposium.
Download


Paper Citation


in Harvard Style

Zernig A., Bluder O., Kästner A. and Pilz J. (2014). Device Level Maverick Screening - Application of Independent Component Analysis in Semiconductor Industry . In Doctoral Consortium - DCPRAM, (ICPRAM 2014) ISBN Not Available, pages 3-10. DOI: 10.5220/0004931500030010


in Bibtex Style

@conference{dcpram14,
author={Anja Zernig and Olivia Bluder and Andre Kästner and Jürgen Pilz},
title={Device Level Maverick Screening - Application of Independent Component Analysis in Semiconductor Industry},
booktitle={Doctoral Consortium - DCPRAM, (ICPRAM 2014)},
year={2014},
pages={3-10},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004931500030010},
isbn={Not Available},
}


in EndNote Style

TY - CONF
JO - Doctoral Consortium - DCPRAM, (ICPRAM 2014)
TI - Device Level Maverick Screening - Application of Independent Component Analysis in Semiconductor Industry
SN - Not Available
AU - Zernig A.
AU - Bluder O.
AU - Kästner A.
AU - Pilz J.
PY - 2014
SP - 3
EP - 10
DO - 10.5220/0004931500030010