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Sensors  2013 

Improving Electronic Sensor Reliability by Robust Outlier Screening

DOI: 10.3390/s131013521

Keywords: semiconductor device testing, zero defect, customer quality incident, robust statistics

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Electronic sensors are widely used in different application areas, and in some of them, such as automotive or medical equipment, they must perform with an extremely low defect rate. Increasing reliability is paramount. Outlier detection algorithms are a key component in screening latent defects and decreasing the number of customer quality incidents (CQIs). This paper focuses on new spatial algorithms (Good Die in a Bad Cluster with Statistical Bins (GDBC SB) and Bad Bin in a Bad Cluster (BBBC)) and an advanced outlier screening method, called Robust Dynamic Part Averaging Testing (RDPAT), as well as two practical improvements, which significantly enhance existing algorithms. Those methods have been used in production in Freescale ? Semiconductor probe factories around the world for several years. Moreover, a study was conducted with production data of 289,080 dice with 26 CQIs to determine and compare the efficiency and effectiveness of all these algorithms in identifying CQIs.


[1]  Wassum, K.M.; Tolosa, V.M.; Wang, J.; Walker, E.; Monbouquette, H.G.; Maidment, N.T. Silicon wafer-based platinum microelectrode array biosensor for near real-time measurement of glutamate in vivo.. Sensors 2008, 8, 5023–5036.
[2]  Hidalgo, J.; Poulakis, P.; K?hler, J.; Del-Cerro, J.; Barrientos, A. Improving planetary rover attitude estimation via MEMS sensor characterization. Sensors 2012, 12, 2219–2235.
[3]  Sang, L.; Liao, M.; Sumiya, M. A comprehensive review of semiconductor ultraviolet photodetectors: From thin film to one-dimensional nanostructures. Sensors 2013, 13, 10482–10518.
[4]  De Pasquale, G.; Somà, A. Reliability testing procedure for MEMS IMUs applied to vibrating environments. Sensors 2010, 10, 456–474.
[5]  Zaal, J.J.M.; Van Driel, W.D.; Zhang, G. Challenges in the assembly and handling of thin film capped MEMS devices. Sensors 2010, 10, 3989–4001.
[6]  Chuang, W.-C.; Hu, Y.-C.; Chang, P.-Z. CMOS-MEMS test-key for extracting aafer-level mechanical properties. Sensors 2012, 12, 17094–17111.
[7]  Migl, D. Zero Defect Mission Requires an Arsenal. Proceedings of IEEE International Test Conference, Santa Clara, CA, USA, October 2006.
[8]  Ooi, M.P.; Kuang, Y.C.; Chan, C.; Demidenko, S. Predictive Die-Level Reliability-Yield Modeling for Deep Sub-Micron Devices. Proceedings of 4th IEEE International Symposium on Electronic Design, Test and Applications, Hong Kong, China, 23–25 Janunary 2008; pp. 216–221.
[9]  Ooi, M.P.; Sim, E.K.; Kuang, Y.C.; Demidenko, S.; Kleeman, L.; Chan, C.W. Getting more from the semiconductor test: Data mining with defect-cluster extraction. IEEE Tran. Instrum. Meas. 2011, 60, 3300–3317.
[10]  Barnett, T.S.; Singh, A.D.; Grady, M.; Purdy, K. Yield-Reliability Modeling: Experimental Verification and Application to Burn-in Reduction. Proceedings of 20th IEEE VLSI Test Symposium, Monterey, CA, USA, 2 May 2002; pp. 75–80.
[11]  Barnett, T.S.; Grady, M.; Purdy, K.; Singh, A.D. Exploiting defect clustering for yield and reliability prediction. IEE Proc. Comput. Digt. Tech. 2005, 152, 407–413.
[12]  Ooi, M.P.; Chan, C.; Lee, S.; Chin, W.L.; Goh, L.Y.; Kuang, Y.C.; Demidenko, S. Critical Assessment of Die Level Predictor Models. Proceedings of 33rd IEEE /CPMT International Electronic Manufacturing Technology Symposium, Penang, Malaysia, 4–6 Novmber 2008; pp. 1–6.
[13]  Ooi, M.P.; Chan, C.; Lee, S.L.; Mohanan, A.A.; Goh, L.Y.; Kuang, Y.C. Towards Identification of Latent Defects: Yield Mining Using Defect Characteristic Model and Clustering. Proceedings of IEEE/SEMI ASMC, Berlin, Germany, 10–12 May 2009; pp. 194–199.
[14]  Barnett, T.S.; Grady, M.; Purdy, K.; Singh, A.D. Combining negative binomial and weibull distributions for yield and reliability modeling. IEEE Des. Test Comput. 2006, 23, 110–116.
[15]  Ohletz, M.J.; Schulze, F. Design, qualification and production of integrated sensor interface circuits for high-quality automotive applications. Microelectron. J. 2009, 40, 1350–1357.
[16]  Solanki, A.; Prasad, K.; Oreilly, R.; Singhal, Y. Inertial MEMS Test Challenges. Proceedings of IEEE 17th International Mixed-Signals, Sensors and Systems Test Workshop, Santa Barbara, CA, USA, 16–18 May 2011; pp. 114–119.
[17]  Mann, W.R. Wafer test methods to improve semiconductor die reliability. IEEE Des. Test Comput. 2008, 25, 528–537.
[18]  Daash, W.R.; Shirley, C.G.; Nahar, A. Statistics in semiconductor test: Going beyond yield. IEEE Des. Test Comput. 2009, 26, 64–73.
[19]  Marinissen, E.J.; Singh, A.; Glotter, D.; Esposito, M.; Carulli, J.M.; Nahar, A.; Butler, K.M.; Appello, D.; Portelli, C. Adapting to Adaptive Testing. Proceedings of the Conference on Design, Automation and Test in Europe, Dresden, Germany, 24–28 March 2010; pp. 556–561.
[20]  Silicon Wafer Processing. Available online: (accessed on 24 September 2013).
[21]  Chang, H.; Shen, Q.; Zhou, Z.; Xie, J.; Jiang, Q.; Yuan, W. Design, fabrication, and testing of a bulk micromachined inertial measurement unit. Sensors 2010, 10, 3835–3856.
[22]  Chou, Y.; Polansky, A.M.; Mason, R.L. Transforming non-normal data to normality in statistical process control. J. Qual. Technol. 1998, 30, 133–141.
[23]  Anderson, T.W.; Darling, D.A. Asymptotic theory of certain goodness of fit criteria. Ann. Math. Stat. 1952, 23, 193–212.
[24]  Grubbs, F. Procedures for detecting outlying observations in samples. Technometrics 1969, 11, 1–21.


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