반도체디스플레이기술학회지 (Journal of the Semiconductor & Display Technology)

한국반도체디스플레이기술학회 (The Korean Society Of Semiconductor & Display Technology)
권호 표지

저주파 노이즈와 BTI의 머신 러닝 모델

Machine Learning Model for Low Frequency Noise and Bias Temperature Instability

  • 김용우 (상명대학교 시스템반도체공학과) ;
  • 이종환 (상명대학교 시스템반도체공학과)
  • Kim, Yongwoo (Department of System Semiconductor Engineering, Sangmyung University) ;
  • Lee, Jonghwan (Department of System Semiconductor Engineering, Sangmyung University)
  • 투고 : 2020.12.04
  • 심사 : 2020.12.10
  • 발행 : 2020.12.31
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초록

Based on the capture-emission energy (CEE) maps of CMOS devices, a physics-informed machine learning model for the bias temperature instability (BTI)-induced threshold voltage shifts and low frequency noise is presented. In order to incorporate physics theories into the machine learning model, the integration of artificial neural network (IANN) is employed for the computation of the threshold voltage shifts and low frequency noise. The model combines the computational efficiency of IANN with the optimal estimation of Gaussian mixture model (GMM) with soft clustering. It enables full lifetime prediction of BTI under various stress and recovery conditions and provides accurate prediction of the dynamic behavior of the original measured data.

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참고문헌

  1. Grasser T. "Stochastic charge trapping in oxides: From random telegraph noise to bias temperature instabilities," Microelectronics Reliability, Vol. 52, pp. 39-70, 2012. https://doi.org/10.1016/j.microrel.2011.09.002
  2. Reisinger H., Grasser T., Gustin W., and Schlünder C., "The statistical analysis of individual defects constituting NBTI and its implications for modeling DC- and AC-stress," In Proceedings of 2010 IEEE International Reliability Physics Symposium, Anaheim, CA, USA, pp. 7-15, 2-6 May 2010.
  3. Puschkarsky K., Reisinger H., Schlünder C., Gustin W., and Grasser T., "Voltage-dependent activation energy maps for analytic lifetime modeling of NBTI without time extrapolation," IEEE Transactions on Electron Devices, Vol. 65, pp.4764-4771, 2018. https://doi.org/10.1109/TED.2018.2870170
  4. Lagger P., Reiner M., Pogany D., and Ostermaier C., "Comprehensive study of the complex dynamics of forward bias-induced threshold voltage drifts in GaN based MIS-MEMTs by stress/recovery experiments," IEEE Transactions on Electron Devices, Vol. 61, pp.1022-1030, 2014. https://doi.org/10.1109/TED.2014.2303853
  5. Putcha V., Franco J., Vais A., Sioncke S., Kaczer B., Linten D., and Groeseneken G., "On the apparent non-Arrhenius temperature dependence of charge trapping in IIIV/high-k MOS stack," IEEE Transactions on Electron Devices, Vol. 65, pp.3689-3696, 2018. https://doi.org/10.1109/TED.2018.2851189
  6. Tewksbury T. L. and Lee H.S., "Characterization, modeling, and minimization of transient threshold voltage shifts in MOSFET's," IEEE Journal of Solid-State Circuits, Vol. 29, pp.239-252, 1994 https://doi.org/10.1109/4.278345
  7. Grasser T., Rott K., Reisinger H., Waltl M., Franco J., and Kaczer B., "A unified perspective of RTN and BTI," In Proceedings of 2014 IEEE International Reliability Physics Symposium, Waikoloa, HI, USA, pp. 4A.5.1-4A.5.7., 1-5 June 2014.
  8. Kaczer B., Grasser T., Martin-Martinez J., Simoen E., Aoulaiche M., Roussel Ph.J., and Groseneken G., "NBTI from the perspective of defect states with widely distributed time scales," In Proceedings of 2009 IEEE International Reliability Physics Symposium, Montreal, QC, Canada, pp. 55-60, 26-30 April 2009.
  9. Grasser T., Reisinger H., Goes W., Aichinger Th., Hehenberger Ph., Wagner P.J., Nelhiebel M., Franco J., and Kaczer B., "Switching oxide traps as the missing link between negative bias temperature instability and random telegraph noise," In Proceedings of 2009 International Electron Device Meeting, Baltimore, MD, USA, pp. 729-732, 7-9 Dec. 2009.
  10. Da Silva R., Wirth G. I., and Brusamarello L., "An appropriate model for the noise power spectrum produced by traps at the Si-SiO2 interface: a study of the influence of a time-dependent Fermi level," Journal of Statistical Mechanics: Theory and Experiment, Vol.2008, pp.10015-10025, 2008.
  11. Wirth G. and da Silva R., "Low-frequency noise spectrum of cyclo-stationary random telegraph signals," Electrical Engineering, Vol.90, pp.435-441, 2008. https://doi.org/10.1007/s00202-007-0094-y
  12. Weckx P., Kaczer M., Toledano-Luque M., Grasser T., Roussel Ph.J., Kukner H., Raghaven P., Catthoor F., and Groeseneken G., "Defect-based methodology for workload-dependent circuit lifetime projectionsApplication to SRAM," Proceedings of 2013 IEEE International Reliability Physics Symposium, Anaheim, CA, USA, pp. 3A.4.1-3A.4.7., 14-18 April 2013.
  13. Puschkarsky K., Reisinger H., Rott G. A., Schlunder C., Gustin W., and T.Grasser, "An efficient analog compact NBTI model for stress and recovery based on activation energy maps," IEEE Transactions on Electron Devices, Vol. 66, pp.4623-4630, 2019. https://doi.org/10.1109/TED.2019.2941889
  14. Fang R., Livingston I., Esqueda I. S., and Kozicki M., "Bias temperature instability model using dynamic defect potential for predicting CMOS aging," Journal of Applied Physics, Vol. 123, pp.225701-1-225701-9, 2018. https://doi.org/10.1063/1.5027856
  15. Wang W., Reddy V., Krishnan A. T., Vattikonda R., Krishnan S., and Cao Y., "Compact modeling and simulation of circuit reliability for 65-nm CMOS technology," IEEE Transactions on Device and Materials Reliability, Vol. 7, pp.509-517, 2007 https://doi.org/10.1109/TDMR.2007.910130
  16. Cao Y., et. al., "Cross-layer modeling and simulation of circuit reliability," IEEE Transaction on Computer - Aided Design of Integrated Circuits and Systems, Vol. 33, pp.8-23, 2014. https://doi.org/10.1109/TCAD.2013.2289874
  17. Velamala J. B., et. al., "Compact modeling of statistical BTI under trapping/detrapping," IEEE Transactions on Electron Devices, Vol. 60, pp.3645-3654, 2013. https://doi.org/10.1109/TED.2013.2281986
  18. Huang A. D., Zhong Z., Wu W., and Guo Y. X., "An artificial neural network-based electrothermal model for GaN HEMTs with dynamic trapping effects consideration," IEEE Transactions on Microwave Theory and Techniques, Vol. 64, no.8, pp.2519-2528, 2016. https://doi.org/10.1109/TMTT.2016.2586055
  19. Li X., Gao J., and Boeck G., "Microwave nonlinear device modeling by using an artificial neural network," Semiconductor Science and Technology, Vol.21, pp.833-840, 2006. https://doi.org/10.1088/0268-1242/21/7/001
  20. Lee J., "Physics-guided neural modeling for low-dimensional thermoelectric module," IEEE Electron Device Letters, Vol.40, pp.1812-1815, 2019. https://doi.org/10.1109/LED.2019.2944395
  21. Zhang L. and Chan M., "Artificial neural network design for compact modeling of generic transistors," Journal of Computational Electronics, Vol.16, pp.825-832, 2017. https://doi.org/10.1007/s10825-017-0984-9
  22. Jarndal A., "On neural networks based electrothermal modeling of GaN devices," IEEE Access, Vol. 7, pp.94205-94214, 2019. https://doi.org/10.1109/ACCESS.2019.2928392
  23. Shimizu H., Awano H., Hiromotto M., and Sato T., "Automation of model parameter estimation for random telegraph noise," IEICE Transactions on Fundamentals, Vol. E97-A, pp.2383-2392, 2014. https://doi.org/10.1587/transfun.E97.A.2383
  24. Singh R., Pal B. C., and Jabr R. A., "Statistical representation of distribution system loads using Gaussian mixture model," IEEE Transactions on Power Systems, Vol.25, pp.29-37, 2010. https://doi.org/10.1109/TPWRS.2009.2030271
  25. Peters G., Crespo F., Lingras P., and Weber R., "Soft clustering - fuzzy and rough approaches and their extensions and derivatives," International Journal of Approximate Reasoning, Vol. 54, pp.307-322, 2013. https://doi.org/10.1016/j.ijar.2012.10.003
  26. D. H. Kim, J. E. Choi, T. M. Ha, and S. J. Hong "Modeling with thin film thickness using machine learning," Journal of the Semiconductor & Display Technology, Vol. 18, pp. 48-52, 2019
  27. J. H. Han and S. S. Hong, "Semiconductor process inspection using mask R-CNN," Journal of the Semiconductor & Display Technology, Vol. 19, pp. 12-18, 2020
  28. Rai R. and Sahu C. K., "Driven by data or derived through physics? A review of hybrid physics guided machine learning techniques with cyber-physical system (CPS) focus," IEEE Access, Vol. 8, pp.71050-71073, 2020. https://doi.org/10.1109/ACCESS.2020.2987324