Skip to main content
SpringerLink
Log in

Machine Learning for VLSI Chip Testing and Semiconductor Manufacturing Process Monitoring and Improvement

  • Chapter
  • First Online:
Machine Learning in VLSI Computer-Aided Design

Abstract

Machine learning and big data analytics are the latest spotlights with all the glare of fame ranging from media coverage to booming start-up companies to eye-catching merges and acquisitions. On the contrary, the $336 billion industry of semiconductor was seen as an “old-fashioned” business, with fading interests from the best and brightest among young graduates and engineers. This chapter argues that this does not have to be that way because many research problems and solutions as studied in the semiconductor industry are in fact closely related to these machine learning and big data problems. To illustrate this point, we discuss a number of practical but challenging problems arising from semiconductor manufacturing process in this chapter. We first show how machine learning techniques, especially those regression-related problems, often under the “disguise” of optimization problems, have been used frequently (often with nontrivial modeling skills and mathematical sophistications) to solve the semiconductor problems. We discuss such examples as process variation modeling and VLSI chip testing. For some other types of semiconductor problems, such as manufacturing process monitoring and improvement, we show that some existing machine learning algorithms are not necessarily well positioned to solve them, and novel machine learning techniques involving temporal, structural, and hierarchical properties need to be further developed. In either scenario, we convey the message that machine learning and existing semiconductor industry researches are closely related, and researchers often contribute to and benefit from each other.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. J. Chen, Y. Chen, X. Du, C. Li, J. Lu, S. Zhao, X. Zhou, Big data challenge: a data management perspective. Front. Comp. Sci. 7(2), 157–164 (2013)

    Article  MathSciNet  Google Scholar 

  2. D. Analytics, Analytics trends 2015: a below-the-surface look, in White Paper (2015)

    Google Scholar 

  3. G. Newell, N. Bekhazi, R. Morgan, Optimizing storage and I/O for distributed processing on enterprise and high performance compute (HPC) systems for mask data preparation software (CATS), Technical Report, Synopsys, Inc., 2007

    Google Scholar 

  4. D. Kurz, C.D. Luca, J. Pilz, Monitoring virtual metrology reliability in a sampling decision system, in Conference on Automation Science and Engineering (2013)

    Google Scholar 

  5. A. Johnson, S. McLoone, A dynamic sampling methodology for within product virtual metrology, in 29th International Manufacturing Conference (2012)

    Google Scholar 

  6. J. Attenberg, K. Weinberger, A. Dasgupta, Collaborative email-spam filtering with the hashing-trick, in Proceedings of the Sixth Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference (2009)

    Google Scholar 

  7. O. Chappelle, P. Shivaswamy, S. Vadrevu, Multi-task learning for boosting with application to web search ranking, in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2010), pp. 1189–1198

    Google Scholar 

  8. A. Torralba, K.P. Murphy, W.T. Freeman, Sharing features: efficient boosting procedures for multiclass object detection, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2004), pp. 762–769

    Google Scholar 

  9. M. Aitkin, N. Longford, Statistical modelling issues in school effectiveness studies. J. R. Stat. Soc. A 149, 1–43 (1986)

    Article  Google Scholar 

  10. M. Daniels, C. Gatsonis, Hierarchical generalized linear models in the analysis of variations in health care utilization. J. Am. Stat. Assoc. 94, 29–38 (1999)

    Article  Google Scholar 

  11. Y. Chen, B. Hu, E.J. Keogh, G.E.A.P.A. Batista, Dtw-d: time series semi-supervised learning from a single example, in KDD (2013), pp. 383–391

    Google Scholar 

  12. B. Hu, Y. Chen, E.J. Keogh, Time series classification under more realistic assumptions, in SDM (2013), pp. 578–586

    Google Scholar 

  13. J. Zakaria, A. Mueen, E.J. Keogh, Clustering time series using unsupervised-shapelets, in ICDM (2012), pp. 785–794

    Google Scholar 

  14. L. Li, B.A. Prakash, Time series clustering: complex is simpler!, in ICML (2011), pp. 185–192

    Google Scholar 

  15. L. Li, B.A. Prakash, C. Faloutsos, Parsimonious linear fingerprinting for time series. J. Proc. VLDB Endow. 3(1), 385–396 (2010)

    Article  Google Scholar 

  16. T. Rakthanmanon, B.J.L. Campana, A. Mueen, G.E.A.P.A. Batista, M.B. Westover, Q. Zhu, J. Zakaria, E.J. Keogh, Searching and mining trillions of time series subsequences under dynamic time warping, in KDD (2012), pp. 262–270

    Google Scholar 

  17. L. Wei, E.J. Keogh, X. Xi, M. Yoder, Efficiently finding unusual shapes in large image databases. Data Min. Knowl. Disc. 17(3), 343–376 (2008)

    Article  MathSciNet  Google Scholar 

  18. B.-K. Yi, N. Sidiropoulos, T. Johnson, H.V. Jagadish, C. Faloutsos, A. Biliris, Online data mining for co-evolving time sequences, in ICDE (2000), pp. 13–22

    Google Scholar 

  19. S. Papadimitriou, J. Sun, C. Faloutsos, Streaming pattern discovery in multiple time-series, in VLDB (2005), pp. 697–708

    Google Scholar 

  20. Y.-J. Chang, Y. Kang, C.-L. Hsu, C.-T. Chang, T.Y. Chan, Virtual metrology technique for semiconductor manufacturing, in International Joint Conference on Neural Networks, 2006. IJCNN ’06 (2006), pp. 5289–5293

    Google Scholar 

  21. A. Yaglom, Some classes of random fields in n-dimensional space, related to stationary random processes. Theory Probab. Appl. 2, 273–320 (1957)

    Article  Google Scholar 

  22. R.L. Bras, I. Rodriguez-Iturbe, Random Functions and Hydrology (Dover Publishers, Mineola, 1985)

    Google Scholar 

  23. T. Coleman, Y. Li, An interior, trust region approach for nonlinear minimization subject to bounds. SIAM J. Optim. 6, 418–445 (1996)

    Article  MathSciNet  Google Scholar 

  24. C. Visweswariah, K. Ravindran, K. Kalafala, S.G. Walker, S. Narayan, First-order incremental block-based statistical timing analysis, in DAC, San Diego, CA, June 2004, pp. 331–336

    Google Scholar 

  25. H. Chang, S.S. Sapatnekar, Statistical timing analysis considering spatial correlations using a single PERT-like traversal, in ICCAD, San Jose, CA, November 2003, pp. 621–625

    Google Scholar 

  26. R. Chen, L. Zhang, V. Zolotov, C. Visweswariah, J. Xiong, Static timing: back to our roots, in Asia and South Pacific Design Automation Conference, Seoul, South Korea, March 2008, pp. 310–315

    Google Scholar 

  27. J. Xiong, V. Zolotov, C. Visweswariah, P. Habitz, Optimal margin computation for at-speed test, in Conference on Design, Automation and Test in Europe, Munich, Germany, March 2008, pp. 622–627

    Google Scholar 

  28. B. Bakker, T. Heskes, Task clustering and gating for Bayesian multitask learning. J. Mach. Learn. Res. 4, 83–99 (2003)

    MATH  Google Scholar 

  29. A. Argyriou, T. Evgeniou, M. Pontil, Convex multi-task feature learning. Mach. Learn. 73(3), 243–272 (2008)

    Article  Google Scholar 

  30. J. Chen, L. Tang, J. Liu, J. Ye, A convex formulation for learning shared structures from multiple tasks, in ICML (2009), p. 18

    Google Scholar 

  31. J. Chen, J. Zhou, J. Ye, Integrating low-rank and group-sparse structures for robust multi-task learning, in KDD (2011), pp. 42–50

    Google Scholar 

  32. P. Kang, D. Kim, H.-J. Lee, S. Doh, S. Cho, Virtual metrology for run-to-run control in semiconductor manufacturing. Expert Syst. Appl. 38, 2508–2522 (2011)

    Article  Google Scholar 

  33. S. Lynn, J. Ringwood, E. Ragnoli, S. McLoone, N. MacGearailt, Virtual metrology for plasma etch using tool variables, in Advanced Semiconductor Manufacturing Conference (2009)

    Google Scholar 

  34. Y. Zhang, J.G. Schneider, Learning multiple tasks with a sparse matrix-normal penalty, in NIPS (2010), pp. 2550–2558

    Google Scholar 

  35. H. Liu, M. Palatucci, J. Zhang, Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery, in ICML (2009), p. 82

    Google Scholar 

  36. D.G. Luenberger, Linear and Nonlinear Programming, 2nd edn. (Addison-Wesley, Massachusetts, 1973)

    MATH  Google Scholar 

  37. R.K. Ando, T. Zhang, A framework for learning predictive structures from multiple tasks and unlabeled data. J. Mach. Learn. Res. 6, 1817–1853 (2005)

    MathSciNet  MATH  Google Scholar 

  38. J. Zakaria, A. Mueen, E.J. Keogh, Clustering time series using unsupervised-shapelets, in ICDM (2012), pp. 785–794

    Google Scholar 

  39. T. Rakthanmanon, B.J.L. Campana, A. Mueen, G.E.A.P.A. Batista, M.B. Westover, Q. Zhu, J. Zakaria, E.J. Keogh, Searching and mining trillions of time series subsequences under dynamic time warping, in KDD (2012), pp. 262–270

    Google Scholar 

  40. D. Chakrabarti, S. Papadimitriou, D.S. Modha, C. Faloutsos, Fully automatic cross-associations, in KDD (2004), pp. 79–88

    Google Scholar 

  41. I.S. Dhillon, S. Mallela, D.S. Modha, Information-theoretic co-clustering, in KDD (2003), pp. 89–98

    Google Scholar 

  42. R. Tibshirani, Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B 58, 267–288 (1996)

    MathSciNet  MATH  Google Scholar 

  43. H. Zou, T. Hastie, Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B (Stat Methodol.) 67(2), 301–320 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA

    Jinjun Xiong & Yada Zhu

  2. Arizona State University, Tempe, AZ, USA

    Jingrui He

Authors
  1. Jinjun Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yada Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jingrui He
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jinjun Xiong .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering and Center for Cyber Physical Systems, Khalifa University, Abu Dhabi, United Arab Emirates

    Ibrahim (Abe) M. Elfadel

  2. Massachusetts Institute of Technology, Cambridge, MA, USA

    Duane S. Boning

  3. Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA

    Xin Li

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Xiong, J., Zhu, Y., He, J. (2019). Machine Learning for VLSI Chip Testing and Semiconductor Manufacturing Process Monitoring and Improvement. In: Elfadel, I., Boning, D., Li, X. (eds) Machine Learning in VLSI Computer-Aided Design. Springer, Cham. https://doi.org/10.1007/978-3-030-04666-8_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-04666-8_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04665-1

  • Online ISBN: 978-3-030-04666-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation