IDEAS home Printed from https://ideas.repec.org/p/aeg/report/2015-03.html
   My bibliography  Save this paper

A SVM Surrogate Model Based Method for Yield Optimization in Electronic Circuit Design

Author

Listed:
  • Angelo Ciccazzo

    (ST Microelectronics, Stradale Primosole 50, 95121 Catania, Italy)

  • Gianni Di Pillo

    (Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza")

  • Vittorio Latorre

    (Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza")

Abstract

Yield optimization is a challenging topic in electronic circuit design. Methods for Yield optimization based on Montecarlo analysis of a circuit whose behavior is reproduced by simulations usually require too many time expensive simulations to be effective for iterative optimization. In this work we take inspiration from both the Montecarlo analysis based methods and machine learning methods in order to realize a methodology able to perform the Yield optimization in a more efficient way. The method we propose tackles the Yield optimization problem by embedding the training of a support vector machine surrogate model and the generation of a Montecarlo analysis into the optimization procedure. We report the numerical results obtained by using the proposed method for the design of two real consumer circuits provided by ST Microelectronics, and we compare these results with the ones obtained using the industrial benchmark currently adopted at ST Microelectronics for Yield optimization. These preliminary results show that the method is promising to be very efficient and capable of reaching design solutions with high values of the Yield.

Suggested Citation

  • Angelo Ciccazzo & Gianni Di Pillo & Vittorio Latorre, 2015. "A SVM Surrogate Model Based Method for Yield Optimization in Electronic Circuit Design," DIAG Technical Reports 2015-03, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    • Handle: RePEc:aeg:report:2015-03
    as

    Download full text from publisher

    File URL: http://www.dis.uniroma1.it/~bibdis/RePEc/aeg/report/2015-03.pdf
    File Function: First version, 2015
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Stephen P. Boyd & Seung-Jean Kim & Dinesh D. Patil & Mark A. Horowitz, 2005. "Digital Circuit Optimization via Geometric Programming," Operations Research, INFORMS, vol. 53(6), pages 899-932, December.
    2. F. Rinaldi & F. Schoen & M. Sciandrone, 2010. "Concave programming for minimizing the zero-norm over polyhedral sets," Computational Optimization and Applications, Springer, vol. 46(3), pages 467-486, July.
    3. G. Liuzzi & S. Lucidi & F. Rinaldi, 2012. "Derivative-free methods for bound constrained mixed-integer optimization," Computational Optimization and Applications, Springer, vol. 53(2), pages 505-526, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Angelo Ciccazzo & Vittorio Latorre & Giampaolo Liuzzi & Stefano Lucidi & Francesco Rinaldi, 2015. "Derivative-Free Robust Optimization for Circuit Design," Journal of Optimization Theory and Applications, Springer, vol. 164(3), pages 842-861, March.
    2. Nikolaos Ploskas & Nikolaos V. Sahinidis, 2022. "Review and comparison of algorithms and software for mixed-integer derivative-free optimization," Journal of Global Optimization, Springer, vol. 82(3), pages 433-462, March.
    3. Tommaso Giovannelli & Giampaolo Liuzzi & Stefano Lucidi & Francesco Rinaldi, 2022. "Derivative-free methods for mixed-integer nonsmooth constrained optimization," Computational Optimization and Applications, Springer, vol. 82(2), pages 293-327, June.
    4. Lu, Hao-Chun, 2020. "Indicator of power convex and exponential transformations for solving nonlinear problems containing posynomial terms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 538(C).
    5. Hao-Chun Lu & Liming Yao, 2019. "Efficient Convexification Strategy for Generalized Geometric Programming Problems," INFORMS Journal on Computing, INFORMS, vol. 31(2), pages 226-234, April.
    6. Manlio Gaudioso & Giovanni Giallombardo & Giovanna Miglionico, 2023. "Sparse optimization via vector k-norm and DC programming with an application to feature selection for support vector machines," Computational Optimization and Applications, Springer, vol. 86(2), pages 745-766, November.
    7. Stefano Lucidi & Massimo Maurici & Luca Paulon & Francesco Rinaldi & Massimo Roma, 2014. "A derivative-free approach for a simulation-based optimization problem in healthcare," DIAG Technical Reports 2014-15, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    8. Warren P. Adams & Stephen M. Henry, 2012. "Base-2 Expansions for Linearizing Products of Functions of Discrete Variables," Operations Research, INFORMS, vol. 60(6), pages 1477-1490, December.
    9. Xinlei Wang & Johan Lim & Seung-Jean Kim & Kyu Hahn, 2015. "Estimating cell probabilities in contingency tables with constraints on marginals/conditionals by geometric programming with applications," Computational Statistics, Springer, vol. 30(1), pages 107-129, March.
    10. Amir Ardestani-Jaafari & Erick Delage, 2016. "Robust Optimization of Sums of Piecewise Linear Functions with Application to Inventory Problems," Operations Research, INFORMS, vol. 64(2), pages 474-494, April.
    11. Rashed Khanjani-Shiraz & Salman Khodayifar & Panos M. Pardalos, 2021. "Copula theory approach to stochastic geometric programming," Journal of Global Optimization, Springer, vol. 81(2), pages 435-468, October.
    12. L. Grippo & F. Rinaldi, 2015. "A class of derivative-free nonmonotone optimization algorithms employing coordinate rotations and gradient approximations," Computational Optimization and Applications, Springer, vol. 60(1), pages 1-33, January.
    13. Dongdong Zhang & Shaohua Pan & Shujun Bi & Defeng Sun, 2023. "Zero-norm regularized problems: equivalent surrogates, proximal MM method and statistical error bound," Computational Optimization and Applications, Springer, vol. 86(2), pages 627-667, November.
    14. Han-Lin Li & Hao-Chun Lu, 2009. "Global Optimization for Generalized Geometric Programs with Mixed Free-Sign Variables," Operations Research, INFORMS, vol. 57(3), pages 701-713, June.
    15. Enrico Civitelli & Matteo Lapucci & Fabio Schoen & Alessio Sortino, 2021. "An effective procedure for feature subset selection in logistic regression based on information criteria," Computational Optimization and Applications, Springer, vol. 80(1), pages 1-32, September.
    16. Guillermo Angeris & Akshay Agrawal & Alex Evans & Tarun Chitra & Stephen Boyd, 2021. "Constant Function Market Makers: Multi-Asset Trades via Convex Optimization," Papers 2107.12484, arXiv.org.
    17. Seyed Ahmad Yazdian & Kamran Shahanaghi & Ahmad Makui, 2016. "Joint optimisation of price, warranty and recovery planning in remanufacturing of used products under linear and non-linear demand, return and cost functions," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(5), pages 1155-1175, April.
    18. Huan Xu & Constantine Caramanis & Shie Mannor, 2012. "A Distributional Interpretation of Robust Optimization," Mathematics of Operations Research, INFORMS, vol. 37(1), pages 95-110, February.
    19. Belleh Fontem, 2023. "Robust Chance-Constrained Geometric Programming with Application to Demand Risk Mitigation," Journal of Optimization Theory and Applications, Springer, vol. 197(2), pages 765-797, May.
    20. Le Thi, H.A. & Pham Dinh, T. & Le, H.M. & Vo, X.T., 2015. "DC approximation approaches for sparse optimization," European Journal of Operational Research, Elsevier, vol. 244(1), pages 26-46.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aeg:report:2015-03. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Antonietta Angelica Zucconi (email available below). General contact details of provider: https://edirc.repec.org/data/dirosit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.