Open Access
ITM Web Conf.
Volume 54, 2023
2nd International Conference on Advances in Computing, Communication and Security (I3CS-2023)
Article Number 04001
Number of page(s) 12
Section VLSI Design and Material Science
Published online 04 July 2023
  1. K.F. Yong, C.T. Lim, W.K. Teng, System level IR drop impact on chip power performance signoff for RISC-v system on chip, in Proc. of the 17th Int. Microsystems, Packaging, Assembly, and Circuits Technology Conference, (IMPACT), 1–4, (2022) [Google Scholar]
  2. M.O. Hossen, A. Kaul, N. Eriko, D.P. Mondira, R. Gutala, D. Aravind, S.B. Muhannad, Analysis of power delivery network (PDN) in bridge-chips for 2.5-d heterogeneous integration, IEEE Trans. Compo., Packaging and Manu. Tech. 12, 11, 1824–1831, (2022) [CrossRef] [Google Scholar]
  3. S.R. Chakraborty, D. Das, Analysis of IR drop and SSN in CNT and GNR power distribution networks, in Proc. of the IEEE International Conference of Electron Devices Society Kolkata Chapter, 650–654, November (2022). [Google Scholar]
  4. M. Lee, C. Choi, D. Seo, B. Bang, Y. Kang, W. Paik, Improving analysis coverage for dynamic IR drop sign-off in finfet SoC design, in proc. of the International SoC Design Conference, (ISOCC) 332–333, October, (2020). [Google Scholar]
  5. E.C. Nwokorie, A.A. Elusoji, A review approach of power grid analysis in VLSI Int. J. Adv. Comput. Engg. and Networking, 1, 6 2320–2106, August, (2013). [Google Scholar]
  6. Y.C. Phong, C.H. Cheng, J.I. Guo, Efficient IR drop analysis and alleviation methodologies using dual threshold voltages with gate resizing techniques, in proc. of the International Conference on Green Circuits and systems, 129–132, June, (2010). [Google Scholar]
  7. S. Köse, E.G. Friedman, Fast algorithms for IR voltage drop analysis exploiting locality, in Proc. of the 48th Design Automation Conference (DAC '11), 996–1001, (2011). [CrossRef] [Google Scholar]
  8. Y. Zhong, M.D.F. Wong, Fast algorithms for IR drop analysis in large power grid, IEEE/ACM International Conference on Computer-Aided Design, 351–357, (2005). [Google Scholar]
  9. Z. Xie, H. Li, X. Xu, J. Hu, Y. Chen, Fast IR drop estimation with machine learning, in Proc. of the 39th International Conference on Computer-Aided Design (ICCAD '20), 18, (2020). [Google Scholar]
  10. R. Liang, H. Xiang, D. Pandey, L. Reddy, S. Ramji, G. Nam, J. Hu, DRC Hotspot Prediction at Sub-10nm Process Nodes Using Customized Convolutional Network, in Proc. of the 2020 International Symposium on Physical Design (ISPD '20), 135–142 (2020). [CrossRef] [Google Scholar]
  11. D. Kim, J. Zhao, J. Bachrach, K. Asanovic, Simmani, Runtime Power Modeling for Arbitrary RTL with Automatic Signal Selection, in Proc. of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO '52), 1050–1062, (2020). [Google Scholar]
  12. R. Liang, Z. Xie, J. Jung, V. Chauha, Y. Chen, J. Hu, H. Xiang, G. Nam, Routing-Free Crosstalk Prediction, International Conference on Computer-Aided Design (ICCAD). (2020). [Google Scholar]
  13. H. Yang, L. Luo, J. Su, C. Lin, B. Yu, Imbalance aware lithography hotspot detection: a deep learning approach. Journal of Micro/Nanolithography, MEMS, and MOEMS 16, 3 (2017). [Google Scholar]
  14. Y. Ma, H. Ren, B. Khailany, H. Sikka, L. Luo, K. Natarajan, B. Yu, High-performance graph convolutional networks with applications in testability analysis Design Automation Conference (DAC), 1–6, (2019). [Google Scholar]
  15. Y.C. Lu, J. Lee, A. Agnesina, K. Samadi, S.K. Lim, A generative adversarial framework for clock tree prediction and optimization, International Conference on Computer-Aided Design (iCcAD). 1–8, (2019). [Google Scholar]
  16. A. Mirhoseini, A. Goldie, M. Yazgan, J. Jiang, E. Songhori, S. Wang, Y.J. Lee, E. Johnson, O. Pathak, S. Bae, Chip Placement with Deep Reinforcement Learning. (2020). [Google Scholar]
  17. C.T. Ho, A.B. Kahng, Fast Learning Based Prediction of Incremental IR Drop. IEEE/ACM International Conference on Computer-Aided Design (ICCAD). IEEE, 1–8 (2019). [Google Scholar]
  18. C.H. Pao, A.Y. Su, Y.M. Lee, An xgboostbased IR drop predictor for power delivery network. Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 1307–1310, (2020). [Google Scholar]
  19. E.C. Barboza, N. Shukla, Y. Chen, J. Hu, Machine Learning-Based Pre-Routing Timing Prediction with Reduced Pessimism in Proc. of the 56th Annual Design Automation Conference 2019 (DAC '19), 1–6 (2019). [Google Scholar]
  20. K. Zhu, M. Liu, Y. Lin, B. Xu, S. Li, X. Tang, N. Sun, D.Z. Pan, G. Route, A New Analog Routing Paradigm Using Generative Neural Network Guidance, In ICCAD, 18, (2019). [Google Scholar]
  21. S.Y. Lin, Y.C. Fang, Y.C. Li, Y.Ch. Liu, T.S. Yang, S.C. Lin, C.M. Li, E.J. Fang, IR drop prediction of ECO-revised circuits using machine learning. IEEE 36th VLSI Test Symposium (VTS), 1–6, (2018). [Google Scholar]
  22. ‘ANSYS RedHawk-SC online documentation’ Available at: [Google Scholar]
  23. Pant, Sanjay. “Design and Analysis of Power Distribution Networks in VLSI Circuits.” Ph.D. diss., 2008. [Google Scholar]
  24. Zhu, Qing K. Power distribution network design for VLSI. John Wiley & Sons, 2004. [CrossRef] [Google Scholar]
  25. C. Zhang, P. Zhou, Improved hierarchical IR drop analysis in homogeneous circuits, in Proc. of the IEEE 15th International Conference on Solid-State & Integrated Circuit Technology, (ICSICT) 1–3, November, (2020). [Google Scholar]
  26. S. Zhao, I. Ahmed, C. Lamoureux, A. Lotfi, V. Betz, O. Trescases, Robust selfcalibrated dynamic voltage scaling in FPGAs with thermal and IR-drop compensation IEEE Trans. Power Electronics, 33, 10, 8500–8511, (2017). [Google Scholar]
  27. Q. Zhu, Data mining and web-based reporting software for power, IR drop and EM analysis in VLSI design in proc. of the International Conference on Infocom Technologies and Unmanned Systems, 334–340, December, 2017. [Google Scholar]
  28. D. Paramesh Kumar, D.R. Ramesh, IR Drop and electro migration reduction techniques in deep sub-micron technologies, 3, 318–327, (2016). [Google Scholar]
  29. D.A. Li, S.R. Nassif A method for improving power grid resilience to electromigration- caused via failures. IEEE Trans.VLSI Sys., 23, 1, 118–130, (2014). [Google Scholar]
  30. Z. Qin, Z. Wang, H. Ma, S. Zhang, D. Yu, IR drop analysis in mobile IC package with consideration of self-heating and leakage power, in proc. of the IEEE 65th Electronic Components and Technology Conference, ECTC, 752–756, May, (2015). [Google Scholar]
  31. Y.H. Huang, M.S. Zhang, H.Z. Tan, A novel method for IR-drop reduction in highperformance printed circuit boards, in Proc. of the 15th Int. Conference on Electronic Packaging Technology, 583–586, August, (2014). [Google Scholar]
  32. V. Sukharev, J.H. Choy, A. Kteyan, X. Huang, IR-Drop Based Electromigration Assessment in IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 428–433, (2014). [Google Scholar]
  33. S.K. Nithin, G. Shanmugam, S. Chandrasekar, Dynamic voltage (IR) drop analysis and design closure: Issues and challenges, in proc. of the 11th International Symposium on Quality Electronic Design, (ISQED), 611–617 March, (2010). [Google Scholar]
  34. C.J. Wei, H. Chen, S.J. Chen, Design and implementation of block-based partitioning for parallel flip-chip power-grid analysis, IEEE Trans. CAD of ICs and Systems, 31, 3, 370–379, (2012). [CrossRef] [Google Scholar]

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