International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

AI-Driven Approaches for Clock Tree Synthesis and Signal Integrity Optimization in Integrated Circuits

Abstract

Clock Tree Synthesis (CTS) is a critical step in integrated circuit (IC) design, directly influencing timing, power, and signal integrity. Traditional CTS methods face significant challenges in achieving an optimal balance between power and timing constraints, especially in large and complex circuits. This paper presents the application of artificial intelligence (AI) techniques to enhance CTS and ensure robust signal integrity in advanced IC designs. By employing AI-driven optimization algorithms, the proposed framework dynamically adjusts clock tree parameters to achieve an average timing improvement of 46%, with skew reductions as high as 50% in certain test cases. Additionally, the approach minimizes power consumption by over 15% across all circuits, demonstrating its ability to deliver energy-efficient designs. Signal integrity is also significantly improved, with crosstalk violations reduced by an average of 41% and up to 44.44% in specific circuits. These results highlight the capability of the AI-enhanced CTS framework to address complex interdependencies within circuits, ensuring reliable performance across distributed networks. This work contributes a novel and effective AI-based methodology for advancing CTS processes, paving the way for the development of next-generation electronic devices with improved performance and efficiency.

Country : USA

1 Rashmitha Reddy Vuppunuthula

  1. Senior Electrical Design Engineer, Austin, Texas – 78741, USA

IRJIET, Volume 7, Issue 2, February 2023 pp. 503-510

doi.org/10.47001/IRJIET/2023.702084

Full Paper
Download

References

  1. Kahng, A. B., Lin, B., & Nath, S. (2013). Enhanced metamodeling techniques for high-dimensional IC design estimation problems. Proceedings of the Design, Automation, and Test in Europe Conference and Exhibition (DATE), 1861–1866.
  2. Kahng, A. B., Lin, B., & Nath, S. (2013). High-dimensional metamodeling for prediction of clock tree synthesis outcomes. ACM/IEEE International Workshop on System Level Interconnect Prediction (SLIP), 1–7.
  3. Lu, T., Sun, J., Wu, K., & Yang, Z. (2018). High-speed channel modeling with machine learning methods for signal integrity analysis. IEEE Transactions on Electromagnetic Compatibility, 60(6), 1957–1964.
  4. Datli, U., Eksi, G., & Isik, G. (2013). A clock tree synthesis flow tailored for low power. Design and Reuse Articles. Available: https://www.design-reuse.com/articles/33873/clock-tree-synthesis-flow-tailored-for-low-power.html.
  5. Lu, Y.-C., Lee, J., Agnesina, A., Samadi, K., & Lim, S. K. (2019). GAN-CTS: A generative adversarial framework for clock tree prediction and optimization. Proceedings of the IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 1–8.
  6. Mola, A. J., &Scholkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14, 199–222.
  7. TensorFlow. (2022). TensorFlow Machine Learning Framework. Available: https://www.tensorflow.org.
  8. Keras. (2022). Keras: Deep Learning Framework. Available: https://keras.io.
  9. Scikit-learn: Machine learning in Python. (2022). Available: https://scikit-learn.org/stable.
  10. Borchani, H., Varando, G., Bielza, C., & Larranaga, P. (2015). A survey on multi-output regression. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5(5), 216–233.
  11. Liu, Z., Jiang, H., Zhu, Z., Chen, L., Sun, Q., & Zhang, W. (2022). Crosstalk Noise of Octagonal TSV Array Arrangement Based on Different Input Signal. Processes, 10(3), 260.
  12. Zhang, Y., Yu, S., Su, D., & Shen, Z. (2018). Finite element modeling on electromigration of TSV interconnect in 3D package. IEEE 20th Electronics Packaging Technology Conference (EPTC), 695–698.
  13. Trinchero, R., & Canavero, F. G. (2018). Modeling of eye diagram height in high-speed links via support vector machine. IEEE 22nd Workshop on Signal and Power Integrity (SPI), 1–4.
  14. Swaminathan, M., Torun, H. M., Yu, H., & Hejase, J. A. (2020). Demystifying Machine Learning for Signal and Power Integrity Problems in Packaging. IEEE Transactions on Components, Packaging, and Manufacturing Technology, 10(7), 1276–1295.
  15. Medico, R., Spina, D., Vande Ginste, D., Deschrijver, D., & Dhaene, T. (2019). Machine-Learning-Based Error Detection and Design Optimization in Signal Integrity Applications. IEEE Transactions on Components, Packaging, and Manufacturing Technology, 9(9), 1712–1720.
  16. Zhang, H. H., Xue, Z. S., Liu, X. Y., & Jiang, L. (2022). Optimization of High-Speed Channel for Signal Integrity with Deep Genetic Algorithm. IEEE Transactions on Electromagnetic Compatibility, 64(5), 1270–1274.
  17. Ooi, K. S., Kong, C. L., &Goay, C. H. (2020). Crosstalk modeling in high-speed transmission lines by multilayer perceptron neural networks. Neural Computing and Applications, 32, 7311–7320.
  18. Park, M. J., Lee, J., & Cho, K. (2023). A 192-Gb 12-High 896-GB/s HBM3 DRAM with a TSV Auto-Calibration Scheme and Machine-Learning-Based Layout Optimization. IEEE Journal of Solid-State Circuits, 58(1), 256–269.
  19. Kwon, Y., Jung, J., Han, I., & Shin, Y. (2018). Transient clock power estimation of pre-CTS netlist. IEEE International Symposium on Circuits and Systems (ISCAS), 1–4.
  20. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778.
  21. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 2672–2680.
  22. S. Kullback & R. A. Leibler. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22(1), 79–86.

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies