Exploration of Artificial Intelligence and Machine Learning Techniques in Enhancing Semiconductor Design Automation and Optimization for VLSI and FPGA Architectures

Ayekpam Lankhonba Meitei; Navneet U Chaurasiya

Authors

Ayekpam Lankhonba Meitei Independent Researcher and IT Expert, India. Author
Navneet U Chaurasiya Independent Researcher and Development Expert, India. Author

Keywords:

Semiconductor Design Automation, Artificial Intelligence, Machine Learning, VLSI, FPGA, Electronic Design Automation

Abstract

The semiconductor industry faces growing challenges in designing and optimizing complex VLSI and FPGA architectures. The integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques into Electronic Design Automation (EDA) offers transformative potential to enhance design accuracy, efficiency, and scalability. This paper explores the latest advancements in AI-driven methodologies for semiconductor design, with a focus on layout optimization, timing analysis, and fault tolerance mechanisms. By conducting a comprehensive literature review and presenting case studies, we highlight the contributions of AI in addressing critical challenges in VLSI and FPGA development. Experimental results reveal significant improvements in design automation processes, underscoring the importance of hybrid AI-EDA solutions in achieving optimal performance. The findings emphasize a path forward for future research and innovation in this rapidly evolving field.

References

Lee, H., et al. (2020). "Convolutional Neural Networks for VLSI Layout Optimization." Microelectronics Journal, 105, 1134-1142.

Sharma, R., et al. (2019). "Genetic Algorithm and Neural Network-based FPGA Layout Optimization." Journal of VLSI Design, 28(4), 231-240.

Reddy, K.V. (2024). UVM Methodology: Industry-Specific Applications in Modern Hardware Verification. International Journal of Computer Engineering and Technology (IJCET), 15(6), 20–32.

Zhou, Q., et al. (2022). "Challenges and Opportunities in AI for EDA." Nature Electronics, 5(1), 15-23.

Kumar, A., et al. (2020). "AI-Assisted Physical Design Automation for Advanced Node Technologies." IEEE Transactions on CAD of Integrated Circuits and Systems, 39(11), 2405-2418.

Reddy, K.V. (2024). Accelerating Functional Coverage Closure Through Iterative Machine Learning. International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 7(2), 1401–1411.

Borkar, K., et al. (2018). "Machine Learning for High-Level Synthesis in FPGA-Based Design." Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology, 90(2), 245-259.

Reddy, K.V. (2024). Optimizing Gate-Level Simulation Performance Through Cloud-Based Distributed Computing. International Journal for Multidisciplinary Research (IJFMR), 6(6), November-December.

Wang, J., et al. (2022). "Deep Learning Approaches for Timing Analysis in VLSI." IEEE Transactions on CAD of Integrated Circuits and Systems, 41(5), 1234-1245.

Reddy, K.V. (2024). Formal Verification with ABV: A Superior Alternative to UVM for Complex Computing Chips. International Journal of Scientific Research in Computer Science Engineering and Information Technology, 10(6), 90–98.

Chen, Y., et al. (2021). "Reinforcement Learning for FPGA Resource Allocation." ACM Transactions on Reconfigurable Technology and Systems, 14(2), 45-58.

Mishra, P., et al. (2022). "Hybrid Algorithms for Multi-Objective Optimization in FPGA Design." Microelectronics Reliability, 128, 114621.

Patel, V., et al. (2017). "Predictive Modeling for Semiconductor Defects Using SVMs." IEEE Transactions on Semiconductor Manufacturing, 30(2), 211-223.

Liu, T., et al. (2021). "Optimization of Placement and Routing in VLSI Using Reinforcement Learning." Integration, the VLSI Journal, 80, 45-57.