Machine Learning Assisted Timing Violation Prediction in Sub-7nm VLSI Physical Design - Rosalin Pradhan; Bibhu Prasad Ganthia (International Journal of VLSI Circuit Design & Technology, 2026)
The continuous scaling of semiconductor technology into the sub-7nm regime has introduced significant challenges in timing closure due to process variability, interconnect delay, power density, and manufacturing uncertainties. Conventional static timing analysis techniques often require extensive computational resources and iterative optimization cycles, resulting in increased design complexity and longer turnaround time. This research proposes a Machine Learning Assisted Timing Violation Prediction framework for sub-7nm VLSI physical design to improve early-stage timing estimation and reduce optimization overhead. The proposed methodology integrates supervised machine learning algorithms with physical design parameters including routing congestion, net length, fan-out, switching activity, and placement density to predict potential timing violations before final signoff analysis. A dataset generated from multiple benchmark circuits is utilized for training and validation using Random Forest, XGBoost, and Deep Neural Network models. The developed framework demonstrates higher prediction accuracy and faster analysis compared with conventional timing verification approaches. Experimental evaluation indicates that the proposed model achieves prediction accuracy above 96%, while reducing timing analysis runtime by nearly 38% in complex VLSI layouts. The framework further assists designers in identifying critical paths and optimization hotspots during early physical design stages. The proposed approach provides an intelligent, scalable, and computationally efficient solution for next-generation nanoscale VLSI system design.
Keywords: Machine Learning; Physical Design Automation; Static Timing Analysis; Sub-7nm VLSI Design; Timing Violation Prediction; deep neural networks