“Frameworks for Advanced Physical Design Optimization in VLSI Systems: A Conceptual Approach”

Abstract

The evolution of Very Large Scale Integration (VLSI) design has been driven by the growing need for higher performance, lower power consumption, and improved scalability. This study explores the role of advanced frameworks and optimization techniques in enhancing physical design automation, focusing on placement, routing, and timing optimization. Through a review of contemporary research, the paper highlights key innovations such as machine learning (ML), reinforcement learning (RL), and hybrid algorithms that improve design efficiency and reliability. It further examines interconnect optimization, clock distribution networks particularly tree-mesh hybrid architectures and power-thermal-aware design strategies that address challenges in sub-nanometer technologies. Despite substantial progress, gaps persist in algorithm scalability, computational efficiency, and holistic framework integration across design stages. The study concludes that the development of standardized, intelligent, and power-aware frameworks integrating AI, 3D ICs, and modular design approaches is vital for achieving scalable, efficient, and high-performance VLSI systems.