VLSI Routing Design Algorithm Implementation and Optimization

Routing design for Very Large Scale Integration (VLSI) circuits represents a critical phase in Electronic Design Automation (EDA), directly impacting chip performance and manufacturing costs. The core challenge can be abstracted as a bipartite graph optimization model: treating millions of logic units as vertices in a bipartite graph with routing channels as edges, where optimization algorithms minimize wire length, crossings, and inter-layer vias while satisfying electrical constraints.

Typical implementation approaches follow a three-stage methodology: - Global routing stage employs grid-based bipartite graph partitioning, combining linear programming or heuristic algorithms (like simulated annealing) for routing region allocation - Detailed routing phase utilizes maze routing algorithms or A* search algorithms for precise path planning with obstacle avoidance - Optimization phase applies iterative refinement techniques (such as rip-up and reroute) to address timing convergence and crosstalk issues

Current technical challenges involve managing parasitic effects at nanometer-scale processes and addressing 3D routing complexities from heterogeneous integration. Deep learning-assisted routing algorithms have emerged as research hotspots, employing neural networks to balance optimization efficiency and solution quality during search space exploration. These algorithms often integrate convolutional neural networks (CNNs) for congestion prediction and reinforcement learning for multi-objective optimization.