Reinforcement Learning Approaches for Layout Optimization in Electronic Design Automation with Electromagnetic Compatibility Constraints

Abstract

Contemporary electronic design automation faces increasingly complex challenges as system integration density continues to escalate while electromagnetic compatibility requirements become more stringent across diverse applications ranging from automotive electronics to wireless communication systems. This research develops a novel reinforcement learning framework for automated layout optimization that simultaneously addresses placement, routing, and electromagnetic compatibility constraints within a unified optimization paradigm. The proposed approach integrates Deep Q-Network (DQN) algorithms with specialized reward functions that incorporate electromagnetic interference metrics, signal integrity assessments, and thermal management considerations to guide the learning process toward layouts that satisfy multiple competing design objectives. Through comprehensive evaluation across representative mixed-signal integrated circuits, automotive electronic control units, and high-frequency wireless systems, our reinforcement learning methodology demonstrates superior performance compared to traditional placement and routing algorithms while maintaining electromagnetic compatibility compliance rates exceeding 94.7% across diverse operating conditions. The framework achieves remarkable improvements in design quality metrics including 23.8% reduction in electromagnetic emissions, 31.2% improvement in signal integrity parameters, and 18.5% decrease in thermal hotspot formation compared to conventional EDA approaches. The adaptive learning mechanism enables the system to continuously improve performance through iterative design exploration, with convergence typically achieved within 2000-5000 training episodes depending on circuit complexity. Real-time layout modification capabilities facilitate interactive design optimization workflows that enable designers to explore trade-offs between electromagnetic performance, power consumption, and area utilization within computationally feasible timeframes. The framework incorporates advanced state representation techniques that capture both local component interactions and global electromagnetic field distributions, enabling comprehensive understanding of electromagnetic coupling mechanisms throughout the design process. Experimental validation against commercial EDA software demonstrates comparable layout quality for standard benchmarks while providing substantial advantages for electromagnetic-critical applications where traditional tools struggle to balance competing design constraints effectively.