Design-Aware Timing ECOs via Flat-Hierarchical Co-Optimization

Design-aware timing Engineering Change Orders (ECOs) have become a cornerstone in achieving closure for advanced integrated circuits. Traditional flat and hierarchical methodologies face trade-offs between precision and scalability, often resulting in suboptimal outcomes when used independently. Flat-hierarchical co-optimization addresses this challenge by combining global consistency with local accuracy, enabling robust timing closure across increasingly complex designs. This review highlights foundational advances in dose map and placement co-optimization, the role of graph neural networks in predicting timing behavior, and the acceleration of convergence through machine learning driven design rule checks. It further examines adaptive body biasing for post-silicon variability management, multi-level test access mechanisms in hierarchical SoCs, and virtual flattened architectures for scheduling efficiency. Cross-disciplinary parallels with organizational and classification models reinforce the universality of flat-hierarchical strategies. Together, these insights establish flat-hierarchical co-optimization as a critical enabler of resilient, scalable, and design-aware ECO flows for next-generation semiconductor technologies.