Presentation
AI-Driven Co-Optimization of Power Delivery Network for High-Power Cores in 2.5D Advanced Packaging
DescriptionThe increasing adoption of chiplet-based 2.5D advanced packaging has introduced formidable power integrity (PI) challenges, requiring the simultaneous optimization of static DC performance and dynamic AC response—a complex, multi-variable task traditionally reliant on manual expertise. This paper presents an integrated, AI-driven workflow that fully automates the PI co-optimization process. For DC performance, the methodology automates the design of the interposer Power-Ground (PG) mesh. It leverages AI-based multi-objective optimization within Redhawk-SC to intelligently adjust metal patterns, directly minimizing IR drop while adhering to process design rules. For system-level AC performance, the workflow automates the selection and configuration of the hierarchical decoupling network. By combining HSPICE simulation with optimizers (optiSLang/ASO.ai), it co-optimizes components from die MIMCAP and interposer Deep-Trench Capacitors (DTC) to package decaps and PCB MLCCs, targeting impedance and noise reduction under constraints of cost, area, and assembly rules. This automated approach achieves a 26% reduction in DC IR drop and a 12% improvement in AC noise margin. Ultimately, it consolidates fragmented manual procedures into a unified flow, reducing the total PI optimization timeline from over three weeks to approximately one week, thereby enhancing design robustness and significantly accelerating time-to-market.
Event Type
Engineering Poster
TimeTuesday, July 285:00pm - 6:00pm PDT
LocationDAC Pavilion, Exhibit Floor
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