Stop Guessing on Multi-Die PG Planning: Let AI Find the IR-Drop Sweet Spot

By Soheil Modirzadeh, Synopsys

Power and ground (PG) bump planning forces a hard trade-off. Too few PG bumps and IR-drop violations put the design at risk. Too many, and engineering teams compensate in routing congestion, die area, and downstream QoR. Engineers have historically closed that gap through manual iteration—place, analyze, adjust, repeat—a loop that breaks down when a single die carries hundreds of thousands to millions of interconnects.

The demo, Multi-Die PG Bump Optimization with Synopsys 3DIC Compiler, shows how the platform collapses that loop with AI, running the full path from prototype PG structures through EMIR analysis to an AI-driven optimized bump configuration.

From bump pattern to PG mesh

The demo starts with a bump pattern specification, including radius, X/Y pitch, and a structured bump type for a regular pattern. Drawing a region on the base die instantly fills it with bumps, and the property editor handles on-the-fly edits like boundary margins. A region acts as a window into the bump canvas and automatically regenerates the bump array when resized, whether it holds 100 or 500,000 bumps. PG nets are then assigned before a template defines the PG mesh aligned to the front-side bumps.

Catching violations before implementation

DRC surfaces a cut-to-cut enclosure violation on the via layer beneath two bumps, and fixes it before the design is implemented. Catching bump errors during prototyping avoids costlier corrections at signoff. 3DIC Compiler platform’s enclosed multi-die checker flags over 60 violation types.

AI-Driven Design Space Exploration for PG Bump Optimization

A scatter plot confirms the expected trend, as PG bump count rises, maximum voltage drop falls. But a second correlation against the Aggregate Design Score (ADS), which combines all selected metrics where lower is better, tells the complete story. Simply maximizing bumps drives congestion and other penalties, so the optimum is a trade-off point, not an extreme. The AI engine sweeps the parameter space, marks better results and worst cases, and identifies the best implementation automatically.

See it for yourself

Manual PG bump planning has always been tedious, iterative, and error-prone, and it does not scale to million-bump multi-die designs. This demo shows the alternative where engineering teams define the problem once, let the tool explore, and let aggregate scoring surface the optimal trade-off point between IR-drop margin and physical cost.