From the power grid you already have to the date it fails. Electromigration,
thermomigration and IR-drop solved as one coupled problem, across the whole chip.
Actual MetalStack output on an industrial RISC-V power grid at 32/28 nm.
One coupled analysis
Electromigration, thermomigration and IR-drop solved together, under your chip’s real thermal map.
Two engines, one physics
A physics-exact numerical engine for sign-off, and a PINN engine fast enough for every iteration.
The flow you already have
Power grids straight from OpenROAD or Synopsys ICC / Fusion Compiler, scriptable end to end.
Capabilities
Physics where the rules only guess.
A rule-based electromigration check assumes one die temperature and one current limit per wire. Real power grids break both assumptions, over-building the wires that will never fail and missing the ones that will. MetalStack solves the governing physics instead, fast enough to run while the design is still moving.
up to 86×Faster with the AI engine than the numerical solve
<0.05%Difference from the physics-exact answer
6Industrial power grids validated at 32/28 nm
01 / COUPLED PHYSICS
One analysis, not three
Electromigration, thermomigration and IR-drop are solved as one connected problem, because on real silicon that is what they are. Voids change the resistance, resistance changes the current, and the current changes the heat that drives both.
Supply a measured or simulated temperature profile and MetalStack uses it directly, including the self-heating each wire generates. Where the hotspot sits relative to the current path decides the verdict; the average die temperature does not.
The grid is run forward through its service life with degradation feeding back into the solve, so you see voltage drop drift year by year instead of a single day-one snapshot.
04 / STATISTICS
A lifetime with odds on it
Manufacturing spread is carried through the analysis, so the answer is a distribution with real probabilities rather than one pessimistic number you have to guess a margin around.
Grids are read straight out of OpenROAD or Synopsys ICC / Fusion Compiler, with material and solver settings in a single YAML file. Nothing to hand-build and no methodology to adopt.
06 / AUTOMATION
Built for agentic flows
Every input and output is scriptable, so MetalStack drops into automated and AI-driven design loops and closes EM and IR-drop sign-off without a human in the middle.
How MetalStack is built
One physics. Two engines.
Same governing equations, two ways to run them. Pick the one the task needs, or use both: explore with the fast engine and confirm with the exact one.
MetalStack Core
The numerical engine. Physics-exact analysis you can defend in a sign-off review, with
every number tracing back to a first-principles solve on the full chip.
Industrial power grids, not sampled test cases
Voltage drop tracked across the product lifetime
The golden reference for everything beside it
MetalStack-AI
The PINN engine. The same analysis up to 86× faster, trained against the governing
equations rather than a corpus of previously solved designs.
Within 0.05% of the exact answer
Manufacturing variation built into one run
Inference only — seconds per run, no cluster
Measured against each other
The fast engine is not a black box. It is benchmarked against Core on the same structures,
so every quick answer carries a known margin and anything that matters can be re-run exactly.
Explore with AI, confirm with Core
Same inputs and outputs either way
Method published at ICCAD 2024
The workflow
Point it at your design. Get an answer.
No model to hand-build and no new methodology to learn. MetalStack takes the power grid already in your layout and tells you where it fails, and when.
01 / INPUT
Hand it the design
The power grid from your existing layout, a YAML file of material and solver settings, and a thermal map if you have one. Without a map, MetalStack works from self-heating over an ambient baseline.
02 / SCREENING
It sets aside what cannot fail
A fast first pass drops the nets that will never break, so the expensive compute goes only where the risk actually is.
03 / AGING
It runs the chip forward
Heat, current and wear compound on each other across the service life, with the solve updating as the grid degrades rather than being evaluated once at time zero.
04 / OUTPUT
You get the failure picture
Where damage forms, how far voltage drop has drifted, the date the design crosses the limit you set, and degraded netlists you can feed back into the flow.
Switch to MetalStack-AI and the same four steps return in seconds rather than
hours — same inputs, same outputs, nothing else about your flow changes.
White papers
The evidence, one paper at a time.
This page stays short on purpose. The method, the designs each claim was measured on, and the full tables and figures live in a growing series of white papers — each a self-contained document you can read, share and cite in a design review.
WP-01 / VALIDATION
Full-chip EM, TM and IR-drop validation
Six industrial power grids at 32/28 nm — voltage drop when new and after aging, time to failure and runtime — including two designs that pass every check you would run today and still fail inside a year.
What MetalStack-AI costs in accuracy for up to 86× in speed, measured against the numerical engine across five structure sizes. Published and peer-reviewed at ICCAD 2024.
Driving EM and IR-drop sign-off from a scripted loop: what to expose to an optimizer, how to budget exact against accelerated runs, and what closing the loop changes about the grid you ship.
Forthcoming papers will cover transient workload benchmarking and multi-corner sign-off. Tell us what you need to see, or ask to be notified when the next one is published.
Bring us a design — or just tell us what you are taping out and on which flow — and we will walk you through a run end to end, including the parts that look bad.