WarpStack predicts thermal warpage for advanced 2.5D and 3D chiplet packages, and gives you three ways to ask: high fidelity at various resolution, so a coarse mesh still gives you the answer; WarpStack-GNN to sweep a whole design space in milliseconds per point; and WarpStack-MOR to explore a bounded box around a nominal design in microseconds. One design description, three ways to get an answer.
Stacking silicon, interposers, and memory bonds materials with very different expansion rates. As the assembly heats and cools, those mismatches make the whole package bow and twist — and that warpage is a first-order reliability risk in modern 2.5D and 3D integration.
Every material in the stack expands by a different amount. Cycle the package from assembly to operating temperature and those mismatches build internal stress that warps the whole structure.
Too much bow leads to cracked dies, delaminated layers, and open solder joints — and it makes assembly yield fall. Catching it early is far cheaper than finding it on the line.
Floorplanning, stack-up choices, and material selection all change how a package warps. Designers need a warpage answer on every iteration — not once, at the very end.
The same package description drives all three. Start where the cost fits the question — a converged answer on a coarse mesh, a whole design space swept by a trained network, or a bounded box explored in microseconds — and move between them without changing your inputs. Each is backed by a technical white paper.
The answer stops depending on the mesh well before the resolution you were told to ask for. Explore at coarse resolution, commit to a fine localized analysis only where it earns its cost.
A network trained on WarpStack's own 3D output returns the full warpage map without solving — fast enough to put warpage inside a live optimization loop.
A physics-based compact model built once over a bounded box around a nominal design — not a fit to data — answering material and thermal what-ifs with an error bound attached.
Bring the stack straight from the ecosystem: WarpStack reads the design, builds the mechanical model itself, and every method writes the same result format — so the view, the comparison and the downstream script are identical whichever one answered.
Import designs described in IEEE 3Dblox (.3dbx) — the IEEE-standard language for physical stacking, dimensions, and connectivity in 2.5D/3D-IC — or describe the stack directly in WarpStack's floorplan JSON. Recursive includes, rotations, mirrors and molding autofill are handled on the way in.
Modulus, Poisson ratio and CTE come from a material profile you supply or the bundled library — never inferred. An unmatched material stops the run rather than quietly guessing, which is what makes a result reproducible.
Rotate the 3D stack and warpage surface, cut XZ/YZ cross-sections on a slider, and exaggerate the z-scale to see a few microns of bow on a 60 mm package — in a self-contained HTML report you can send on.
Put any two answers side by side — two methods, two resolutions, two material sets — and read the signed difference across the whole surface, not just the peak-to-peak number.
Warpage CSVs, geometry.json and simulation_report.json out of every run — ready to feed straight into your own flow.
A production-class accelerator package imports to 457 mechanical layers, 273 footprint regions and 138 instances — with one line changed. Every layer is resolved through the full thickness; no plate approximation, no smeared stiffness.
WarpStack is designed to drop straight into agentic EDA and system-design workflows. A first-class CLI and structured data interface let autonomous design agents call a full 3D solve, WarpStack-MOR, or WarpStack-GNN, read back machine-readable warpage maps and peak-to-peak margins, and feed them into floorplanning, stack-up, and material-selection loops. With millisecond AI inference, warpage finally runs at the speed of the loop itself.
Warpage analysis becomes a callable step inside your agentic flow — not a hand-run GUI task.
Every number on this page is backed by a short technical white paper — the full method, the package it was measured on, the tables and figures, and the scope and caveats. Read the ones that matter to your design.
The convergence evidence on an AMD MI350-class package, and the cost of the fine mesh you did not need to solve.
Read →Global design-space exploration: accuracy on 512 held-out points, training cost, and the break-even point.
Read →Localized exploration: method agreement on an out-of-box what-if, the validation set, and the cost model.
Read →WarpStack is in active development. Request a demo or a walkthrough on your own 2.5D/3D chiplet designs, and we'll get you set up.