Applied AI · Real engineering
Verify RSFQ circuits
at simulation speed.
Fluxus is a learned physics model for RSFQ circuits that predicts delay, current, and DRV violations — with calibrated confidence on every output — 200–500× faster than SPICE.
import cogitan
# load your cell library and run verification
results = cogitan.verify(
path = "./layouts/sfq5ee+",
pdk = "mit-ll-sfq5ee+",
batch = True,
)
for cell, r in results.items():
print(
f"{cell:<14} {r.delay:>5.1f}
f" ± {r.sigma:.1f} ps {r.status}"
)$ cogitan verify ./layouts/sfq5ee+
SPLIT_v1_a 17.2 ± 1.4 ps ✓
SPLIT_v2_b 18.9 ± 1.1 ps ✓
DFF_x3_c 22.1 ± 5.8 ps → spice
AND2_v1_d 14.4 ± 1.6 ps ✓
...
─────────────────────────────────
1,000 cells 4.8s (SPICE: ~85 min)
997 pass · 3 → spice fallback
Wall-clock time · chip-scale verification
200–500×
faster than SPICE at chip scale
100%
functional classification on validated types
sub-3 ps
delay accuracy on SPLIT / DFF / AND2
99.2%
design rule violation detection
The problem
SPICE doesn't scale.
At RSFQ process nodes, a 1,000-cell verification block takes 5–10 minutes in SPICE. A full 100,000-cell chip is an entire engineering day — per iteration. There is no design-space exploration. There is no confidence scoring. There is no gradient-based optimization.
The constraint isn't compute. It's the simulation paradigm itself.
Workflow
01
Provide your PDK
Cell library and SPICE simulation data for your process node. MIT-LL SFQ5ee+, IPHT, SeeQC, AIST, SkyWater, or proprietary.
02
We train the surrogate
A learned surrogate calibrated to your junction parameters, cell geometry, and design rules. Training runs on your data.
03
Explore at 200–500×
Run design-space exploration with per-cell confidence scoring. Low-sigma predictions pass. High-sigma cells are flagged.
04
SPICE on the shortlist only
SPICE validates final candidates. 5–10× wall-clock savings per design loop. Every shipped design is SPICE-verified.
Cogitan
RSFQ is the first application.
The same approach — learned physics surrogate, calibrated uncertainty, differentiable for optimization, trained on your data — applies to any domain where simulation is the bottleneck and ground truth is expensive to generate.
We are actively developing for additional domains. If you have a hard simulation problem, we'd like to understand it.
Get in touchProduct roadmap
Have an RSFQ process?
We're onboarding now.
Bring your cell library and SPICE simulation data. We handle the rest. SFQ5ee+ is the proof of concept. Your process is next.
Request early access