Sub-brand · Research Preview
ChipletOS Photonic Signoff
Photonic IC signoff for silicon-photonics designers. Six primitives (waveguide, MZI, MMI, ring, grating, photonic crystal), AIM-Photonics-class DRC, and Lumerical / OmniSim / GDSII export — under the same validation discipline as the ChipletOS chiplet stack.
Research Preview
Analytical physics engine (~21% mean error vs FDFD-grade references); trained surrogate pending FDTD corpus.
One platform, two markets: chiplet packaging and silicon photonics. Same surrogate stack, same validation suite, same cross-vendor exporter discipline.
Six primitives
Waveguide → MZI → MMI → Ring → Grating → Photonic Crystal
Each primitive ships with a fast closed-form analytical hot path for inverse-design loops (~21% mean error vs FDFD-grade references, honestly disclosed). Trained AI surrogates are pending the FDTD training corpus — all six primitives serve the analytical engine today.
Waveguide
Research preview (analytical)Effective-index model for rectangular SOI strip waveguides + propagation loss + bend loss. Pure-Python at ~0.04 ms / call.
Surrogate target
Closed-form analytical (~21% mean error vs FDFD-grade references); trained surrogate pending FDTD corpus
API
POST /v1/photonics/predict-waveguide-mode
Mach-Zehnder Interferometer
Research preview (analytical)MZI transmission spectrum from arm-length imbalance + phase shifter + coupler ratios.
Surrogate target
Analytical model today (~21% mean error vs FDFD-grade references); trained surrogate pending FDTD corpus
API
POST /v1/photonics/waveguide-from-target (chained)
MMI Coupler
Research preview (analytical)1×2, 2×2 self-imaging-length MMI power splitter. Length, insertion loss, port imbalance.
Surrogate target
Analytical model today (~21% mean error vs FDFD-grade references); trained surrogate pending FDTD corpus
API
POST /v1/photonics/predict-mmi (chained)
Ring Resonator
Research preview (analytical)Add-drop / all-pass ring transfer function. FSR, FWHM, Q from coupling coefficients + round-trip loss.
Surrogate target
Analytical model today (~21% mean error vs FDFD-grade references); trained surrogate pending FDTD corpus
API
POST /v1/photonics/ring-from-target
Grating Coupler
Research preview (analytical)Fiber-to-chip grating coupler — peak wavelength, 1-dB bandwidth, peak coupling efficiency from pitch + duty + etch depth.
Surrogate target
Analytical model today (~21% mean error vs FDFD-grade references); trained surrogate pending FDTD corpus
API
POST /v1/photonics/predict-grating (chained)
Photonic Crystal
Research preview (analytical)2D triangular-lattice band-gap predictor. Lattice topology surrogate lifts full-wave band-structure runs from hours to milliseconds.
Surrogate target
Analytical model today (~21% mean error vs FDFD-grade references); trained surrogate pending FDTD corpus
API
POST /v1/photonics/predict-photonic-crystal (chained)
Live API endpoints
Curated /v1/photonics/* endpoints
11 of the 12 new photonic routes shown below — 3 public + 8 auth-gated (40 total photonic surface incl. inherited PROV 4 RF). Try the public ones below; auth-gated ones are listed for completeness — request access via the buyer app.
/v1/photonics/signoff-healthPublicReturns availability of the analytical hot path, full-wave reference solver, validation-suite status, and surrogate-version metadata.
/v1/photonics/drc-photonicPublicAIM-Photonics-class DRC: minimum waveguide width, bend-radius vs material, ring gap, grating pitch. Returns violation list keyed to AIM rule IDs.
{"geometries":[{"type":"waveguide","width_nm":50,"bend_radius_um":10,"material":"Si"}]}/v1/photonics/validate-against-ieeePublicRuns the analytical photonic stack against an IEEE literature corpus (SOI Neff papers) and returns per-paper MAE + a pooled verdict.
{}/v1/photonics/predict-waveguide-modePublicReturns effective index Neff, group index ng, propagation loss (dB/cm), and bend loss (dB/turn) for a rectangular SOI strip waveguide.
{"width_nm":450,"height_nm":220,"wavelength_nm":1550,"material":"Si"}/v1/photonics/waveguide-from-targetAuth-gatedInverse design: given target Neff, return manufacturable (width_nm, height_nm) and back-checked Neff.
{"target_neff":2.4,"wavelength_nm":1550}/v1/photonics/ring-from-targetAuth-gatedGiven target FSR and Q, return ring radius + coupling coefficient + predicted ER.
{"target_fsr_ghz":100,"target_q":10000}/v1/photonics/resonator-paretoAuth-gatedMulti-objective Pareto front over ring resonator design space.
{"objectives":["fsr","q","footprint"]}/v1/photonics/waveguide-paretoAuth-gatedMulti-objective Pareto front over waveguide design space.
{"objectives":["loss","footprint","neff_band"]}/v1/photonics/export-lumericalAuth-gatedExport a photonic design to a Lumerical-importable .lsf / .ldf bundle. Bit-perfect native validation runs customer-side in the licensed Lumerical instance.
{"design_id":"..."}/v1/photonics/export-omnisimAuth-gatedExport to Photon Design OmniSim format. Bit-perfect native validation runs customer-side in the licensed OmniSim instance.
{"design_id":"..."}/v1/photonics/export-gdsiiAuth-gatedExport a closed-loop-verified photonic layout to GDSII for any AIM-Photonics-class foundry.
{"design_id":"..."}Solver stack
Reference FDTD → analytical models → AI surrogate (pending)
Native solvers never belong in an optimizer inner loop — a hard-won lesson from our chiplet stack that the photonic sub-brand inherits on day one. The reference FDTD solver runs in isolation; the fast analytical models do all the hot-path work today (trained AI surrogates land with the FDTD corpus).
Full-wave FDTD reference solver
Process-isolated for stability. Generates training corpora and verifies the surrogate before promotion. Never called in an optimizer inner loop.
Independent FDTD
Independent solver implementation for the cross-solver agreement check. Currently on the roadmap.
Closed-form analytical models
Pure-Python effective-index + transfer-matrix + coupled-mode models. ~0.04 ms / call. The hot path for inverse-design loops.
Trained MLP surrogates — pending FDTD training corpus
All 6 primitives serve the analytical physics engine today (~21% mean error vs FDFD-grade references, honestly disclosed). Trained surrogates with per-prediction confidence intervals land once the FDTD training corpus is generated.
Scope
What is live today and what is on the roadmap
Live today
- Research preview: all 6 primitives on the analytical physics engine (~21% mean error vs FDFD-grade references, honestly disclosed)
- Waveguide-mode predictor on the closed-form analytical model
- AIM-Photonics-class design-rule check (8 rule families)
- Published-paper cross-check against 5 silicon-photonic references
- Eight auth-gated routes: inverse design, Pareto, 5 cross-vendor exporters
On the roadmap
- Trained AI surrogates for all 6 primitives (pending the FDTD training corpus)
- Per-prediction confidence intervals on surrogate calls (lands with the surrogates)
- Independent cross-solver agreement check (Meep vs gprMax disagreement matrix)
- Vendor-native bit-perfect validation in licensed Lumerical / OmniSim / ANSYS / KLA instances
- Photonic wet-lab VNA campaign for measured-truth verification (analogous to our chiplet wet-lab pack)
See Trust & Validation for the full validation-suite breakdown.
For strategic acquirers
Widens the acquirer pool 2×
Chiplet alone narrows the natural acquirer set to Cadence, Synopsys, ANSYS, Intel Foundry, and Corning. Adding the photonic sub-brand doubles it: Synopsys-Lumerical, ANSYS-Lumerical-adjacent, Photon Design, and the captive Marvell SiPh / Acacia (Cisco) / Lumentum design teams all become natural strategic fits.
Synopsys (Lumerical)
Owns Lumerical (~$80M photonic EDA revenue). ChipletOS Photonic Signoff is the AI-native distribution layer Lumerical doesn't have.
View buyer page →
ANSYS (Lumerical FDTD)
No AI-EDA story, no photonic enterprise pull-through. ChipletOS photonic + chiplet stack is the build-vs-buy decision.
View buyer page →
Photon Design (Omnisim)
Smaller incumbent; natural partnership candidate for joint distribution under the ChipletOS Photonic Signoff brand.
View buyer page →
Try the live waveguide predictor
Three public endpoints, no auth, real Modal compute. The same audit pattern as the chiplet stack — applied to silicon photonics.