Industry · Photonics

Photonics & optical devices

A photonic integrated circuit lives or dies at its interfaces: how light couples from fiber to chip, whether the package can hold nanometer alignment, and whether a design that simulates beautifully still performs once it is fabricated. Agentriq designs optical devices to be low-loss and manufacturable at once, then verifies every result in the loop.

01The problem

NVIDIA is scaling co-packaged optics, its Spectrum-X and Quantum-X Photonics switches, to move light directly into the switch silicon of AI clusters; NVIDIA reports that this co-packaged design uses about four times fewer lasers, three and a half times better power efficiency and sixty three times better signal integrity than conventional pluggable optics. Across silicon photonics the hard part is rarely the waveguide, it is the interface: fiber-to-chip coupling has to be held to nanometers, packaging can exceed 80% of a PIC's cost and testing as much as a further 29%, and every decibel of insertion loss you chase tends to make the device harder to yield.

package substrateSwitch ASICcompute siliconSilicon PICphotonic circuitlaserfiber arraycoupling · nanometer alignmentpackaging · over 80% of costtesting · up to 29%co-packaged vs pluggable optics (reported)4x fewer lasers · 3.5x power · 63x signal integrity
Where a PIC wins or loses: the interface. Take NVIDIA's co-packaged optics as an example of the field. The device is only as good as its interfaces, where fiber meets chip and where the PIC meets the package.

02How we solve it

Optimize for loss and manufacturability at once.

We drive fabrication-aware inverse design so the geometry that minimizes insertion loss is also the one the foundry can actually print.

TWO simulations per iteration: one forward, one adjointidealas printedTarget specloss · BW · sizeInverse designfree-form optimizerLitho modelDUV / EBL biasFDTD / FEMfull-wave evalAdjointone extra solvegradient over every pixel, back to the optimizer
Fabrication-aware inverse design. The optimizer folds the mask model into the loop, so the geometry it converges on is the one the foundry actually prints, not an idealized shape that degrades on the wafer.

Adjoint-method inverse design

Compute the gradient over thousands of geometry parameters from only two simulations per iteration, one forward and one adjoint, so free-form devices converge in a fraction of the solver calls.

Fabrication-aware, yield-optimized design

Fold DUV and e-beam lithography models into the optimization so the design corrects for process bias up front; published fabrication-aware work reports up to 0.6 dB lower insertion loss per device.

Neural and surrogate-accelerated inverse design

Train surrogate and neural models on solver data to predict device response near-instantly, steering the search toward promising geometries before spending full-wave simulations.

Multi-scale FDTD and FEM to circuit

Simulate components with full-wave FDTD and FEM, distill them into compact models, and validate the whole PIC at circuit level so component gains hold at system scale.

03What it produces

Low-loss, compact, manufacturable by design.

0.000.250.500.751.000.550.28Y-branch0.900.30SWG-to-stripInsertion loss (dB), lower is betterconventionalfabrication-awaresimulated target
Insertion loss: as-designed vs as-fabricated. Folding the mask model into the search closes the gap between simulated and measured loss; published fabrication-aware inverse design reports up to 0.6 dB lower loss per device.

Devices that hold their insertion-loss budget in silicon and not just in simulation, with fabrication-aware inverse design demonstrating up to 0.6 dB lower loss on real fabricated components and free-form geometries reaching footprints of tens of square microns.

04The agentic loop

From layout to a verified device.

Agentriq runs the whole cycle as a closed agent loop: propose a geometry, simulate it under fabrication-aware models, verify it against loss and design-rule targets, diagnose any failure, and write the winning design and its evidence back to the PDK.

EVERY RESULT becomes a decisionLayoutcandidate deviceVerifyre-check, convergeRoot-cause+ Agentic RAGJudgeAcceptIterateEscalateoutcome + evidence written back to structured memory
From work to a verified decision. Every candidate geometry is simulated, verified against loss and design rules, diagnosed on a miss, judged, and written back to the PDK.
  1. 01Generate a candidate geometry with adjoint or surrogate-guided inverse design, then simulate it with fabrication-aware full-wave FDTD and FEM plus compact-model circuit checks.
  2. 02A verification agent re-checks insertion loss, bandwidth and foundry design rules against spec, across process corners.
  3. 03On a miss, a root-cause agent with Agentic RAG traces the failure to geometry, lithography bias or coupling, drawing on PDK documents and prior runs.
  4. 04A judge agent decides iterate, accept or escalate.
  5. 05The accepted design, its figures of merit and its evidence are written back to the PDK and the project record.

05Tooling

Simulation, solvers & frameworks.

  • Ansys Lumerical FDTD
  • Ansys Lumerical MODE
  • Lumopt
  • Lumerical INTERCONNECT
  • CML Compiler
  • COMSOL Wave Optics
  • gdsfactory
  • Foundry PDKs (AIM · imec · GlobalFoundries)

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