TARGET
Target-to-geometry workflows
Translate optical performance, fabrication rules, and tolerance limits into machine-readable objectives.
MEASUREConcept cycles / Target
AI + AUTOMATION FOR PHOTONICS
Move photonics from simulation to automated execution.
Aryo helps photonics and optical-system teams connect inverse design, simulation, lab automation, test data, and manufacturing controls into one governed workflow.
More design exploration, faster validation, and a cleaner path from optical intent to repeatable production.ARYO IMPLEMENTATION SYSTEMWORKFLOW / CONTROL / VALUE
START WITH A BOUNDED SYSTEMMore design exploration, faster validation, and a cleaner path from optical intent to repeatable production.
01Optical targets + constraintsCONNECT
02Inverse design + simulationCONNECT
03Lab + production feedbackGOVERN
01Design-space coverage
02Simulation throughput
03Test automation
04Yield learning
WHERE AI EARNS ITS KEEP
Redesign the workflow, not just the task.
We prioritize decisions that repeat, have usable evidence, and can be measured by an accountable technical or operating owner.
EXPLORE
Inverse design orchestration
Coordinate optimization, surrogate models, FDTD/FEM solvers, and compute across viable designs.
MEASUREIterations / Compute
TEST
Automated lab and test
Link instruments, scripts, experiment plans, and analysis into traceable closed-loop validation.
MEASUREHands-on time / Coverage
LEARN
Design-to-yield learning
Connect measured performance and process variation back to design rules and next-run decisions.
MEASUREYield / Root-cause time
PAST / PRESENT / FUTURE
Adoption is moving from tools to operating systems.
Benchmark progress shows what is becoming technically possible. Implementation determines whether that capability becomes safe, repeatable enterprise value.
Inverse-design studies
Optimization produced promising geometries, while simulation, lab work, and fabrication remained separate loops.
Orchestrated toolchains
AI and automation connect targets, solvers, instruments, analysis, and experiment records around a bounded device.
Design-to-fab learning
Measured performance and process variation continuously inform the next design and manufacturing decision.
CONVENTIONAL VS. AI-ENABLED
Make the operating hypothesis visible.
Illustrative person-hours for a bounded engineering workflow. Aryo replaces these assumptions with client baselines during the diagnostic.
01
−38%Passive photonic component
Coupler / splitter / filter
02
−34%PIC building-block library
Cells / models / design rules
03
−33%Automated optical test cell
Instruments / motion / analytics
THE ARYO ENGAGEMENT
From use case to governed capability.
A fixed-timeline model establishes the baseline, proves the workflow, connects the stack, and transfers the operating model.
- 01WEEKS 1–3
Design-to-test diagnostic
Map solvers, experiment handoffs, instrumentation, data lineage, and failure loops.
- 02WEEKS 4–8
Closed-loop pilot
Automate one device or test family against a defined optical target and baseline.
- 03WEEKS 9–16
Toolchain integration
Connect design environments, compute, lab control, result stores, and manufacturing.
- 04ONGOING
Platform and operating model
Scale reusable modules with evaluation, change control, access, and monitoring.
EXTERNAL SIGNALS
Capability is moving quickly. Production discipline matters more.
BEGIN WITH A REAL WORKFLOW
Define what the first implementation must prove.
Bring one product family, device class, line, or automation loop. Aryo will frame the baseline, systems, controls, pilot scope, and measurable decision gate.