Layout / mesh input
Import an existing mesh, or begin from GDS/layout information for MEMS workflows.
Technology
The technology behind structured nonlinear insight
From layout and mesh input to physical-model foundations, Zenoriq builds the technical bridge between nonlinear simulation models, structured insight records, and engineering decisions.
Engine Atlas Insight records
Device definition
layout · mesh · technology stack
Physical model foundation
geometry · materials · domains · actuation
Structured insight records
Operating states Coupling paths Attribution Decision records
Technology philosophy
Built for understanding. Not only computation
Zenoriq is not positioned as a FEM replacement. FEM remains part of the physical foundation. Zenoriq adds the nonlinear insight layer that turns model structure into engineering decisions.
Input foundation
From layout or mesh to a physical model foundation
Zenoriq can start from an existing mesh/FEM model, or from GDS/layout data combined with a technology stack to generate the device geometry and mesh foundation.
Layer definitions, materials, thicknesses, conductors, domains, boundaries, and actuation information become the physical foundation for the structured nonlinear insight model.
Technology stack
Connect layers, materials, thicknesses, conductors, and domains to the physical setup.
Geometry & mesh foundation
Generate or import the geometry and mesh used to build the nonlinear insight model.
Workflow
From device description to decision layer
Zenoriq connects layout, mesh, technology-stack, and physical-model information into one structured path from device definition to nonlinear engineering insight.
Layout / Mesh Input Technology Stack Physical Model Foundation Insight Model Insight Records Decision
Core principle
A different path to nonlinear understanding
Instead of relying on dense response sampling or repeated full-order studies, Zenoriq builds a structured model from the physical system. Once extracted, the model can be queried directly for nonlinear behavior, relationships, and decision-relevant records.
Common route
Repeated response studies
Many workflows depend on dense sweeps or repeated full-order studies before nonlinear trends become visible.
Zenoriq route
Structured nonlinear insight model
A structured model preserves the relationships needed to evaluate behavior, coupling paths, root causes, and device KPIs directly.
Outcome
Direct insight extraction
The structure turns nonlinear model information into decision-ready records instead of isolated plots.
Zenoriq Engine · Compute
Build structured nonlinear insight models
Zenoriq Engine is the backend layer for automated computation. It prepares structured nonlinear insight models, evaluates operating states, generates linked records, and supports batch-oriented analysis without repeating full-order studies for every question.
- Build structured nonlinear insight models from device physics foundations
- Compute operating states, branches, sensitivities, and insight records
- Generate device-aware KPIs, observables, reports, graphs, and exports
- Evaluate design variants without launching a new full-order study for every question
Zenoriq Atlas · Discover
Explore insights interactively
Zenoriq Atlas is the interactive discovery environment. It is where computed records become maps, graphs, rankings, operating-window views, and geometry-linked explanations.
- Navigate insight records, operating windows, and branch behavior
- Explore coupling graphs, rankings, and root-cause paths
- Connect KPIs back to geometry-linked fields and regions
- Compare operating states, variants, and decision-relevant margins
Insight records
Structured outputs, not isolated plots
Each insight record carries scope, provenance, related operating states, linked observables, coupling paths, attribution results, and device-aware KPI meaning. This is what turns model output into reusable engineering knowledge.
Operating windows
Where the device remains usable, stable, and inside configured engineering limits.
Root causes
Which mode, coefficient, domain, conductor, or region dominates a KPI.
Coupling paths
How modes, domains, signals, and observables interact.
Device-aware KPIs
Engineering metrics such as scale factor, scan angle, purity, gap margin, or operating range.
Observables
Electrical and mechanical signals linked to modal and geometric behavior.
Trust indicators
Validity, robustness, and confidence checks for reduced-model interpretation.
Engineering questions
One model · Many engineering questions
One structured model can support operating-point analysis, bias exploration, coupling analysis, observable evaluation, KPI extraction, attribution, and design comparison. The goal is not one isolated result, but many connected engineering questions from one traceable foundation.
Amplitude Bias Operating point Coupling Observable KPI
Attribution
Trace decisions back to physics
A target KPI can be traced back through the dominant coupling path, involved modes and domains, relevant conductors or regions, and finally to the engineering decision it supports.
KPI Coupling path Mode / domain Conductor / region Design decision
Why it matters
Designed for physics fidelity and exploration speed
Physics-based
Built from the underlying physical model instead of relying on extensive response sampling.
Structured
The nonlinear model keeps the relationships needed to trace behavior back to modes, domains, regions, and device functions.
Instant insight
KPIs, coupling paths, and root-cause indicators can be evaluated directly from the model structure.