From Design Space to Verified Electromagnetic Performance

At ILKM, we believe that artificial intelligence should augment the rigorous physics-based simulation workflows used by RF engineers, rather than attempt to bypass them. Our technical approach is structured across five critical layers.

Layer 1

Engineering Requirements

Optimization cannot begin without a mathematically rigorous definition of the goal. RF design is an exercise in managing tradeoffs between competing objectives.

Frequency band
Physical envelope
Substrate properties
Input power
Gain & Directivity
Radiation pattern
Efficiency
Bandwidth
Cost limits
Manufacturing limits

Layer 2

Parametric Models

Engineering geometries are represented using controlled parameters. Rather than allowing an AI to propose un-manufacturable voxel noise, we constrain the generative and optimization processes to valid topological variables.

  • Patch dimensions and feed positions
  • Stub lengths and widths
  • Transmission-line widths and gaps
  • Substrate thickness and dielectric variations
  • Array spacing and lattice geometries
  • Matching network component values
  • Via geometry and transition structures
  • Ground-plane defect structures

Layer 3

Simulation Integration

Workflows may incorporate results exported from commercial or open electromagnetic and circuit simulation environments. We do not replace the solver; we automate the orchestration of design evaluations.

Note: Workflows are conceptually compatible with industry-standard tools such as Ansys HFSS, Keysight ADS, Cadence AWR, Altair Feko, CST Studio Suite, openEMS, and scikit-rf. Trademarks belong to their respective owners. Mention does not imply affiliation.

Layer 4

AI & Optimization

Once a parametric space is defined and connected to a solver, we apply machine learning and optimization algorithms to explore the design space efficiently.

Surrogate Regression & Neural NetworksTraining fast-evaluating approximations of the EM solver based on sampled datasets to drastically speed up optimization.
Bayesian Optimization & Gaussian ProcessesIntelligently selecting the next geometry to simulate by balancing the exploration of unknown parameter space with the exploitation of known high-performance regions.
Evolutionary AlgorithmsNavigating multi-objective problems to find the Pareto front.
Inverse & Generative DesignProposing initial candidate geometries based directly on target S-parameter or radiation objectives.

Layer 5

Physics-Based Verification

Every candidate design generated or optimized by AI must be validated using appropriate electromagnetic or circuit simulation before engineering conclusions are drawn. Machine learning models are inherently interpolative and can be blind to complex physical coupling at the edges of their training distribution. Final verification ensures that the predicted performance holds true against the uncompromising laws of physics.