Engineering Capabilities

ILKM focuses on AI-assisted engineering workflows across four primary domains of high-frequency electronics.

AI-Assisted Antenna Design

Accelerate the exploration of complex antenna topologies, balancing competing requirements across multiple frequency bands.

Focus Areas

  • Microstrip antennas
  • Patch antennas
  • Antenna arrays
  • Phased arrays
  • MIMO structures
  • Broadband antennas
  • Multi-band antennas
  • mmWave antennas
  • Reconfigurable concepts
  • Antenna placement studies

Optimization Objectives

S11VSWRBandwidthGainEfficiencyBeamwidthSide-lobe levelFront-to-back ratioPolarizationIsolation

RF & Microwave Circuit Optimization

Automated tuning and optimization of passive and active microwave structures to meet stringent RF performance criteria.

Focus Areas

  • Matching networks
  • Filters
  • Power amplifiers
  • Low-noise amplifiers
  • RF front ends
  • Passive networks
  • Transmission lines
  • Microwave structures

Optimization Objectives

S11S21S22GainNoise figureP1dBIP3StabilityEfficiencyHarmonics

High-Frequency PCB Engineering

Optimize layout geometries to maintain signal integrity and impedance control in multi-layer high-frequency boards.

Focus Areas

  • Controlled-impedance routing
  • Microstrip
  • Stripline
  • Coplanar waveguide
  • Via transitions
  • Launch structures
  • RF grounding
  • Connector transitions
  • Parasitic coupling
  • Stackup optimization
  • EM-aware layout
  • Signal integrity interactions
  • Manufacturing constraints

Electromagnetic Optimization

Implement advanced mathematical techniques to navigate the electromagnetic design space.

Focus Areas

  • Full-wave simulation
  • Parameterized geometries
  • Design-of-experiments
  • Surrogate models
  • Bayesian optimization
  • Evolutionary optimization
  • Gradient-based methods
  • Multi-objective optimization
  • Sensitivity analysis
  • Robust optimization
  • Tolerance analysis

Physics-Informed AI

Engineering AI should respect or incorporate physical constraints, simulation data, and electromagnetic relationships rather than operate as an unconstrained black box.

Constraint-Aware Optimization

Ensuring generated geometries do not violate manufacturing rules (DRC) or physical envelope limits.

Physics-Informed Loss Functions

Penalizing neural networks when their predictions violate Maxwell's equations or boundary conditions.

Simulation-in-the-Loop

Active learning workflows that continuously query the EM solver during optimization to refine the surrogate model.