Properties

Properties defines the material property or simulation property of a geometrical shape, such as a metal, a thin conducting sheet, a dielectric metarial, a magnetic material, or a lumped element (resistor, capacitor, inductor). Properties are always associated with one or more geometrical shapes, known as primitives.

Technically, excitation sources, probes, and field dump boxes are also Properties, which is used to enable a specific function to a location or a shape.

Important

Remember to always add at least one Primitives (such as a Box) to any property.

Metal

A metal is a modeled as a Perfect Electric Conductor (PEC) with infinite conductivity.

Internally, the PEC is implemented by forcing the tangential electric field in this region to be zero, which is characteristic of an ideal conductor that can’t be penetrated by electric field lines. If resistive losses are unimportant, one can use PEC rather than a realistic material model for simplicity and efficiency.

Example

It’s added by the AddMetal() method in Matlab/Octave, or by the AddMetal() method in Python.

Create a Perfect Electric Conductor named plate:

% Matlab/Octave
csx = InitCSX();
csx = AddMetal(csx, 'plate');

# Python
import CSXCAD
csx = CSXCAD.ContinuousStructure()
metal = csx.AddMetal('plate')

Thin Conducting Sheet

A Thin Conducting Sheet is a simplified model of a resistive conductor, and is the standard choice for modeling resistive metal sheets, plates, and traces.

Modeling thin metal sheets is challenging in FDTD. To capture effects like surface current (skin effect) requires an impractically high resolution mesh. Thus, Thin Conducting Sheet treats the metal as a zero-thickness 2D plane. The resistive loss in metals is simulated using a simplified, behavioral model to “fit” the observed loss rather than the full physics.

Important

A Thin Conducting Sheet can only be used to create a zero-thickness geometrical object, the created 3D shape (primitive) mush be a plane.
Surface roughness modeling is currently not supported.

Example

It’s added by the AddConductingSheet() method in Matlab/Octave, or by the AddConductingSheet() method in Python.

The following example creates a Thin Conducting Sheet material named copper_foil, with a conductivity of 59.6e6 S/m and a simulated thickness of 35 µm (the shape created from it must be a 2D plane with zero thickness). This is typical for a 1-oz circuit board:

% Matlab/Octave
csx = InitCSX();
csx = AddConductingSheet(csx, 'copper', 59.6e6, 35e-6);

# Python
import CSXCAD
csx = CSXCAD.ContinuousStructure()
sheet = csx.AddConductingSheet('copper_foil', conductivity=59.6e6, thickness=35e-6)

General Material

A general material is defined by a relative permittivity \(\epsilon_r\), a relative permeability \(\mu_r\), an electric conductivity \(\kappa\), and a hypothetical magnetic conductivity \(\sigma\).

Warning

Kappa (\(\kappa\)) always stands for electric conductivity in openEMS. It’s not to be confused with electric permittivity \(\epsilon\), which is sometimes also denoted as \(\kappa\) in the literature (e.g. high-κ dielectric). This convention is never used in openEMS, its use in simulation code is strongly discouraged.

All parameters are constants that don’t vary with frequency. It can model dielectric materials (such as circuit board substrate), magnetic materials (such as magnetic cores), resistive materials, and 3D metals. Due to the constant-property assumption, this model is not realistic. But it produces acceptable results in simpler applications, and has no simulation overhead.

Example

It’s added by the AddMaterial() method in Matlab/Octave, or by the AddMaterial() method in Python.

Create a plexiglass material:

% Matlab/Octave
csx = InitCSX();
csx = AddMaterial(csx, 'plexiglass');
csx = SetMaterialProperty(csx, 'plexiglass', 'Epsilon', 2.22);

# Python
import CSXCAD
csx = CSXCAD.ContinuousStructure()
plexiglass = csx.AddMaterial('plexiglass', epsilon=2.22)

Lumped Element

Lumped elements are ideal resistors, capacitors and inductors with sizes assumed to be negligible. They’re especially useful for modeling surface-mount circuit components.

Important

Axis Alignment. Lumped elements must have an orientation aligned to the X, Y, or Z axis. If a misaligned resistor or capacitor must be used, defining a distributed element based on a hypothetical material (via AddMaterial() with an artificial conductivity or permittivity) may be a workaround.

Parasitics: Even though lumped elements are “ideal”, their connections to the circuit still introduce parasitic effects (e.g., inductance from the overall loop area, partial inductance of terminal leads or mounting height). For example, an SMD capacitor’s inductance primarily comes from its mounting height, not internal to the capacitor itself. Full-wave simulations inherently capture these parasitics through the electric and magnetic fields in space. Thus, non-internal parasitics don’t need to be added, such as the series inductance of an SMD capacitor. However, datasheets often combine internal and external-loop effects, making it hard to isolate the “pure” lumped element values for simulation - some may argue it isn’t a well-defined concept to begin with.

Example

It’s added by the AddLumpedElement() method in Matlab/Octave, or by the AddLumpedElement() method in Python. If argument caps is enabled, a small PEC plate is added to each end of the lumped element to ensure electrical contact to the connected lines.

Create a lumped 1 pF capacitor in y direction:

% Matlab/Octave
csx = InitCSX();
CSX = AddLumpedElement(CSX, 'capacitor', 'y', 'Caps', 1, 'C', 1e-12);

# Python
import CSXCAD
csx = CSXCAD.ContinuousStructure()
capacitor = csx.AddLumpedElement('capacitor', 'y', C=1e-12, caps=True)

For most of the project history, a lumped element can only be an isolated resistor or capacitor (even inductors are not implemented). In the latest development version of openEMS (v0.0.37, unreleased), a contributed new extension has been submitted to openEMS, allowing the lumped element to be an entire RLC circuit, with resistance, capacitor, inductance values simultaneously. It’s controlled by the parameter LEtype in Python’s AddLumpedElement(). A value of 0 denotes a parallel RLC circuit, when a value of 1 denotes a series RLC circuit. It’s not implemented by the Matlab/Octave binding as of now.

Dispersive Materials

Debye, Drude, Lorentz materials are advanced models to model dispersive materials. They’re added by the functions AddDebyeMaterial() and AddLorentzMaterial().

Nearly all real-world materials exhibit a phenomenon known as dispersion. That is, the speed of light in the medium depends on the EM wave’s frequency. In optics, it manifests as a frequency-dependent refractive index. In RF/microwave engineering, it appears as a frequency-dependent permittivity and permeability. In metamaterial research, one can even deliberately introduce dispersion to control electromagnetic wave propagation in unusual ways.

As a result, while the basic material model with constant permittivity and permeability is sufficient if dispersion is negligible, more demanding simulations call for dispersion models for accurately calculating a material’s wideband response.

See Dispersive Materials for detailed descriptions.

Probes and Dumps

Technically, excitation sources, probes, and field dump boxes are also Properties. The associated geometrical shape determines the locations of these excitations, probes and dumps.

They’re added by Matlab/Octave functions AddExcitation(), AddPlaneWaveExcite(), AddProbe(), and AddDump(). In Python, they’re added by AddExcitation(), AddProbe() and AddDump().

To learn more, see Excitation Sources and Field Dump.