Gyromagnetic material characterization and error analysis using a partially-filled waveguide technique

The use of composite materials has increased tremendously in fields that require the properties of materials to be tailored for specific applications. In addition to showing extraordinary properties during mechanical testing, many of these materials are appealing to engineers for their special electromagnetic properties that can be applied in the areas of electronic systems or antennas. Since many of these materials exhibit anisotropic behavior and their characteristics are often described using tensor constitutive parameters, it is crucial to develop techniques to accurately characterize the materials experimentally. Rectangular waveguides are often used in material characterization since they are readily available and provide high signal strength due to field confinement. Also, samples can be easily machined and placed in the cross section of the waveguide. While a great deal of work has been done in studying materials with tensors consisting of diagonal entries only, it is also important to develop techniques to characterize materials with off-diagonal terms in the tensor, such as gyromagnetic materials. Unfortunately, the limited size of many samples often precludes using methods that require the waveguide cross-section to be completely filled. Recently the authors have developed a method for characterizing gyromagnetic materials by placing a narrow sample into a rectangular waveguide such that the sample only partially fills the cross-section. Mode-matching techniques are used to calculate the S-parameters, which have been validated using full-wave simulations. The benefits of this approach are that it eliminates the influence of air gaps from the sidewalls, improves transmitted signal strength, and does not require a special sample holder. The effects of sample placement and biasing field strength on the extraction of the material parameters will be investigated, and the sensitivity of the inversion to instrumentation uncertainty will be evaluated using Monte Carlo error analysis.