Abstract
Designed and manufactured set of measuring test fixtures for printed circuit board (PCB) materials research and characterization is described in this article. Attention is paid to special designed test vehicle “General board” which includes six types of transmission lines that are used for PCB materials characterization by two measurement methods. Measurements of the frequency dependencies of the dielectric constant and dielectric loss for a number of the most widely used PCB substrates are presented. The comparison of the measurement results made by two measurement methods is also shown. The influence of the inhomogeneities of the PCB dielectric and some differences in the extracted material parameters data correlated with electro-magnetic fields structure in different transmission lines is demonstrated. In addition it is shown that measurements performed using “General board” can be used for characterization of the losses due to PCB surface plating.
Highlights
A process of electronic devices design has significantly changed and become more efficientwhen various numerical electromagnetic (EM) simulation tools have been involved in the research and development
The major advantage of the new design process is that the number of iterations of the optimization “M” is significantly less than “N”, which results in the prototype manufacturing time and costs decrease
The number of iterations of the numerical model optimization “K” could be quite large that increases the costs of the required computational resources, as well as simulation time
Summary
A process of electronic devices design has significantly changed and become more efficientwhen various numerical electromagnetic (EM) simulation tools have been involved in the research and development. The major advantage of the new design process is that the number of iterations of the optimization (including prototype manufacturing) “M” is significantly less than “N”, which results in the prototype manufacturing time and costs decrease. The number of iterations of the numerical model optimization “K” could be quite large that increases the costs of the required computational resources, as well as simulation time. That is why the number “K” critically depends on numerical model adequacy to the manufactured prototype
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