Computationally Sampling Surface and Volume Current Densities of Liquid Crystal Non-Planar Phase Shifters for Low-Loss 5G IoT and 6G AIoT

Dielectric reconfigurable transmission lines (DTTLs) provide electromagnetic properties (e.g., differential phase shift) that can be tuned (mainly by varying the dielectric constants) to meet a variety of agile specifications of millimeter-wave beam steering, a key technology underpinning next-generation communication systems and beyond. Liquid crystals (LCs) are particularly promising materials for tuning their dielectric properties continuously with low power consumptions (e.g., up to 10 V) and are therefore instrumental for integration into DTTLs as phase shifters for electronic beam steering, featuring the richness of the angular resolution data catering to satellite internet and Internet of Things (IoT). However, insertion loss remains a blocker for the commercial success of LC DTTLs to unlock the future Artificial Intelligence of Things (AIoT) ecosystem. For the first time, we quantify the variations of the surface current density of the LC-surrounded coaxial core line and grounding conductors, as well as the volume current density of the filled dielectric (LC itself) at 60 GHz, based on a modified computational sampling strategy. The results formulate backbones for precise characterization of the LC coaxial DTTL device's conductor loss and dielectric loss, respectively. The approach is scalable and extendable to terahertz and optical wavelengths to facilitate dielectric-reconfigurable coaxial device characterization and standardization of electronic design automation (EDA) with LC.