Leveraging AI/ML Based Optimization Techniques for Modeling and Simulation of Microstrip-to-Media Transition

The basic concept of microstrip line to media transition predominantly defines how electric fields couple when connecting planar microstrip transmission line to a different type of transmission medium, such as a waveguide, slot line, or another microstrip line for multilayer planar technologies such as Printed Circuit Board (PCB) or Low Temperature Co-fired Ceramic (LTCC). It is critical to maximize signal/power transfer characterized by low insertion loss and good return loss (impedance matching) while maintaining broad operational frequency bandwidth using impedance tapering, via transition or aperture coupling techniques. Both manufacturing tolerances and material properties on transition performance studies are crucial to compensate and maintain smooth electromagnetic field matching while configuring the characteristic impedance tolerances along the transition section. Engineers typically run a large quantity of simulations to conduct parametric studies and design optimization which would not only be time-consuming, costly, and unrealistic, but it would also be unguided, resulting in blind and broad searches. AI/ML algorithms guiding simulation can eliminate random searches that produce unneeded results while increasing efficiency and leading to better understanding of the design more deeply, including parameter influences, coherences, and output selection. The goal of the session is to identify the key design aspects of microstrip to media transitions and share best practices examples and methods of tuning and optimization. Running parametric studies, adjoint derivative analysis can help to understand the model behavior and narrow down the list of design parameters that can be impactful. Moreover, selecting the appropriate design optimization algorithm could be tricky and time consuming. Selection of the best metamodeling approach is a key. Thus, the session will study advanced optimization algorithms and with the use of physics-based full wave EM simulation software to automatically create metamodel of optimal prognosis for the defined outputs. This technique can help find regions where the metamodeling is favorable and regions where new observations could improve quality. This way will enable redefining the DOE where needed to achieve the best metamodel quality, requiring less manual input and simulations.