Performance Evaluation and Optimization of a FOWLP-based Microstrip Antenna

This paper investigates the performance evaluation and optimization of an antenna in package based on FOWLP technology. An X-band antenna is designed, precisely modeled in HFSS, and optimized using a neural network-based surrogate model. The measurement results show that the antenna achieves a gain of 6.5 dBi at 8.5 GHz with an S<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</inf> value of -21.84 dB, confirming a good match between simulation and measurement. The machine learning-based approach significantly reduces optimization costs and time compared to traditional finite element simulation. The simulation results of the optimized structure demonstrate that the $S_{11}$ at the center frequency decreases to 24.18 dB. This study validates the efficacy of FOWLP for highintegration antenna applications and offers a new method for efficient antenna design and optimization.