Optimal Design of Planar Microwave Microfluidic Sensors Based on Deep Reinforcement Learning

Abstract

Automatic optimization of resonant structure is highly desired in the design of high-sensitivity microwave microfluidic sensors. In this paper, the design of resonant structures is abstracted as a decision model, and a deep deterministic policy gradient algorithm based on joint simulation is applied to achieve automatic optimal design through a learned strategy. The agent’s action strategy is decomposed into multiple movement actions against the pixelated structure and finally outputs the optimized structure. Through the optimal structure adjustment strategy, the sensor sensitivity can be dramatically improved. Depending on the design requirement, the liquid consumption volume can be set to be a constant or variable. To evaluate the performance of the proposed strategy, two optimized prototypes are prepared and tested. Compared with the original complementary split-ring resonator-based sensor which obtains a sensitivity of 0.522% for water measurement, the optimized sensors achieve high sensitivities of 0.666% and 0.805%, respectively, implying that the deep deterministic policy gradient-based agent can effectively explore the optimization strategy. This study is a meaningful attempt to develop automatic design procedure for planar microwave microfluidic sensors.