This article presents a novel class of dual-mode substrate-integrated waveguide (SIW) bandpass filters exploiting perturbed SIW rectangular cavities. By introducing metallized via-holes along the central line of an SIW rectangular cavity, the resonant frequency of TE ₁₀₁ mode can be shifted close to that of TE ₂₀₁ mode, thus creating a dual-mode resonator. This structure implements a doublet, which provides two poles and one or two transmission zeros (TZs). The pass bandwidth, spurious-free bandwidth, and positions of the TZs can be fully controlled by modifying the length of the perturbation, the side ratio of the rectangular cavity, and the offset of the input/output ports, respectively. Eight two-pole SIW filters are designed and investigated to demonstrate the high design flexibility. Moreover, a four-pole SIW filter with the asymmetric response and a five-pole SIW filter with quasi-elliptic response are designed, fabricated, and measured to describe the extension of the proposed approach to higher order filters.

Microwave microfluidic sensors have been employed for dielectric characterization of different liquids. Intuitively, the microfluidic channel plays a vital role in determining the sensor performance. In this article, for the first time, numerical optimization design of microfluidic channel route is carried out with the aim of improving the sensor sensitivity. Two swarm intelligence algorithms, i.e., particle-ant colony optimization algorithm and wolf colony algorithm, are implemented for the route optimization. Through the developed optimization procedure, the sensor sensitivity of the original design can be increased significantly. Several prototypes of optimized sensors are fabricated and tested, and they exhibit good capability in retrieving the liquid properties. In comparison with original complementary split-ring resonator-based sensor with a sensitivity of 0.308% for water measurement, the optimized sensor achieves a high sensitivity value of 0.55%, i.e., the sensor sensitivity is increased by 78.6% after optimization. The developed methodology can also be used in other designs, such as series LC -based sensor, whose sensitivity can be improved by about 50%. It is demonstrated that the developed methodology possesses good automatic optimization ability and universality for the optimal design of microwave microfluidic sensors.

This article presents a novel wideband bandpass filter based on the integration of a substrate integrated waveguide (SIW) and a spoof surface plasmon polariton (SSPP). An SIW cavity with periodic arrays of meander-slot units is etched on the top metallic layer to achieve the characteristics of a multi-order filter with good performance. The passbands can be flexibly selected by varying the geometric parameters of the SIW and SSPP to adjust the lower and upper sidebands independently. Using a redistribution layer (RDL) process, a novel 3D capacitive interconnection called a through-dielectric capacitor (TDC) is proposed and collaboratively designed with an interdigital capacitor to achieve capacitive source-load cross-coupling. The proposed filter has a center frequency of 60 GHz, with a wide 3-dB fractional bandwidth of about 45.8%. The improved simulated sideband suppression has a 30 dB rejection at 40 GHz and 75.4 GHz, corresponding to a 30-dB rectangular coefficient of 1.28.

In this article, the common-mode suppression filters (CMF) are synthesized using deep reinforcement learning algorithm called proximal policy optimization (PPO). The Latin hypercube is firstly employed to sample the parameters of complementary split-ring resonator structure, which is used as a reflective CMF. Then, with the help of PPO algorithm, a resistor-equipped defected ground structure is developed to absorb the reflective common-mode noise. To prove the feasibility of the proposed optimization method, two samples are designed and tested. The first sample is an absorptive CMF with absorption band located at 5 GHz, and the second sample is with dual absorption bands located at 2.4 GHz and 5 GHz. The results demonstrate that the proposed approach enables the automatic design of CMF.

In this paper, a genetic algorithm (GA)-based optimal design of thermoelectric cooler (TEC) and a hybrid Bayesian optimization (BO)-artificial neural network (ANN) based tuning method are presented to implement efficient thermal management in 3D integrated microsystems. The design approach is implemented based on the MATLAB-COMSOL co-simulation platform, where parametric modeling, simulation setup, and intelligent optimization modules are programmed in MATLAB and the Multiphysics simulations are conducted in COMSOL. The numerical simulation model is validated through comparison with the measured data from a previous work firstly. After that, embedded TECs are designed for the thermal management of a 3D integrated microsystem including two power chips with dimensions of 3×3×0.15 mm ³ and 2.5×2.5×0.1 mm ³ , utilizing the proposed design approach. The maximum of 14.4 K and 7.3 K drops in temperature are achieved in power chips with heat flux of 166 W/cm ² and 128 W/cm ² , respectively. At last, the mechanism and implementation of the hybrid BO-ANN based dynamic thermal management method are presented in detail. The numerical experiment results indicate that the power dissipation of the cooler is significantly improved through utilizing the proposed management approach.