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In this work, 1.5-level interconnections of RF microsystems with good RF performance, integration process compatibility, and high reliability are developed to meet the future demand for wireless communication microsystems in the millimeter wave band. Numerical models of 1.5-level interconnections based on solder balls with different diameters are established and analyzed using HFSS. The optimized structure parameters of 0.2 mm diameter Sn96.5Ag3Cu0.5 (SAC305) solder balls and 0.3 mm diameter Sn63Pb37 solder balls are selected for the interconnection between glass micro substrates and silicon micro substrates and between silicon micro substrates and HTCC substrates, respectively. Integration process parameters of the vertical interconnection are optimized. The micro substrate interconnection samples manufactured based on optimized integration methods and parameters show high reliability.

Partial discharge (PD) is the dominant insulating defect in Gas-Insulated Switchgear (GIS). The existing detection methods are mainly divided into built-in wire-connected disk antennas with destructive drilling and external ultra-high frequency antennas with poor anti-interference ability. This research introduces a passive wireless PD sensor implanted inside GIS on the observation window. The sensor is implemented by a sheeting branch-inductor with multiple resonances which is able to enhance detection sensitivity. A coaxially aligned readout circuit, positioned outside the GIS, interrogates the PD sensor to wirelessly obtain the PD signal. The proposed sensing scheme improves signal-to-noise ratio and ensures minimal disruption to the electric field distribution inside GIS. An experimental setup was established in a controlled laboratory environment to benchmark the multi-resonant sensor against the commercial UHF sensor. A 2.5-times enhancement of signal strength was observed. Since our sensor was implanted inside the GIS, a high signal-to-noise ratio (68.82 dB) was obtained. Moreover, we constructed a wireless calibration test to investigate the accuracy of the proposed sensor. The precision of the signal test was as high as 0.72 pC. The pulse phase distribution information was collected to demonstrate a phase-resolved partial discharge (PRPD) pattern. The experiment results validate the effectiveness of the proposed method and demonstrate excellent performance in PD detection.

Abstract Cross‐project defect prediction (CPDP) is an important research direction in software defect prediction. Traditional CPDP methods based on hand‐crafted features ignore the semantic information in the source code. Existing CPDP methods based on the deep learning model may not fully consider the differences among projects. Additionally, these methods may not accurately classify the samples near the classification boundary. To solve these problems, the authors propose a model based on multi‐adaptation and nuclear norm (MANN) to deal with samples in projects. The feature of samples were embedded into the multi‐core Hilbert space for distribution and the multi‐kernel maximum mean discrepancy method was utilised to reduce differences among projects. More importantly, the nuclear norm module was constructed, which improved the discriminability and diversity of the target sample by calculating and maximizing the nuclear norm of the target sample in the process of domain adaptation, thus improving the performance of MANN. Finally, extensive experiments were conducted on 11 sizeable open‐source projects. The results indicate that the proposed method exceeds the state of the art under the widely used metrics.

This communication investigated the dust effect on microelectromechanical system (MEMS) thermal wind sensors, with an aim to evaluate performance in practical applications. An equivalent circuit was established to analyze the temperature gradient influenced by dust accumulation on the sensor’s surface. The finite element method (FEM) simulation was carried out to verify the proposed model using COMSOL Multiphysics software. In experiments, dust was accumulated on the sensor’s surface by two different methods. The measured results indicated that the output voltage for the sensor with dust on its surface was a little smaller than that of the sensor without dust at the same wind speed, which can degrade the measurement sensitivity and accuracy. Compared to the sensor without dust, the average voltage was reduced by about 1.91% and 3.75% when the dustiness was 0.04 g/mL and 0.12 g/mL, respectively. The results can provide a reference for the actual application of thermal wind sensors in harsh environments.

This article presents a method for detecting rotational speed by LC (inductor-capacitor) wireless sensors. The sensing system consists of two identical LC resonant tanks. One is mounted on the rotating part and the other, as a readout circuit, is placed right above the rotating part. When the inductor on the rotating part is coaxially aligned with the readout inductor during rotation, the mutual coupling between them reaches the maximum, resulting in a peak amplitude induced at the readout LC tank. The period of the readout signal corresponds to the rotation speed. ADS (Advanced Design System) software was used to simulate and optimize the sensing system. A synchronous detection circuit was designed. The rotational speed of an electric was measured to validate this method experimentally, and the results indicated that the maximum error of the rotation speed from 16 rps to 41 rps was 0.279 rps.

Porous polytetrafluoroethylene (PTFE) is physically flexible, thermally and chemically stable, relatively inexpensive, and commercially available. It is attractive for various flexible sensors. This paper has studied flexible capacitive humidity sensors fabricated on porous PTFE substrates. Graphene oxide (GO) was used as a sensing material, both hydrophobic and hydrophilic porous PTFE as the substrates, and interdigitated electrodes on the PTFE substrates were screen-printed. SEM and Raman spectrum were utilized to characterize GO and PTFE. An ethanol soak process is developed to increase the yield of the humidity sensors based on hydrophobic porous PTFE substrates. Static and dynamic properties of these sensors are tested and analyzed. It demonstrates that the flexible capacitive humidity sensors fabricated on the ethanol-treated hydrophobic PTFE exhibit high sensitivity, small hysteresis, and fast response/recovery time.

An innovative micro-test structure for detecting the thermal expansion coefficient (TEC) of metal materials is presented in this work. Throughout this method, a whole temperature sensing moveable structures are supported by four groups of cascaded chevrons beams and packed together. Thermal expansion of the metal material causes the deflection of the cascaded chevrons, which leads to the capacitance variation. By detecting the capacitance value at different temperatures, the TEC value of the metal materials can be calculated. A finite element model has been established to verify the relationship between the TEC of the material and the displacement of the structure on horizontal and vertical directions, thus a function of temperature for different values of TEC can be deduced. In order to verify the analytical model, a suspended-capacitive micro-test structure has been fabricated by MetalMUMPs process and tested in a climate chamber. Test results show that in the temperature range from 30 °C to 80 °C, the TEC of the test material is 13.4 × 10−6 °C−1 with a maximum relative error of 0.8% compared with the given curve of relationship between displacement and temperature.