Piezoelectric energy harvesters have proven to have the potential to be a power source in a wide range of applications. As the harvester dimensions scale down, the resonance frequencies of these devices increase drastically. Proof masses are essential in micro-scale devices in order to decrease the resonance frequency and increase the strain along the beam to increase the output power. In this work, the effects of proof mass geometry on piezoelectric energy harvesters are studied. Different geometrical dimension ratios have significant impact on the resonance frequency, e.g., beam to mass lengths, and beam to mass widths. A piezoelectric energy harvester has been fabricated and tested operating at a frequency of about 4 kHz within the audible range. The responses of various prototypes were studied, and an optimized T-shaped piezoelectric vibration energy harvester design is presented for improved performance.

This work presents a laterally rotating micromachined platform integrated under optical waveguides to control the in-plane propagation direction of light within a die to select one of multiple outputs. The platform is designed to exhibit low constant optical losses throughout the motion range and is actuated electrostatically using an optimized circular comb drive. An angular motion of ±9.5° using 180 V is demonstrated. To minimize the optical losses between the moving and fixed parts, a gap-closing mechanism is implemented to reduce the initial air gap to submicron values. A latch structure is implemented to hold the platform in place with a resolution of 0.25° over the entire motion range. The platform was integrated with silicon nitride waveguides to create a crossbar switch and preliminary optical measurements are reported. In the bar state, the loss was measured to be 14.8 dB with the gap closed whereas in the cross state it was 12.2 dB. To the authors’ knowledge, this is the first optical switch based on a rotating microelectromechanical device with integrated silicon nitride waveguides reported to date.

This paper presents a bulk-mode disk resonator utilizing a transverse piezoelectric actuation technique to drive the single crystalline silicon disk structure in its wine-glass bulk mode of resonance. The device is fabricated by post-processing dies from a commercial microelectromechanical systems process and combines high quality factor and relatively low motional resistance. Post-process fabrication steps are used to reduce the width of the anchor beam supports, and consequently reduce the anchor losses. This results in a factor of 3 increase in the quality factor. Post-processed resonators were measured to have resonance frequencies of 23 MHz and quality factors of 13000 in atmospheric pressure, increasing to 31000 in 1 mTorr vacuum. The devices also demonstrate motional resistances of 21 kQ in atmospheric pressure, reducing to 8.8 kQ in 1 mTorr vacuum without requiring any dc voltage for operation.

This paper presents an oscillator based on a fully differential adjustable-bandwidth transimpedance amplifier (TIA) and a piezoelectric micromachined bulk-mode disk resonator. The TIA includes a regulated cascode stage and a common source active feedback topology. The TIA is designed in 65-nm CMOS and consumes 1.4 mA from a 1-V supply. The measured maximum mid-band transimpedance gain is ~80 dBQ and the TIA features an adjustable bandwidth of up to 214 MHz, when driving a shunt parasitic capacitance C _P of 4 pF. The measured input-referred current noise of the TIA at mid-band (i.e., ~20 kHz-40 MHz) is below 3.7 pA/√Hz. The TIA is connected with the disk resonator in [1], and the oscillator performance in terms of phase noise and frequency stability is reported. The measured phase noise in air and under vacuum, at a 1-kHz offset, is -104 and -116 dBc/Hz, respectively. The close-to-carrier phase noise is also low in air and under vacuum with -40 and -60 dBc/Hz, respectively, at a 10-Hz offset, while the far-from-carrier phase noise reaches -130 dBc/Hz. The measured short-term stability of the MEMS oscillator is of ±0.38 ppm.

Ultrasonic diagnostics is the earliest way to predict industrial faults. Usually, a contact microphone is employed for detection, but the recording will be contaminated with noise. In this paper, a dataset that contains 10 main faults of pipelines and motors is analyzed from which 30 different features in the time and frequency domains are extracted. Afterward, for dimensionality reduction, principal component analysis (PCA), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) are performed. In the subsequent phase, recursive feature elimination (RFE) is employed as a strategic method to analyze and select the most relevant features for the classifiers. Next, predictive models consisting of k-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM) are employed. Then, in order to solve the classification problem, a stacking classifier based on a meta-classifier which combines multiple classification models is introduced. Furthermore, the k-fold cross-validation technique is employed to assess the effectiveness of the model in handling new data for the evaluation of experimental results in ultrasonic fault detection. With the proposed method, the accuracy is around 5% higher over five cross folds with the least amount of variation. The timing evaluation of the meta model on the 64 MHz Cortex M4 microcontroller unit (MCU) revealed an execution time of 11 ms, indicating it could be a promising solution for real-time monitoring.