FG Mikro- und Nanosysteme
Refine
Year of publication
Document Type
Way of publication
- Open Access (6)
Keywords
- Accelerometer (1)
- Acoustic electrostatic actuators (1)
- Acoustic transducers (1)
- Capacitors (1)
- Duffing oscillator (1)
- Electrostatic actuator (1)
- Electrostatics (1)
- Engineering (1)
- Force (1)
- Harmonic distortions (1)
Institute
For electrostatic actuators, the pull-in marks an upper limit for the operation range. Once reached, the electrodes come into contact and are shorted without further protection. A non-destructive measurement technique to predict this failure mode is of high interest to allow, e.g. fabrication monitoring or reliability studies. To this end, we develop a surprisingly simple nonlinear lumped parameter model (LPM) for a rather complex electrostatic actuator, designed for an in-ear loudspeaker application. It turns out that a single degree-of-freedom model with only one parameter is sufficient. Our key approach is to experimentally determine this free model parameter by analysing harmonic distortions at low frequencies. Harmonic distortions are a very sensitive tool for nonlinearities. Our method is suggested by simulations with a 2D stationary finite element method (FEM), demonstrating how the analysis of harmonic distortions for voltages far below the pull-in can predict not only the DC pull-in but also the quasi-static AC pull-in voltages at different working points. The distortion analysis of electrostatic actuator ensembles therefore seems a viable route for their non-destructive characterization in the nonlinear domain.
Predictive maintenance of equipment requires a set of data collected through sensors, from which models will learn behaviors that will allow the automatic detection or prediction of these behaviors. The objective is to anticipate unexpected situations such as sudden equipment stoppages. Industries are noisy environments due to production lines that involve a series of components. As a result, the data will always be obstructed by noise. Noise-robust predictive maintenance models, which include ensemble and deep learning models with and without data fusion, are proposed to enhance the monitoring of industrial equipment. The work reported in this article is based on two components, a milling tool, and a motor, with sound, vibration, and ultrasound data collected in real experiments. Four main tasks were performed, namely the construction of the datasets, the training of the monitoring models without adding artificial noise to the data, the evaluation of the robustness of the previously trained models by injecting several levels of noise into the test data, and the optimization of the models by a proposed noisy training approach. The results show that the models maintain their performances at over 95% accuracy despite adding noise in the test phase. These performances decrease by only 2% at a considerable noise level of 15-dB signal-to-noise ratio (SNR). The noisy training method proved to be an optimal solution for improving the noise robustness and accuracy of convolutional deep learning models, whose performance regression of 2% went from a noise level of 28 to 15 dB like the other models.
Schottky photodetectors based on internal photoemission have the potential of an adjustable detection range in the near-infrared range and Si compatible technology integration. The external quantum efficiency of Schottky photodetectors can be improved using nanophotonic structures, which enhance the absorption of the device. However, the electrical properties of Schottky photodetectors deviate under an altered metal-semiconductor interface topography. We characterize a common layer system for metal contacts consisting of a TiN interstitial layer between the p-Si wafer and the AlSiCu metallization. By varying the thickness of the TiN we discuss how homogeneity of the sputtered layers influence device properties with different interface topographies. Through electrical wafer-level characterization, the characteristics of the Schottky contact are evaluated and compared to the ideal physical modelling.
Integrated optical interconnections in silicon chips have the potential to replace electrical interconnects between integrated circuits thanks to their high bandwidth and low energy communication promises. A silicon dielectric waveguide can be etched through the full thickness of a silicon substrate providing a monolithically integrated solution for 2.5D and 3D photonic architectures. The optical through-silicon waveguide design presented here is based on Finite Difference Time Domain simulations, while the fabrication takes advantage of the innovative plasma cryo-etching process that provides smooth sidewalls and tapering angle tunability. Such structures can provide effective mode size conversion, therefore favoring the coupling of external light sources to photonic integrated circuits and the stacking of multiple optically interconnected dies.