FG Mikro- und Nanosysteme
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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.
MEMS transducer for interacting with a volume flow of a fluid, and method for producing the same
(2023)
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.