Uncertainty evaluation plays a key role in assessing and comparing measurement results, e.g. towards the design of experiments and the design of measurement systems. Nevertheless, it is frequently neglected by engineers particularly in early design phases, which may cause problems during the design process or even lead to system failures. In prior work, the use of software tools for uncertainty calculation in measurement science education has been suggested in order to raise awareness and increase the use of such tools to obtain GUM conform uncertainty estimates. However, we found that uncertainty of complex-valued quantities represents a major challenge in this context. Therefore, we present a proposal to extend the teaching concept towards such quantities, based directly on the utilization of an "Uncertainty Toolbox" for MATLAB maintained by our research group. The approach evaluates the uncertainty in complex parameters considering the real and imaginary components separately, with potential correlations between them arising from shared input quantities. As teaching example we study the Maxwell-Wien Bridge, as it is commonly taught in measurement science courses and brings in aspects such as above mention correlations and different representations of the measurement result (magnitude/phase, real/complex part of impedance). Based on this example, advantages and disadvantages of the presented teaching philosophy are discussed, emphasizing how problems arising from uncertainty may be identified in early design phases also considering complex-valued quantities.
To guarantee high performance of Micro Optical Electro Mechanical Systems (MOEMS), precise position feedback is crucial. To overcome drawbacks of widely used optical feedback, we propose an inkjet-printed capacitive position sensor as smart packaging solution. Printing processes suffer from tolerances in excess of those from standard processes. Thus, FEM simulations covering assumed tolerances of the system are adopted. These simulations are structured following a Design Of Computer Experiments (DOCE) and are then employed to determine a optimal sensor design. Based on the simulation results, statistical models are adopted for the dynamic system. These models are to be used together with specifically designed hardware, considered to cope with challenging requirements of ≈50nm position accuracy at 10MS/s with 1000μm measurement range. Noise analysis is performed considering the influence of uncertainties to assess resolution and bandwidth capabilities.
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