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首页> 外文期刊>Sensors and Actuators, A. Physical >Automated extraction of multi-energy domain reduced-order models demonstrated on capacitive MEMS microphones
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Automated extraction of multi-energy domain reduced-order models demonstrated on capacitive MEMS microphones

机译:在电容式MEMS麦克风上演示的自动提取多能量域降阶模型

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We present a methodology to systematically extract efficient and physically-based reduced-order models based on a mixed-level simulation approach and demonstrate its practicality for the design of a capacitive MEMS microphone. The method has been implemented in a MATLAB toolbox. Starting from a FEM discretization, it enables the automated generation of mixed-level VHDL-AMS based macromodels, which can be straightforwardly fed into a standard circuit simulator. The whole model generation process is described in all details. The resulting macromodels are highly efficient and moreover, in contrast to other equivalent network approaches, physically-based. Therefore they allow for the predictive simulation of microstructures with complex geometry, as it is demonstrated by the numerical analysis of a capacitive microphone.
机译:我们提出了一种基于混合级仿真方法来系统地提取高效且基于物理的降阶模型的方法,并论证了其在电容性MEMS麦克风设计中的实用性。该方法已在MATLAB工具箱中实现。从FEM离散化开始,它可以自动生成基于混合级VHDL-AMS的宏模型,可以直接将其输入到标准电路模拟器中。整个模型生成过程将在所有细节中进行描述。与其他等效的基于物理的网络方法相比,生成的宏模型效率很高,而且效率更高。因此,它们可以对具有复杂几何形状的微结构进行预测性仿真,如电容式麦克风的数值分析所示。

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