Modeling high signal-to-noise ratio in a novel silicon MEMS microphone with comb readout

Strong competition within the consumer market urges the companies to constantly improve the quality of their devices. For silicon microphones excellent sound quality is the key feature in this respect which means that improving the signal-to-noise ratio (SNR), being strongly correlated with the sound quality is a major task to fulfill the growing demands of the market. MEMS microphones with conventional capacitive readout suffer from noise caused by viscous damping losses arising from perforations in the backplate [1]. Therefore, we conceived a novel microphone design based on capacitive read-out via comb structures, which is supposed to show a reduction in fluidic damping compared to conventional MEMS microphones. In order to evaluate the potential of the proposed design, we developed a fully energy-coupled, modular system-level model taking into account the mechanical motion, the slide film damping between the comb fingers, the acoustic impact of the package and the capacitive read-out. All submodels are physically based scaling with all relevant design parameters. We carried out noise analyses and due to the modular and physics-based character of the model, were able to discriminate the noise contributions of different parts of the microphone. This enables us to identify design variants of this concept which exhibit a SNR of up to 73 dB (A). This is superior to conventional and at least comparable to high-performance variants of the current state-of-the art MEMS microphones [2].