Efficient nonlinear simulink models of MEMS gyroscopes generated with a novel model order reduction method

Parent, Arnaud; Krust, Arnaud; Lorenz, G.; Favorskiy, I.; Piirainen, Tommi

doi:10.1109/transducers.2015.7181393

Cited by 14 publications

(4 citation statements)

References 7 publications

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“…Another positive feature, useful in this context, is the non-intrusiveness of POD-DL-ROMs as snapshots can be readily generated with commercial codes. These capabilities, which are crucial in proposing realistic applications to microstructures, are underlined in this section by addressing an electromechanical disk resonating gyroscope (DRG) taken from the library of Coventor MEMS+ TM [ 62 , 63 , 64 ], a leading tool for the analysis of MEMS. The original model available in the software, inspired by the device proposed in [ 65 ], has been slightly modified in our benchmarks, and all of the details are provided in what follows.…”

Section: Applicationsmentioning

confidence: 99%

Reduced Order Modeling of Nonlinear Vibrating Multiphysics Microstructures with Deep Learning-Based Approaches

Gobat

Fresca

Manzoni

et al. 2023

Sensors

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Micro-electro-mechanical-systems are complex structures, often involving nonlinearites of geometric and multiphysics nature, that are used as sensors and actuators in countless applications. Starting from full-order representations, we apply deep learning techniques to generate accurate, efficient, and real-time reduced order models to be used for the simulation and optimization of higher-level complex systems. We extensively test the reliability of the proposed procedures on micromirrors, arches, and gyroscopes, as well as displaying intricate dynamical evolutions such as internal resonances. In particular, we discuss the accuracy of the deep learning technique and its ability to replicate and converge to the invariant manifolds predicted using the recently developed direct parametrization approach that allows the extraction of the nonlinear normal modes of large finite element models. Finally, by addressing an electromechanical gyroscope, we show that the non-intrusive deep learning approach generalizes easily to complex multiphysics problems.

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Section: Applicationsmentioning

confidence: 99%

Reduced Order Modeling of Nonlinear Vibrating Multiphysics Microstructures with Deep Learning-Based Approaches

Gobat

Fresca

Manzoni

et al. 2023

Sensors

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show abstract

“…In this section, as an example with industrial relevance, we will address a disk resonating gyroscope (DRG) inspired by the device proposed in [47]. The DRG is modelled with the software Coventor MEMS+ TM 7.1 using one of the templates available in the software itself, slightly modified for the purposes of this work [48][49][50].…”

Section: Application: Electromechanical Disk Resonating Gyroscopementioning

confidence: 99%

Modelling the Periodic Response of Micro-Electromechanical Systems through Deep Learning-Based Approaches

et al. 2023

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We propose a deep learning-based reduced order modelling approach for micro- electromechanical systems. The method allows treating parametrised, fully coupled electromechanical problems in a non-intrusive way and provides solutions across the whole device domain almost in real time, making it suitable for design optimisation and control purposes. The proposed technique specifically addresses the steady-state response, thus strongly reducing the computational burden associated with the neural network training stage and generating deep learning models with fewer parameters than similar architectures considering generic time-dependent problems. The approach is validated on a disk resonating gyroscope exhibiting auto-parametric resonance.

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“…Electrical tuning of a resonance frequency is a well-known method [10,11]. However, making near zero resonance frequency is by no means easy, since the frequency shift ∆f induced by a voltage shift ∆V will become very large when the spring constant k becomes small.…”

Section: Device Proof Mass Resonance Frequencymentioning

confidence: 99%

“…To simulate the device performance, Coventor's multi-physics simulation tool, MEMS+ ® ,is employed [14]. Past studies proved MEMS+ accurately predicts experimental electrostatic softening for manufactured gyroscopes [11,15]. The top view of the MEMS+ simulated model is shown in Figure 4b.…”

Section: Demonstration Of Multi-step Tuningmentioning

confidence: 99%

A Sub-1 Hz Resonance Frequency Resonator Enabled by Multi-Step Tuning for Micro-Seismometer

Maekoba

Parent

et al. 2021

Micromachines

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We propose a sub-1 Hz resonance frequency MEMS resonator that can be used for seismometers. The low resonance frequency is achieved by an electrically tunable spring with an ultra-small spring constant. Generally, it is difficult to electrically fine-tune the resonance frequency at a near-zero spring constant because the frequency shift per voltage will diverge at the limit of zero spring constant. To circumvent this issue, we propose a multi-step electrical tuning method. We show by simulations that the resonance frequency can be tuned by 0.008 Hz/mV even in the sub-1 Hz region. The small spring constant, however, reduces the shock robustness and dynamic range of the seismometer. To prevent this, we employ a force-balanced method in which the mass displacement is nulled by the feedback force. We show that the displacement can be obtained from the voltage that generates the feedback force.

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Efficient nonlinear simulink models of MEMS gyroscopes generated with a novel model order reduction method

Cited by 14 publications

References 7 publications

Reduced Order Modeling of Nonlinear Vibrating Multiphysics Microstructures with Deep Learning-Based Approaches

Reduced Order Modeling of Nonlinear Vibrating Multiphysics Microstructures with Deep Learning-Based Approaches

Modelling the Periodic Response of Micro-Electromechanical Systems through Deep Learning-Based Approaches

A Sub-1 Hz Resonance Frequency Resonator Enabled by Multi-Step Tuning for Micro-Seismometer

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