Physically-Based Reduced Order Modelling of a Uni-Axial Polysilicon MEMS Accelerometer

Ghisi, Aldo; Mariani, Stefano; Corigliano, Alberto; Zerbini, Sarah

doi:10.3390/s121013985

Cited by 12 publications

(4 citation statements)

References 35 publications

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“…Specifically, the mechanical properties of polysilicon are characterized by a neural network, trained with the same data used in this work for upscaling its elastic properties [21][22][23][24]. Next step of the procedure is therefore the multiscale [25,26], fully data-driven analysis of the designed device and, to a larger extent, of the scattering measured in ever downsizing MEMS devices.…”

Section: Discussionmentioning

confidence: 99%

A Stochastic Model to Describe the Scattering in the Response of Polysilicon MEMS

Dassi

Merola

Riva

et al. 2021

7th International Electronic Conference on Sensors and Applications

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The current miniaturization trend in the market of inertial microsystems is leading to movable device parts with sizes comparable to the characteristic length-scale of the polycrystalline silicon film morphology. The relevant output of micro electro-mechanical systems (MEMS) is thus more and more affected by a scattering, induced by features resulting from the micro-fabrication process. We recently proposed an on-chip testing device, specifically designed to enhance the aforementioned scattering in compliance with fabrication constraints. We proved that the experimentally measured scattering cannot be described by allowing only for the morphology-affected mechanical properties of the silicon films, and etch defects must be properly accounted for too. In this work, we discuss a fully stochastic framework allowing for the local fluctuations of the stiffness and of the etch-affected geometry of the silicon film. The provided semi-analytical solution is shown to catch efficiently the measured scattering in the C-V plots collected through the test structure. This approach opens up the possibility to learn on-line specific features of the devices, and to reduce the time required for their calibration.

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Section: Discussionmentioning

confidence: 99%

A Stochastic Model to Describe the Scattering in the Response of Polysilicon MEMS

Dassi

Merola

Riva

et al. 2021

7th International Electronic Conference on Sensors and Applications

Self Cite

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“…In fact, shocks still represent an important issue as for the reliability of inertial MEMS devices and failures linked to cracks spreading in high stressed regions of the MEMS can suddenly occur [ 26 ]. Focusing on the physical effects of shocks and drops on the MEMS accelerometer, it can be shown that the mechanical side of the problem is by far the most prominent one and the electrical side can be disregarded [ 27 ]. This is due to the fact the inertial and possible contact forces turn out to exceed by orders of magnitude the electrical ones, when more than

is induced by the shock loading [ 24 ].…”

Section: Simulation Analysismentioning

confidence: 99%

“…Because of the re-entrant corners at the connection with the anchor and the substrate,

is actually located in critical regions very close to the end of the cross-sections of the supporting beams [ 29 ]. What turned out from previous experiments is that standard accelerometers can sustain high-g acceleration levels without malfunctioning, since the stress field in critical regions results to be much below the characteristic tensile strength of silicon (typically higher than 1 GPa) [ 27 ]. As for traditional single torsional beam structures, the critical regions are near the symmetry line of the differential masses, where the moment of force is the maximum.…”

Section: Simulation Analysismentioning

confidence: 99%

A 4 mm2 Double Differential Torsional MEMS Accelerometer Based on a Double-Beam Configuration

Miao

Xiao

et al. 2017

Sensors

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This paper reports the design and simulation of a 4 mm2 double differential torsional MEMS accelerometer based on a double-beam configuration. Based on the structure of conventional torsional accelerometers, normally composed of one pair of proof masses and one torsional beam, this work explores the double differential configuration: a torsional accelerometer with two pairs of unbalanced proof masses rotating in reverse. Also, the torsional beam is designed as a double-beam structure, which is a symmetrical structure formed by two torsional beams separated by a certain distance. The device area of the novel accelerometer is more than 50 times smaller than that of a traditional double differential torsional MEMS accelerometer. The FEM simulation results demonstrate that the smaller device does not sacrifice other specifications, such as mechanical sensitivity, nonlinearity and temperature robustness. The mechanical sensitivity and nonlinearity of a ±15 g measuring range is 59.4 fF/g and 0.88%, respectively. Compared with traditional single-beam silicon structures, the novel structure can achieve lower maximum principle stress in critical regions and reduce the possibility of failure when high-g acceleration loading is applied along all three axes. The mechanical noise equivalent acceleration is about 0.13 mg/Hz in the theoretical calculations and the offset temperature coefficient is 0.25 mg/℃ in the full temperature range of −40 ℃ to 60 ℃.

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“…The mentioned length scale separation is very important for computing the homogenized properties of the polysilicon films in multi-scale and, often, multi-physics analyses [13,14]. To avoid any surrogate or smoothing procedure of the available results, upscaling through a new Monte Carlo analysis thus looks necessary whenever the considered value does not match those already investigated.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Polycrystalline Microstructure-Statistical Property Prediction

Quesada-Molina

Mariani

2021

Computational Science – ICCS 2021

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Upscaling of the mechanical properties of polycrystalline aggregates might require complex and time-consuming procedures, if adopted to help in the design and reliability analysis of micro-devices. In inertial micro electro-mechanical systems (MEMS), the movable parts are often made of polycrystalline silicon films and, due to the current trend towards further miniaturization, their mechanical properties must be characterized not only in terms of average values but also in terms of their scattering. In this work, we propose two convolutional network models based on the ResNet and DenseNet architectures, to learn the features of the microstructural morphology and allow automatic upscaling of the statistical properties of the said film properties. Results are shown for film samples featuring different values of a length scale ratio, so as to assess accuracy and computational efficiency of the proposed approach.

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Physically-Based Reduced Order Modelling of a Uni-Axial Polysilicon MEMS Accelerometer

Cited by 12 publications

References 35 publications

A Stochastic Model to Describe the Scattering in the Response of Polysilicon MEMS

A Stochastic Model to Describe the Scattering in the Response of Polysilicon MEMS

A 4 mm2 Double Differential Torsional MEMS Accelerometer Based on a Double-Beam Configuration

A Deep Learning Approach for Polycrystalline Microstructure-Statistical Property Prediction

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