Mechanical Characterization of Polysilicon MEMS Devices: a Stochastic, Deep Learning-based Approach

Quesada-Molina, José Pablo; Rosafalco, Luca; Mariani, Stefano

doi:10.1109/eurosime48426.2020.9152690

Cited by 5 publications

(5 citation statements)

References 20 publications

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“…Currently, the proposed strategy is going to be re-formulated within the frame of a data-driven approach. 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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“…They found that the micro-grooves between the grains are the critical reason leading to structural fracture. Mariani et al [114][115][116] The variation of the tensile strength of MEMS materials makes it difficult to predict the failure threshold or quantify reliability through numerical analysis. Therefore, some studies have proposed probabilistic models for MEMS structural materials, which regard the tensile strength of the material as a random variable.…”

Section: Strength Model Of Brittle Materialsmentioning

confidence: 99%

“…They found that the micro-grooves between the grains are the critical reason leading to structural fracture. Mariani et al [114][115][116] produced a series of studies. In 2011, Mariani et al performed multi-scale modeling of MEMS structures.…”

Section: Strength Model Of Brittle Materialsmentioning

confidence: 99%

“…In 2019 and 2020, José Pablo Quesada Molina et al used deep learning to characterize the mechanical properties of polysilicon materials based on the data set (which was generated numerically, as shown in Figure 12). The deep learning model could predict the relationship between the grain characteristics and the mechanical properties of polysilicon [115,116].…”

Section: Strength Model Of Brittle Materialsmentioning

confidence: 99%

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Reliability of MEMS in Shock Environments: 2000–2020

Peng

You

2021

Micromachines

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The reliability of MEMS in shock environments is a complex area which involves structural dynamics, fracture mechanics, and system reliability theory etc. With growth in the use of MEMS in automotive, IoT, aerospace and other harsh environments, there is a need for an in-depth understanding of the reliability of MEMS in shock environments. Despite the contributions of many articles that have overviewed the reliability of MEMS panoramically, a review paper that specifically focuses on the reliability research of MEMS in shock environments is, to date, absent. This paper reviews studies which examine the reliability of MEMS in shock environments from 2000 to 2020 in six sub-areas, which are: (i) response model of microstructure, (ii) shock experimental progresses, (iii) shock resistant microstructures, (iv) reliability quantification models of microstructure, (v) electronics-system-level reliability, and (vi) the coupling phenomenon of shock with other factors. This paper fills the gap around overviews of MEMS reliability in shock environments. Through the framework of these six sub-areas, we propose some directions potentially worthy of attention for future research.

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“…Finally, a linear activation function is applied to the output neuron, to get the output in terms of the estimated Young's modulus Ê . More details about training, validation and testing of this model can be found in [25].…”

Section: E =mentioning

confidence: 99%

A Two-Scale Multi-Physics Deep Learning Model for Smart MEMS Sensors

Quesada-Molina¹,

Mariani²

2021

MSCE

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Smart materials and structures, especially those bio-inspired, are often characterized by a hierarchy of length-and time-scales. Smart Micro Electro-Mechanical Systems (MEMS) are also characterized by different physical phenomena affecting their properties at different scales. Data-driven formulations can then be helpful to deal with the complexity of the multi-physics governing their response to the external stimuli, and optimize their performances. As an example, Lorentz force micro-magnetometers working principle rests on the interaction of a magnetic field with a current flowing inside a semiconducting, micro-structured medium. If an alternating current with a properly set frequency is let to flow longitudinally in a slender beam, the system is driven into resonance and the sensitivity to the magnetic field may result largely enhanced. In our former activity, a reduced-order physical model of the movable structure of a single-axis Lorentz force MEMS magnetometer was developed, to feed a multi-objective topology optimization procedure. That model-based approach did not account for stochastic effects, which lead to the scattering in the experimental data at the micrometric length-scale. The formulation is here improved to allow for stochastic effects through a two-scale deep learning model designed as follows: at the material scale, a neural network is adopted to learn the scattering in the mechanical properties of polysilicon induced by its polycrystalline morphology; at the device scale, a further neural network is adopted to learn the most important geometric features of the movable parts that affect the overall performance of the magnetometer. Some preliminary results are discussed, and an extension to allow for size effects is finally foreseen.

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Mechanical Characterization of Polysilicon MEMS Devices: a Stochastic, Deep Learning-based Approach

Cited by 5 publications

References 20 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

Reliability of MEMS in Shock Environments: 2000–2020

A Two-Scale Multi-Physics Deep Learning Model for Smart MEMS Sensors

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