Multi-scale analysis of polysilicon MEMS sensors subject to accidental drops: Effect of packaging

Ghisi, Aldo; Fachin, Fabio; Mariani, Stefano; Zerbini, Sarah

doi:10.1016/j.microrel.2008.12.010

Cited by 42 publications

(30 citation statements)

References 17 publications

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“…Wafer level packaging increases the durability of the sensor as compared to component level packaging, especially for the sensors, which require a vacuum environment such as inertia sensors (Gooch et al ., 1999). Packaging infl uences the damage level for accidental drops (Ghisi et al ., 2009). …”

Section: Long Term Technical Challengesmentioning

confidence: 99%

Micro-electro-mechanical-systems (MEMS) for assessing and monitoring civil infrastructures

Ozevin

2014

Sensor Technologies for Civil Infrastructures

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Section: Long Term Technical Challengesmentioning

confidence: 99%

Micro-electro-mechanical-systems (MEMS) for assessing and monitoring civil infrastructures

Ozevin

2014

Sensor Technologies for Civil Infrastructures

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“…For polysilicon MEMS, the effects of the crystalline morphology on the reliability of inertial devices subjected to impacts and also under operational conditions were studied by the authors in [8][9][10][11][12][13][14][15][16] to possibly drive their optimization. To characterize the micro-devices on the basis of real experimental data, an on-chip test procedure was also proposed and analyzed in [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Mechanical Characterization of Polysilicon MEMS: A Deep Learning Approach

Quesada-Molina

Rosafalco

Mariani

2019

The 6th International Electronic Conference on Sensors and Applications

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Deep Learning strategies recently emerged as powerful tools for the characterization of heterogeneous materials. In this work, we discuss an approach for the characterization of the mechanical response of polysilicon films that typically constitute the movable structures of microelectro-mechanical systems (MEMS). A dataset of microstructures is digitally generated and a neural network is trained to provide the appropriate scattering in the values of the overall stiffness (in terms of the Young's modulus) of the grain aggregate. Since results are framed within a stochastic procedure, the aim of the learning strategy is not to accurately reproduce the microstructure-informed response of the polysilicon film, but instead to provide a fast tool to be used at the device level for Monte Carlo analysis of the relevant performance indices. Accuracy of the proposed approach is assessed for very small samples of the polycrystalline aggregate to check if size effects are correctly captured.

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“…on the basis of a stress criterion accounting for the pressure distribution over the bonding ring of any MEMS on the wafer. On the basis of the good results already obtained in [9][10][11][12][13] when dealing with drop effects on inertial MEMS sensors, we adopt here an uncoupled (or hierarchical), top-down approach to the problem. Information is therefore always transferred from the higher scale to the smaller one.…”

Section: Introductionmentioning

confidence: 99%

A multi-scale approach to wafer to wafer metallic bonding in MEMS

Ghisi

Corigliano

Mariani

et al. 2013

2013 14th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and

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A three-scale approach to thermo-compression, metallic wafer bonding is here presented. The approach focuses on the purely mechanical side of the process, identifying three different length-scales: the macro-scale, at which the whole wafer is considered, to define the average contact pressure within each single die; the mesoscale, at which the aforementioned average pressure at the die level is applied to the MEMS bonding ring, to study stress diffusion in it; and the micro-scale, at which a micro-mechanically informed morphology of a representative volume of the two metallic rings in contact is considered along with their surface roughness, to get insights into local features of the sealing. The proposed approach can describe local effects due to a space-varying pressure, and can help to enhance and speedup the design phase of the bonding rings

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Multi-scale analysis of polysilicon MEMS sensors subject to accidental drops: Effect of packaging

Cited by 42 publications

References 17 publications

Micro-electro-mechanical-systems (MEMS) for assessing and monitoring civil infrastructures

Micro-electro-mechanical-systems (MEMS) for assessing and monitoring civil infrastructures

Stochastic Mechanical Characterization of Polysilicon MEMS: A Deep Learning Approach

A multi-scale approach to wafer to wafer metallic bonding in MEMS

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