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Соболев, Я. А.; Popov, B. G.

doi:10.1023/a:1013992131362

Cited by 1 publication

(1 citation statement)

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“…According to the derivation in earlier studies [18,19,20,21] and the material mechanics theory, the strain along the depth of the model can be expressed as:

ε = ε_{0} + \frac{z_{c} - z}{r}

where ε is the total strain; ε 0 , the uniform strain; z , the coordinate along the depth; and r , the radius of curvature of the neutral plane. Therefore, the stress in the layers can be expressed as:

σ = E_{i} (ε - ε_{i})

where E i is Young’s modulus and ε i , the shrinkage strain induced by the doping of layer i .…”

Section: Process and Modelmentioning

confidence: 99%

Modeling the Microstructure Curvature of Boron-Doped Silicon in Bulk Micromachined Accelerometer

Zhou

Peng

et al. 2013

Materials

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Microstructure curvature, or buckling, is observed in the micromachining of silicon sensors because of the doping of impurities for realizing certain electrical and mechanical processes. This behavior can be a key source of error in inertial sensors. Therefore, identifying the factors that influence the buckling value is important in designing MEMS devices. In this study, the curvature in the proof mass of an accelerometer is modeled as a multilayered solid model. Modeling is performed according to the characteristics of the solid diffusion mechanism in the bulk-dissolved wafer process (BDWP) based on the self-stopped etch technique. Moreover, the proposed multilayered solid model is established as an equivalent composite structure formed by a group of thin layers that are glued together. Each layer has a different Young’s modulus value and each undergoes different volume shrinkage strain owing to boron doping in silicon. Observations of five groups of proof mass blocks of accelerometers suggest that the theoretical model is effective in determining the buckling value of a fabricated structure.

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“…According to the derivation in earlier studies [18,19,20,21] and the material mechanics theory, the strain along the depth of the model can be expressed as: