A Deep Learning-Based Approach to Uncertainty Quantification for Polysilicon MEMS

The path towards miniaturization for micro-electro-mechanical systems (MEMS) has recently increased the effects of stochastic variability at the (sub)micron scale on the overall performance of the devices. We recently proposed and designed an on-chip testing device to characterize two sources of variability that majorly affect the scattering in response to the external actions of inertial (statically determinate) micromachines: the morphology of the polysilicon film constituting the movable parts of the device, and the environment-affected over-etch linked to the microfabrication process. A fully stochastic model of the entire device has been set to account for these two sources on the measurable response of the devices, e.g., in terms of the relevant C-V curves up to pull-in. A complexity in the mentioned model is represented by the need to assess the stochastic (local) stiffness of polysilicon, depending on its unknown (local) microstructure. In this work, we discuss a deep learning approach to the micromechanical characterization of polysilicon films, based on densely connected neural networks (NNs). Such NNs extract relevant features of the polysilicon morphology from SEM-like Voronoi tessellation-based digital microstructures. The NN-based model or surrogate is shown to correctly catch size effects at a varying ratio between the characteristic size of the structural components of the device, and the morphology-induced length scale of the aggregate of silicon grains. This property of the model looks to indeed be necessary to prove the generalization capability of the learning process, and to next feed Monte Carlo simulations resting on the model of the entire device.

Dual-Functional Carbon-Doped Polysilicon Films for Passivating Contact Solar Cells: Regulating Physical Contacts while Promoting Photoelectrical Properties

Passivating contact crystalline-silicon solar cells are among the most promising industrially feasible photovoltaic (PV) technologies and require an excellent physical contact to handle the device performance. Here, we report a...

Stochastic Mechanical Characterization of Polysilicon MEMS: A Deep Learning Approach

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 micro-electro-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.

A Novel Measurement Method of Mechanical Properties for Individual Layers in Multilayered Thin Films

Various multilayered thin films are extensively used as the basic component of some micro-electro-mechanical systems, requiring an efficient measurement method for material parameters, such as Young’s modulus, residual stress, etc. This paper developed a novel measurement method to extract the Young’s moduli and residual stresses for individual layers in multilayered thin films, based on the first resonance frequency measurements of both cantilever beams and doubly-clamped beams. The fabrication process of the test structure, the corresponding modeling and the material parameter extraction process are introduced. To verify this method, the test structures with gold/polysilicon bilayer beams are fabricated and tested. The obtained Young’s moduli of polysilicon films are from 151.38 GPa to 154.93GPa, and the obtained Young’s moduli of gold films are from 70.72 GPa to 75.34GPa. The obtained residual stresses of polysilicon films are from −14.86 MPa to −13.11 MPa (compressive stress), and the obtained residual stresses of gold films are from 16.27 to 23.95 MPa (tensile stress). The extracted parameters are within the reasonable ranges, compared with the available results or the results obtained by other test methods.