Abstract
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.
Highlights
Over the last years, we have witnessed an accelerated development in the technology of computer systems, with a huge increase in the available computation power
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
We propose a two-scale machine learning (ML) approach based on an assembly of artificial neural networks (ANNs) (CNNs and multilayer perceptron (MLP)) at different length-scales, to provide an accurate property-performance mapping for polysilicon Mechanical Systems (MEMS) devices
Summary
We have witnessed an accelerated development in the technology of computer systems, with a huge increase in the available computation power. As shown in [5], diverse ML approaches have been successfully implemented to perform descriptive, predictive and prescriptive tasks in a straightforward data-driven manner. Within this context, a very popular type of ML algorithms is represented by the artificial neural networks (ANNs). A popular subtype of ANNs is given by the convolutional neural networks (CNNs) These architectures are formulated as a variant of FFNNs, to be well suited for data featuring a spatial correlation [9]. We propose a two-scale ML approach based on an assembly of ANNs (CNNs and MLPs) at different length-scales, to provide an accurate property-performance mapping for polysilicon MEMS devices.
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More From: Journal of Materials Science and Chemical Engineering
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