Quality factor control of mechanical resonators using variable phononic bandgap on periodic microstructures

The quality factor (Q-factor) is an important parameter for mechanical resonant sensors, and the optimal values depend on its application. Therefore, Q-factor control is essential for microelectromechanical systems (MEMS). Conventional methods have some restrictions, such as additional and complicated equipment or nanoscale dimensions; thus, structural methods are one of the reasonable solutions for simplifying the system. In this study, we demonstrate Q-factor control using a variable phononic bandgap by changing the length of the periodic microstructure. For this, silicon microstructure is used because it has both periodicity and a spring structure. The bandgap change is experimentally confirmed by measuring the Q-factors of mechanical resonators with different resonant frequencies. The bandgap range varies depending on the extended structure length, followed by a change in the Q-factor value. In addition, the effects of the periodic structure on the Q-factor enhancement and the influence of stress on the structural length were evaluated. Although microstructures can improve the Q-factors irrespective of periodicity; the result of the periodic microstructure is found to be efficient. The proposed method is feasible as the novel Q-factor control technique has good compatibility with conventional MEMS.

A parabolic local problem with exponential decay of the resonance error for numerical homogenization

This paper aims at an accurate and efficient computation of effective quantities, e.g. the homogenized coefficients for approximating the solutions to partial differential equations with oscillatory coefficients. Typical multiscale methods are based on a micro–macro-coupling, where the macromodel describes the coarse scale behavior, and the micromodel is solved only locally to upscale the effective quantities, which are missing in the macromodel. The fact that the microproblems are solved over small domains within the entire macroscopic domain, implies imposing artificial boundary conditions on the boundary of the microscopic domains. A naive treatment of these artificial boundary conditions leads to a first-order error in [Formula: see text], where [Formula: see text] represents the characteristic length of the small scale oscillations and [Formula: see text] is the size of microdomain. This error dominates all other errors originating from the discretization of the macro and the microproblems, and its reduction is a main issue in today’s engineering multiscale computations. The objective of this work is to analyze a parabolic approach, first announced in A. Abdulle, D. Arjmand, E. Paganoni, C. R. Acad. Sci. Paris, Ser. I, 2019, for computing the homogenized coefficients with arbitrarily high convergence rates in [Formula: see text]. The analysis covers the setting of periodic microstructure, and numerical simulations are provided to verify the theoretical findings for more general settings, e.g. non-periodic microstructures.

Multiscale Topology Optimization With Gaussian Process Regression Models

Multiscale topology optimization (MSTO) is a numerical design approach to optimally distribute material within coupled design domains at multiple length scales. Due to the substantial computational cost of performing topology optimization at multiple scales, MSTO methods often feature subroutines such as homogenization of parameterized unit cells and inverse homogenization of periodic microstructures. Parameterized unit cells are of great practical use, but limit the design to a pre-selected cell shape. On the other hand, inverse homogenization provide a physical representation of an optimal periodic microstructure at every discrete location, but do not necessarily embody a manufacturable structure. To address these limitations, this paper introduces a Gaussian process regression model-assisted MSTO method that features the optimal distribution of material at the macroscale and topology optimization of a manufacturable microscale structure. In the proposed approach, a macroscale optimization problem is solved using a gradient-based optimizer The design variables are defined as the homogenized stiffness tensors of the microscale topologies. As such, analytical sensitivity is not possible so the sensitivity coefficients are approximated using finite differences after each microscale topology is optimized. The computational cost of optimizing each microstructure is dramatically reduced by using Gaussian process regression models to approximate the homogenized stiffness tensor. The capability of the proposed MSTO method is demonstrated with two three-dimensional numerical examples. The correlation of the Gaussian process regression models are presented along with the final multiscale topologies for the two examples: a cantilever beam and a 3-point bending beam.

Fabrication of silver helix microstructures in a large area

The fabrication of metamaterials working in the wavelength region from visible to infrared is very attractive for beam treatment toward negative refraction and beyond the diffraction limit. The two photon absorption direct laser writing (TPA-DLW) method is a powerful tool to fabricate metamaterials with compact, dense, intricate, and periodic microstructures on the micrometer and submicrometer scales and is sensitive in the wavelength region from visible to infrared. In this study, large-area helix microstructures intended for metamaterials were fabricated using a negative photoresist, SU-8. To stabilize the fabricated free-standing helix microstructures with a 1 µm radius, circular foundations with a radius of 1.3 µm and elevation angle of 10, 12, or 14 ° were built in advance. The foundation is useful to avoid collapsing the helix microstructures. Due to the useful foundation, over 18,000 helical structures were fabricated in a large area. The obtained helical structures have the potential for metamaterials to control the handedness of a circularly polarized infrared beam.