Yield-aware multi-objective optimization of a MEMS accelerometer system using QMC-based methodologies

Pak, Murat; Fernández, Francisco V.; Dündar, Günhan

doi:10.1016/j.mejo.2020.104876

Cited by 2 publications

(2 citation statements)

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“…Therefore, in order to alleviate these design challenges, a more advanced optimization approach must be followed. Although formal design optimization techniques exist and are quite common in the design of RFIC components [11][12][13][14] or typical MEMS devices [15,16], such approaches have been overlooked for RF-MEMS devices which combine the two domains. For instance, an earlier work utilized metaheuristic optimization techniques in order to obtain optimal sets of design variables for planar CMOS RF inductors [13].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Modeling and Generic Design Optimization Methodology for Radio-Frequency Microelectromechanical Devices

Bajwa

Yapıcı

2023

Sensors

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RF-MEMS technology has evolved significantly over the years, during which various attempts have been made to tailor such devices for extreme performance by leveraging novel designs and fabrication processes, as well as integrating unique materials; however, their design optimization aspect has remained less explored. In this work, we report a computationally efficient generic design optimization methodology for RF-MEMS passive devices based on multi-objective heuristic optimization techniques, which, to the best of our knowledge, stands out as the first approach offering applicability to different RF-MEMS passives, as opposed to being customized for a single, specific component. In order to comprehensively optimize the design, both electrical and mechanical aspects of RF-MEMS device design are modeled carefully, using coupled finite element analysis (FEA). The proposed approach first generates a dataset, efficiently spanning the entire design space, based on FEA models. By coupling this dataset with machine-learning-based regression tools, we then generate surrogate models describing the output behavior of an RF-MEMS device for a given set of input variables. Finally, the developed surrogate models are subjected to a genetic algorithm-based optimizer, in order to extract the optimized device parameters. The proposed approach is validated for two case studies including RF-MEMS inductors and electrostatic switches, in which the multiple design objectives are optimized simultaneously. Moreover, the degree of conflict among various design objectives of the selected devices is studied, and corresponding sets of optimal trade-offs (pareto fronts) are extracted successfully.

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Section: Introductionmentioning

confidence: 99%

“…For instance, an earlier work utilized metaheuristic optimization techniques in order to obtain optimal sets of design variables for planar CMOS RF inductors [13]. Similarly, in another work, a yield-aware multi-objective optimization strategy was introduced in order to optimize the accelerometer performance [16].…”

Section: Introductionmentioning

confidence: 99%