Simplification by pruning as a model order reduction approach for RF-MEMS switches. Issue 2 (29th November 2019)

• Record Type: Journal Article
• Authors: Ciuprina, Gabriela; Ioan, Daniel; Lup, Aurel-Sorin; Silveira, Luis Miguel; Duca, Anton; Kraft, Michael
• Is Part Of: Compel. Volume 39:Issue 2(2020)
• Page Start: 511
• Page End: 523
• Publication Date: 2019-11-29
• Subjects: Multiphysics -- Model order reduction -- MEMS modeling -- Computational electromagnetics; Electrical engineering -- Data Processing -- Periodicals; Electrical engineering -- Mathematics -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Data Processing -- Periodicals; Electronics -- Mathematics -- Periodicals; 621.3
• DOI: 10.1108/COMPEL-06-2019-0266
• Languages: English
• ISSNs: 0332-1649

Abstract

Abstract : Purpose: This paper proposes an algorithm for the extraction of reduced order models of MEMS switches, based on using a physics aware simplification technique. Design/methodology/approach: The reduced model is built progressively by increasing the complexity of the physical model. The approach starts with static analyses and continues with dynamic ones. Physical phenomena are introduced sequentially in the reduced model whose order is increased until accuracy, computed by assessing forces that are kept in the reduced model, is acceptable. Findings: The technique is exemplified for RF-MEMS switches, but it can be extended for any device where physical phenomena can be included one by one, in a hierarchy of models. The extraction technique is based on analogies that are carried out for both the multiphysics and the full-wave electromagnetic phenomena and their couplings. In the final model, the multiphysics electromechanical phenomena is reduced to a system with lumped components with nonlinear elastic and damping forces, coupled with a system with distributed and lumped components which represents the reduced model of the RF electromagnetic phenomena. Originality/value: Contrary to the order reduction by projection methods, this approach has the advantage that the simplified model can be easily understood, the equations and variables have significance for the user and the algorithm starts with a model of minimal order, which is increased until the approximation error is acceptable. The novelty of the proposed method is that, being tailored to a specific application, it is able to keep physical interpretation inside the reduced model. This is the reason why, the obtained model has an extremely low order, much lower than the one achievable with general state-of-the-art procedures.