SUGAR is an open source simulation tool for micro-electromechanical systems (MEMS) based on nodal analysis techniques from the world of integrated circuit simulation. Beams, electrostatic gaps, circuit elements, and other elements are modeled by small, coupled systems of differential equations. In less than a decade, the MEMS community has leveraged nearly all the integrated-circuit community’s fabrication techniques, but little of the wealth of simulation capabilities. A wide range of student and professional circuit designers regularly use circuit simulation tools like SPICE, while MEMS designers often resort to back-of-the-envelope calculations. For three decades, development of IC CAD tools has gone hand-in-hand with the development of IC processes. Tools for simulation will play a similar role in future advances in the design of complicated MEMS devices. SUGAR inherits its name and philosophy from SPICE. MEMS designers can describe a device in a compact netlist format, and quickly simulate the device’s behavior. Early in the design process, quick simulations help designers explore the solution space of their problem and prototype viable designs. Later in the design process, a designer might use other available software packages to run slower but more detailed simulations to check for subtle second-order effects. For more in-depth information, please see the list of publications or read the SUGAR 3.0 user’s manual (pdf).

References in zbMATH (referenced in 13 articles )

Showing results 1 to 13 of 13.
Sorted by year (citations)

  1. Vakilzadeh, Mohsen; Vatankhah, Ramin; Eghtesad, Mohammad: Vibration control of micro-scale structures using their reduced second order bilinear models based on multi-moment matching criteria (2020)
  2. Vakilzadeh, M.; Eghtesad, M.; Vatankhah, R.; Mahmoodi, M.: A Krylov subspace method based on multi-moment matching for model order reduction of large-scale second order bilinear systems (2018)
  3. Vakilzadeh, Mohsen; Eghtesad, Mohammad; Vatankhah, Ramin; Mahmoodi, Masih: Model order reduction of second-order systems with nonlinear stiffness using Krylov subspace methods and their symmetric transfer functions (2018)
  4. Xu, Kang-Li; Yang, Ping; Jiang, Yao-Lin: Structure-preserving model reduction of second-order systems by Krylov subspace methods (2018)
  5. Lu, Ding; Su, Yangfeng; Bai, Zhaojun: Stability analysis of the two-level orthogonal Arnoldi procedure (2016)
  6. Chern, Ruey-Lin; Hsieh, Han-En; Huang, Tsung-Ming; Lin, Wen-Wei; Wang, Weichung: Singular value decompositions for single-curl operators in three-dimensional Maxwell’s equations for complex media (2015)
  7. Li, Yung-Ta; Bai, Zhaojun; Lin, Wen-Wei; Su, Yangfeng: A structured quasi-Arnoldi procedure for model order reduction of second-order systems (2012)
  8. Beattie, Christopher; Gugercin, Serkan: Interpolatory projection methods for structure-preserving model reduction (2009)
  9. Lin, Yiqin; Bao, Liang; Wei, Yimin: Model-order reduction of large-scale second-order MIMO dynamical systems via a block second-order Arnoldi method (2007)
  10. Bai, Zhaojun; Skoogh, Daniel: A projection method for model reduction of bilinear dynamical systems (2006)
  11. Salimbahrami, Behnam; Lohmann, Boris: Order reduction of large scale second-order systems using Krylov subspace methods (2006)
  12. Nayfeh, Ali H.; Younis, Mohammad I.; Abdel-Rahman, Eihab M.: Reduced-order models for MEMS applications (2005)
  13. Bai, Zhaojun: Krylov subspace techniques for reduced-order modeling of large-scale dynamical systems (2002)

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