1 / 15

Modeling MEMS Sensors [SUGAR: A Computer Aided Design Tool for MEMS ]

Modeling MEMS Sensors [SUGAR: A Computer Aided Design Tool for MEMS ]. UC Berkeley James Demmel, EECS & Math Sanjay Govindjee , CEE Alice Agogino, ME Kristofer Pister, EECS Roger Howe, EECS UC Davis Zhaojun Bai, CS January, 2004. Sugar Project Objective.

zuri
Download Presentation

Modeling MEMS Sensors [SUGAR: A Computer Aided Design Tool for MEMS ]

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Modeling MEMS Sensors[SUGAR: A Computer Aided Design Tool for MEMS ] UC Berkeley James Demmel, EECS & Math Sanjay Govindjee, CEE Alice Agogino, ME Kristofer Pister, EECS Roger Howe, EECS UC Davis Zhaojun Bai, CS January, 2004

  2. Sugar Project Objective • “Be SPICE to the MEMS world” • open source and more Design Fast, Simple, Capable Measurement Simulation

  3. SUGAR: Simulation Capabilities Hierarchical Scripting Language Solvers • Transient • Steady-State • Static • Sensitivity System Assembler Models MATLAB Web Interface

  4. Resonant MEMS Systems • Essential element in RF MEMS signal processing • Specific signal amplification in physical and chemical sensors • Bulk Acoustic Waves for 1 - 100 GHz • Traditional analytic design methods frustratingly inadequate; Abdelmoneum, Demirci, and Nguyen 2003

  5. Checkerboard Resonator

  6. Bode Plot Sun Ultra 10: Exact 1474 sec Reduced 28 sec

  7. Challenges in Simulation of Resonator Based MEMS Sensors • Coupled energy domains with differing temporal and spatial scales; boundary layer effects • Accurate material models: thermoelastic damping, Akhieser mechanism, uncertainty • Radiation boundaries for semi-infinite half-spaces: anchor losses • Large sparse systems for which parallelism needs to be exploited (cluster computing) • Automated generation of reduced order models to accelerate large simulations

  8. Design Synthesis and Optimization • Beyond a quick design tool we are looking to design development and constrained optimization • Multi-objective genetic algorithms (combinatorial type problems) • Specialized gradient methods (continuous type problems)

  9. Simulation is not enough Design synthesis is needed • Symmetric Leg Constraint case • Manhattan Angle and Symmetric Leg Constraints case • Unconstrained case

  10. Experimental Measurements • Modeling is not enough; verification is needed • Integrated modeling and testing is the ideal • Tight coupling of simulation and testing with automatic model extraction and comparison (using SMIS)

  11. Synthesized Structures

  12. Simulation - Measurement Comparison Generate Parameters Refine Parameters Sense Data Extract Features Correspond Extract Features Simulate

  13. Other current and future activities • Bounding sets for expected performance variation • Material parameter extraction • Single crystal Silicon models; CMOS processes; Si-Ge etc • Other reduced order models; e.g. electrostatic gap models directly from EM-field equations • Real-time dynamic experiment-simulation coupling • Advanced design synthesis and optimization technologies

  14. Graduate Students • David Bindel, CS • Jason Clark, AST • David Garmire, CS • Raffi Kamalian, ME • Tsuyoshi Koyama, CEE • Shyam Lakshmin, CS • Jiawang Nie, Math

  15. Torsional Micro-mirror (M. Last)

More Related