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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.

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

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  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)

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