modeling mems sensors [sugar: a computer aided design tool for mems ]
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
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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
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Sugar Project Objective• “Be SPICE to the MEMS world”
– open source and more
Design
SimulationMeasurement
Fast, Simple,
Capable
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SUGAR: Simulation Capabilities
Hierarchical Scripting Language
MATLAB Web Interface
Models
System Assembler
Solvers
•Transient
•Steady-State
•Static
•Sensitivity
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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
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Checkerboard Resonator
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Bode PlotSun Ultra 10:
Exact 1474 sec
Reduced 28 sec
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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
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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)
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Simulation is not enough Design synthesis is needed
Symmetric Leg Constraint case
Manhattan Angle and Symmetric Leg Constraints case
Unconstrained case
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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)
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Synthesized Structures
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Simulation - Measurement Comparison
SimulateSense Data Extract Features Extract FeaturesCorrespond
Generate ParametersRefine Parameters
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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
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• David Bindel, CS• Jason Clark, AST• David Garmire, CS• Raffi Kamalian, ME• Tsuyoshi Koyama, CEE• Shyam Lakshmin, CS• Jiawang Nie, Math
Graduate Students
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Torsional Micro-mirror (M. Last)