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

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Modeling MEMS SensorsSUGAR 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
Fast, Simple, Capable
Simulation
Measurement
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SUGAR Simulation Capabilities
Hierarchical Scripting Language
Solvers

• Transient
• Steady-State
• Static
• Sensitivity

System Assembler
Models
MATLAB
Web Interface
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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 Plot
Sun 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
Generate Parameters
Refine Parameters
Simulate
Sense Data
Extract Features
Extract Features
Correspond
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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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Graduate Students

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

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Torsional Micro-mirror (M. Last)