Transcript Modeling MEMS Sensors [SUGAR: A Computer Aided Design Tool for MEMS ] •UC Berkeley –James Demmel, EECS & Math –Sanjay Govindjee, CEE –Alice Agogino, ME –Kristofer.

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
• “Be SPICE to the MEMS world”
– open source and more
Design
Fast, Simple,
Capable
Measurement
Simulation
SUGAR: Simulation Capabilities
Hierarchical
Scripting
Language
Solvers
•Transient
System Assembler
Models
MATLAB
Web Interface
•Steady-State
•Static
•Sensitivity
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
Checkerboard Resonator
Bode Plot
Sun Ultra 10:
Exact
1474 sec
Reduced 28 sec
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
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)
Simulation is not enough
Design synthesis is needed

Unconstrained case

Symmetric Leg Constraint case

Manhattan Angle and Symmetric
Leg Constraints case
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)
Synthesized Structures
Simulation - Measurement
Comparison
Generate Parameters
Refine Parameters
Sense Data
Extract Features
Correspond
Extract Features
Simulate
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
Graduate Students
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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
Torsional Micro-mirror (M. Last)