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
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
SimulationMeasurement
Fast, Simple,
Capable
SUGAR: Simulation Capabilities
Hierarchical Scripting Language
MATLAB Web Interface
Models
System Assembler
Solvers
•Transient
•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
Symmetric Leg Constraint case
Manhattan Angle and Symmetric Leg Constraints case
Unconstrained 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
SimulateSense Data Extract Features Extract FeaturesCorrespond
Generate Parameters
Refine Parameters
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
• David Bindel, CS• Jason Clark, AST• David Garmire, CS• Raffi Kamalian, ME• Tsuyoshi Koyama, CEE• Shyam Lakshmin, CS• Jiawang Nie, Math
Graduate Students
Torsional Micro-mirror (M. Last)