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)