Transcript BSAC Research Review Individual Project

SUGAR: A MEMS Simulation Tool
http://www-bsac.eecs.berkeley.edu/cadtools/sugar
David Bindel, Jason V. Clark, David Garmire, Babak Jamshidi, Shyam Lakshmin, Jiawang Nie, Ningning Zhou
Alice Agogino, Zhaojun Bai, James Demmel, Sanjay Govindjee, Ming Gu, Kristofer S.J. Pister
Introduction:
Software architecture:
Most MEMS designers use:
Most of the IC fabrication technology
Netlist
Analysis Results
Solvers
uses mumps.net
Little of the simulation technology
transient analysis
param ox=0, oy=0, oz=0
Our goal: Be SPICE to the MEMS world
gap3dV2 p1 [b c D E]
[l=100u w1=5u w2=5u
gap=2u t1=0u t2=500u
Challenges
System Assembly
V1=5 V2=12
Device Models
steady-state analysis
static analysis
sensitivity analysiss
ox=ox oy=oy oz=oz]
Must be fast enough for design (not just
verification)
MATLAB™ Web
Must handle coupled physical effects
User Interfaces
M&MEMS: SUGAR on the Web
SUGAR in action:
Moment
arm
Torsional
hinge
Mirror
Library
Perforated
beams
Cosineshaped
beams
Actuation
direction
Recessed
inner plate
Perforated comb drive array
Torsional micromirror.
MEMS Design by: M. Last, K.S.J. Pister
Might use O(106) continuum finite elements
SUGAR system uses 2621 elements
Described with a few parameterized subnets
Measurement feedback:
Hosted on Berkeley Millennium cluster
Requires only a web browser
Java required for 3D graphics
Used in Fall 2001 MEMS introduction course
Related work:
SPICE and related network simulators
CAD/Simulation Tool
ANSYS, ABAQUS, FEAP, other finite element codes
idea
design
rough draft
NODAS from CMU
mathematic model
-
simulation
evaluation
Coventorware from Coventor
of
model
Production Tools
manufacturing
measurement
sample
-
Characterization Tools
optical measurement
KSJP15
methods
Ongoing and future work:
More and better mechanical (and other) models
prototype
Figure courtesy R. Muller.
Optical measurement facilities provided by
Muller’s Matisse group
Validate simulations by comparison to
measured data
Use measurement to improve models
Contact detection and simulation
Exploiting sparsity and parallelism
Reduced-order modeling
Sensitivity analysis
Design synthesis and optimization
Improved numerical treatment of multi-scale
multiphysics problems