David Bindel, Jason V' Clark, David Garmire, Babak Jamshidi, Shyam Lakshmin, Jiawang Nie, Ningning Z

1
David Bindel, Jason V. Clark, David Garmire,
Babak Jamshidi, Shyam Lakshmin, Jiawang Nie,
Ningning ZhouAlice Agogino, Zhaojun Bai, James
Demmel, Sanjay Govindjee, Ming Gu, Kristofer S.J.
Pister
SUGAR A MEMS Simulation Tool
http//www-bsac.eecs.berkeley.edu/cadtools/sugar
Introduction
Software architecture

• Most MEMS designers use
• Most of the IC fabrication technology
• Little of the simulation technology
• Our goal Be SPICE to the MEMS world
• Challenges
• Must be fast enough for design (not just
  verification)
• Must handle coupled physical effects

Netlist
Analysis Results
Solvers
transient analysis
uses mumps.net
steady-state analysis
param
ox0,
oy
0, oz0
System Assembly
gap3dV2 p1 b c D E
static analysis
l100u w15u w25u
gap2u t10u t2500u
Device Models
sensitivity analysiss
V15 V212
oxox
oy

oy
ozoz
Library
MATLAB

Web
User Interfaces
MMEMS SUGAR on the Web
SUGAR in action

• Hosted on Berkeley Millennium cluster
• Requires only a web browser
• Java required for 3D graphics
• Used in Fall 2001 MEMS introduction course

• Might use O(106) continuum finite elements
• SUGAR system uses 2621 elements
• Described with a few parameterized subnets

Measurement feedback
Related work

• SPICE and related network simulators
• ANSYS, ABAQUS, FEAP, other finite element codes
• NODAS from CMU
• Coventorware from Coventor

Ongoing and future work

• More and better mechanical (and other) 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

Figure courtesy R. Muller.
KSJP15

• Optical measurement facilities provided by
  Mullers Matisse group
• Validate simulations by comparison to measured
  data
• Use measurement to improve models