An Information-Driven FEA Model Generation Approach for Chip Package Applications

1
An Information-Driven FEA Model Generation
Approach for Chip Package Applications

• Sai Zeng1, Russell Peak2, Ryuichi Matsuki3,
• Angran Xiao4, Miyako Wilson2, Robert E. Fulton1
• 1 Engineering Information Systems Lab
• 2 Manufacturing Research Center
• 4 Systems Realization Lab
• Georgia Institute of Technology, Atlanta, GA
  30332-0405, USA
• 3 Advanced Product Design Development Division,
  
• Shinko Electric Industries Co., Ltd., Nagano,
  Japan

23rd Computers and Information in Engineering
Conference September 26, 2003, Chicago, Illinois
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Example Chip Package Products Source
www.shinko.co.jp
Quad Flat Packs (QFPs)
Plastic Ball Grid Array (PBGA) Packages
Wafer Level Package (WLP)
System-in-Package (SIP)
Glass-to-Metal Seals
3
Variable Topology Multi-Body (VTMB) FEA Meshing
Challenges
Idealized Analytical Bodies
Decomposed FEA Geometry Models
Meshing Solving
1a
1b
Design Model
1
2
2
1c
3a
3b
3c
3
original
Labor-intensive chopping
1a
1b
1c
1
2
2
1d
1e
3
3a
3b
topology change (no body change)
1a
1b
1
2
1c
2
3
1d
3
4
4a
4b
4c
body change (includes topology change)
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Traditional Approach

• Small topology changes force mesh model
  rebuilding from scratch
• Mesh models are barely reusable using traditional
  approach

FEA Model Planning Sketches in Traditional
Approach
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Motivation

• Competition in Chip Package Industry
• Needs for new technologies and approaches
  facilitating seamless design and analysis
  integration
• Difficulty in analysis model generation
• Hundreds of components
• Variable materials
• Complex geometric shapes
• Changeable connectivity configurations
• Modifications resulting in tedious and time
  consuming FEA modeling process
• package design
• analysis discipline
• idealization

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Objective

• Integrate chip package design using Finite
  Element Analysis
• Automate the FEA modeling process to save the
  modeling time and reduce the human errors
• Increase reusability of the mesh models during
  chip package modification and redesign

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Frame of Reference Multi-Representation
Architecture (MRA)for CAD-CAE Interoperability

• Composed of four representations (information
  models)
• Provides flexible, modular mapping between design
  analysis models
• Creates automated, product-specific analysis
  modules (CBAMs)
• Represents design-analysis associativity
  explicitly

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Information-Driven FEA Modeling Approach

• Mapping process ABB?RMM transforms the ABB model
  into a ready-to-mesh model (RMM) by geometry
  decomposition.
• Mapping process RMM?SMM transforms the RMM into
  the solvable FEA-based SMM in an automated
  manner.
• ABB captures analytical concepts
• FEA based SMM object wrapper
• Integrates pre-processor, solver and post
  processor information
• Includes vendor-specific script file format

Information-Driven FEA Modeling Approach
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Analysis Building Block Models (ABBs)

• An ABB model represents engineering analytical
  concepts as a set of computable information
  entities
• Independent from specific solution techniques
• Analysis knowledge is captured by employing
  object-orient information representation
  technology

Information Content for Example ABB Concepts
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Analysis Building Block Models (ABBs)

• A diving board example is presented to illustrate
  an ABB system

A Graphical View of an ABB System and its
Analytical Bodies and Connectivity
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Ready-to-Mesh Models (RMMs)

• A RMM is obtained by geometric decomposition from
  a corresponding ABB
• The geometry of a RMM model is composed of
  geometry pieces that are convex-shaped and
  meshable using efficient and cheap meshing
  techniques.
• Building blocks of an ABB can be reused to
  construct a RMM
• Associativity of building blocks is changed
  before and after decomposition

A Graphical View of an RMM System and its
Decomposed Bodies and Connectivity
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Decomposition Architecture

• Decomposition is implemented to obtain conformal
  mesh along the interfaces of connected bodies
• Decomposition deals with geometry exclusively
• Decomposed model consists of decomposed bodies
  connected along equivalent faces

Decomposition Process
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Decomposition Associativity Mechanism

• An mechanism is required to keep track of the
  information associativity during the geometry
  decomposition

Compositional Relations for Boundary Condition
Building Blocks and Continuum Building Blocks
after Decomposition
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Solution Method Model (SMM)

• It is an information entity that wraps solution
  tool inputs and outputs into a single logical
  package
• SMM includes the SMM information objects and the
  SMM tool agent

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ABB - SMM- Solution Tool Interaction

• ABB systems generate SMMs based on solution
  method considerations
• Via RMMs in these problem types
• Solution tool capabilities are also usually
  considered

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A Chip Package Thermomechanical Analysis Case
An ABB system

• Four linear elastic thermomechanical continua
• Continua are glued together
• One rigid pin support
• Uniform temperature drop as thermal load

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A Chip Package Thermomechanical Analysis Case
An RMM

• A RMM is obtained after automatic decomposition
  of a ABB system
• With composition mechanism, information
  associated with geometry can be assigned on the
  corresponding decomposed geometry
• This model can be directly input into the SMM to
  generate a conformal FEA meshed model

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A Chip Package Thermomechanical Analysis Case
An SMM

• The tool agent translates the model information
  into the tool-specific computable formats, e.g. a
  PATRAN command language ASCII file
• Modeling time is counted as information instance
  object creation time
• Modeling time is dramatically reduced comparing
  to traditional FEA modeling approach

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Complex Chip Package Thermomechanical Analysis
Case
Decomposition
ABB Model consisting 182 Input bodies
RMM consisting 9056 Decomposed bodies
FEA SMM
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Closure

• Presentation of information-driven FEA modeling
  approach
• Demonstration representing product-independent
  analysis concepts as knowledge-based objects
• semantically rich
• reusable
• modular and tool-independent
• Reduction of FEA modeling time (variable topology
  multi-body application) - reduced from days/hours
  to hours/minutes
• Enhancement of knowledge capture and automation
  level vs. traditional direct FEA modeling
  approaches

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Acknowledgements

• We are particularly grateful for the support of
  the following
• people
• Kuniyuki Tanaka, Yukiharu Takeuchi, and Shinichi
  Wakabayashi of Shinko Electric Ltd.
• Greg Bettencourt of Shinko Electric America, Inc.
• Rod Dreisbach of The Boeing Company
• Mike Dickerson of the NASA Jet Propulsion Lab
  (JPL)
• Manas Bajaj, Greg M. Mocko, Edward J. Kim,
  Injoong Kim at the Engineering Information
  Systems Lab, Georgia Tech

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Question?