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Semiconductor Modeling An Introduction

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					Semiconductor Modeling
An Introduction


     Ryan McKenzie
     Hui Tan
     Ben Pullen
     Lei You
Semiconductor Modeling
 Motivations
 Basic Procedure
 Example Simulations
 Available Tools   and Services
 Resources List
Motivation :
It’s all about industry.
       The intent of integrated circuit fabrication
  is to produce a wafer with specific electrical
  and mechanical characteristics, usually in the
  form of electronic circuits or chips, via some
  number of processing transformations.
  Accurate Modeling does the following:
 Cuts Cost
 Cuts Development Time
 Allows Producers to Compete on the
  “Cutting Edge”
Procedure:
How the real work gets done.
 Pick Appropriate Conduction Equations
 Augment With Conductor Property         Data
 Isolate “Interesting” Variables
 Discretize Resultant System
 Apply Schrödinger-Poisson Cycle
 Use Converged Resultant Data to:
   PlotRelevant Data Points (2D or 3D)
   Produce Visual Representation
Summary of Basic
Semiconductor Equations
Discretization
 Select Points   to Populate Fine Grid
   System   Solving Points
   All Points within Range Space and
    Appropriate Boundary Conditions
 Reduce the Grid Resolution for Courser
 Sub-Grids if Necessary
Schrödinger-Poisson Cycle
 Solve Current   Grid with Schrödinger
  Equations
 Apply a Poisson Solution to Resultant
  Grid Having a Decomposed Domain
 Continue Until Convergence is
  Achieved
Example Simulation
Gate-All-Around Transistor – a base
level electronic device that is useful for
its ability to collect an electrical signal
and output an amplified version of that
signal.
Optimum Thickness ( 2D )
 The goal of this simulation is to map the
  relationship between conductive film
  thickness of the device to electron
  concentration at the output region.
 The simulation is done in a software package
  called ATLAS, so the appropriate equations
  are automatically selected based on the
  desired output variables you chose.
Device Characteristics
 Substrate =  Silicone Dioxide
 Conductor = Graphite (Carbon)
 ATLAS substitutes in appropriate
  constants for electric permeativity and
  relative conductivity.
More Device Characteristics
                Gate Thickness: 25nm
                Silicon Film Thickness:
                 1.5nm to 20nm
                Doping: 1x10^18 cm^-3
Schrödinger-Poisson Steps:
Automatic in ATLAS (OH YEAH!!!)
     ATLAS solves the one dimensional
 Schrödinger's equation along a series of
 slices across the device. Each slice is taken
 along an existing set of grid points in the
 device mesh. Carrier concentrations
 calculated from this are substituted into the
 charge part of the Poisson's equation. The
 potential derived from this is substituted back
 to Schrödinger's equation. This solution
 process continued until a self-consistent
 solution of Schrödinger's and Poisson's
 equation is obtained.
What Does it All Mean?
 The Domain of    Possible Solutions are
  Cyclically Decomposed Until the Unique
  and Continuous solution is determined.
 Think of it as a Glorified Process of
  Elimination.
The Pretty Results


                     Electron
                     Concentration
                     with base device
                     characteristics.
The Pretty Results Continued


                  Electron
                  Concentration
                  with variable
                  silicone sheet
                  thickness.
Some Other Pretty Results

   Random Examples of
   Semiconductor Modeling Results
Potential Difference Around a Parallel
Plate Capacitor
Electrostatic Difference in a Conductive
Box (3 sides grounded, 1 side charged)
Heat Diffusion Along a Finned Box
Containing a Specified Circuit Device
Electron Diffusion Into a Substrate
    Resources
   http://www.cae.wisc.edu/~hitchon/
   http://www-mtl.mit.edu/CIDM/papers/generic-process-
    model/generic-process-model.html
   http://www.sauna.com/
   http://www.celestry.com/
   http://www.semiseek.com/Pag00011.htm
   http://www.ecse.rpi.edu/homepages/shur/1.3/1-3.html
   http://www.cerfacs.fr/algor/reports/2001/TR_PA_01_51.
    pdf
   http://www.scicomp.ucsd.edu/~mholst/codes/pmg/
   http://nina.ecse.rpi.edu/shur/SDM1/Notes/Index.htm
   http://www.britneyspears.ac/ (NOT A JOKE)
Integrated Circuit Etching
IC Etching Topics
 What is it?
 How was it done?
 How is it done?
 How does it relate to CS521?
IC Etching
              Integrated circuit
               starts as a wafer of
               silicon (picture on
               the left.)
              We want to etch a
               trench into the wafer
               to create a
               transistor.
IC Etching
              A mask is placed
               over the silicon to
               act as a stencil for
               our etching
               technique.
              The desired effect is
               a square, uniform
               trench.
IC Etching
 Foryears, etching was done with liquid
 chemicals. This process is "directionally
 blind" -- that is, when liquids move on a
 surface, their direction cannot be
 controlled.
IC Etching
 As chip architectures were reduced in
  size, gate and trench sizes had to be
  reduced to match.
 As the trenches are reduced in size, the
  importance of precise, uniform, and
  square etching grows dramatically.
IC Etching
                          accomplished
 Modern silicon etching is
  through a technique known as “Plasma
  Etching.”
 Plasma-based etching is done in
  plasma chemical reactors consisting of
  a vacuum chamber, power supply, and
  gas handling system.
IC Etching
 The gas-phase chemical compounds are
  separated into neutral fragments, positive and
  negative ions, and electrons.
 Some of the neutral fragments of the plasma
  react with the material in the trench to
  produce a protective film.
 Ions bombard the wafer surface vertically --
  thus removing the protective film on the
  horizontal surface, but not on the sidewall.
IC Etching
A short film:
http://www.nas.nasa.gov/Main/Features/2
  000/Fall/plasma_flash.html
IC Etching
 Problems with   this model?
IC Etching
 Cost, It is very expensive to slowly etch
  a perfectly square trench.
 We need a techniques that produce
  quality results quickly.
IC Etching And Us
A software package entitled SPELS has
 been developed to aid in the modeling
 of the etching process on high
 performance computers.
IC Etching
 Trench etching is done in a machine
 called a reactor; etch rates depend on
 reactor conditions such as operating
 power and gas pressure, as well as
 material properties of the wafer and
 reacting gases used.
IC Etching
 Using theSPELS code, scientists can
 manipulate these factors to find the best
 conditions for creating ideal etch
 profiles.
Etching References
 http://www.nas.nasa.gov/Main/Features/
  2000/Fall/plasma.html
 http://www.nas.nasa.gov/Main/Features/
  2000/Fall/plasma_flash.html
 http://www.che.caltech.edu/faculty/kpg/k
  pg.html
 http://www.aip.org/tip/INPHFA/vol-2/iss-
  1/p26.pdf
SEMICONDUCTOR

PROCESS MODELING
Why need semiconductor
modeling?
   It’s a computational modeling.
   What is computational modeling?
   Evaluation and optimization of various design is
    possible, without resorting to costly and time-consuming
    trial fabrication and measurement steps.
   Provides valuable insight into important physical
    quantities.
   Shortened development cycles.
   Reduced cost.
   Increased quality and reliability of final products.

    A important field of computational modeling related to
    semiconductor manufacturing belongs to process
    modeling.
Semiconductor Modeling
 Process Modeling
 -- In technology development phase
 -- In technology characterization phase

 Device Modeling


 Circuit Modeling
Semiconductor Process Modeling

The aim of process modeling:
Predict geometries and material properties
of the wafer structures and semiconductor
devices as they result from the
manufacturing process.
Semiconductor Process Modeling
…

 Two traditional branches

  Wafer to’pography   modeling

  Bulk process modeling
Semiconductor Process Modeling
…

    Two steps

    Physical Modeling


    Discrete Modeling
Physical Modeling
 What is the physical modeling?
 A hierarchy of physical model
 -- Bottom: derived from principles using
 mechanisms of atomic level or fundamental
 laws
  -- Top: simple analytical models
  -- Middle: allow a tradeoff of model generality
 for their simplicity
 Mathematical form: systems of       non-
 linear PDEs or by algorithms
Sub-models of Physical Model
       Photolithography

       Etching and   Deposition
       Ion Implantation

       Bulk Particle Transport

       Mechanical Deformation
Photolithography
  Lithography process
  Photolithography technology
  Factors that   the model must account for
  -- light intensity distribution in the photoresist
  film
  -- chemical reaction that changes photoresist
  etching properties
  -- resulting photoresist profile after dvelopment
Etching and Deposition
   The formation of multilayer wafer structure
   The role of physical model in this process is to
    relate the propagation velocity of the surface to
    material properties and processing conditions.
   Process techniques used for Etching and
    Deposition range from isotropic chemical
    process to directional physical process.
   Most important model parameters
    Ion Implantation
   The Ion Implantation process
   The process model concerns the distributions
    of stopped particles, the produced damage, and
    the energy
   The produced damage occurs when ions collide
    with a lattice atom and when they cause it to
    leave its original site in the lattice.
    Bulk Particle Transport
 One of the most important group of physical
  models is related to the transport of particles
  within the bulk region.
 The principal physical mechanism for particle
  transport is diffusion. But the governing
  equations for particle transport should also
  account for advection due to electric field and
  various chemical reactions among particles.
 Hierarchically organization: range from single
  species diffusion equations to complex
  coupled systems of diffusion-drift-reaction
  PDEs.
Mechanical Deformation
   The models for mechanical deformation
    follows the evolution of the stress field in
    different material layers during
    manufacturing.
   Generally, the cumulative mechanical
    stress represents an important factor that
    could affect the reliability of semiconductor
    devices and the interconnection system.
Discrete Modeling
   Principal task: generation and control of
    appropriate grid structures for arbitrarily
    shaped multilayer material domains and the
    derivation of the discrete analog of the
    governing mathematical description.


   The practical application of process modeling
    is enabled by simulation tools that integrate
    various physical and discrete models.
 Issues in Discrete Modeling
               the complete physical
 Subdivision of
 domain into small subdomains (cells).


 Two phases: discretization and solution
 of algebraic problem.
Choosing Cells
 Methods of choosing structured and
 unstructured meshes


 Finite-Difference Method   (FD)
 Finite-Volume Discretization (FV)
 Finite-Element Method   (FE)
How to select discretization
method
    The final selection of the grid and the
    discretization method should depend on:
   Geometry of the domain
   The PDE (including boundary conditions) to
    be solved
   The coordinate system used to describe the
    continuous problem
 Grid-generation Technique
 Any grid-generation technique has to take care
  of problems arising from:
 strongly varying quantities
 multilayer devices
 geometrical singularities
 time-dependent structures


 These typical problems for process simulation
 and the desired efficiency automatically lead to
 the requirement of grid adaptation.
Grid-generation Technique …
   As the mesh size cannot be determined in
    advance, the solution process on a given
    relatively coarse mesh has to provide the
    information about where to refine the mesh.


   Two ways of improving the accuracy
    -- increase the order of approximation
    -- decrease the local mesh size
Within practically used design
environments, the steps of grid
generation, grid adaptation, and solution
of the resulting systems of equations
have to be performed automatically and
without an interaction from outside.
This is mandatory for technology
computer-aided design (TCAD) where
complete processing sequences are
intended to be simulated.
References
1. Semiconductor Process Modeling
   Wolfgang Joppich
   Wiley Encyclopedia of Electrical and Electronics
   Engineering

2. A General Semiconductor Process Modeling
   Framework
  Duane S. Boning, Michael B. McIlrath, Paul
   Penfield, Jr., and Emanuel M. Sachs
Semiconductor Process
Modeling in Future Trends
 State of technology
 the Semiconductor Industry Association
  Roadmap
 Monte Carlo simulation algorithms
 Interconnections
 The lack of accurate experimental
  verification
 The trends towards 3D
 The object-oriented programming approach
 The next-generation process simulation
  software
State of technology
 Role: Semiconductor process modeling
 has become an essential technology in
 semiconductor industry.

 Impressive progress in process
 modeling has been achieved,but there
 is still much more potential to be
 exploited.
State of technology
 Lack of predictive capabilities.
 The improved models, required for a
  new technology, usually are not
  available before the technology itself.
 The process modeling is   required
  accelerate so that the application is
  more effective than at present .
State of technology and future
trends
 Process modeling has to provide general
  concepts, guidance,and insights at a very
  early stage of process or technology
  development for the engineers.
 The most important needs for future
  processing modeling is the Semiconductor
  Industry Association Roadmap.
Semiconducter Industry
Assosiation Roadmap
   the Semiconductor Industry Association
    Roadmap’s priorities are:
    -automatic grid-generation and adaptation algorithms.
    -Defect-mediated dopant profile evolution.
    -combined equipment and feature scale topography
    models.
    -2D and 3D doping profile measurement tools.
    -etch model predict ability .
    -Silicidation models.
    Great effect is directed towards 3D process
    simulation tools.
Monte Carlo simulation
algorithms
 Defect-based dopant models for implantation,
  diffusion,and activation must start with
  underlying first-principle calculation and
  characterization methods.
 Monte Carlo simulation algorithms will
  become increasingly important.because
  Monte Carlo method are inherently three-
  dimensional.
    Interconnections
 For the determination of the overall chip
  performance, interconnections have
  become as important as the active
  semiconductor devices.
 Interconnection technology includes
  dielectric and metal-film formation as well
  as the etch process.
Interconnections
The accurate evaluation of
-the process variation,
-their effects on the performance,
-their effects on the reliability of interconnection.
depends on :
the integration of equipment ,feature-scale
  topography modeling of
  deposition,lithography,and etching.
Interconnections
 Thisincludes a critical need for improved
 physical modeling of topography
 processes.
 The formulation of predictive models for
 deposition and etching is essential for the
 interconnect modeling
 These models are expected to have more
 improved statistical analysis methods and
 tools.
The lack of accurate
experimental verification
 The lack of accurate experimental verification
  is a important obstacle for process model
  development and model calibration that
  should be overcome in the future.
 The problem is even more emphasized with
  damage distribution that are induced by
  implantation and their evolution during
  subsequent annealing processes.
    (this phenomenon can’t be measured directly and is only
    verified indirectly by its effect on dopant distribution.)
The lack of accurate
experimental verification
   A better understanding of the physics of buck
    particle transport increasingly demands
    further improvements in metrology.
   The limitation in measurement technology
    severely hampers the development of
    accurate multi-domain process modeling
    tools.
The trends towards 3D
 The trends  towards 3D with more
  complex models leads to :
  -larger systems of coupled PDEs,
  -to more complex topologies,
  -to multilayer structures.
 This requires computing power provided
  in a ideal way by scalable parallel
  architecture.
The trends towards 3D
 Parallelization is innovative technique ,it can
  be used for new algorithmic developments.
 A straightforward loop parallelization of
  initially sequential programs will be made on
  shared-memory machines.
 Grid partitioning is a typical approach to
  parallelize grid oriented PDE application.
 This technique is independent of the
  particular partial differential equation or
  system to be solved.
The trends towards 3D
 Load balancing and locality should be taken
  into account for an efficient parallelization.
 All processors are responsible for
  approximately the same number of discrete
  equations and variables.
 The data structure should be more regular.
 For low communication cost the algorithm
  should offer a large amount of locality.
    The next-generation process
    simulation software
 Many improvements both on the physical and
  on the discrete approximation level can be
  expect in the near future.
 The combination of these improvements
  requires flexible and reliable software.
 The next-generation process simulation tools
  have to be designed to be modular in such a
  way that innovative models or algorithms can
  easily be added.
    The object-oriented programming
    approach

 The object-oriented programming approach
  significantly simplifies the tool development
  by providing a simple and unified access
  mechanism to objects .
 These objects represent wafer and device
  structure without going into details of the data
  structures used.
 This approach provides the possibility for
  code structuring that may allow an active
  participation of a large community in the
  development of widely used software
  packages.
The next-generation process
simulation software
 Due to   below become more complex:
   - model development,
  - automatic grid generations,
  - adaptive meshing,
  - regridding of time-dependent domain,
  - search for optimal solvers,
  - parallel programming,
  - pre and post processing of single simulation
 step
  - approximately complete simulation of processing
 step.

 Theseposes new challenges to the
 developers of software tools
The next-generation process
simulation software
 Apart from the need of portability with
  respect to parallel programming,It also
  needs:
   - separate modeling,
  - discrete description,
  - solving from one another.
 A parallel programming environment keeps
  the formulation of the application, and away
  from particular solver.
 This idea represents the approach of the
  future.

				
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