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Solving Device PDEs with the PROPHET Simulator

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Solving Device PDE's with the PROPHET Simulator. Dan Yergeau Stanford University ... range clamping. damping. Small signal AC, preliminary harmonic balance. NACDM 2002 ... – PowerPoint PPT presentation

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Title: Solving Device PDEs with the PROPHET Simulator


1
Solving Device PDEs with the PROPHET Simulator
http//www-tcad.stanford.edu/prophet/
  • Dan Yergeau Stanford University

With contributions from Zhiping Yu and Gaofeng
Wang (Stanford) Paco Leon (Mixed Technology
Associates) Conor Rafferty (Agere Systems)
2
Outline
  • What is PROPHET?
  • Specifying PDEs for PROPHET to solve
  • Discretization on a mesh
  • Applications
  • Summary

3
What is PROPHET?
  • Developed at Bell Labs as a process simulator
  • Released externally as a TCAD simulation platform
    (Stanford, UT Austin, and Conor Rafferty added
    device simulation capabilities)
  • Permits user-level specification of PDEs created
    from reusable operators

4
PROPHET Overview
User interface
Input parser
Graphics/postprocessing
Modules
Solve
Grid
Field
Bias

Libraries
Database
Structure
Linear solver
PDE Engine
Assembly
Discretization (geometric)
Models (physical)
Solvers
5
Representing Physical Systems in PROPHET
Global system is composed of blocks of PDEs
Loosely Coupled
Tightly Coupled
6
Representing PDEs in PROPHET
  • PDE is a sum of terms (well, almost)
  • Terms are a combination of geometric and physical
    operators
  • Geometric operator
  • Physical operator
  • Functions permit evaluation of intermediate
    values with chaining back to solution variables

7
Geometric Operators
  • Spatial operators divergence, nodal
  • Differentiation wrt time
  • Interface flux
  • Dirichlet
  • Constraint
  • Interface algebraic

8
Physical Operators
  • Algebraic building blocks , -, , /, sqrt, exp,
    etc.
  • Fluxes
  • Many domain-specific expressions
  • space charge density
  • mobility
  • device contact boundary conditions
  • Shockley-Reed-Hall and Auger recombination

9
Specifying Terms
ltgeo_opgt.ltphy_opgt(ltinputsgtltoutputsgt)_at_ltwheregt
box_div.lapflux(psipsi)_at_silicon,poly,oxide
nodal.nscd(electrons,holes,netdopepsi)_at_silicon,p
oly
constraint.continuity(0psi)_at_silicon/oxide,poly/o
xide
dirichlet.device_dirichlet(netdopepsi)_at_CONTACTS
10
Specifying Systems (PDE block)
system namesilicon_poisson sysvarspsi
term0ndiv_fbm.lapflux(psipsi)_at_SEMICONDUCTORS,IN
SULATORS term1nodal.nscd(electrons,holes,netdo
pepsi)_at_SEMICONDUCTORS term2dirichlet.device_
dirichlet(netdopepsi)_at_CONTACTS
term3constraint.continuity(psipsi)_at_ALL_INTERFAC
ES tmpvarselectrons,holes
func0quasiFermi(psielectrons,holes)_at_SEMICONDUCT
ORS
11
Solution Methods
  • Timestepping
  • typically TR/BDF2 with LTE-based timestep
    control
  • PDE block staggering
  • Newton nonlinear algorithm on a single block
  • convergence detection in residual and/or update
    norm
  • range clamping
  • damping
  • Small signal AC, preliminary harmonic balance

12
Discretization
Apply Gauss Theorem
Approximate on a mesh
13
Interfaces
Control volume integration is closed by
including external flux
Thus, the natural boundary condition is zero-flux
if equation has no interface flux terms
14
Evaluating the Real Flux
15
Applications
  • Modeling quantum effects via Density Gradient
  • Laser simulation
  • Interconnect interactions with substrate (device
    level frequency domain)

16
Density Gradient
Models quantum confinement of particles adjacent
to silicon/oxide interface. The large chemical
potential barrier of the insulator requires the
wave function to vanish at the interface.
Continuity of this wave function pushes
carriers away from the barrier.
Quantum potential
17
Density Gradient System
18
Classical DD vs. Density Gradient
MIT 50nm well-tempered MOSFET
19
Photon Generation in a Laser
1.55 micron InGaAs/InP Edge Emitting
Laser Intensity of dominant mode
Last equation is lumped (scalar), but dependent
on distributed spontaneous emission rate
20
Interconnect/substrate
21
Bias Effects on the Substrate
22
Summary -- Advantages
  • Rapid prototyping
  • Simulation on real structures
  • Reasonable efficiency (extra assembly overhead is
    fairly minimal)
  • Partial box method
  • Code reuse means fewer errors
  • Model debugging aids

23
Drawbacks and Needs
  • Diagnostics
  • Still looking for faster and more robust linear
    solvers. We use Berkeley Sparse and PETSc
    (sparse direct, ILUGMRES, ILUBiCGstab)
  • Recovery from failed Newton is through load
    control (reduce time step or bias change). What
    alternatives have been tried and how well do they
    work?
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