Title: Technology CAD: Technology Modeling, Device Design and Simulation S. Saha and B. Gadepally
1 Technology CAD Technology Modeling Device Design and SimulationS. Saha and B. Gadepally 2004 VLSI Design Tutorial January 5 2004 Mumbai India 2 Technology CAD Technology Modeling Device Design and Simulation
Coordinator Prof. Bhaskar Gadepally
Adjunct Prof. Electrical Engineering IIT Bombay
Chairman Reliance Software Consulting Inc.
155 E. Campbell Ave. Campbell CA 95008 (USA)
2004 VLSI Design Tutorial January 5 2004
3 Technology CAD Technology Modeling Device Design and Simulation
Instructor Dr. Samar Saha
Silicon Storage Technology Inc.
1171 Sonora Court
Sunnyvale CA 94086 (USA)
2004 VLSI Design Tutorial January 5 2004
4 Tutorial Outline
Prof. B. Gadepally
Introduction and Tutorial Overview.
Dr. S. Saha
Front-end Process Technology CAD (TCAD) Models and Process Simulations
Device TCAD Models and Device Simulations
Industrial Application of TCAD
Calibration of Process and Device Models
Industrial Application of TCAD in
Compact / SPICE Modeling.
5 Technology CAD Technology Modeling Device Design and Simulation Introduction and Tutorial Overview 2004 VLSI Design Tutorial January 5 2004 Mumbai India 6 Overview of IC Technology
In the past three decades
device densities have grown exponentially
device and technology complexities have increased significantly
design constraints are many-fold
ultra thin oxide
technology development cost has increased enormously.
7 Overview of IC Technology 8 Overview of IC Devices
New device and device physics are continuously evolving
quantum mechanical carrier transport
high-frequency interconnect behavior.
9 Technology CAD
With the increased complexities in IC process and device physics
intuitive analysis is no longer possible to design advanced IC processes and devices
TCAD tools are crucial for efficient technology and device design
to quantify potential roadblocks
to indicate new solutions
for continuos scaling of devices.
10 Technology CAD
Scope of TCAD
front-end process modeling and simulation
implant diffusion oxidation etc.
numerical device modeling and simulation
I - V C - V etc. simulation
topography modeling and simulation
deposition lithography etching etc.
device modeling for circuit simulation
compact / SPICE modeling
capacitance inductance etc.
11 Tutorial Objective
Offer insight into the physical basis of TCAD especially bulk-process and device TCAD.
Describe systematic methodologies for an effective application of TCAD tools.
Describe systematic calibration methodology for predictive usage of TCAD tools
Offer users sufficient insight to leverage new tools.
12 Session 1 Bulk-Process Simulation
Front-end process models implemented in process TCAD tools
ion implantation models
microscopic diffusion models
transient enhanced diffusion.
13 Session 2 Device Simulation
Device models implemented in device TCAD tools
fundamentals of carrier transport
carrier mobility models
device physics of nanoscale technology
inversion layer quantization
fundamental limits of MOSFETs.
14 Session 3 Industry Application
Introduction to process and device simulation tools.
Predictive usage of TCAD
process model calibration
device model calibration.
Predictive simulation of CMOS technology.
15 Session 3 Industry Application - Calibration 16 Session 4 TCAD in Research Modeling
Simulation tools in device research
sub-100 nm MOSFETs
DG-MOSFETs - FinFETs.
TCAD in device (compact) modeling
substrate current model
flash memory cell macro-model.
17 Technology CAD Technology Modeling Device Design and SimulationBulk-Process Simulation 2004 VLSI Design Tutorial January 5 2004 Mumbai India 18 Outline
Front-end IC fabrication processes include
implant S/D and halo (low energy) well (high energy) etc.
diffusion Rapid thermal annealing (RTA) Þ Transient Enhanced Diffusion (TED) and other anomalous effects
oxidation gate oxide STI liner oxide etc.
Objective of this session
understanding of physical models implemented in a process TCAD tool
building new models
basic understanding of general purpose simulator internals
TCAD models in general without considering any particular tools.
21 Ion Implantation
Ion Implantation Mechanisms.
Ion Implant Models
Monte Carlo (MC).
Implant-induced Damage Modeling.
22 Ion Implantation
Bombard wafers with energetic ions energy E 0.5 KeV - 1 MeV gt Ebinding.
Ions collide elastically with target atoms creating
ion deflections energy loss
displaced target atoms (recoils).
Ions suffer inelastic drag force from target electrons
ion energy loss
23 Ion Implantation
Channeling is caused by ions traveling with few collisions and little drag along certain crystal directions.
Ions come to rest after losing all the energy on
elastic collisions (nuclear stopping)
inelastic drag (electronic stopping).
24 Ion Energy Loss Mechanisms
Nuclear stopping (Sn(E))
ion energy loss to target atom by interaction with the electric field of the target atoms nucleus
classical relationship of two colliding particles
the scattering potential with the exponential screening function is given by
Z1 atomic number of incoming ion
Z2 atomic number of target atom.
25 Ion Energy Loss Mechanisms
Electronic stopping (Se(E)) is due to the viscous drag force on moving ion in a dielectric medium.
ke is a model parameter.
Accurate model must account for the variation of Se in space.
Stopping power S of an ion is given by
26 Ion Range Distribution
Ions come to rest over a distribution of locations.
Peak depth and lateral spread of distribution are determined by
ion mass energy dose and incident angle
target atom composition geometry structure and temperature.
Implanted profile can be represented by
27 Ion Range Distribution 28 Ion Range Distribution
The as-implanted 1D distribution function is described by a series of coefficients called moments.
2D distribution of the implanted profile is constructed from 1D distribution function taking lateral spread vertical spread.
29 1D Analytical Ion Implantation Models
Q implant dose (/cm-2)
Rp projected range º normalized first moment
sp straggle/standard deviation º second moment.
30 1D Analytical Ion Implantation Models
crystalline targets without channeling
four coefficients (Rp sp skewness kurtosis)
crystalline targets with channeling tilt and rotation.
crystalline targets with channeling tilt and rotation
second profile to model the channeling
Legendre Polynomials - 19 coefficients.
31 1D Analytical Ion Implantation Models
Coefficients are fit to the measured doping profiles.
Coefficient-set for each distribution is tabulated for different
ion mass (As B In P Sb)
dose energy tilt and rotation
each material is treated separately and scaled by its Rp.
dose absorbed on the top layer is calculated and is used as the dose matching thickness for the layer below.
32 2D/3D Analytical Ion Implantation Models
Each 1D profile along a vertical line is converted to 2D or 3D distribution by multiplying it by a function of lateral coordinates
here lateral straggle sl sp
Multi-layer targets and sloped surfaces are converted to 2D/3D by dose matching approach.
More complex models have sl(x).
Low energy profiles need non-separable point-response functions.
33 Monte Carlo Modeling of Ion Implantation
The collision energy loss is modeled by binary collision approximation (BCA) that is each ion collides with one target atom at a time.
The energy loss (DE) is modeled in terms of
incident energy E0 and scattering angle q0 of ion
separation between two particles
coulomb potential between two particles
BCA requires special formulation for
low energies when lattice movements come into play.
34 Monte Carlo Modeling of Ion Implantation
Ongoing development in MC modeling is to improve
speed of calculations
electronic stopping power Se model
detailed local model for Se
local and non-local split in energy loss due to Se
fnl fraction of non-local energy split
a universal screening length
p impact parameter.
Overall accuracy of MC implant model is excellent.
35 Ion Channeling in Crystalline Silicon
Along certain angles in crystal ion may encounter no target atoms.
Repeated small-angle collisions steer the ion back into the channel.
Channeling was first discovered by MC simulation.
important at any energy
critical at low energy where lt110gt channels steer Boron ions under MOS gate.
Analytic channeling model is complex.
36 Ion Channeling in Crystalline Silicon 37 Damage Creation Models
Each incoming ions generates damage seen by subsequent ions
recoils target atoms knocked out of lattice sites
The effect of damage is significant on as-implanted profile as well as during subsequent diffusion.
Models based on Kinchin-Pease formulation is used to estimate damage density n Er/2Ed
Er recoil energy
Ed target displacement energy ( 15 eV for Silicon).
38 Plus-one Damage Model
Most recoiled interstitials (I) find a vacancy (V) and recombine rapidly either during the implantation or the first instants of annealing.
Distribution of remaining recoils shows
net excess of V near the surface
net excess of I toward bulk.
At low ion mass and/or moderate energy
population of net I and net V is less than the population of I due to dopant atoms taking substitutional sites
one extra-ion is created for each dopant atom taking a substitutional site.
39 Deviation from Plus-one Model
Plus-one approximation often fails for
as the population of recoils can become quite large relative to extra ion population
An effective plus-n factor as a function of ion species energy and dose is used. Typical values
As n 3.5 _at_ E 5 KeV n 1.2 _at_ E 500 KeV
B n 1.2 _at_ E 5 KeV n 1.0 for E gt 20 KeV
P n 2.2 _at_ E 5 KeV n 1.0 _at_ E 500 KeV.
40 Ion Implantation Summary
Ion implantation with ion energy gt Ebinding of target atoms is used to implant impurity atoms into target.
Analytical ion implantation model
the impurity profile is represented by moments for different species dose energy tilt and rotation
the moments are extracted from the experimental profile to create look-up table
simulation is performed using this look-up table.
MC ion implantation model is more accurate particularly for low energy.
The implant damage is modeled by plus-n model.
Fundamentals of Dopant Diffusion
Oxidation Enhanced Diffusion (OED)
Oxidation Retarded Diffusion (ORD)
Transient Enhanced Diffusion (TED).
Point Defect Model.
Clusters and Precipitates.
42 Ficks Laws of Diffusion
Ficks first law
describes flux (F) through any surface
diffusion is downhill - high low concentration - sign
Coupled process and device simulations using Phase 1 calibration data.
Target output (electrical) parameters
C - V curves
Input variables (5 - 8 process model parameters)
point-defect distributions from implants
key impurity segregation coefficients
parabolic oxidation rate.
147 2D Calibration Example RSCE 148 Process Modeling Summary
Systematic process model calibration methodology is critical.
Observed success within a (CMOS) technology
process re-optimization offered a significant improvement in device performance
process centering achieved at manufacturing co-location with minimum development effort.
each successive technology generation requires a significant calibration effort (model update).
149 Device TCAD
Role of device simulation in TCAD
Key physical models and examples
mobility models for deep sub-micron CMOS
quantum effects in scaled CMOS devices
Device model calibration
impact ionization with DD model.
150 Device Simulation Role in TCAD
Simulate device electrical behavior with sufficient accuracy to calibrate process simulation models
primarily 2D electrostatic simulation
Vth DIBL Ioff body effect capacitances
expect DD model is sufficient for most requirements for MOSFETs with Leff ³ 0.1 mm.
Provide capability for the physical simulation of wide range of device parameters
substrate current latch-up ESD and so on.
Support exploratory device simulation for research.
151 Device Simulation CPU Burden
Numerical issues associated with device simulation are well established
core issue is repeated solution of large sparse il
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