Date post: | 04-Apr-2018 |
Category: |
Documents |
Upload: | engahmed25 |
View: | 352 times |
Download: | 10 times |
of 18
7/31/2019 Introduction to Silvaco ATHENA Tool and Basic Concepts in Process Modeling 1
1/18
EEE 533 Semiconductor Device and Process Simulation
EEE 533: Semiconductor Device andProcess Simulation
Spring 2001
Introduction to Silvaco ATHENA Tool andBasic Concepts in Process Modeling
Part - 1
Instructor: Dragica Vasileska
Department of Electrical EngineeringArizona State University
7/31/2019 Introduction to Silvaco ATHENA Tool and Basic Concepts in Process Modeling 1
2/18
EEE 533 Semiconductor Device and Process Simulation
1. Introduction to Process Simulation
The fabrication process of an integrated circuit consists ofthe following main steps:
u Epitaxial growthv oxidation, passivation of the silicon surfacew Photolithography diffusion metalization
A schematic description of a planar process for the fabricati-on of a pn-junction, consists of the following steps:
1. Epitaxial growth:
Epitaxialn-layer
p-substrate
High-temperature process (~1000 C) The amount of dopant atoms
determines the conductivity of the layer
7/31/2019 Introduction to Silvaco ATHENA Tool and Basic Concepts in Process Modeling 1
3/18
EEE 533 Semiconductor Device and Process Simulation
2. Oxidation and Photolithography
3. Diffusion and Metalization steps
Epitaxial
n-layer
p-substrate
SiO2 Diffusion window
Thermal oxidation leads toformation of oxide layer forsurface passivation
Photolithography allowsproper formation of thediffusion window
oxidation
Epitaxial
n-layer
p-substrate
photolithography
n-layer
p-substrate
diffusion
p
n-layer
p-substrate
metalization
p The diffusion process gives
rise to the pn-junction
(takes place at ~1000 C)
Electrical contacts areformed via the metalizationprocess
7/31/2019 Introduction to Silvaco ATHENA Tool and Basic Concepts in Process Modeling 1
4/18
EEE 533 Semiconductor Device and Process Simulation
The sequence of events that lead to successful fabricationof the device structure are the following:
Fabricate device
structure
Perform electrical
characterization
Designcondition met?
yes
optimization
Simulation replacingexperimental steps:
no
ATHENA
Process simulation tool
ATLAS
Device simulation tool
7/31/2019 Introduction to Silvaco ATHENA Tool and Basic Concepts in Process Modeling 1
5/18
EEE 533 Semiconductor Device and Process Simulation
Physically-based process simulation predicts the structurethat results from specified process sequence
Accomplished by solving systems of equations that describethe physics and chemistry of semiconductor processes
Physically-based process simulation provides three major
advantages:u it is predictivev it provides insightw captures theoretical knowledge in a way that makes
the knowledge available to non-experts
Factors that make physically-based process simulationimportant:
u quicker and cheaper than experimentsv provides information that is difficult to measure
7/31/2019 Introduction to Silvaco ATHENA Tool and Basic Concepts in Process Modeling 1
6/18
EEE 533 Semiconductor Device and Process Simulation
The processing steps that one needs to follow, for example,for fabricating a 0.1 m MOSFET device, include (in randomorder):
Ion implantation process
Diffusion process
Oxidation process
Etching models
Deposition models
In the following set of slides, each of this process is
described in more details with the appropriate statementsand parameter specification.
7/31/2019 Introduction to Silvaco ATHENA Tool and Basic Concepts in Process Modeling 1
7/18
EEE 533 Semiconductor Device and Process Simulation
Some historical dates:
- Bipolar transistor: 1947 - DTL - technology 1962
- Monocrystal germanium: 1950 - TTL - technology 1962
- First good BJT: 1951 - ECL - technology 1962
- Monocrystal silicon: 1951 - MOS integrated circuit 1962
- Oxide mask, - CMOS 1963
Commercial silicon BJT: 1954 - Linear integrated circuit 1964
- Transistor with diffused - MSI circuits 1966
base: 1955 - MOS memories 1968- Integrated circuit: 1958 - LSI circuits 1969
- Planar transistor: 1959 - MOS processor 1970
- Planar integrated circuit: 1959 - Microprocessor 1971
- Epitaxial transistor: 1960 - I2L 1972
- MOS FET: 1960 - VLSI circuits 1975
- Schottky diode: 1960 - Computers using- Commercial integrated VLSI technology 1977
circuit (RTL): 1961 - ...
7/31/2019 Introduction to Silvaco ATHENA Tool and Basic Concepts in Process Modeling 1
8/18
EEE 533 Semiconductor Device and Process Simulation
2. Description of the Ion Implantation Process
Ion implantation is the most-frequently applied doping
technique in the fabrication of Si devices, particularlyintegrated circuits.
Two models are frequently used to describe the ionimplantation process:
u Analytical models:
do not contribute to physical understanding can be adequate for many engineering appli-
cations because of its simplicity
v Statistical (Monte Carlo technique):
first principles calculation (time consuming) can describe parasitic effects such as:
- lattice disorder and defects- back scattering and target sputtering- channeling (important in crystalline mater.)
7/31/2019 Introduction to Silvaco ATHENA Tool and Basic Concepts in Process Modeling 1
9/18
EEE 533 Semiconductor Device and Process Simulation
(A) Analytical Models
For all of the analytical models, the real ion distribution in1D is given the following functional form:
D total implanted dose per unit areaf(x) probability density function, frequency function -described with the following four characteristic quantities:
u Projected range Rp: v Standard deviation RP:
w Skewness :
x
Excess or kurtosis :
)()( xDfxC =
=+
dxxxfRp )( ( )
2/12
)(
=+
dxxfRxR pp
( )
( )3
3)(
p
p
R
dxxfRx
=
+
( )
( )4
4)(
p
p
R
dxxfRx
=
+
7/31/2019 Introduction to Silvaco ATHENA Tool and Basic Concepts in Process Modeling 1
10/18
EEE 533 Semiconductor Device and Process Simulation
Analytical distributions most frequently used for describingdoping profiles are:
u Simple Gaussian or normal distributionv Joined half-Gaussian distributionw Pearson type IV distribution
Simple Gaussian or normal distribution 1D model
Makes use of the projected range Rp and the standard
deviation Rp:
Has =0 and =3.The approximation of the true profileis only correct up to first order, since it gives symmetricprofiles around the peak of the distribution.
Range parametersRp and Rp for all the impurity-material combinations are stored in the ATHENAIMP file.
( )
( )
=
2
2
2exp
2)(
p
p
p R
Rx
R
DxC
7/31/2019 Introduction to Silvaco ATHENA Tool and Basic Concepts in Process Modeling 1
11/18
EEE 533 Semiconductor Device and Process Simulation
The model is activated via the GAUSS parameter onthe IMPLANT statement; Rp (RANGE) and Rp (STD.DEV) Other parameter that has to be specified is the dose D(via the parameterDOSE on the IMPLANT statement)
Pearson distribution 1D model
This is a standard model in SSUPREM4, and is used forgenerating asymmetrical doping profiles.
The family of Pearson distribution functions is obtainedas a solution of a differential equation:
( )
( ) ( )
( )
+
+
++=
++
=
2102
22
2102
21
2/101
22
2210
4
2arctan
4
/2exp
)(
)()(
2
bbb
bRxb
bbb
bba
bRxbRxbKxf
xbxbb
fax
dx
xdf
p
bpp
7/31/2019 Introduction to Silvaco ATHENA Tool and Basic Concepts in Process Modeling 1
12/18
EEE 533 Semiconductor Device and Process Simulation
The type of the Pearson distribution depends upon thesign of the term: D= 4b0b2 - b1
2. Only the Pearson IV (D>0)distribution has the proper shape and a single maximum.
The constants a, b0, b1 and b2 are related to themoments off(x) in the following manner:
The vertical dopant concentration is then proportional to
the ion dose:
This simple model can fail in the case when channelingeffects are important (dual Pearson model has to be used)
( )
81210,632
,34
,3
2
2
1
22
0
==
=
=+
=
AA
b
abA
Rb
A
Ra
pp
)()( xDfxC =
7/31/2019 Introduction to Silvaco ATHENA Tool and Basic Concepts in Process Modeling 1
13/18
EEE 533 Semiconductor Device and Process Simulation
The model is activated via the PEARSON parameter onthe IMPLANT statement.
Other parameters that can be specified in conjunctionwith the model choice include:
Lattice structure type: CRYSTAL orAMORPHOUS
Implant material type: ARSENIC, BORON, etc.
Implant energy in keV via ENERGY parameter
For dual-Pearson model, another parameter isimportant and describes the screen oxide (S.OXIDE)through which ion implantation process takes place
7/31/2019 Introduction to Silvaco ATHENA Tool and Basic Concepts in Process Modeling 1
14/18
EEE 533 Semiconductor Device and Process Simulation
Two-dimensional implant profiles
2D analytical implant models are quite rudimentary andusually based on a simple convolution of a quasi-one
dimensional profile C(x, tmask(y)) with a Gaussian distribu-tion in the y-direction:
y - independent of depth (problem)
In the case of an infinitely high mask extending to thepoint y= a, the convolution can be performed analytically, togive:
( )'
2
'exp))'(,(
2
1),(
2
2
dyyy
ytxCyxC
y
masky
=+
MASK IONS
x (depth)
y (lateral)
=
=
7/31/2019 Introduction to Silvaco ATHENA Tool and Basic Concepts in Process Modeling 1
15/18
EEE 533 Semiconductor Device and Process Simulation
Additional Parameters that need to be specified for 2Dion-implantation profiles are:
Tilt angle: TILT Angle of rotation of the implant: ROTATION
Implant performed atall rotation angles: FULLROTATIO
Print moments used for all ion/material combinations:PRINT.MOM
Specification of a factor by which all lateral standard de-viations for the first and second Pearson distribution aremultiplied: LAT.RATIO1 and LAT.RATIO2
7/31/2019 Introduction to Silvaco ATHENA Tool and Basic Concepts in Process Modeling 1
16/18
EEE 533 Semiconductor Device and Process Simulation
(B) Monte Carlo Models
Analytical models can give very good results when applied toion-implantation in simple planar structures. For non-planar
structures, more sophisticated models are required.
SSUPREM4 contains two models for Monte Carlo simulation:
Amorphous material model
crystaline material model
The Monte Carlo model can also deal with the problem of ionimplantation damage:
Damage types: Frankel pairs (Interstitial and Vacancyprofiles), clusters, Dislocation loops
Two models exist for ion implantation damage modeling:
Kinchin-Pease model (for amorphous material)
Crystalline materials model
7/31/2019 Introduction to Silvaco ATHENA Tool and Basic Concepts in Process Modeling 1
17/18
EEE 533 Semiconductor Device and Process Simulation
(C) Some examples for analytical models
Implant of phosphorus with a dose of 1014 cm-2 and Gaussian model usedfor the distribution function. The range and standard deviation are speci-fied in microns instead of using table values.
IMPLANT PHOS DOSE=1E14 RANGE=0.1 STD.DEV=0.02 GAUSS
100 keV implant of phosphorus done with a dose of 1014 cm-2 and a tiltangle of 15 to the surface normal. Pearson model is used for the distribu-
tion function.IMPLANT PHOSPH DOSE=1E14 ENERGY=100 TILT=15
60 keV implant of boron is done with a dose of 41012 cm-2, tilt angle of 0and rotation of 0. Pearson model for the distribution function is used thattakes into account channeling effect via the specification of the CRYSTAL
parameter.IMPLANT BORON DOSE=4.0E12 ENERGY=60 PEARSON \
TILT=0 ROTATION=0 CRYSTAL
7/31/2019 Introduction to Silvaco ATHENA Tool and Basic Concepts in Process Modeling 1
18/18
EEE 533 Semiconductor Device and Process Simulation
(D) Characteristic values for the ion-implantation process
Dose: 1012 to 1016 atoms/cm2
Current: 1 A/cm2 to 1 A/cm2
Voltage-energy: 10 to 300 kV
After the fact annealing: 500 to 800 C
Advantages of the ion implantation process:
Relatively low-temperature process that can be used atarbitrary time instants during the fabrication sequence.