Introduction to Silvaco ATHENA Tool and Basic Concepts in Process Modeling 1

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


2/18


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


3/18


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


4/18


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


5/18


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


6/18


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.


7/18


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 - ...


8/18


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.)


9/18


(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

=

+


10/18


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


11/18


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


12/18


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 =


13/18


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


14/18


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)

=

=


15/18


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


16/18


(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


17/18


(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


18/18


(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.

Documents

Introduction to Silvaco ATHENA Tool and Basic Concepts in Process Modeling 1