Index of /intel-research/silicon
Nano-materials &
Silicon Nanotechnology
C. Michael Garner Intel Corporation
C. Michael Garner
(2)
Agenda
Agenda
•
•
Technology Scaling and Moore's Law
Technology Scaling and Moore's Law
•
•
Technology Challenges
Technology Challenges
•
•
Nanotechnology Building Blocks
Nanotechnology Building Blocks
•
•
Nano
Nano
-
-
material Opportunities
material Opportunities
•
•
Beyond the roadmap
Beyond the roadmap
…
…
.
.
•
(3)
Key Messages
Key Messages
•
•
Silicon Nanotechnology is production reality
Silicon Nanotechnology is production reality
and follows Moore’s law
and follows Moore’s law
•
•
Experimental data on 22nm
Experimental data on 22nm
-
-
node/10nm
node/10nm
-
-minimum
minimum
-
-
feature
feature
-
-
size
size
•
•
We believe that silicon nanotechnology is
We believe that silicon nanotechnology is
extendable to 2015
extendable to 2015
•
•
Open minded about post
Open minded about post
-
-
2015 options
2015 options
•
•
Nano-
Nano
-
materials will play an important role in
materials will play an important role in
the silicon nanotechnology platform
the silicon nanotechnology platform
Nanotechnology could deliver critical materials Nanotechnology could deliver critical materials Nanotechnology could deliver critical materials
(4)
Technology Scaling
Technology Scaling
100 100 10 10 0.01 0.011970 1980 1990 2000 2010 2020
Nominal feature size
Nominal feature size
Nanotechnology
Nanotechnology
130nm 130nm 90nm 90nm 70nm 70nm 50nm 50nmGate Width
Gate Width
10000 10000 10 10 1000 1000 1 1 Nanometer Nanometer Micron Micron 0.1 0.1(5)
Intel’s Transistor Research in
Intel’s Transistor Research in
Deep Nanotechnology Space
Deep Nanotechnology Space
Experimental transistors for future process generations
Experimental transistors for future process generations
30nm 30nm
20nm 20nm
15nm 15nm
10nm 10nm 65nm process
65nm process 2005 production
2005 production 45nm process45nm process 2007 production
2007 production 32nm process32nm process 2009 production
2009 production 22nm process22nm process 2011 production 2011 production
Transistors will be improved
for production
Transistors will be improved
Transistors will be improved
for production
(6)
Moore's Law Continues
Moore's Law Continues
1,000 10,000 100,000 1,000,000 10,000,000 100,000,000 1,000,000,000
1970 1980 1990 2000 2010
4004 8080
8086
8008
Pentium® Processor
486™ DX Processor 386™ Processor
286
Pentium® II Processor Pentium® III Processor
Itanium® Processor
Heading toward 1 billion transistors in 2007
Pentium® 4 Processor Itanium® 2 Processor
(7)
Flash
Flash
(ETOX
(ETOX
®
®
)
)
Technology Scaling
Technology Scaling
1986 / 1.5µm 1988 / 1.0µm 1991 / 0.8µm 1993 / 0.6µm1996 / 0.4µm 1998 / 0.25µm 2000 / 0.18µm 2002 / 0.13µm
5.4X
1986 / 1.5µm 1988 / 1.0µm 1991 / 0.8µm 1993 / 0.6µm
1996 / 0.4µm 1998 / 0.25µm 2000 / 0.18µm 2002 / 0.13µm 1986 / 1.5µm
1986 / 1.5µm 1988 / 1.01988 / 1.0µµmm 1991 / 0.81991 / 0.8µµmm 1993 / 0.61993 / 0.6µµmm
1996 / 0.4µm
1996 / 0.4µm 1998 / 0.251998 / 0.25µµmm 2000 / 0.182000 / 0.18µµmm 2002 / 0.13µm
5.4X
234 X
Volume Production Year / Technology Generation
18 years and 8 Generations of ETOX® to 0.13
µ
m
18 years and 8 Generations of ETOX
(8)
Silicon Scaling Leads to Material
Silicon Scaling Leads to Material
Challenges
Challenges
•
•
Lithography
Lithography
•
•
Transistors
Transistors
•
•
Interconnects
Interconnects
•
(9)
PPT Shrink Source: Intel
Material Challenges
Material Challenges
50nm
Print Features
Print Features Line Edge Roughness(LER)Line Edge Roughness(LER)
10nm 10nm
Resist Nano-domains
Resist Nano-domains
Low K Interlevel Dielectric Micelle Assembled….
Low K Interlevel Dielectric Micelle Assembled….
Barrier Layer ~20nm
Barrier Layer ~20nm
What Device Next? What Materials? How to Assemble? What Device Next? What Device Next? What Materials? What Materials? How to Assemble? How to Assemble?
Materials Challenges Everywhere
(10)
Nanotech
Nanotech
Building Blocks
Building Blocks
Sub 100nm particles
Sub 100nm particles
c.
Molecular Assembly (directed and self assembly)
c.
Molecular Assembly (directed and self assembly)
Macromolecules
Macromolecules
10nm 10nm
Sub 100nm structures
(11)
Lithography Challenges
Lithography Challenges
1000 1000
100 100
10 10
’89
’89 ’91’91 ’93’93 ’95’95 ’97’97 ’99’99 ’01’01 ’03’03 ’05’05 ’07’07 ’09’09 ’11’11 Initial Production
Initial Production
Feature size
Feature size
13nm (EUVL)
13nm (EUVL)
Lithography
Lithography
Wavelength
Wavelength
193nm 193nm 248nm
248nm
Gap
Gap
nm
New Mask, Design Techniques, and Materials Needed to Support future Lithography Scaling
New Mask, Design Techniques, and Materials Needed to Support future Lithography Scaling
(12)
Future Lithography Resist Challenges
Future Lithography Resist Challenges
Line Edge Roughness(LER)
Atomic Force Microscope
Picture of Resist Nano-domains Atomic Force Microscope
Picture of Resist Nano-domains Line Edge Roughness(LER)
•Resist nano-domains limiting feature resolution and defects.
•Requires control at the molecular level
•Resist nano-domains limiting feature resolution and defects.
(13)
New Materials, Devices Extend Si
New Materials, Devices Extend Si
Scaling
Scaling
Gate
Gate
Silicide Silicide added addedChannel
Channel
Strained Strained silicon siliconChanges
Changes
Made
Made
Future
Future
Options
Options
High
High
-
-
k
k
gate
gate
dielectric
dielectric
Transistor
Transistor
Source: Source: Intel Intel PolySi Silicon PolySi Silicon Gate dielectric less than 3 atomic layers thickSource: Intel
(14)
New Materials, Devices Extend Si
New Materials, Devices Extend Si
Scaling
Scaling
Metal lines
Metal lines
Al Cu Al Cu
Insulating
Insulating
dielectric
dielectric
SiO
SiO22 SiOFSiOF CDO
CDO (low (low--k)k)
Changes
Changes
Made
Made
Future
Future
Options
Options
Ultra
Ultra
Low
Low
-
-
k
k
Dielectric
Dielectric
Interconnects
Interconnects
New
Thinner
Barrier
Layers
New
Thinner
Barrier
Layers
(15)
Molecular Self
Molecular Self
-
-
Assembly
Assembly
Low
Low
-
-
K Dielectric
K Dielectric
Source: J. Brinker, UNM/Sandia National Labs
Source: J. Brinker, UNM/Sandia National Labs
• Materials of the gel self-organize into a Low K dielectric
(16)
Conductivity Challenge
Conductivity Challenge
Nanomaterials to reduce roughness & grain scattering Nanomaterials to reduce roughness & grain scattering
(17)
Integrated Thermal and Power
Integrated Thermal and Power
Delivery Management
Delivery Management
Heat spreader for high heat flux from die
Capacitors for high current, low noise power delivery
Thermal Challenge
Ultra low thermal resistance Thermal Interface Material
Thermal Challenge
Ultra low thermal resistance Thermal Interface Material
Power Challenge Ultra fast,
high charge density capacitors
Power Challenge Ultra fast,
high charge density capacitors
Nano-material Opportunities in Thermal and Power Delivery
(18)
Characterization Techniques
Characterization Techniques
Characterization Techniques
Quantitative Understanding
Physics-based Computer Vision
Scientific
measurements Model Knowledge
•
•
Interpret images in conjunction with physical modelsInterpret images in conjunction with physical modelsÎ
ÎFocus on nanoscale sources such as AFMs, STMs, FIBFocus on nanoscale sources such as AFMs, STMs, FIB
•
•
New metrology is needed……New metrology is needed……Î
ÎFaster structural analysisFaster structural analysis
Î
(19)
Beyond the roadmap….
Beyond the roadmap….
•
•
Many device options….
Many device options….
•
•
Compatibility with CMOS for evolutionary
Compatibility with CMOS for evolutionary
introduction
introduction
•
•
Directed or self assembly of arrays???
Directed or self assembly of arrays???
Î
Î
Defect density or purity required….
Defect density or purity required….
•
•
Self correcting architectures??
Self correcting architectures??
•
Nanotechnology needs a richer suite of
functionality…
Collaboration between Industry, Universities,
and Government is essential
Collaboration between Industry, Universities,
Collaboration between Industry, Universities,
and Government is essential
(20)
What are we looking for?
What are we looking for?
•
•
Required characteristics:Required characteristics:Î
ÎScalabilityScalability
Î
ÎPerformancePerformance
Î
ÎEnergy efficiencyEnergy efficiency
Î
ÎGainGain
Î
ÎOperational reliability Operational reliability
Î
ÎRoom temp. operationRoom temp. operation
•
•
Preferred approach:Preferred approach:Î
ÎCMOS process CMOS process compatibility
compatibility
Î
ÎCMOS architectural CMOS architectural compatibility
Alternative state variables
Alternative state variables
•
•
Spin
Spin
–
–
electron, nuclear,
electron, nuclear,
photon
photon
•
•
Phase
Phase
•
•
Quantum state
Quantum state
•
•
Magnetic flux quanta
Magnetic flux quanta
•
•
Mechanical deformation
Mechanical deformation
•
•
Dipole orientation
Dipole orientation
•
•
Molecular state
Molecular state
(21)
Some Alternative Logic Devices
Some Alternative Logic Devices
Fas
ter
Sm
al
le
r
Cheaper
ITRS, 2000
George Bourianoff
(22)
Future Nanotechnology will compliment
Future Nanotechnology will compliment
& extend Silicon Technology
& extend Silicon Technology
*Source: Holmes et al, University *Source: Holmes et al, University College Cork
College Cork
**Source: Blau et al, Trinity **Source: Blau et al, Trinity College Dublin
College Dublin
Silicon
Silicon
Nanowire*
Nanowire*
Nanotube/Nanowire
Nanotube/Nanowire
Transistors
Transistors
Carbon
Carbon
Nanotube**
Nanotube**
Many options, but no clear winners…
Many options, but no clear winners…
(23)
Summary
Summary
•
•
Many new materials required for scaling
Many new materials required for scaling
Î
ÎLithographyLithography
Î
ÎTransistorTransistor
Î
ÎInterconnectsInterconnects
Î
ÎNonNon--volatile Memoryvolatile Memory
Î
ÎThermal & Power Delivery Materials Thermal & Power Delivery Materials
•
•
Silicon is the platform for the future
Silicon is the platform for the future
•
•
Nanotechnology could deliver critical materials to
Nanotechnology could deliver critical materials to
support Silicon Nanotechnology
support Silicon Nanotechnology
•
•
For technology beyond 2015 collaboration between
For technology beyond 2015 collaboration between
Industry, Universities, and Government is essential
(24)
Back
(25)
Quest for New Materials
Quest for New Materials
Metal Al Æ Cu Æ Æ Æ Æ ?
ILD SiO2 SiOF Æ SiOC Æ Æ ? ?
Gate Ox SiO2 Æ Æ Æ High-k ? ? ?
Gate
Electrode Poly Æ Æ Æ Æ Metal ? ?
Year 1997 1999 2001 2003 2005 2007 2009 2011 P856 P858 PX60 P1262 P1264 P1266 P1268 P1270 Node 0.25µm 0.18 µm 130nm 90nm 65nm 45nm 32nm 22nm
Extending Moore’s Law with Novel Materials Extending Moore’s Law with Novel Materials
(26)
Transistors Shipped Per Year
Transistors Shipped Per Year
Moore’s Law in Action...
'68 '70 '72 '74 '76 '78 '80 '82 '84 '86 '88 '90 '92 '94 '96 '98 '00 '02
1016 1014 1012 1010 Units 1018 S o u rce : Da ta ques t/I n te l, 12/02
Average Transistor Price By Year
Average Transistor Price By Year
0.0000001 0.000001 0.00001 0.0001 0.001 0.01 0.1 1 10
'68 '70 '72 '74 '76 '78 '80 '82 '84 '86 '88 '90 '92 '94 '96 '98 '00 '02
$ $
Moore’s Law is driven by economics
Moore’s Law is driven by economics
(27)
Precision Biology
Precision Biology
Precision Biology
Create a new generation of
Create a new generation of
bio
bio--instruments capable of instruments capable of operating in the
operating in the singlesingle- -molecule
molecule regimeregime Silicon Fluid
reservoirs and channel
20µm
280 nm
100nm
100nm
50nm
50nm
Transistor for
Transistor for
90nm Process
90nm Process
Source: Intel
Source: Intel
Influenza virus
Influenza virus
Source: CDC
(28)
The road to smart dust.…
The road to smart dust.…
The road to smart dust.…
Configurable Configurable Silicon Radio Silicon Radio
MOTE MOTE
(a small piece of silicon)
(a small piece of silicon)
Sensing +
Sensing +
Computing Computing + +
Communicating
Communicating
Storage +
Storage +
(29)
Nanotechnology Opportunities
Nanotechnology Opportunities
Extending Moore’s Law
Extending Moore’s Law
•
•
Synergistic extension of Silicon Technology
Synergistic extension of Silicon Technology
Î
ÎSiSi--based CMOS transistors through 2015based CMOS transistors through 2015
Î
ÎRole for new materials based on nanotechnologyRole for new materials based on nanotechnology
Î
ÎOpen minded about options beyond 2015Open minded about options beyond 2015
Expanding Moore’s Law
Expanding Moore’s Law
•
•
Proactive Computing Vision
Proactive Computing Vision
Collaboration between Industries, Universities, and
Collaboration between Industries, Universities, and
Governments is essential
(30)
What is Nanotechnology?
What is Nanotechnology?
a.
a.
New structures like carbon nanotubes
New structures like carbon nanotubes
b.
b.
Silicon devices made smaller
Silicon devices made smaller
c.
c.
Arranging atoms and molecules
Arranging atoms and molecules
d.
d.
Letting atoms assemble themselves
Letting atoms assemble themselves
e.
e.
Something far in the future
Something far in the future
f.
f.
In production today
In production today
g.
g.
All of the above
All of the above
Correct answer: g.
(31)
$ per transistor decreased by > 6 orders in 30
$ per transistor decreased by > 6 orders in 30
years: This drives investments
years: This drives investments
0.0000001 0.000001 0.00001 0.0001 0.001 0.01 0.1 1 10
'68 '70 '72 '74 '76 '78 '80 '82 '84 '86 '88 '90 '92 '94 '96 '98 '00 '02 $
$
CPU ~3
micro
-$/xtor
DRAM ~30
nano
-$/xtor
1. We expect this trend to continue;
2. And benchmark the alternatives
(32)
What are the Alternative Devices?
What are the Alternative Devices?
(From ITRS ERD TWIG 2003)
Logic Device Perf. Arch. compat Reliab ility Proc. compa t Op. temp Energ eff Sensitiv ity Scala bility Flux quanta
3 2 3 2 1 1 2 1
1D 2 3 1 2 3 3 3 3
Resonant Tunneling Devices 29
2 2 2 2 2 3 1 2
SETs 1 1 1 2 1 2 1 2
Molecular 1 1 3 2 2 2 3 3
QCA 1 1 2 1 1 2 3 2
Spin 2 2 1 2 1 2 1 3
Quantum 3 2 1 1 1 3 1 3
(33)
Architecture Devices State variables Data represent ations CNT FETs Molecular Spintronics Quantum CNN Crossbar Quantum Boolean Molecular state Spin orientation
Flux quanta Quantum state Electric charge Associative Patterns Analog Digital Scaled CMOS Probabilities Hierarchy Biotech Logic/Memory Sensors NEMS
A Taxonomy for Nano
A Taxonomy for Nano
-
-
computing
computing
Time Courtesy: George Bourianoff
(1)
28
28 C. Michael Garner Sept.16, 2003C. Michael Garner Sept.16, 2003
The road to smart dust.…
The road to smart dust.…
The road to smart dust.…
Configurable Configurable Silicon Radio Silicon Radio
MOTE MOTE
(a small piece of silicon) (a small piece of silicon)
Sensing + Sensing +
Computing Computing + +
Communicating
Communicating
Storage +
(2)
Nanotechnology Opportunities
Nanotechnology Opportunities
Extending Moore’s Law
Extending Moore’s Law
•
•
Synergistic extension of Silicon Technology
Synergistic extension of Silicon Technology
Î
ÎSiSi--based CMOS transistors through 2015based CMOS transistors through 2015 Î
ÎRole for new materials based on nanotechnologyRole for new materials based on nanotechnology Î
ÎOpen minded about options beyond 2015Open minded about options beyond 2015
Expanding Moore’s Law
Expanding Moore’s Law
•
•
Proactive Computing Vision
Proactive Computing Vision
Collaboration between Industries, Universities, and
Collaboration between Industries, Universities, and
Governments is essential
(3)
30
30 C. Michael Garner Sept.16, 2003C. Michael Garner Sept.16, 2003
What is Nanotechnology?
What is Nanotechnology?
a.
a. New structures like carbon nanotubes New structures like carbon nanotubes b.
b. Silicon devices made smallerSilicon devices made smaller c.
c. Arranging atoms and moleculesArranging atoms and molecules d.
d. Letting atoms assemble themselves Letting atoms assemble themselves e.
e. Something far in the futureSomething far in the future f.
f. In production todayIn production today g.
g. All of the aboveAll of the above
Correct answer: g.
(4)
$ per transistor decreased by > 6 orders in 30
$ per transistor decreased by > 6 orders in 30
years: This drives investments
years: This drives investments
0.0000001 0.000001 0.00001 0.0001 0.001 0.01 0.1 1 10
'68 '70 '72 '74 '76 '78 '80 '82 '84 '86 '88 '90 '92 '94 '96 '98 '00 '02 $
$
CPU ~3 micro-$/xtor
DRAM ~30 nano-$/xtor
1. We expect this trend to continue;
2. And benchmark the alternatives
(5)
32
32 C. Michael Garner Sept.16, 2003C. Michael Garner Sept.16, 2003
What are the Alternative Devices?
What are the Alternative Devices?
(From ITRS ERD TWIG 2003)
Logic Device
Perf. Arch. compat
Reliab ility
Proc. compa t
Op. temp
Energ eff
Sensitiv ity
Scala bility Flux
quanta
3 2 3 2 1 1 2 1
1D 2 3 1 2 3 3 3 3
Resonant Tunneling Devices 29
2 2 2 2 2 3 1 2
SETs 1 1 1 2 1 2 1 2
Molecular 1 1 3 2 2 2 3 3
QCA 1 1 2 1 1 2 3 2
Spin 2 2 1 2 1 2 1 3
Quantum 3 2 1 1 1 3 1 3
(6)
Architecture
Devices
State variables
Data represent
ations
CNT FETs
Molecular
Spintronics Quantum CNN
Crossbar Quantum Boolean
Molecular state
Spin orientation
Flux quanta Quantum state Electric charge
Associative Patterns
Analog
Digital
Scaled CMOS
Probabilities
Hierarchy
Biotech Logic/Memory
Sensors
NEMS