Transcript Slide 1

Next Generation Electronics from Silicon Carbide
to Carbon Nanotubes and Smart Sensors:
Paradigms for UMD-ARO/ARL
Collaboration
Neil Goldsman
Dept. of Electrical and Computer Engineering
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SiC, Nanotubes and Smart Sensors
Outline
• Existing Program:
– Silicon Carbide Electronics
– A mutually beneficial, synergistic collaboration
• Potential Collaborations
– Nanotechnology: Carbon Nanotube
Electronics
– Low Power Wireless Sensor Networks: Smart
Dust
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Existing Program
Modeling, Characterization and Design of
Wide Bandgap MOSFETs for High
Temperature and Power Applications
Applications include:
•Electronics for harsh environments including automotive
and aircraft engines.
•Extending micro-electronics revolution to power IC’s.
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Personnel Currently Involved
UMCP: Neil Goldsman
Gary Pennington (Research Associate)
Siddharth Potbhare (MS-Ph.D)
ARL:
Skip Scozzie
Aivars Lelis (& UMCP Ph.D)
Bruce Geil (& UMCP MS)
Dan Habersat (& Former Merit)
Gabriel Lopez (& Former Merit)
ARO STAS: Barry Mclean & Jim McGarrity
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Personnel Development: Contribution to
ARL
• Gary Pennington: Finished PhD 2003, now scientist
postdoctoral research associate on SiC for ARL
• Steve Powell: Finished PhD 2003, now at NSA
• Gabriel Lopez: Former UMD MERIT student, now ARL
employee
• Aivars Lelis: ARL employee, PhD at UMD under
Goldsman (transferring our software to ARL for use and
more development)
• Bruce Geil: ARL employee, MS at UMD under Goldsman
(transferring our software to ARL for use and more
development)
• Currently interviewing several students (US citizens) for
internships and possible positions at ARL
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SiC Research Strategy
Device Modeling
Drift-Diffusion
(UMD)
Experiment
(ARL)
Material Modeling
Monte Carlo
(UMD)
SiC Device
Research & Design
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4H-SiC Monte Carlo
Goals:
• Understand high-field, high-temperature
transport in 4H-SiC.
• Develop transport properties for drift-diffusion
device simulator.
(interpret device experiments at ARL)
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4H-SiC Monte Carlo
Atomic Level Quantum Mechanical Investigation.
Calculate SiC Band Structure: Obtain Electronic Properties
c
c
M-L
(Γ M K) plane
(A L H) planes
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Monte Carlo for SiC: Bulk
• Simulation of temperature-dependent propeties of bulk
electron transport in SiC that agree with experiment.
Exp: I. Khan, and J. Cooper, “Measurement of high-field electron transport
in silicon carbide” IEEE Trans. Elec. Dev. Vol. 47, No. 2 pp. 269, 2000.
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Monte Carlo for SiC: inversion layer
•Extend bulk method to the inversion layer using bulk bandstructure along
with bulk phonon and impurity scattering rates.
•Scattering by trapped interface charge, interface roughness and surface
reflections. Scattering increases as electron distance to interface y decreases.
MC Extracted Interface Trap Density for SiC
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Advanced Drift Diffusion
Simulator for
4H-SiC MOSFETs
Allows device designers to probe inside
device to determine what’s going on!
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SiC MOSFET:
Characterizing Internal Device Physics
Gate metal
source
drain
p-type epilayer
Electrical Characteristics
• I-V Curves
• Charge Pumping Data
• Extracted Mobility Values
• Threshold and Flatband
Voltages
Physical Characteristics
p+ substrate
•Device Geometry
•Doping Profile
•Semiconductor
•Gate Metal
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MOSFET Device Simulation
MOSFET Device Structure
Semiconductor Equations
Poisson Equation:
 2  

n p N

q

D
 N A

n
   J n  q R  G 
t
Electron current
continuity equation:
q
Hole current
continuity equation:
q
Electron current
equation:
J n  qnn  q(nDn )
Hole current
equation:
J p  qp p   q( pD p )
p
   J p  q R  G 
t
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Mobility Models
Oxide
Low field mobility:
Matthiessen's rule
1
 LF

1
B

1
 SP

1
 SR

1
Electron Flow
C
LF = Low Field Mobility B = Bulk Mobility
Bulk
SP = Surface Phonon Mobility
Electron
Surface Phonon
SR = Surface Roughness mobility
Trap
Surface Roughness
C = Trapped interface charge mobility
High Field Mobility:
High field mobility:
 HF 
Fixed Charge
 LF
1
   LF E||   
 
1  
  vsat  
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New Model for Interface Trap Mobility:
2D Coulomb Scattering
Coulomb Potential:
e2 1

V (r ) 

4 r
Fermi’s Golden Rule:
2D Fourier Transform of V(r):
e2 1

Vq2 D , z, zi  
exp q2 D z  zi 
 q2 D
    


2


2

S q2 D , z, zi  
| Vq2 D , z, zi  |   k   k

Scattering Rate:
1


1
4 2


S
q
 2 D , z 1  cos  k dk d
k ,
e 4 N IT 1


4  2 
 2

0


2 m*
exp 4 z
sin   d
2



z dependence of Mobility:
e
16   2
 IT z, T   *      * 3
m
m e N IT
*

3 
15
2
m
kB
T 
zT 2
2


4 


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Agrees with Experiment
Extracts Surface State Structure
4H SiC MOSFET: L = 5m W = 5m
Id – Vg T = 27oC
I-V Characteristics
Interface States Extracted
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Combined Effect of Interface and Surface
Roughness Scattering
IDS vs VDS
IDS vs VGS
Reducing surface roughness scattering only improves
mobility after interface trap density is significantly reduced!
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Key Results for Recent 4H SiC
Technology
• Significant improvement in numerical attributes of simulator:
– Allows for much higher resolution mesh
• Improved physical model for interface state mobility
– Depends on 2D coulomb scattering
• Developing new model for device instability
– Use gate current injected from channel
– Related to oxide charging and interface trap generation
• New Monte Carlo simulations show energy of carriers in channel
– Needed for interface trap generation
– Needed for oxide state occupation
• Shows potential improvement if interface states are reduced.
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Very Recent Publications (Mostly Collaborative)
1)
G. Pennington, and N. Goldsman, "Empirical Pseudopotential Band Structure of 3C, 4H,
and 6H SiC Using Transferable Semiempirical Si and C Model Potentials,” Phy. Rev. B,
vol 64, pp. 45104-1-10, 2001.
2)
G. Pennington, N. Goldsman, C. Scozzie, J. McGarrit, F.B. Mclean., “Investigation of
Temperature Effects on Electron Transport in SiC using Unique Full Band Monte Carlo
Simulation,” International Semiconductor Device Research Symposium Proceedings, pp.
531-534, 2001.
3)
S. Powell, N. Goldsman, C. Scozzie, A. Lelis, J. McGarrity, “Self-Consistent Surface
Mobility and Interface Charge Modeling in Conjunction with Experiment of 6H-SiC
MOSFETs,” International Semiconductor Device Research Symposium Proceedings, pp.
572-574, 2001.
4)
S. Powell, N. Goldsman, J. McGarrity, J. Bernstein, C. Scozzie, A. Lelis,
“Characterization and Physics-Based Modeling of 6H-SiC MOSFETs”’ Journal of
Applied Physics, V.92, N.7, pp 4053-4061, 2002
5)
S Powell, N. Goldsman, J. McGarrity, A. Lelis, C. Scozzie, F.B McLean., “Interface
Effects on Channel Mobility in SiC MOSFETs,” Semiconductor Interface Specialists
Conference, 2002
6)
G. Pennington, S. Powell, N. Goldsman, J.McGarrity, A. Lelis, C.Scozzie., “Degradation
of Inversion Layer Mobility in 6H-SiC by Interface Charge,” Semiconductor Interface
Specialists Conference, 2002.
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Very Recent Publications Continued
7) G. Pennington and N. Goldsman, ``Self-Consistent Calculations for n-Type Hexagonal SiC
Inversion Layers,” Journal of Applied Physics, Vol. 95, No. 8, pp. 4223-4234, 2004
8) G. Pennington, N. Goldsman, J. McGarrity, A Lelis and C. Scozzie, ``Comparison of 1120
and 0001 Surface Orientation in 4H SiC Inversion Layers,” Semiconductor Interface
Specialists Conference, 2003.
9) S. Potbhare, N. Goldsman, A. Lelis, “Characterization and Simulation of Novel 4H SiC
MOSFETs”, UMD Research Review Day Poster, March 2004.
10) G. Pennington, N. Goldsman, J. McGarrity, A. Lelis, C. Scozzie, ``(001) Oriented 4H-SiC
Quantized Inversion Layers," International Semiconductor Device Research Symposium,
pp. 338-339, 2003.
11) X. Zhang, N. Goldsman, J.B. Bernstein, J.M. McGarrity, S. Powell, ``Numerical and
Experimental Characterization of 4H-SiC Schottky Diodes,” International
Semiconductor Device Research Symposium, pp. 120-121, 2003.
12) S. K. Powell, N. Goldsman, A. Lelis, J. M. McGarrity and F.B. McLean, High
Temperature Modeling and Characterization of 6H SiC MOSFETs, submitted for
publication, 2004
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Potential Program
Designing Carbon Nanotube MOSFETs
(CNTFETs)
(Currently Supported Elsewhere)
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Physical CNT in Channel System
We characterize:
• Transport in the nanotube, and
through the surrounding silicon.
• Quantization of the nanotube
d=0.8-1.7nm
• Interaction with Silicon (charge
transport through the CNT-Si
barrier)
• Transport and quantization in
the
surrounding Silicon
CNT in the quantum well
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Motivation: Improve MOSFET
Performance with CNT
Theory indicates that:
• CNT has about 4x higher mobility than Si ([1], exp. [2])
• CNT usage reduces surface scattering
– Surface roughness
– Interface states
– Impurities
• CNT can be used to engineer subband structure
• CNT increases oxide capacitance (better drive current)
[1] G. Pennington, N. Goldsman, “Semiclassical Transport and Phonon Scattering
on Electrons in Semiconducting Carbon Nanotubes,” Phys. Rev. B, vol. 86,
pp. 45426-37, 2003.
[2] T. Durkop, S. A. Getty, E. Cobas, and M. S. Fuhrer, “Extraordinary Mobility
in Semiconducting Carbon Nanotubes,” Nano Letters, vol. 4, pp. 35-9, 2004.
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Device Modeling Equations:
Solve Numerically
qq
    ppnnDD 

nn 1 1
 .
J n.JnRn GR
Gn n
tt q q
pp  11 .J  GR
  .J p R  Gp
tt qq p p p
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Poisson Eqn.
Quantum CNT/Si Electron Current Continuity Eqn.
Quantum CNT/Si Hole Current Continuity Eqn.
Current Equations with Quantum and CNT-Si Barrier Effects:
 qn
n
J nJ n-qn
nnQMQM eHSn  q nnVkTth 
p
J p  qp  p    QM  HS
   p kT p
J p  -qp  p    QM  h   q  pVth p
Electron Current Density
Hole Current Density
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Calculated I-V Characteristics for CNTMOSFET with different layers of d=0.8nm CNTs
Show 3X Improvement in Current Drive
VGS= 1.5V
VDS= 1.0V
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Potential Program
Smart Dust: Unique Low Power
Flexible Sensor Networks
Maryland Sensor Network Group
(Currently Supported Elsewhere)
Dept. of Electrical and Computer Engineering
University of Maryland
College Park
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Overview: Smart Dust Network
•A network of smart sensors (dust
particles) that sense the environment,
communicate with each other
wirelessly to perform distributed
computations and make decisions.
•Dust particle to be mm size (grain of
sand).
•Network to be seamlessly integrated
into environment for flexible application.
•Each dust particle usually contains
sensors, a micro-controller, a transceiver,
and powering mechanisms
•The network can contain several hundreds
or even thousands of dust particles.
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Smart Dust Animation
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Maryland Sensor Network Group:
Synergistically Combining a Broad Expertise
Electromagnetics & Antennas
3D Microelectronics
Digital Design &
Control
SMART
DUST
Sensors and MEMS
Scalable Power, Energy
Harvesting with 3D Integration
Communication Networking, Data
Fusion & Signal Processing
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Hardware Already Prototyped
Output
Driver
Output
Driver
VCO
PFD
12-Bit Counter
Smart Pebble Transceiver Custom IC
PLL FSK Tx Chip Fabricated in 0.5μ CMOS
VCO
VCO
Output
Driver
Digital Switching Noise Testing Circuit 1
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Smart Dust Network
• Applications:
– Motion and Distance tracking
– Biological and Chemical Environmental
Factors
– Distributed Image Recognition and Optical
Sensing
– Acoustic and Vibrational Sensing
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Future Work
Modeling and Characterization of SiC Devices
• Design Next Generation SiC MOS Power Devices
• Advanced models for gate leakage, oxide trap
generation and interface trap generation
• Modeling temperature dependence of inversion layer
saturation velocity
• Understand high temperature 4H-SiC MOSFET
• Incorporate models based on Boltzmann Transport
Equation into the simulator
• Expand collaboration with ARL, Cree Inc. Penn State.
• Inversion layer Monte Carlo for SiC Power MOSFETs
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Future Work
• Cooperative Agreement established between
UMD and ARL on SiC extending 6-1 PEER basic
research to 6-2 applications.
• Collaboration between ARL and UMD on
Nanotechnology?
– Nanotube electronics and fluidics
• Collaboration between ARL and UMD on Smart
Sensor Networks?
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