1. Neil Goldsman Dept. of Electrical and Computer Engineering
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

2. 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

3. 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.

4. 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

5. 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

6. SiC Research Strategy Material Modeling Device Modeling Monte Carlo
(UMD)
Device Modeling
Drift-Diffusion
(UMD)
Experiment
(ARL)
SiC Device
Research & Design

7. 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)

8. 4H-SiC Monte Carlo Atomic Level Quantum Mechanical Investigation.
Calculate SiC Band Structure: Obtain Electronic Properties
(A L H) planes c
(Γ M K) plane
M-L

9. 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.

10. 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

11. Advanced Drift Diffusion Simulator for 4H-SiC MOSFETs
Allows device designers to probe inside device to determine what’s going on!

12. Characterizing Internal Device Physics
SiC MOSFET:
Characterizing Internal Device Physics
Gate metal
source
drain
Electrical Characteristics
I-V Curves
Charge Pumping Data
Extracted Mobility Values
Threshold and Flatband Voltages
p-type epilayer
Physical Characteristics
Device Geometry
Doping Profile
Semiconductor
Gate Metal
p+ substrate

13. MOSFET Device Simulation
MOSFET Device Structure
Semiconductor Equations
Poisson Equation:
Electron current continuity equation:
Hole current continuity equation:
Electron current equation:
Hole current equation:

14. Mobility Models Low field mobility: High field mobility: Oxide
Bulk
Electron Flow
Electron
Surface Phonon
Surface Roughness
Trap
Fixed Charge
Matthiessen's rule
mLF = Low Field Mobility mB = Bulk Mobility
mSP = Surface Phonon Mobility
mSR = Surface Roughness mobility
mC = Trapped interface charge mobility
Low field mobility:
High field mobility:
High Field Mobility:

15. New Model for Interface Trap Mobility: 2D Coulomb Scattering
Coulomb Potential:
2D Fourier Transform of V(r):
Fermi’s Golden Rule:
Scattering Rate:
z dependence of Mobility:

16. Agrees with Experiment Extracts Surface State Structure
4H SiC MOSFET: L = 5mm W = 5mm
Id – Vg T = 27oC
I-V Characteristics
Interface States Extracted

17. Combined Effect of Interface and Surface Roughness Scattering
IDS vs VGS
IDS vs VDS
Reducing surface roughness scattering only improves mobility after interface trap density is significantly reduced!

18. 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.

19. Very Recent Publications (Mostly Collaborative)
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 , 2001.
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 , 2001.
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 , 2001.
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 , 2002
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
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.

20. 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 , 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 , 2003.
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 , 2003.
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

21. Potential Program Designing Carbon Nanotube MOSFETs (CNTFETs) (Currently Supported Elsewhere)

22. Physical CNT in Channel System
We characterize:
Transport in the nanotube, and
through the surrounding silicon.
Quantization of the nanotube
Interaction with Silicon (charge
transport through the CNT-Si barrier)
Transport and quantization in the
surrounding Silicon
d= nm
CNT in the quantum well

23. 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 , 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.

24. Device Modeling Equations: Solve Numerically
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:
Electron Current Density
Hole Current Density

25. Calculated I-V Characteristics for CNT-MOSFET with different layers of d=0.8nm CNTs Show 3X Improvement in Current Drive
VGS= 1.5V
VDS= 1.0V

26. Maryland Sensor Network Group (Currently Supported Elsewhere)
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

27. 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.

28. Smart Dust Animation

29. Maryland Sensor Network Group:
Synergistically Combining a Broad Expertise
Electromagnetics & Antennas
Sensors and MEMS
3D Microelectronics
SMART DUST
Scalable Power, Energy Harvesting with 3D Integration
Digital Design & Control
Communication Networking, Data Fusion & Signal Processing

30. Hardware Already Prototyped Smart Pebble Transceiver Custom IC PLL FSK Tx Chip Fabricated in 0.5μ CMOS

31. Applications: Smart Dust Network Motion and Distance tracking
Biological and Chemical Environmental Factors
Distributed Image Recognition and Optical Sensing
Acoustic and Vibrational Sensing

32. 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

33. 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?