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Next Generation Electronics from Silicon Carbide to Carbon Nanotubes and Smart Sensors:

Paradigms for UMD-ARO/ARLCollaboration

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 ModelingDrift-Diffusion

(UMD)

Material ModelingMonte Carlo

(UMD)

Experiment(ARL)

SiC Device Research & Design

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4H-SiC Monte Carlo4H-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 Carlo4H-SiC Monte Carlo

Atomic Level Quantum Mechanical Investigation.Calculate SiC Band Structure: Obtain Electronic Properties

(A L H) planes

c

(Γ M K) plane

c

M-L

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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 transportin silicon carbide” IEEE Trans. Elec. Dev. Vol. 47, No. 2 pp. 269, 2000.

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•Extend bulk method to the inversion layer using bulk bandstructure alongwith bulk phonon and impurity scattering rates.

•Scattering by trapped interface charge, interface roughness and surfacereflections. Scattering increases as electron distance to interface y decreases.

Monte Carlo for SiC: inversion layer

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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Physical Characteristics•Device Geometry•Doping Profile•Semiconductor•Gate Metal

Electrical Characteristics• I-V Curves• Charge Pumping Data• Extracted Mobility Values• Threshold and Flatband Voltages

SiC MOSFET:

Characterizing Internal Device Physics

p+ substrate

p-type epilayer

Gate metal

source drain

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MOSFET Device StructureMOSFET Device Structure Semiconductor EquationsSemiconductor Equations

AD NNpnq

2Poisson Equation:

GRqJt

nq n Electron current

continuity equation:

GRqJt

pq p

Hole current continuity equation:

)( nnn nDqqnJ Electron current equation:

)( ppp pDqqpJ Hole current equation:

MOSFET Device Simulation

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Mobility Models

High Field Mobility:

Matthiessen's rule

CSRSPBLF 11111

LF = Low Field Mobility B = Bulk Mobility

SP = Surface Phonon Mobility

SR = Surface Roughness mobility

C = Trapped interface charge mobility

Low field mobility:

1

||1

sat

LF

LFHF

v

EHigh field mobility:

Oxide

Bulk

Electron Flow

Electron Surface Phonon

Surface RoughnessTrap

Fixed Charge

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New Model for Interface Trap Mobility: 2D Coulomb Scattering

r

erV

1

4)(

2

Coulomb Potential:

iDD

iD zzqq

ezzq 2

2

2

2 exp1

,,V

2D Fourier Transform of V(r):

kkzzqzzqS iDiD

2

22 |,,V|2

,,

Fermi’s Golden Rule:

dm

zNe IT sin

24exp

1

4

2

02

*

2

4

dkdkzqSk

D

cos1,4

11

,

22

Scattering Rate:

2

3

2

*

3*

2

*

2

4

15

16 , Tz

kmT

Nemm

eTz B

ITIT

z dependence of Mobility:

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Agrees with ExperimentExtracts Surface State Structure

Id – Vg T = 27oC

4H SiC MOSFET: L = 5m W = 5m

I-V Characteristics Interface States Extracted

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Combined Effect of Interface and Surface Roughness Scattering

IDS vs VDSIDS 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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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.

Very Recent Publications (Mostly Collaborative)

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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

Very Recent Publications Continued

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Potential Program

Designing Carbon Nanotube MOSFETs (CNTFETs)

(Currently Supported Elsewhere)

22CNT in the quantum well

We characterize:

• Transport in the nanotube, and through the surrounding silicon.

• Quantization of the nanotube

• Interaction with Silicon (chargetransport through the CNT-Si barrier)

• Transport and quantization in the surrounding Silicon

Physical CNT in Channel System

d=0.8-1.7nm

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Motivation: Improve MOSFET Performance with CNT

• 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)

Theory indicates that:

[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

Current Equations with Quantum and CNT-Si Barrier Effects:

-

-

n n QM e n th

p p QM h p th

J qn q V n

J qp q V p

Electron Current Density

Hole Current Density

nn n QM HS nJ qn kT n

pp p QM HS pJ qp kT p

2

1.

1.

n n n

p p p

qp n D

nJ R G

t q

pJ R G

t q

Poisson Eqn.

Quantum CNT/Si Electron Current Continuity Eqn.

Quantum CNT/Si Hole Current Continuity Eqn.

2 qp n D

1. n n

nJ GR

t q

1

. p p

pJ GR

t q

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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

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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 EngineeringUniversity 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

3D Microelectronics

Electromagnetics & Antennas Sensors and MEMS

Scalable Power, Energy Harvesting with 3D Integration

Communication Networking, Data Fusion & Signal Processing

Digital Design & Control

SMART DUST

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Hardware Already Prototyped Smart Pebble Transceiver Custom IC

PLL FSK Tx Chip Fabricated in 0.5μ CMOS

12-B

it C

oun

ter Ou

tpu

tD

rive

r

Ou

tpu

tD

rive

r

OutputDriver

PF

D

VCO VCO VCO

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?