Next Generation Electronics from Silicon Carbide to Carbon Nanotubes and Smart Sensors: Paradigms for UMD-ARO/ARL Colla

1. 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

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

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

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

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

16. Agrees with ExperimentExtracts 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. 45104-1-10, 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. 531-534, 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. 572-574, 2001. • S. Powell, N. Goldsman, J. McGarrity, J. Bernstein, C. Scozzie, A. Lelis, “Characterization and Physics-Based Modeling of 6H-SiC MOSFETs”’ Journal ofApplied Physics, V.92, N.7, pp 4053-4061, 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. 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. • 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. • 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 ProgramDesigning 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=0.8-1.7nm CNT in the quantum well

23. 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.

24. Poisson Eqn. Quantum CNT/Si Electron Current Continuity Eqn. Quantum CNT/Si Hole Current Continuity Eqn. Electron Current Density Hole Current Density Device Modeling Equations: Solve Numerically Current Equations with Quantum and CNT-Si Barrier Effects:

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. Potential ProgramSmart 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 PrototypedSmart Pebble Transceiver Custom ICPLL FSK Tx Chip Fabricated in 0.5μ CMOS

31. Smart Dust Network • Applications: • 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?