Novel Dielectric Materials for Future Transistor Generations

  • Gennadi Bersuker
  • Byoung H. Lee
  • Anatoli Korkin
  • Howard R. Huff
Part of the Nanostructure Science and Technology book series (NST)


Responding to the market growth for computational power in various applications, semiconductor technology continues unabated in its drive toward higher transistor densities and faster transistors. The general direction of this trend is scaling down the critical dimensions of the integrated circuit (IC) components. Transistor scaling is governed by the duality of speed versus power: transistor speed, which depends on the drive current, should be increased while decreasing transistor power consumption. The static power consumption depends on the total transistor leakage current (it includes the subthreshold, p-n junction, and leakage currents), of which the gate leakage current may constitute the major component. According to the International Technology Roadmap for Semiconductors (ITRS) [1], the electrical gate oxide thickness (EOT) needed by 2007 for high-performance applications such as microprocessors (MPUs) should be less than 1 nm and the gate leakage current should be less than 103 A/cm2 at 100°C. For low standby power applications, such as for cell phones, where the static power consumption is the major limiting factor, by the year 2006 the leakage current should be less than 1.5 × 10−2 A/cm2 at 100°C, with the EOT ≤ 1.6 nm. The SiO2 gate dielectric cannot support these requirements for the leakage current since the electron direct tunneling and the associated leakage current in the case of such thin dielectrics are too high. Although the exact limit of SiO2 thickness for different electronic application is disputable, the dielectrics with the higher permittivity can offer a potential solution by providing the equivalent EOT for the larger thickness of the gate oxide [2].


Atomic Layer Deposition Gate Dielectric Electron Trapping Shallow Trap Static Power Consumption 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Gennadi Bersuker
    • 1
  • Byoung H. Lee
    • 1
  • Anatoli Korkin
    • 2
  • Howard R. Huff
    • 1
  1. 1.SEMATECHAustin
  2. 2.Nano & Giga SolutionsGilbert

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