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Russian Microelectronics

, Volume 44, Issue 2, pp 89–100 | Cite as

A tunnel field-effect transistor with a graphene derivatives (COH) n -(CF) n -(CH) n three-layer quantum well with the middle (CF) n drain layer

  • V. A. Zhukov
  • V. G. Maslov
Article

Abstract

A tunnel field-effect quantum-well nanotransistor, in which the drive voltage of 0.6 V is applied to the barriers surrounding the well, and electrons are drained from the quantum well, is considered. Electrons are tunneled into the quantum well through the first half of the double-humped tunnel barrier (double heterojunction) formed by a three-layer sandwich of broad-band 2D semiconductors (graphene derivatives, such as perhydroxy graphene (COH) n , fluorographene (CF) n , and graphane (CH) n ) with sharply different levels of the bottoms of the conduction band. The middle fluorographene (CF) n layer has the lowest conduction band bottoms, which forms the quantum well with a depth of ∼3 eV and a width of ∼0.6 nm in the common tunnel potential barrier with a width of 1.8 nm and serves as a drain channel for electrons. The source and gate metallic electrodes are adjacent to the outer layers of the sandwich 2D semiconductors, i.e., perhydroxy graphene (COH) n and graphane (CH) n , respectively, forming the common gate sandwich of 20 × 20 nm2 in size. The metallic drain electron with a width of 10 nm and a potential, which is 1 V higher than that of the first (source) electrode, is adjacent to the middle fluorographene (CF) n layer, which extends outside the sandwich, being 35 nm wider than the outer 2D semiconductor layers of the inner three-layer sandwich. The gate opening potential is 0.62 V. The maximum working voltage I sd = 2 × 10−5 A. The drain off-state current is zero, and the leakage on-state current through the gate electrode is I g = I leak ∼ 10−10 A. The quantum capacitance of the transistor enables its operation at a frequency of up to 1012 Hz.

Keywords

RUSSIAN Microelectronics Tunnel Barrier Energy Band Diagram Quantum Capacitance Double Barrier 
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

© Pleiades Publishing, Ltd. 2015

Authors and Affiliations

  1. 1.St. Petersburg Institute for Informatics and AutomationRussian Academy of SciencesSt. PetersburgRussia
  2. 2.St. Petersburg State University of Information Technologies, Mechanics, and OpticsSt. PetersburgRussia
  3. 3.St. Petersburg State Polytechnical UniversitySt. PetersburgRussia

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