Analytical Modelling Of Breakdown Effect In Graphene Nanoribbon Field Effect Transistor
Since 2004, graphene as transistor channel has drawn huge amount of attention due to its extraordinary scalability and high carrier mobility. In order to open required bandgap, its nanoribbon form is used in transistors. Breakdown effect modelling of the graphene nanoribbon field effect transistors...
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| 格式: | Thesis |
| 語言: | English |
| 出版: |
2014
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| 主題: | |
| 在線閱讀: | http://eprints.usm.my/46146/1/Mahdiar%20Hosseinghadiry24.pdf |
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| 總結: | Since 2004, graphene as transistor channel has drawn huge amount of attention due to its extraordinary scalability and high carrier mobility. In order to open required bandgap, its nanoribbon form is used in transistors. Breakdown effect modelling of the graphene nanoribbon field effect transistors (GNRFET) is needed to investigate the limits on operating voltage of the transistor. However, until now there is no study in analytical approach and modelling of the breakdown voltage (BV) effects on the graphene-based transistors. Thus, in this project, semi-analytical models for lateral electric field, length of velocity saturation region (LVSR), ionization coefficient (α), and breakdown voltage (BV) of single- and double-gate graphene nanoribbon field effect transistors (GNRFET) are proposed. As the methodology, the application of Gauss’s law at drain and source regions is employed in order to derive surface potential and lateral electric field equations. Then, LVSR is calculated as a solution of surface potential at saturation condition. The ionization coefficient is modelled and calculated by deriving equations for probability of collisions in ballistic and drift modes based on lucky drift theory of ionization. Then the threshold energy of ionization is computed using simulation and an empirical equation is derived semi-analytically. Finally avalanche breakdown condition is employed to calculate the lateral BV. As a result of this research, simple analytical and semi-analytical models are proposed for the LVSR,α, and BV, which could be used in design and optimization of semiconductor devices and sensors. |
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