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首页> 外文会议>International Conference on Energy, Communication, Data Analytics and Soft Computing >Parameter estimation of carbon nano tubes field effect transistor (CNFET) using whale optimization algorithm
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Parameter estimation of carbon nano tubes field effect transistor (CNFET) using whale optimization algorithm

机译：使用鲸鱼优化算法的碳纳米管场效应晶体管（CNFET）的参数估计

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Carbon Nano-Tube Field Effect Transistors (CNFETs) are better in performance compared to traditional CMOS-FET. It is due to their high current driving ability, low power delay product, ballistic transport and improved thermal constancy. Logical Effort (LE) technique is used for immediate as well as precise estimation of delay in CMOS circuits. Even though CNFETs have high switching and current driving capability than conventional CMOS FET, many factors can disturb the performance of CNFETs which include parasitic capacitance, radiation, leakage current etc. Some of the factors can be effectively controlled by optimizing the design parameters of CNFET. The capacitance and delay are greatly dependent upon diameter and pitch of the carbon nanotubes. In case, if any increase in diameter of the carbon nano tubes shrinks the pitch of carbon nano tubes, this will lead to increase of inter-capacitance between the CNTs. Therefore, an optimization principle should be mostly needed to forecast the optimal diameter and pitch of CNFETs with maximum yield and minimum delay and capacitance. The optimization is achieved with the help of whale optimization algorithm. CNFETs specific parameters, such as pitch, width and diameter of CNTs are used to predict power, inter capacitance, fringe capacitance, delay comparison and current vs. voltage characteristics of circuits were analysed. The results exhibited that the proposed models relatively deliver reduced power consumption; inter capacitance, delay and fringe capacitance with whale optimization technique in CNFET circuits. The CNFET technology with whale optimization model ensures enhancement in current voltage characteristics.
机译：与传统的CMOS-FET相比，碳纳米管场效应晶体管（CNFET）的性能更好。这是由于它们的高电流驱动能力，低功率延迟乘积，弹道运输和改善的热稳定性。逻辑工作量（LE）技术用于即时以及精确估计CMOS电路中的延迟。尽管CNFET具有比常规CMOS FET高的开关和电流驱动能力，但许多因素仍会​​干扰CNFET的性能，包括寄生电容，辐射，泄漏电流等。可以通过优化CNFET的设计参数来有效地控制某些因素。电容和延迟在很大程度上取决于碳纳米管的直径和间距。如果碳纳米管的直径的任何增加缩小了碳纳米管的间距，则这将导致CNT之间的互电容增加。因此，最需要以最优化原理来预测CNFET的最佳直径和间距，从而获得最大的良率，最小的延迟和电容。借助于鲸鱼优化算法来实现优化。 CNFET的特定参数（例如CNT的间距，宽度和直径）用于预测功率，内部电容，边缘电容，延迟比较以及电路的电流与电压特性。结果表明，所提出的模型相对降低了功耗。 CNFET电路中使用鲸鱼优化技术实现内部电容，延迟和边缘电容。具有鲸鱼优化模型的CNFET技术可确保提高电流电压特性。

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