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A new smart nanoforce sensor based on suspended gate SOIMOSFET using carbon nanotube

机译:一种基于碳纳米管的悬浮门SoimosfeT的新型智能纳米型传感器

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This paper presents a new nanoforce sensor based on a suspended carbon nanotube gate field-effect transistor. To do so, a numerical investigation of Suspended Gate Silicon-on-Insulator MOSFET (SG-SOIMOSFET) is carried out using ATLAS 2D simulator. Based on the relationship between the nanotube's deflection and the applied force, a comprehensive study of the proposed nanoforce sensor behavior is performed. Moreover, we describe the evolution of the drain current characteristics as a function of the applied force while examining the influence of capacity variation of the insulating gate on the drain current in the saturation region. It is found that the sensor has a good sensitivity of 230.68 ln(A)/pN. Our second contribution in this paper is to develop a model based on artificial neural networks (ANNs). We successfully integrate our neural model of nanoforce sensor as a new component in the ORCAD-PSPICE electric simulator library; this component must accurately express the behavior of the sensor. A second model based on neural networks, which deals with correction and linearization of the sensor output signal, is designed and implemented into the same simulator. The proposed device can be considered as a potential alternative for CMOS-based nanoforce sensing.
机译:本文介绍了一种基于悬浮碳纳米管栅极场效应晶体管的新型纳米型传感器。为此,使用ATLAS 2D模拟器进行悬浮栅极硅与绝缘体MOSFET(SG-SOIMOSFET)的数值研究。基于纳米管的偏转与施加力之间的关系,进行了对所提出的纳米集传感器行为的综合研究。此外,我们描述了作为施加力的函数的漏极电流特性的演变,同时检查绝缘栅极的容量变化对饱和区中的漏极电流的影响。发现传感器具有230.68Ln(a)/ pn的良好敏感性。我们本文的第二份贡献是开发基于人工神经网络(ANNS)的模型。我们成功将我们的神经模型集成为orcad-PSPICE电动模拟器库中的新组件;该组件必须准确地表达传感器的行为。基于神经网络的第二模型,其涉及传感器输出信号的校正和线性化,设计和实现到相同的模拟器中。所提出的装置可以被认为是基于CMOS的纳米型感测的潜在替代方案。

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