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NanoNET is a tool to simulate the Nanobundle Network Thin Film Transistors (NB-TFTs). Random networks of carbon nanotubes with thousands of tubes and random orientation can be simulated using this tool. The final answer can be compactly formulated in the formula shown in the picture. Here ID is current and LC and LS is channel length and tube length of the transistor and m is the current exponent.
For a normal Si MOSFET, m = 1 and the current is simply inversely proportional to channel length. But for these nanotube networks, m > 1 is also possible. Indeed, m = 1 for very high density networks but the value of m increases with decreasing tube density of the network
This abnormal behavior can be simply understood as follows:
When the density of tubes is very high, most of the tubes take part in conduction and the current simply doubles on halving the channel length. But for a lower density network, there are some islands of pools of nanotubes that are not taking part in the conduction which start to connect as channel length is reduced. So not only the average path length reduces, but the number of paths also goes up with decreasing channel length which causes this super-linear increase in the current with channel length or m > 1. The smaller the density, the more pronounced is this effect and higher is the m.
Note: The version 1.7 of the nanoNet tool includes a new option: ‘Temperature Distribution in CNT Network’. This new option in the tool allows analyzing the temperature distribution in CNT-network as a function of different device parameters such as channel length and tube density.
This work was supported by Network for Computational Nanotechnology (NCN) and Lilly Foundation.
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