Dependence of enhancement factor on electrode size for field emission current from carbon nanotube on silicon wafer

Funayama, Keita; Tanaka, Hiroya; Hirotani, Jun; Shimaoka, Keiichi; Ohno, Yutaka; Tadokoro, Yukihiro

doi:10.1088/1361-6528/ab33c8

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…Indeed, the average value of V th , 32.43 V, was larger than the median. Based on this observation, we also calculated the theoretical performance of the field-emission current using the Fowler–Nordheim law 42 , 76 ; the parameters were g = 80 nm, L = 1.0 μm, and the enhancement factor was set to 1.75 to fit the peak of the histogram corresponding to V th = 26.20 and I th = 0.10 nA. The resulting curve is plotted in red in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…We found that an individual CNT-based field emitter physically realized one of the key functions of the neural network, namely, the activation function 73 . The output current from the emitter exponentially responds to the applied voltage 29,39,42,60,[74][75][76] . This typical nonlinear response realizes the activation function used in this study.…”

Section: Introductionmentioning

confidence: 99%

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Artificial-intelligence-assisted mass fabrication of nanocantilevers from randomly positioned single carbon nanotubes

et al. 2023

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Nanoscale cantilevers (nanocantilevers) made from carbon nanotubes (CNTs) provide tremendous benefits in sensing and electromagnetic applications. This nanoscale structure is generally fabricated using chemical vapor deposition and/or dielectrophoresis, which contain manual, time-consuming processes such as the placing of additional electrodes and careful observation of single-grown CNTs. Here, we demonstrate a simple and Artificial Intelligence (AI)-assisted method for the efficient fabrication of a massive CNT-based nanocantilever. We used randomly positioned single CNTs on the substrate. The trained deep neural network recognizes the CNTs, measures their positions, and determines the edge of the CNT on which an electrode should be clamped to form a nanocantilever. Our experiments demonstrate that the recognition and measurement processes are automatically completed in 2 s, whereas comparable manual processing requires 12 h. Notwithstanding the small measurement error by the trained network (within 200 nm for 90% of the recognized CNTs), more than 34 nanocantilevers were successfully fabricated in one process. Such high accuracy contributes to the development of a massive field emitter using the CNT-based nanocantilever, in which the output current is obtained with a low applied voltage. We further showed the benefit of fabricating massive CNT-nanocantilever-based field emitters for neuromorphic computing. The activation function, which is a key function in a neural network, was physically realized using an individual CNT-based field emitter. The introduced neural network with the CNT-based field emitters recognized handwritten images successfully. We believe that our method can accelerate the research and development of CNT-based nanocantilevers for realizing promising future applications.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial-intelligence-assisted mass fabrication of nanocantilevers from randomly positioned single carbon nanotubes

et al. 2023

Self Cite

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“…Due to the high aspect ratio and the small radius of the curvature of the CNTs, extremely high electric field strength can be generated near the top of the CNTs (figure 2(a)) [25][26][27].…”

Section: Resultsmentioning

confidence: 99%

Modelling of carbon nanotube film based temperature sensor: thermal emission and gas discharge

Pan

Zhang

2021

Nanotechnology

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The carbon nanotube (CNT) film based ionized temperature sensor is sensitive to gas temperature, and shows good sensitivity compared with other temperature sensors. But it is still unclear about the effect of CNT film on thermal emission and gas discharge at different temperatures. In this article, we established a gas discharge model of the CNT film temperature sensor. Field assisted thermal emission is simulated at the tip of CNTs by analysing the field enhancement effect and effective work function. Ionization collision, excitation, recombination collision, Penning ionization and quenching of argon are considered in order to obtain the interaction of various particles at different temperature. The current density-temperature characteristic of the temperature sensor was obtained at 24-80 V. The increase of the working voltage is helpful to improve the output current and sensitivity of the temperature sensor. Response time of the sensor will not change in the temperature range of 293-373 K. However, the change of temperature will affect the current density, secondary electron emission and reaction rate. In addition, the sensor has different temperature sensitivity in argon and helium. The above simulation results are helpful to understand the role of CNT film and temperature sensitivity of the ionized sensor. It can also be used to study and improve the sensitivity of this type of sensor.

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The selection and design of electrode materials for field emission devices

Zhao,

Ding,

et al. 2023

Materials Science in Semiconductor Processing

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Dependence of enhancement factor on electrode size for field emission current from carbon nanotube on silicon wafer

Cited by 5 publications

References 30 publications

Artificial-intelligence-assisted mass fabrication of nanocantilevers from randomly positioned single carbon nanotubes

Artificial-intelligence-assisted mass fabrication of nanocantilevers from randomly positioned single carbon nanotubes

Modelling of carbon nanotube film based temperature sensor: thermal emission and gas discharge

The selection and design of electrode materials for field emission devices

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