“…By comparing the current map with a corresponding topography image (Figure S1a), we found that a CNT bundle (marked by (2)) exhibited a rather large current compared with individual CNTs (marked by (1)). It was presumably due to a large number of current paths in the bundles . The measured currents did not change with different gate biases, confirming a typical metallic property (Figure S1b).…”
Much progress has been made in the nanoscale analysis of nanostructures, while the mapping of key charge transport properties such as a carrier mobility remains a challenge, especially for one-dimensional systems. Here, we report the nanoscale mapping of carrier mobilities in carbon nanotube (CNT) networks and show that charge transport behaviors varied depending on network structures. In this work, the spatial distribution of localized charge transport properties such as mobilities and charge trap densities in CNT networks were mapped via a scanning noise microscopy. The mobility map was obtained from the conductivity maps measured at different back-gate biases, showing up to two orders of mobility variations depending on localized network structures. Furthermore, from the maps, correlations between mobility/ conductivity and charge trap density were analyzed to determine charge transport mechanisms. In metallic CNT networks, the regions with rather high (low) or low (high) charge trap densities (mobilities) exhibited a dif fusive or ballistic transport behavior, respectively. Interestingly, semiconducting CNT networks also exhibited a gradual transition from a diffusive to a ballistic transport behavior as the CNT mobility was increased by reaching the on-state with negative gate biases. The mapping of the cross-patterned CNT network showed that metallic CNT electrodes could achieve a good electrical contact with semiconducting CNTs without high contact resistance regions. Since this method allowed one to map versatile charge transport properties such as mobility, conductivity, and charge trap density, it can be a powerful tool for basic research about charge transport phenomena and practical device applications.
“…By comparing the current map with a corresponding topography image (Figure S1a), we found that a CNT bundle (marked by (2)) exhibited a rather large current compared with individual CNTs (marked by (1)). It was presumably due to a large number of current paths in the bundles . The measured currents did not change with different gate biases, confirming a typical metallic property (Figure S1b).…”
Much progress has been made in the nanoscale analysis of nanostructures, while the mapping of key charge transport properties such as a carrier mobility remains a challenge, especially for one-dimensional systems. Here, we report the nanoscale mapping of carrier mobilities in carbon nanotube (CNT) networks and show that charge transport behaviors varied depending on network structures. In this work, the spatial distribution of localized charge transport properties such as mobilities and charge trap densities in CNT networks were mapped via a scanning noise microscopy. The mobility map was obtained from the conductivity maps measured at different back-gate biases, showing up to two orders of mobility variations depending on localized network structures. Furthermore, from the maps, correlations between mobility/ conductivity and charge trap density were analyzed to determine charge transport mechanisms. In metallic CNT networks, the regions with rather high (low) or low (high) charge trap densities (mobilities) exhibited a dif fusive or ballistic transport behavior, respectively. Interestingly, semiconducting CNT networks also exhibited a gradual transition from a diffusive to a ballistic transport behavior as the CNT mobility was increased by reaching the on-state with negative gate biases. The mapping of the cross-patterned CNT network showed that metallic CNT electrodes could achieve a good electrical contact with semiconducting CNTs without high contact resistance regions. Since this method allowed one to map versatile charge transport properties such as mobility, conductivity, and charge trap density, it can be a powerful tool for basic research about charge transport phenomena and practical device applications.
“…The PV-RAMs were connected in series to sum the photovoltages, and the output voltage V ph = Σ( R 4×4 * P 4×4 ) performs a multiplication and sum operation (a convolution integral), with R 4×4 being the photovoltage responsivity matrix that represents the weights, P 4×4 being the photo intensity on each pixel as input. We note that there have been methods that massively fabricate carbon nanotube network devices 42 – 44 . Homogeneous WS 2 nanotube networks can be obtained with further investigation into the synthesis, purification, dispersion, and fabrication processes.…”
Intelligent materials with adaptive response to external stimulation lay foundation to integrate functional systems at the material level. Here, with experimental observation and numerical simulation, we report a delicate nano-electro-mechanical-opto-system naturally embedded in individual multiwall tungsten disulfide nanotubes, which generates a distinct form of in-plane van der Waals sliding ferroelectricity from the unique combination of superlubricity and piezoelectricity. The sliding ferroelectricity enables programmable photovoltaic effect using the multiwall tungsten disulfide nanotube as photovoltaic random-access memory. A complete “four-in-one” artificial vision system that synchronously achieves full functions of detecting, processing, memorizing, and powering is integrated into the nanotube devices. Both labeled supervised learning and unlabeled reinforcement learning algorithms are executable in the artificial vision system to achieve self-driven image recognition. This work provides a distinct strategy to create ferroelectricity in van der Waals materials, and demonstrates how intelligent materials can push electronic system integration at the material level.
“…mobility of 17.4 cm 2 V −1 ·s −1 , SS of 120 mV·dec −1 , and on/off ratio of ∼10 7 . Such CNT mask strategy could be further improved by developing guided/self-aligned CNT growth methods to achieve ultra-clean SWCNT arrays with more controllable bundle density and very high degree of alignment (angle SD of ∼0.03°), which are essential to achieve large-scale fabrication of short-channel devices with superior performance and excellent uniformity ( Guo et al., 2022 ; Hong et al., 2010 ). Figure 5 Channel length scaling of 2D transistors with nanogap (A) Upper: schematic diagram of nanogaps achieved by suspended SWCNT masks; Below: SEM image of the corresponding nanogaps in 7.5 nm.…”
Section: Channel Length Scaling Of Transistorsmentioning
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