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Open Access

Carbon Nanotube Transistor with Short-Term Memory

Changqing YinYuxing LiJiabin WangXuefeng WangYi YangTian-Ling Ren( )
Institute of Microelectronics, Tsinghua University, Beijing 100084, China.
both the Institute of Microelectronics and Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China.

These authors contributed equally to this work.

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Abstract

Short-Term Memory (STM) is a primary capability of the human brain. Humans use STM to remember a small amount of information, like someone’s phone number, for a short period of time. Usually the duration of STM is less than 1 minute. Synapses, the connections between neurons, are of vital importance to memory in biological brains. For mimicking the memory function of synapses, Carbon Nanotube (CNT) networks based thin-film transistors with Electric Double Layers (EDL) at the dielectric/channel interface were researched in this work. A response characteristic of pre-synaptic potential pulses on the gate electrode of this CNT synaptic transistor was shown remarkably similar to Excitatory Post-Synaptic Current (EPSC) of biological synapses. Also a multi-level modulatable STM of CNT synaptic transistors was investigated. Post-synaptic current was shown with tunable peak values, on-off ratio, and relaxation time.

References

[1]
Drachman D. A., Do we have brain to spare? Neurology, vol. 64, no. 12, pp. 2004–2005, 2005.
[2]
Bi G. Q. and Poo M. M., Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type, The Journal of Neuroscience, vol. 18, no. 24, pp. 1046410472, 1998.10.1523/JNEUROSCI.18-24-10464.1998
[3]
Schacter D. L. and Wagner A. D., Learning and memory, in Principles of Neural Science, E. R. Kandel, J. H. Schwartz and T. M. Jessell, eds. New York, NY, USA: McGraw-Hill, 2000, pp. 1227-1246.
[4]
Chapman R. M., McCrary J. W., and Chapman J. A., Short-term memory: The “storage” component of human brain responses predicts recall, Science, vol. 202, no. 4373, pp. 1211–1214, 1978.
[5]
Strukov D. B., Snider G. S., Stewart D. R., and Williams R. S., The missing memristor found, Nature, vol. 453, no. 7191, pp. 80–83, 2008.
[6]
Jo S. H., Chang T., Ebong I., Bhadviya B. B., Mazumder P., and Lu W., Nanoscale memristor device as synapse in neuromorphic systems, Nano Letters, vol. 10, no. 4, pp. 1297–1301, 2010.
[7]
Park S., Kim H., Choo M., Noh J., Sheri A., Jung S., Seo K., Park J., Kim S., Lee W., et al., RRAM-based synapse for neuromorphic system with pattern recognition function, presented at Electron Devices Meeting (IEDM), San Francisco, CA, USA, 2012.
[8]
Yu S., Chen H. Y., Gao B., Kang J., and Wong H.-S. P., HfOx-based vertical resistive switching random access memory suitable for bit-cost-effective three-dimensional cross-point architecture, ACS Nano, vol. 7, no. 3, pp. 2320–2325, 2013.
[9]
Shi J., Ha S. D., Zhou Y., Schoofs F., and Ramanathan S., A correlated nickelate synaptic transistor, Nature Communications, vol. 4, no. 2676, pp. 1–9, 2013.
[10]
Zhu L. Q., Wan C. J., Guo L. Q., Shi Y., and Wan Q., Artificial synapse network on inorganic proton conductor for neuromorphic systems, Nature Communications, vol. 5, no. 3158, pp. 1–7, 2014.
[11]
Tian H., Mi W., Wang X. F., Zhao H., Xie Q. Y., Li C., Li Y. X., Yang Y., and Ren T. L., Graphene dynamic synapse with modulatable plasticity, Nano Letters, vol. 15, no. 12, pp. 8013–8019, 2015.
[12]
Baughman R. H., A Zakhidov A., and de Heer W. A., Carbon nanotubes—The route toward applications, Science, vol. 297, no. 5582, pp. 787–792, 2002.
[13]
Shulaker M. M., Hills G., Patil N., Wei H., Chen H. Y., Wong H.-S. P., and Mitra S., Carbon nanotube computer, Nature, vol. 501, no. 7468, pp. 526–530, 2013.
[14]
Agnus G., Zhao W., Derycke V., Filoramo A., Lhuillier Y., Lenfant S., Vuillaume D., Gamrat C., and Bourgoin J. P., Two-terminal carbon nanotube programmable devices for adaptive architectures, Advanced Materials, vol. 22, no. 6, pp. 702–706, 2010.
[15]
Kim K., Chen C. L., Truong Q., Shen A. M., and Chen Y., A carbon nanotube synapse with dynamic logic and learning, Advanced Materials, vol. 25, no. 12, pp. 1693–1698, 2013.
[16]
Chen C. L., Kim K., Truong Q., Shen A., Li Z., and Chen Y., A spiking neuron circuit based on a carbon nanotube transistor, Nanotechnology, vol. 23, no. 27, p. 275202, 2012.
[17]
Zhu L. Q., Sun J., Wu G. D., Zhang H. L., and Wan Q., Self-assembled dual in-plane gate thin-film transistors gated by nanogranular SiO2 proton conductors for logic applications, Nanoscale, vol. 5, no.5, pp. 1980–1985, 2013.
[18]
Javey A., Guo J., Wang Q., Lundstrom M., and Dai H., Ballistic carbon nanotube field-effect transistors, Nature, vol. 424, no. 6949, pp. 654–657, 2003.
[19]
Wang S., Zeng Q., Yang L., Zhang Z., Wang Z., Pei T., Ding L., Liang X., Gao M., Li Y., et al., High-performance carbon nanotube light-emitting diodes with asymmetric contacts, Nano Letters, vol. 11, no. 1, pp. 23–29, 2010.
[20]
Ortiz-Conde A., Sánchez F. J., Liou J. J., Cerdeira A., Estrada M., and Yue Y., A review of recent MOSFET threshold voltage extraction methods, Microelectronics Reliability, vol. 42, no. 4, pp. 583–596, 2002.
[21]
Zhou J., Wan C., Zhu L., Shi Y., and Wan Q., Synaptic behaviors mimicked in flexible oxide-based transistors on plastic substrates, Electron Device Letters, IEEE, vol. 34, no. 11, pp. 1433–1435, 2013.
[22]
Appenzeller J., Knoch J., Derycke V., Martel R., Wind S., and Avouris Ph., Field-modulated carrier transport in carbon nanotube transistors, Physical Review Letters, vol. 89, no. 12, p. 126801, 2002.
[23]
Kim U. J., Lee E. H., Kim J. M., Min Y. S., Kim E., and Park W., Thin film transistors using preferentially grown semiconducting single-walled carbon nanotube networks by water-assisted plasma-enhanced chemical vapor deposition, Nanotechnology, vol. 20, no. 29, p. 295201, 2009.
[24]
Jo S. H., Chang T., Ebong I., Bhadviya B. B., Mazumder P., and Lu W., Nanoscale memristor device as synapse in neuromorphic systems, Nano Letters, vol. 10, no. 4, pp. 1297–1301, 2010.
Tsinghua Science and Technology
Pages 442-448
Cite this article:
Yin C, Li Y, Wang J, et al. Carbon Nanotube Transistor with Short-Term Memory. Tsinghua Science and Technology, 2016, 21(4): 442-448. https://doi.org/10.1109/TST.2016.7536722

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Received: 08 December 2015
Accepted: 11 January 2016
Published: 11 August 2016
© The author(s) 2016
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