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Mimicking of pulse shape-dependent learning rules with a quantum dot memristor
G.-Q. Bi and M.-M. Poo, J. Neurosci. 18, 10464 (1998).
K. Seo, I. Kim, S. Jung, M. Jo, S. Park, J. Park, J. Shin, K. P. Biju, J. Kong, K. Lee, B. Lee, and H. Hwang, Nanotechnology 22, 254023 (2011).
W. He, K. Huang, N. Ning, K. Ramanathan, G. Li, Y. Jiang, J. Y. Sze, L. Shi, R. Zhao, and J. Pei, Sci. Rep. 4, 4755 (2014).
Y. Li, Y. Zhong, J. Zhang, L. Xu, Q. Wang, H. Sun, H. Tong, X. Cheng, and X. Miao, Sci. Rep. 4, 4906 (2014).
C. Schneider, A. Huggenberger, T. Sünner, T. Heindel, M. Strauß, S. Göpfert, P. Weinmann, S. Reitzenstein, L. Worschech, M. Kamp, S. Höfling, and A. Forchel, Nanotechnology 20, 434012 (2009).
P. Maier, F. Hartmann, T. Mauder, M. Emmerling, C. Schneider, M. Kamp, S. Höfling, and L. Worschech, Appl. Phys. Lett. 106, 203501 (2015).
V. Villière and E. M. McLachlan, J. Neurophysiol. 76, 1924 (1996).
P. Maier, F. Hartmann, M. Rebello Sousa Dias, M. Emmerling, C. Schneider, L. K. Castelano, M. Kamp, G. E. Marques, V. Lopez-Richard, L. Worschech, and S. Höfling, Appl. Phys. Lett. 109, 023501 (2016).
H. Tan, G. Liu, X. Zhu, H. Yang, B. Chen, X. Chen, J. Shang, W. D. Lu, Y. Wu, and R.-W. Li, Adv. Mater. 27, 2797–2803 (2015).
T. You, L. P. Selvaraj, H. Zeng, W. Luo, N. Du, D. Bürger, I. Skorupa, S. Prucnal, A. Lawerenz, T. Mikolajick, O. G. Schmidt, and H. Schmidt, Adv. Electron. Mater. 2, 1500352 (2016).
M.-J. Lee, C. B. Lee, D. Lee, S. R. Lee, M. Chang, J. H. Hur, Y.-B. Kim, C.-J. Kim, D. H. Seo, S. Seo, U.-I. Chung, I. K. Yoo, and K. Kim, Nat. Mater. 10, 625 (2011).
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We present the realization of four different learning rules with a quantum dot memristor by tuning the shape, the magnitude, the polarity and the timing of voltage pulses. The memristor displays a large maximum to minimum conductance ratio of about 57 000 at zero bias voltage. The high and low conductances correspond to different amounts of electrons localized in quantum dots, which can be successively raised or lowered by the timing and shapes of incoming voltage pulses. Modifications of the pulse shapes allow altering the conductance change in dependence on the time difference. Hence, we are able to mimic different learning processes in neural networks with a single device. In addition, the device performance under pulsed excitation is emulated combining the Landauer-Büttiker formalism with a dynamic model for the quantum dot
charging, which allows explaining the whole spectrum of learning responses in terms of structural parameters that can be adjusted during fabrication, such as gating efficiencies and tunneling rates. The presented memristor may pave the way for future artificial synapses with a stimulus-dependent capability of learning.
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