TY - GEN
T1 - Artificial Neuron using Ag/2D-MoS/Au Threshold Switching Memristor
AU - Dev, Durjoy
AU - Krishnaprasad, Adithi
AU - He, Zhezhi
AU - Das, Sonali
AU - Shawkat, Mashiyat Sumaiya
AU - Manley, Madison
AU - Aina, Olaleye
AU - Fan, Deliang
AU - Jung, Yeonwoong
AU - Roy, Tania
N1 - Funding Information:
In summary, we have demonstrated a Ag/MoS2/Au threshold switching memristor-based artificial neuron that emulates all four critical behaviors of a biological neuron. The ability of emulating a biological neuron makes this threshold switching memristor a potential candidate for future neuromorphic computing. References: [1] Z. Wang et al., Nat. Electron., vol. 1, p. 137, (2018). [2] X. Zhang et al., IEEE Electron Dev. Lett., vol. 39, p. 308, (2018). [3] Y. Zhang et al., Small, vol. 14, p. 1802188, (2018). [4] H. Kalita et al., Sci. Rep., vol. 9, p. 53, (2019). [5] L. O. Chua, Semicond. Sci. Technol., vol. 29, p. 104001, (2014). [6] P.U. Diehl et al. Front Comput Neurosci., vol. 9, p. 99, (2015). Acknowledgment: D. D. and A. K. acknowledge support of BAE Systems through Award no. 1020180. T. R. acknowledges support of NSF CAREER Award no. NSF-ECCS-1845331.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - The phenomenal evolution of information and communication drives future technologies towards highly parallel, energy-efficient self-learning systems like the human brain. The limitations of current von Neumann computation systems have paved the way for artificial neural networks (ANN) to meet these criteria. The memristor has become an emerging candidate to realize ANN through emulating biological synapse and neuron behavior [1]-[3]. We previously reported an artificial neuron with 2D Mos2 and graphene electrode, but the operating voltage was high, and the output current was low [4]. In this work, we harness threshold switching in Mos2 enabled by Ag electrode, to emulate integration and firing behavior of neuron and demonstrate digit recognition application with these devices. The simple vertical structure of Ag/MoS/ Au threshold switching memristor (TSM), with very low threshold voltage (\mathrm{V}-{\mathrm{t}\mathrm{h}}=0.4-0.5\mathrm{V}), displays the four crucial features of neuron all-or-nothing spiking, threshold-driven firing, post firing refractory period and stimulus strength based frequency response.
AB - The phenomenal evolution of information and communication drives future technologies towards highly parallel, energy-efficient self-learning systems like the human brain. The limitations of current von Neumann computation systems have paved the way for artificial neural networks (ANN) to meet these criteria. The memristor has become an emerging candidate to realize ANN through emulating biological synapse and neuron behavior [1]-[3]. We previously reported an artificial neuron with 2D Mos2 and graphene electrode, but the operating voltage was high, and the output current was low [4]. In this work, we harness threshold switching in Mos2 enabled by Ag electrode, to emulate integration and firing behavior of neuron and demonstrate digit recognition application with these devices. The simple vertical structure of Ag/MoS/ Au threshold switching memristor (TSM), with very low threshold voltage (\mathrm{V}-{\mathrm{t}\mathrm{h}}=0.4-0.5\mathrm{V}), displays the four crucial features of neuron all-or-nothing spiking, threshold-driven firing, post firing refractory period and stimulus strength based frequency response.
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U2 - 10.1109/DRC46940.2019.9046335
DO - 10.1109/DRC46940.2019.9046335
M3 - Conference contribution
AN - SCOPUS:85083252908
T3 - Device Research Conference - Conference Digest, DRC
SP - 193
EP - 194
BT - 2019 Device Research Conference, DRC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 Device Research Conference, DRC 2019
Y2 - 23 June 2019 through 26 June 2019
ER -