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Title: Natural Organic Materials Based Memristors and Transistors for Artificial Synaptic Devices in Sustainable Neuromorphic Computing Systems
Natural organic materials such as protein and carbohydrates are abundant in nature, renewable, and biodegradable, desirable for the construction of artificial synaptic devices for emerging neuromorphic computing systems with energy efficient operation and environmentally friendly disposal. These artificial synaptic devices are based on memristors or transistors with the memristive layer or gate dielectric formed by natural organic materials. The fundamental requirement for these synaptic devices is the ability to mimic the memory and learning behaviors of biological synapses. This paper reviews the synaptic functions emulated by a variety of artificial synaptic devices based on natural organic materials and provides a useful guidance for testing and investigating more of such devices.  more » « less
Award ID(s):
2104976
NSF-PAR ID:
10393687
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Micromachines
Volume:
14
Issue:
2
ISSN:
2072-666X
Page Range / eLocation ID:
235
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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    In conclusion, the SiO2memristors with different metallization were established. To tune the property of RS layer, the sputtering conditions of RS were varied. To investigate the influence of TE selections on switching performance of memristor, we integrated Cu, Ag and Cu/Ag alloy as TEs and compared the switch characteristics. Our encouraging results clearly demonstrate that SiO2with Cu/Ag is a promising memristor device with synaptic switching behavior in neuromorphic computing applications.

    Acknowledgement

    This work was supported by the U.S. National Science Foundation (NSF) Award No. ECCS-1931088. S.L. and H.W.S. acknowledge the support from the Improvement of Measurement Standards and Technology for Mechanical Metrology (Grant No. 22011044) by KRISS.

    References

    [1] Younget al.,IEEE Computational Intelligence Magazine,vol. 13, no. 3, pp. 55-75, 2018.

    [2] Hadsellet al.,Journal of Field Robotics,vol. 26, no. 2, pp. 120-144, 2009.

    [3] Najafabadiet al.,Journal of Big Data,vol. 2, no. 1, p. 1, 2015.

    [4] Zhaoet al.,Applied Physics Reviews,vol. 7, no. 1, 2020.

    [5] Zidanet al.,Nature Electronics,vol. 1, no. 1, pp. 22-29, 2018.

    [6] Wulfet al.,SIGARCH Comput. Archit. News,vol. 23, no. 1, pp. 20–24, 1995.

    [7] Wilkes,SIGARCH Comput. Archit. News,vol. 23, no. 4, pp. 4–6, 1995.

    [8] Ielminiet al.,Nature Electronics,vol. 1, no. 6, pp. 333-343, 2018.

    [9] Changet al.,Nano Letters,vol. 10, no. 4, pp. 1297-1301, 2010.

    [10] Qinet al., Physica Status Solidi (RRL) - Rapid Research Letters, pssr.202200075R1, In press, 2022.

     
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    Abstract

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