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Ta/HfO2-based Memristor and Crossbar Arrays for In-Memory Computing

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Memristor Computing Systems

Abstract

Computing hardware systems based on memristors can eliminate the data shuttling between the memory and processing units, offer massive parallelism, and hence promise substantial boost in computing throughput and energy efficiencies. However, such non-von Neumann computational approach imposes high requirements on specific characteristics of memristive devices. In this chapter, we first describe the electrical behavior of our newly developed Ta/HfO2 memristor, in particular, how it meets most requirements for in-memory computing in artificial neural networks. We then examine the underlying mechanism of this device by using electrical and physical characterizations, and attribute the resistance switching to the composition modulation of conduction channel(s) through motion of both cation and anions. Finally, we showcase large-scale one-transistor-one-resistance switch (1T1R) arrays fabricated by integrating the Ta/HfO2 memristors with foundry-made metal–oxide–semiconductor transistors, and their applications in artificial neural networks and hardware security.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Massachusetts Amherst, Amherst, MA, 01003, USA

    Hao Jiang, Can Li, Peng Lin, Zhongrui Wang, J. Joshua Yang & Qiangfei **a

Authors
  1. Hao Jiang
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  2. Can Li
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  3. Peng Lin
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  4. Zhongrui Wang
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  5. J. Joshua Yang
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  6. Qiangfei **a
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Corresponding author

Correspondence to Qiangfei **a .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering & Computer Science, University of California, Berkeley, Berkley, CA, USA

    Leon O. Chua

  2. Fundamentals of Electrical Engineering, TU Dresden, Dresden, Germany

    Ronald Tetzlaff

  3. Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Angela Slavova

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Jiang, H., Li, C., Lin, P., Wang, Z., Yang, J.J., **a, Q. (2022). Ta/HfO2-based Memristor and Crossbar Arrays for In-Memory Computing. In: Chua, L.O., Tetzlaff, R., Slavova, A. (eds) Memristor Computing Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-90582-8_8

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  • DOI: https://doi.org/10.1007/978-3-030-90582-8_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90581-1

  • Online ISBN: 978-3-030-90582-8

  • eBook Packages: EngineeringEngineering (R0)

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