skip to main content

Title: A Dynamical Compact Model of Diffusive and Drift Memristors for Neuromorphic Computing

Different from nonvolatile memory applications, neuromorphic computing applications utilize not only the static conductance states but also the switching dynamics for computing, which calls for compact dynamical models of memristive devices. In this work, a generalized model to simulate diffusive and drift memristors with the same set of equations is presented, which have been used to reproduce experimental results faithfully. The diffusive memristor is chosen as the basis for the generalized model because it possesses complex dynamical properties that are difficult to model efficiently. A data set from statistical measurements on SiO2:Ag diffusive memristors is collected to verify the validity of the general model. As an application example, spike‐timing‐dependent plasticity is demonstrated with an artificial synapse consisting of a diffusive memristor and a drift memristor, both modeled with this comprehensive compact model.

more » « less
Award ID(s):
Author(s) / Creator(s):
 ;  ;  ;  ;  ;  ;  ;  ;  ;  ;  
Publisher / Repository:
Wiley Blackwell (John Wiley & Sons)
Date Published:
Journal Name:
Advanced Electronic Materials
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. By mimicking biomimetic synaptic processes, the success of artificial intelligence (AI) has been astounding with various applications such as driving automation, big data analysis, and natural-language processing.[1-4] Due to a large quantity of data transmission between the separated memory unit and the logic unit, the classical computing system with von Neumann architecture consumes excessive energy and has a significant processing delay.[5] Furthermore, the speed difference between the two units also causes extra delay, which is referred to as the memory wall.[6, 7] To keep pace with the rapid growth of AI applications, enhanced hardware systems that particularly feature an energy-efficient and high-speed hardware system need to be secured. The novel neuromorphic computing system, an in-memory architecture with low power consumption, has been suggested as an alternative to the conventional system. Memristors with analog-type resistive switching behavior are a promising candidate for implementing the neuromorphic computing system since the devices can modulate the conductance with cycles that act as synaptic weights to process input signals and store information.[8, 9]

    The memristor has sparked tremendous interest due to its simple two-terminal structure, including top electrode (TE), bottom electrode (BE), and an intermediate resistive switching (RS) layer. Many oxide materials, including HfO2, Ta2O5, and IGZO, have extensively been studied as an RS layer of memristors. Silicon dioxide (SiO2) features 3D structural conformity with the conventional CMOS technology and high wafer-scale homogeneity, which has benefited modern microelectronic devices as dielectric and/or passivation layers. Therefore, the use of SiO2as a memristor RS layer for neuromorphic computing is expected to be compatible with current Si technology with minimal processing and material-related complexities.

    In this work, we proposed SiO2-based memristor and investigated switching behaviors metallized with different reduction potentials by applying pure Cu and Ag, and their alloys with varied ratios. Heavily doped p-type silicon was chosen as BE in order to exclude any effects of the BE ions on the memristor performance. We previously reported that the selection of TE is crucial for achieving a high memory window and stable switching performance. According to the study which compares the roles of Cu (switching stabilizer) and Ag (large switching window performer) TEs for oxide memristors, we have selected the TE materials and their alloys to engineer the SiO2-based memristor characteristics. The Ag TE leads to a larger memory window of the SiO2memristor, but the device shows relatively large variation and less reliability. On the other hand, the Cu TE device presents uniform gradual switching behavior which is in line with our previous report that Cu can be served as a stabilizer, but with small on/off ratio.[9] These distinct performances with Cu and Ag metallization leads us to utilize a Cu/Ag alloy as the TE. Various compositions of Cu/Ag were examined for the optimization of the memristor TEs. With a Cu/Ag alloying TE with optimized ratio, our SiO2based memristor demonstrates uniform switching behavior and memory window for analog switching applications. Also, it shows ideal potentiation and depression synaptic behavior under the positive/negative spikes (pulse train).

    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.


    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.


    [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.

    more » « less
  2. Abstract

    A diffusive memristor is a promising building block for brain‐inspired computing hardware. However, the randomness in the device relaxation dynamics limits the wide‐range adoption of diffusive memristors in large arrays. In this work, the device stack is engineered to achieve a much‐improved uniformity in the relaxation time (standard deviation σ reduced from ≈12 to ≈0.32 ms). The memristor is further connected with a resistor or a capacitor and the relaxation time is tuned between 1.13 µs and 1.25 ms, ranging from three orders of magnitude. The hierarchy of time surfaces (HOTS) algorithm, to utilize the tunable and uniform relaxation behavior for spike generation, is implemented. An accuracy of 77.3% is achieved in recognizing moving objects in the neuromorphic MNIST (N‐MNIST) dataset. The work paves the way for building emerging neuromorphic computing hardware systems with ultralow power consumption.

    more » « less
  3. Abstract

    An efficient strategy for addressing individual devices is required to unveil the full potential of memristors for high‐density memory and computing applications. Existing strategies using two‐terminal selectors that are preferable for compact integration have trade‐offs in reduced generality or functional window. A strategy that applies to broad memristors and maintains their full‐range functional window is proposed. This strategy uses a type of unipolar switch featuring a transient relaxation or retention as the selector. The unidirectional current flow in the switch suppresses the sneak‐path current, whereas the transient‐relaxation window is exploited for bidirectional programming. A unipolar volatile memristor with ultralow switching voltage (e.g., <100 mV), constructed from a protein nanowire dielectric harvested fromGeobacter sulfurreducens, is specifically employed as the example switch to highlight the advantages and scalability in the strategy for array integration.

    more » « less
  4. Abstract

    Recent studies of resistive switching devices with hexagonal boron nitride (h-BN) as the switching layer have shown the potential of two-dimensional (2D) materials for memory and neuromorphic computing applications. The use of 2D materials allows scaling the resistive switching layer thickness to sub-nanometer dimensions enabling devices to operate with low switching voltages and high programming speeds, offering large improvements in efficiency and performance as well as ultra-dense integration. These characteristics are of interest for the implementation of neuromorphic computing and machine learning hardware based on memristor crossbars. However, existing demonstrations of h-BN memristors focus on single isolated device switching properties and lack attention to fundamental machine learning functions. This paper demonstrates the hardware implementation of dot product operations, a basic analog function ubiquitous in machine learning, using h-BN memristor arrays. Moreover, we demonstrate the hardware implementation of a linear regression algorithm on h-BN memristor arrays.

    more » « less
    more » « less