Structural and Parametric Identification of Knowm Memristors

Ostrovskii, Valerii Y.; Fedoseev, Petr; Bobrova, Yulia O.; Butusov, Denis N.

doi:10.3390/nano12010063

Cited by 32 publications

(34 citation statements)

References 46 publications

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“…It should be mentioned that the DMM does not only provide a simple SPICE-compatible implementation for the resistive memory devices but also a versatile one, as it can accurately fit the I-V loops experimentally measured in different RRAM devices, while accounting for their particular features (as the current limitation in Figure 10b and the snapback effect in Figure 10c). Note that in Figure 10c, the x-axis accounts for the voltage effectively applied to the memristive device, (V-IR) that results after subtracting the voltage drop in the series resistance (RS, which in the experiments is reported to be 46.25 kΩ [75]). (h) LTP dependence on the pulses amplitude [78] can also be captured with the DMM.…”

Section: Experimental Validationmentioning

confidence: 99%

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SPICE Implementation of the Dynamic Memdiode Model for Bipolar Resistive Switching Devices

2022

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This paper reports the fundamentals and the SPICE implementation of the Dynamic Memdiode Model (DMM) for the conduction characteristics of bipolar-type resistive switching (RS) devices. Following Prof. Chua’s memristive devices theory, the memdiode model comprises two equations, one for the electron transport based on a heuristic extension of the quantum point-contact model for filamentary conduction in thin dielectrics and a second equation for the internal memory state related to the reversible displacement of atomic species within the oxide film. The DMM represents a breakthrough with respect to the previous Quasi-static Memdiode Model (QMM) since it describes the memory state of the device as a balance equation incorporating both the snapback and snapforward effects, features of utmost importance for the accurate and realistic simulation of the RS phenomenon. The DMM allows simple setting of the initial memory condition as well as decoupled modeling of the set and reset transitions. The model equations are implemented in the LTSpice simulator using an equivalent circuital approach with behavioral components and sources. The practical details of the model implementation and its modes of use are also discussed.

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Section: Experimental Validationmentioning

confidence: 99%

“…The arrows indicate the direction of the applied bias: (e) Figure 10. Experimental I-V loops of different memristor structures reported in the literature fitted with the DMM model: (a) Ta/HfO 2 /Pt [72], (b) TaO X[73], and (c) W dopped Ge 2 Se 3[74,75]. As reference, the HRS and LRS curves are indicated in (a).…”

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confidence: 99%

SPICE Implementation of the Dynamic Memdiode Model for Bipolar Resistive Switching Devices

2022

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“…However, the use of memristors in AI/ML accelerators is still very much in the research phase. A company call Knowm is working towards commercialization of a memristor-based accelerator [152], but that is probably a few years away. They do sell a memristors and an evaluation kit on their website.…”

Section: B Other Technologiesmentioning

confidence: 99%

AI and ML Accelerator Survey and Trends

Reuther

Michaleas

Jones

et al. 2022

2022 IEEE High Performance Extreme Computing Conference (HPEC)

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This paper updates the survey of AI accelerators and processors from past three years. This paper collects and summarizes the current commercial accelerators that have been publicly announced with peak performance and power consumption numbers. The performance and power values are plotted on a scatter graph, and a number of dimensions and observations from the trends on this plot are again discussed and analyzed. Two new trends plots based on accelerator release dates are included in this year's paper, along with the additional trends of some neuromorphic, photonic, and memristor-based inference accelerators.

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“… The W-dopant SDC memristor [ 31 ]. ( a ) Materials ( left ) and a graphical representation of the switching mechanism ( right ); ( b ) symbol of the SDC memristor.…”

Section: Figurementioning

confidence: 99%

Electromagnetic Interference Effects of Continuous Waves on Memristors: A Simulation Study

Ma¹,

Man²,

Zhang

et al. 2022

Sensors

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As two-terminal passive fundamental circuit elements with memory characteristics, memristors are promising devices for applications such as neuromorphic systems, in-memory computing, and tunable RF/microwave circuits. The increasingly complex electromagnetic interference (EMI) environment threatens the reliability of memristor systems. However, various EMI signals’ effects on memristors are still unclear. This paper selects continuous waves (CWs) as EMI signals. It provides a deeper insight into the interference effect of CWs on the memristor driven by a sinusoidal excitation voltage, as well as a method for investigating the EMI effect of memristors. The optimal memristor model is obtained by the exhaustive traversing of the possible model parameters, and the interference effect of CWs on memristors is quantified based on this model and the proposed evaluation metrics. Simulation results indicate that CW interference may affect the switching time, dynamic range, nonlinearity, symmetry, time to the boundary, and variation of memristance. The specific interference effect depends on the operating mode of the memristor, the amplitude, and the frequency of the CW. This research provides a foundation for evaluating EMI effects and designing electromagnetic protection for memristive neuromorphic systems.

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Structural and Parametric Identification of Knowm Memristors

Cited by 32 publications

References 46 publications

SPICE Implementation of the Dynamic Memdiode Model for Bipolar Resistive Switching Devices

SPICE Implementation of the Dynamic Memdiode Model for Bipolar Resistive Switching Devices

AI and ML Accelerator Survey and Trends

Electromagnetic Interference Effects of Continuous Waves on Memristors: A Simulation Study

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