3-D Physical Electro-Thermal Modeling of Nanoscale Y<sub>2</sub>O<sub>3</sub> Memristors for Synaptic Application

Kumar, Sanjay; Gautam, Mohit Kumar; Gill, Gurpreet Singh; Mukherjee, Shaibal

doi:10.1109/ted.2022.3166858

Cited by 10 publications

(10 citation statements)

References 32 publications

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“…During each switching cycle, the emergence of an undefined current path (due to stochastic nature) results in variations in the resistance of the memristor, thereby causing C2C variations [15]. Figure 4b demonstrates the impact of C2C variations in the Ni/Mo/MoO 3 /Ni memristor during continuous operation spanning 40 cycles.…”

Section: C2c Variations In Ni/mo/moo 3 /Ni Memristormentioning

confidence: 99%

“…4c. The voltage ramp rate (V RR ) for memristors defines how quickly the input voltage changes [15]. The impact of V RR on synaptic weights is examined, as depicted in Figure 4d.…”

Section: C2c Variations In Ni/mo/moo 3 /Ni Memristormentioning

confidence: 99%

“…This letter proposes the utilization of Ni/Mo/MoO 3 /Ni memristor as an ‘Electronic synapse’ to emulate the synaptic plasticity exhibited by biological synapses. The memristor undergoes training through the application of 300 consecutive voltage pulses at +1.25 V and −1.75 V [15]. The memristor demonstrates ‘Potentiations (learning)’ by exhibiting conductance strengthening in response to positive pulses, and ‘Depressions (forgetting)’ by displaying conductance weakening for negative pulses, as illustrated in Figure 4c.…”

Section: Ni/mo/moo3/ni Memristor For Synaptic Applicationsmentioning

confidence: 99%

“…Niraula et al introduced a thermodynamic framework for HfO 2 RS behaviour, emphasizing conductive filament (CF) formation and rupture [12,13]. Sung et al explored memristors as synapses in neuromorphic circuits [14], and Sanjay et al investigated cycle-to-cycle (C2C) variations in the SET and RE-SET modules of Y 2 O 3 -based memristors, demonstrating the emulation of synaptic plasticity behaviour [15]. However, Y 2 O 3 , a rare earth critical material, requires a growth temperature of 450°C to grow on yttrium [16].…”

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confidence: 99%

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Nanoscale Ni/Mo/MoO₃/Ni memristor for synaptic applications

Praveen,

Nishad,

Nishad

2024

Electronics Letters

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For the first time, a physics‐based modelling of a nanoscale Ni/Mo/MoO3/Ni memristor is presented in this letter by inserting a ‘Mo:Capping layer’ between the top electrode (Ni) and the insulating layer (MoO3). The proposed memristor has stable hysteresis I–V characteristics as well as a significant reduction in ‘Forming voltage’ (VFORM) to 0.75 V. The simulated resistive switching responses using the COMSOL Multiphysics package demonstrate consistently low values of coefficient of variability (CV) with 14.31% and 14.85% for the SET and RESET modules, respectively, during cycle‐to‐cycle variations along with a low compliance current (ICC) of 193 µA. In addition to observing synaptic plasticity behaviour, it also examines how ramp‐rates impact ‘Potentiation’ and ‘Depression’ as memristor conductance (G) is closely related to synaptic weights.

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Section: C2c Variations In Ni/mo/moo 3 /Ni Memristormentioning

confidence: 99%

“…4c. The voltage ramp rate (V RR ) for memristors defines how quickly the input voltage changes [15]. The impact of V RR on synaptic weights is examined, as depicted in Figure 4d.…”

Section: C2c Variations In Ni/mo/moo 3 /Ni Memristormentioning

confidence: 99%

Section: Ni/mo/moo3/ni Memristor For Synaptic Applicationsmentioning

confidence: 99%

mentioning

confidence: 99%

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Nanoscale Ni/Mo/MoO₃/Ni memristor for synaptic applications

Praveen,

Nishad,

Nishad

2024

Electronics Letters

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“…The growth of the gap originates due to rupturing of the CFs, as shown in Figure b. The formation of the gap in the RESET process is also of stochastic nature. ,− The corresponding physics-based equations utilized in COMSOL Multiphysics for SET and RESET processes, various physical parameters, and their physical interpretation with numerical values and incorporated various simulation modules are given in the Supporting Information.…”

Section: Device Structure and Modelingmentioning

confidence: 99%

Experimental Validation of Switching Dependence of Nanoscale Y₂O₃ Memristors on Electrode Symmetry via Physical Electrothermal Modeling

Gautam,

Kumar,

Chaudhary

et al. 2023

ACS Appl. Electron. Mater.

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In this work, the impact of symmetric and asymmetric electrodes on the resistive switching (RS) behavior of the nanoscale Y 2 O 3 -based memristor is investigated with experiments. In addition, the extracted switching parameters are validated with systemic modeling. Memristor growth is deployed by utilizing a dual ion beam sputtering (DIBS) system, and simulation is carried out in a semiconductor physicsbased tool, i.e., COMSOL Multiphysics with a defined MATLAB script. The performed simulation work is based on the minimum free energy of the used materials at an applied certain voltage. The simulated results exhibit a stable pinched hysteresis loop in the RS responses either in symmetric or asymmetric electrode combinations with an efficient ON/ OFF current ratio and show a close match with the experimental results. Moreover, the simulated devices show synaptic plasticity functionalities in terms of potentiation and depression processes with an almost ideal linearity factor for both electrode combinations similar to the realistic experimental data. Therefore, the present work efficiently depicts the suitability of the electrode material with the Y 2 O 3 switching layer to enhance electrical performance to integrate into the artificial synapse and neuromorphic computations.

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Investigation of Filament Formation and Surface Perturbation in Nanoscale-Y2O3 Memristor: A Physical Modeling Approach

Kumar,

Dubey,

Nawaria

et al. 2024

J. Electron. Mater.

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3-D Physical Electro-Thermal Modeling of Nanoscale Y₂O₃ Memristors for Synaptic Application

Cited by 10 publications

References 32 publications

Nanoscale Ni/Mo/MoO₃/Ni memristor for synaptic applications

Nanoscale Ni/Mo/MoO₃/Ni memristor for synaptic applications

Experimental Validation of Switching Dependence of Nanoscale Y₂O₃ Memristors on Electrode Symmetry via Physical Electrothermal Modeling

Investigation of Filament Formation and Surface Perturbation in Nanoscale-Y2O3 Memristor: A Physical Modeling Approach

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3-D Physical Electro-Thermal Modeling of Nanoscale Y2O3 Memristors for Synaptic Application

Cited by 10 publications

References 32 publications

Nanoscale Ni/Mo/MoO3/Ni memristor for synaptic applications

Nanoscale Ni/Mo/MoO3/Ni memristor for synaptic applications

Experimental Validation of Switching Dependence of Nanoscale Y2O3 Memristors on Electrode Symmetry via Physical Electrothermal Modeling

Investigation of Filament Formation and Surface Perturbation in Nanoscale-Y2O3 Memristor: A Physical Modeling Approach

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Product

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3-D Physical Electro-Thermal Modeling of Nanoscale Y₂O₃ Memristors for Synaptic Application

Nanoscale Ni/Mo/MoO₃/Ni memristor for synaptic applications

Nanoscale Ni/Mo/MoO₃/Ni memristor for synaptic applications

Experimental Validation of Switching Dependence of Nanoscale Y₂O₃ Memristors on Electrode Symmetry via Physical Electrothermal Modeling