On the design and analysis of a compact array with 1T1R RRAM memory element

Mbarek, Khaoula; Rziga, Faten Ouaja; Ghedira, Sami; Besbes, Kamel

doi:10.1007/s10470-019-01488-w

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…Linear Ion Drift model (HP) [19] Voltage Threshold Adative Memristor model (VTEAM) [28] Enhanced Generalized Memristor model (EGM) [ We simulate the studied models with the Spectre simulator in Virtuoso® Custom IC Design in order to fit the I-V results of an experimental Ta2O5-RRAM memristor device and then to validate their capabilities to emulate synaptic functions.…”

Section: Memristor Modelsmentioning

confidence: 99%

“…• Simulation of the Linear Ion Drift model The linear ion drift model has been developed based on the first fabricated HP's structure of the memristor device [19]. The above memristor behavior is mathematically described using equations in Table 1.…”

Section: State Variable Derivativementioning

confidence: 99%

“…Several complex tasks were performed efficiently using the STDP learning rule based on memristors such as unsupervised learning [12], data classification [13] and pattern recognition [14].Memristor component was first introduced by L. Chua in 1971 as the fourth circuit component, alongside the three fundamental passive components, which are the resistor, the capacitor, and the inductor [15]. Memristors are widely used as synaptic devices due to their adjustable resistance [16,17], low-power operation, nano-scale physical structure, and highdensity integration [18,19].In the literature, several research teams have fabricated memristive devices to emulate synaptic functions using a variety of materials [21][22][23][24]. The large variety of the memristor devices structure and materials composition has led to the…”

mentioning

confidence: 99%

“…Memristor component was first introduced by L. Chua in 1971 as the fourth circuit component, alongside the three fundamental passive components, which are the resistor, the capacitor, and the inductor [15]. Memristors are widely used as synaptic devices due to their adjustable resistance [16,17], low-power operation, nano-scale physical structure, and highdensity integration [18,19].…”

mentioning

confidence: 99%

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Spike-time dependent plasticity rule in memristor models for circuits design

Elhamdaoui¹,

Rziga²,

Mbarek³

et al. 2021

Preprint

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Spike Time-Dependent Plasticity (STDP) represents an essential learning rule found in biological synapses which is recommended for replication in neuromorphic electronic systems. This rule is defined as a process of updating synaptic weight that depends on the time difference between the pre- and post-synaptic spikes. It is well known that pre-synaptic activity preceding post-synaptic activity may induce long term potentiation (LTP) whereas the reverse case induces long term depression (LTD). Memristors, which are two-terminal memory devices, are excellent candidates to implement such a mechanism due to their distinctive characteristics. In this article, we analyze the fundamental characteristics of three of the most known memristor models, and then we simulate it in order to mimic the plasticity rule of biological synapses. The tested models are the linear ion drift model (HP), the Voltage ThrEshold Adaptive Memristor (VTEAM) model and the Enhanced Generalized Memristor (EGM) model. We compare the I-V characteristics of these models with an experimental memristive device based on Ta2O5. We simulate and validate the STDP Hebbian learning algorithm proving the capability of each model to reproduce the conductance change for the LTP and LTD functions. Thus, our simulation results explore the most suitable model to operate as a synapse component for neuromorphic circuits.

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Section: Memristor Modelsmentioning

confidence: 99%

Section: State Variable Derivativementioning

confidence: 99%