Mimicking the spike-timing dependent plasticity in HfO2-based memristors at multiple time scales

Maestro-Izquierdo, Marcos; González, M.B.; Campabadal, F.

doi:10.1016/j.mee.2019.111014

Cited by 19 publications

(12 citation statements)

References 17 publications

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“…The results for non-laminated ZrO2 results are in a good accordance with the ones presented earlier by Ossorio et al [69]. demonstrated to be useful for neuromorphic applications, i.e., artificial neural networks, as they might behave as electronic synapses [72].…”

Section: Electrical and Magnetic Propertiessupporting

confidence: 90%

Atomic layer deposited nanolaminates of zirconium oxide and manganese oxide from manganese(III)acetylacetonate and ozone

et al. 2021

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Atomic layer deposition method was used to grow thin films consisting of ZrO2 and MnOx layers. Magnetic and electric properties were studied of films deposited at 300 ºC. Some deposition characteristics of the manganese(III)acetylacetonate and ozone process were investigated, such as the dependence of growth rate on the deposition temperature and film crystallinity. All films were partly crystalline in their as-deposited state. Zirconium oxide contained cubic and tetragonal phases of ZrO2, while the manganese oxide was shown to consist of cubic Mn2O3 and tetragonal Mn3O4 phases. All the films exhibited nonlinear saturative magnetization with hysteresis, as well as resistive switching characteristics.

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Section: Electrical and Magnetic Propertiessupporting

confidence: 90%

Atomic layer deposited nanolaminates of zirconium oxide and manganese oxide from manganese(III)acetylacetonate and ozone

et al. 2021

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“…For this application, the experimental STDP windows obtained in [33] were fitted using the above described model. The experimental STDP measurements were obtained by means of applying identical pre and post-synaptic waveforms with a spike width of 1 ms and a maximum voltage of |0.7V peak | (Figure 7a), which corresponds to the voltage required to set the conductivity state of the device at g SHmin ~15G o (Figure 6b).…”

Section: Methodsmentioning

confidence: 99%

“…( a ) Identical pre (red) and post-synaptic (blue) waveforms are applied to the tested samples, which result in an equivalent voltage drop waveform (black) showing a dependence on the time delay Δt. ( b ) Experimental [33] (dots) and modeled (line) STDP functions.…”

Section: Figurementioning

confidence: 99%

Self-Organizing Neural Networks Based on OxRAM Devices under a Fully Unsupervised Training Scheme

et al. 2019

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A fully-unsupervised learning algorithm for reaching self-organization in neuromorphic architectures is provided in this work. We experimentally demonstrate spike-timing dependent plasticity (STDP) in Oxide-based Resistive Random Access Memory (OxRAM) devices, and propose a set of waveforms in order to induce symmetric conductivity changes. An empirical model is used to describe the observed plasticity. A neuromorphic system based on the tested devices is simulated, where the developed learning algorithm is tested, involving STDP as the local learning rule. The design of the system and learning scheme permits to concatenate multiple neuromorphic layers, where autonomous hierarchical computing can be performed.

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“…In order to test and validate the STDP function using the memristor models detailed in our work, we employ a spike pair protocol [31,32] in which the lower and upper memristor electrodes are assigned to the presynaptic and postsynaptic inputs respectively, as shown in Fig. 6.…”

Section: Validation Of the Spike-time-dependent Plasticitymentioning

confidence: 99%

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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Mimicking the spike-timing dependent plasticity in HfO2-based memristors at multiple time scales

Cited by 19 publications

References 17 publications

Atomic layer deposited nanolaminates of zirconium oxide and manganese oxide from manganese(III)acetylacetonate and ozone

Atomic layer deposited nanolaminates of zirconium oxide and manganese oxide from manganese(III)acetylacetonate and ozone

Self-Organizing Neural Networks Based on OxRAM Devices under a Fully Unsupervised Training Scheme

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

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