Ferroelectric Second-Order Memristor

Mikheev, V. P.; Chouprik, Anastasia; Lebedinskiǐ, Yu. Yu.; Zarubin, Sergei; Matveyev, Yury; Kondratyuk, Ekaterina; Kozodaev, Maxim G.; Markeev, Andrey M.; Zenkevich, Andrei; Negrov, Dmitrii

doi:10.1021/acsami.9b08189

Cited by 94 publications

(79 citation statements)

References 45 publications

(63 reference statements)

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“…The change in the polarization (P) of the ferroelectric domain provides a significant basis for the conductance change of ferroelectric synapses, as shown in Figure 2d. [80][81][82][83][84][85]120] Metals and semiconductors have been used for two-electrode terminals where the two-terminal junction is separated by a ferroelectric layer (e.g., lead zirconate titanate (PZT) and Hf 0.5 Zr 0.5 O 2 (HZO)). [80][81][82][83][84][85]120] The orientation of the P in the ferroelectric material can be aligned differently, depending on the polarity of the external electric field.…”

Section: Ferroelectric Synapsementioning

confidence: 99%

“…[80][81][82][83][84][85]120] Metals and semiconductors have been used for two-electrode terminals where the two-terminal junction is separated by a ferroelectric layer (e.g., lead zirconate titanate (PZT) and Hf 0.5 Zr 0.5 O 2 (HZO)). [80][81][82][83][84][85]120] The orientation of the P in the ferroelectric material can be aligned differently, depending on the polarity of the external electric field. [121,122] The modulation of P for a metal/ferroelectric layer/semiconductor (MFS) junction can alter the junction resistance, which might be attributed to asymmetric screening effects at the interfaces between the metal/ferroelectric and the semiconductor/ferroelectric layers.…”

Section: Ferroelectric Synapsementioning

confidence: 99%

“…As shown in Figure 3c, the PPF and PPD can be emulated using a relatively high and low frequency of V INPUT in the ferroelectric memristive synapse with stacked TiN/HZO/Si, respectively. [120] Reports have postulated that the STP of biological synapses might be associated with a high-or low-pass filtering effect on information transmission, working memory, and sound-source localization. [128][129][130] The memristive synapse that is applied by a relatively strong input pulse can not only strengthen the G value (i.e., ∆G > 0) but also retain the changed state for long-term plasticity.…”

Section: Mimicking Various Synaptic Functionalitiesmentioning

confidence: 99%

Section: Memristive Artificial Synapsesmentioning

confidence: 99%

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Emerging Memristive Artificial Synapses and Neurons for Energy‐Efficient Neuromorphic Computing

2020

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as the thermal budget has increased rapidly in recent years, which could surpass the future revenue generated worldwide by the semiconductor industry. [4,5] Consequently, electronic miniaturization limits the sustainable growth of computing technology. The von Neumann design is a conventional computing architecture, which separates the processor and memory unit. This architecture performs a computational task through sequential procedures and has functioned a mainstay of modern computing since 1945. [6] In fact, the architecture is significantly beneficial to both hardware and software developers because each component can be improved and readily extended into a united electronic system, even without a comprehensive understanding of all the components. However, the von Neumann bottleneck generates substantial power consumption and latency during the computing operation. [2,7] This limitation results from data transmission between two functional units (processor and memory), particularly when the memory is accessed through a bus with restricted bandwidth. [2,7] Moreover, according to the International Data Corporation (IDC), global data will rapidly increase to 175 ZB (1.75 × 10 23 B) by 2025. [8] Consequently, the von Neumann bottleneck will be more detrimental under tremendous workload because it may be produced by the daily operation of the design. Recently, there has been increasing demand for intellectual computers that can efficiently process substantial global data, thereby resulting in the development of a wide range of softwareor hardware-based artificial neural networks (ANNs) aimed at achieving the computing ability of the human brain. [2,9] Softwarebased ANNs have recently exhibited remarkable capabilities, such as image recognition, [10,11] natural language processing, [12,13] and performing specific tasks, [14] some beyond the human level. However, since existing ANNs are built on conventional computing architecture, that is, the von Neumann architecture, the learning parameters stored in memory are iteratively introduced to the processor to perform a task. The bottleneck issue arising from the movement of large datasets would eventually reduce the energy and time efficiency when operating the ANN software. [2,7] To alleviate this problem, several advanced software-and hardware-based ANN approaches have been suggested. [2,7,15-17] Advanced algorithms, such as network pruning, quantization, Huffman coding, and knowledge distillation, have been Memristors have recently attracted significant interest due to their applicability as promising building blocks of neuromorphic computing and electronic systems. The dynamic reconfiguration of memristors, which is based on the history of applied electrical stimuli, can mimic both essential analog synaptic and neuronal functionalities. These can be utilized as the node and terminal devices in an artificial neural network. Consequently, the ability to understand, control, and utilize fundamental switching principles and various types of device architectures of the...

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Section: Ferroelectric Synapsementioning

confidence: 99%

Section: Ferroelectric Synapsementioning

confidence: 99%

Section: Mimicking Various Synaptic Functionalitiesmentioning

confidence: 99%

Section: Memristive Artificial Synapsesmentioning

confidence: 99%

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Emerging Memristive Artificial Synapses and Neurons for Energy‐Efficient Neuromorphic Computing

2020

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“…From a biological point of view, since the device exhibits bidirectional continuous adjustable characteristics, it can be applied to a wide range of neural network systems. Therefore, it is possible to further explore synaptic plasticity functions [18,20,43,[45][46][47][48]. A schematic representation of a synapse, which is the interconnection between two neurons, is depicted in Fig.…”

Section: Science Chinamentioning

confidence: 99%

Hf0.5Zr0.5O2-based ferroelectric memristor with multilevel storage potential and artificial synaptic plasticity

Zhao

et al. 2020

Sci. China Mater.

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Memristors are designed to mimic the brain's integrated functions of storage and computing, thus breaking through the von Neumann framework. However, the formation and breaking of the conductive filament inside a conventional memristor is unstable, which makes it difficult to realistically mimic the function of a biological synapse. This problem has become a main factor that hinders memristor applications. The ferroelectric memristor overcomes the shortcomings of the traditional memristor because its resistance variation depends on the polarization direction of the ferroelectric thin film. In this work, an Au/Hf 0.5 Zr 0.5 O 2 /p +-Si ferroelectric memristor is proposed, which is capable of achieving resistive switching characteristics. In particular, the proposed device realizes the stable characteristics of multilevel storage, which possesses the potential to be applied to multi-level storage. Through polarization, the resistance of the proposed memristor can be gradually modulated by flipping the ferroelectric domains. Additionally, a plurality of resistance states can be obtained in bidirectional continuous reversibility, which is similar to the changes in synaptic weights. Furthermore, the proposed memristor is able to successfully mimic biological synaptic functions such as longterm depression, long-term potentiation, paired-pulse facilitation, and spike-timing-dependent plasticity. Consequently, it constitutes a promising candidate for a breakthrough in the von Neumann framework.

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Effect of Film Microstructure on Domain Nucleation and Intrinsic Switching in Ferroelectric Y:HfO₂ Thin Film Capacitors

Buragohain

Erickson

Mimura

et al. 2021

Adv Funct Materials

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One of the general features of ferroelectric systems is a complex nature of polarization reversal, which involves domain nucleation and motion of domain walls. Here, time‐resolved nanoscale domain imaging is applied in conjunction with the integral switching current measurements to investigate the mechanism of polarization reversal in yttrium‐doped HfO2 (Y:HfO2)—currently one of the most actively studied ferroelectric systems. More specifically, the effect of film microstructure on the nucleation process is investigated by performing a comparative study of the polarization switching behavior in the epitaxial and polycrystalline Y:HfO2 thin film capacitors. It is found that although the epitaxial Y:HfO2 capacitors tend to switch slower than their polycrystalline counterparts, they exhibit a significantly higher nucleation density and rate, suggesting that this is a rate‐limiting mechanism. In addition, it is observed that under the external fields approaching the activation field value, the switching kinetics can be described equally well by the nucleation limited switching and the Kolmogorov‐Avrami‐Ishibashi models for both types of capacitors. This signifies convergence of two different mechanisms implying that the polarization reversal proceeds via a homogeneous nucleation process unaffected by the film microstructure, which can be considered as approaching the intrinsic switching limit.

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Ferroelectric Second-Order Memristor

Cited by 94 publications

References 45 publications

Emerging Memristive Artificial Synapses and Neurons for Energy‐Efficient Neuromorphic Computing

Emerging Memristive Artificial Synapses and Neurons for Energy‐Efficient Neuromorphic Computing

Hf0.5Zr0.5O2-based ferroelectric memristor with multilevel storage potential and artificial synaptic plasticity

Effect of Film Microstructure on Domain Nucleation and Intrinsic Switching in Ferroelectric Y:HfO₂ Thin Film Capacitors

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Ferroelectric Second-Order Memristor

Cited by 94 publications

References 45 publications

Emerging Memristive Artificial Synapses and Neurons for Energy‐Efficient Neuromorphic Computing

Emerging Memristive Artificial Synapses and Neurons for Energy‐Efficient Neuromorphic Computing

Hf0.5Zr0.5O2-based ferroelectric memristor with multilevel storage potential and artificial synaptic plasticity

Effect of Film Microstructure on Domain Nucleation and Intrinsic Switching in Ferroelectric Y:HfO2 Thin Film Capacitors

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Effect of Film Microstructure on Domain Nucleation and Intrinsic Switching in Ferroelectric Y:HfO₂ Thin Film Capacitors