Relevant bibliographies by topics / Ferroelectric memristor

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Ferroelectric memristor'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Ferroelectric memristor"

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Lin, Zhuosheng, and Zhen Fan. "A Ferroelectric Memristor-Based Transient Chaotic Neural Network for Solving Combinatorial Optimization Problems." Symmetry 15, no. 1 (2022): 59. http://dx.doi.org/10.3390/sym15010059.

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A transient chaotic neural network (TCNN) is particularly useful for solving combinatorial optimization problems, and its hardware implementation based on memristors has attracted great attention recently. Although previously used filamentary memristors could provide the desired nonlinearity for implementing the annealing function of a TCNN, the controllability of filamentary switching still remains relatively poor, thus limiting the performance of a memristor-based TCNN. Here, we propose to use ferroelectric memristor to implement the annealing function of a TCNN. In the ferroelectric memrist
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Margolin, I., A. Chouprik, V. Mikheev, S. Zarubin, and D. Negrov. "Flexible HfO2-based ferroelectric memristor." Applied Physics Letters 121, no. 10 (2022): 102901. http://dx.doi.org/10.1063/5.0102290.

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The development of the next generation of flexible electronics for biomedical applications requires the implementation of flexible active elements, potentially microcontrollers. The further step in this direction includes the development of devices for data processing directly on-chip, in particular, devices for neuromorphic computing. One of the key elements put forward within this paradigm is the memristor—the device emulating the plasticity of biological synapses. Due to the internal temporal dynamics of conductance, second-order memristors exhibit the most natural emulation of a biological
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Andreeva, N. V., E. A. Ryndin, A. E. Petukhov, and O. Yu Vilkov. "Resistive Switching Mechanism in Ferroelectric Memristors with Thin Polycrystalline Barium Titanate Film." Nano- i Mikrosistemnaya Tehnika 26, no. 2 (2024): 70–80. http://dx.doi.org/10.17587/nmst.26.70-80.

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In a ferroelectric memristor, the manifestation of resistive effects is most often associated with the influence of polarization and dynamics of the domain structure on the charge transport. The role of point defects is either not taken into account or is reduced to the modulation of potential barriers at the interfaces with electrodes. However, the similarity of charge transport mechanisms in memristors based on thin ferroelectric and metal-oxide films suggests that the contribution of point defects in the anionic sublattice, namely, oxygen vacancies, to the resistive switching in ferroelectr
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Gao, Zhan, Yan Wang, Ziyu Lv, et al. "Ferroelectric coupling for dual-mode non-filamentary memristors." Applied Physics Reviews 9, no. 2 (2022): 021417. http://dx.doi.org/10.1063/5.0087624.

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Memristive devices and systems have emerged as powerful technologies to fuel neuromorphic chips. However, the traditional two-terminal memristor still suffers from nonideal device characteristics, raising challenges for its further application in versatile biomimetic emulation for neuromorphic computing owing to insufficient control of filament forming for filamentary-type cells and a transport barrier for interfacial switching cells. Here, we propose three-terminal memristors with a top-gate field-effect geometry by employing a ferroelectric material, poly(vinylidene fluoride–trifluoroethylen
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Wang, Lulu, Jiameng Sun, Yinxing Zhang, et al. "Ferroelectric memristor based on Li-doped BiFeO3 for information processing." Applied Physics Letters 121, no. 24 (2022): 241901. http://dx.doi.org/10.1063/5.0131063.

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As a nanoscale semiconductor memory device, a ferroelectric memristor has promising prospects to break through the von Neumann framework in terms of artificial synaptic function, information processing, and integration. This study presents the fabrication of Li0.09Bi0.91FeO3 as the functional layer for a memristor device based on the Si substrate, enabling the integration of silicon complementary metal oxide semiconductor technology. In addition, it exhibits bipolar resistance switching characteristics in a direct current mode and can rapidly achieve stable conductance tunability at higher fre
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Chanthbouala, André, Vincent Garcia, Ryan O. Cherifi, et al. "A ferroelectric memristor." Nature Materials 11, no. 10 (2012): 860–64. http://dx.doi.org/10.1038/nmat3415.

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Kim, D. J., H. Lu, S. Ryu, et al. "Ferroelectric Tunnel Memristor." Nano Letters 12, no. 11 (2012): 5697–702. http://dx.doi.org/10.1021/nl302912t.

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Wang, Lei, Shiqing Sun, Jianhui Zhao, et al. "HfO2:Gd-based ferroelectric memristor as bio-synapse emulators." Applied Physics Letters 121, no. 25 (2022): 253502. http://dx.doi.org/10.1063/5.0101026.

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In this work, a memristor device with Pd/HfO2:Gd/La0.67Sr0.33MnO3/SrTiO3/Si was prepared, and its synaptic behavior was investigated. The memristor shows excellent performance in I–V loops and ferroelectric properties. Through polarization, the conductance modulation of the memristor is achieved by the reversal of the ferroelectric domain. In addition, we simulate biological synapses and synaptic plasticities such as spike-timing-dependent plasticity, paired-pulse facilitation, and an excitatory postsynaptic current. These results lay the foundation for the development of synaptic functions in
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Mikheev, Vitalii, Anastasia Chouprik, Yury Lebedinskii, et al. "Ferroelectric Second-Order Memristor." ACS Applied Materials & Interfaces 11, no. 35 (2019): 32108–14. http://dx.doi.org/10.1021/acsami.9b08189.

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McConville, James P. V., Haidong Lu, Bo Wang, et al. "Ferroelectric Domain Wall Memristor." Advanced Functional Materials 30, no. 28 (2020): 2000109. http://dx.doi.org/10.1002/adfm.202000109.

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Dissertations / Theses on the topic "Ferroelectric memristor"

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Boyn, Sören. "Ferroelectric tunnel junctions : memristors for neuromorphic computing." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLS089/document.

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Les architectures d’ordinateur classiques sont optimisées pour le traitement déterministe d’informations pré-formatées et ont donc des difficultés avec des données naturelles bruitées (images, sons, etc.). Comme celles-ci deviennent nombreuses, de nouveaux circuits neuromorphiques (inspirés par le cerveau) tels que les réseaux de neurones émergent. Des nano-dispositifs, appelés memristors, pourraient permettre leur implémentation sur puce avec une haute efficacité énergétique et en s’approchant de la haute connectivité synaptique du cerveau.Dans ce travail, nous étudions des memristors basés s
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Khanas, Anton. "Multiferroic oxide nanostructures : multi-resistance states." Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS039.pdf.

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Les nouveaux défis de la microélectronique depuis déjà deux décennies nécessitent le développement de matériaux et dispositifs bien au-delà des exigences de compatibilité avec la technologie silicium. Parmi ces défis figurent la miniaturisation des éléments de circuit intégrés et l’intégration 3D, pour surmonter le goulot d’étranglement de von Neumann, ainsi que l’élaboration de dispositifs neuromorphiques à base de memristors. Les fonctionnalités supplémentaires, offertes par la mise en œuvre de matériaux ferroélectriques et magnétiques, permettraient de réaliser des progrès significatifs dan
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Book chapters on the topic "Ferroelectric memristor"

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Peng, Baocheng, and Qing Wan. "Oxide Neuromorphic Transistors for Brain-like Computing." In Advanced Memory Technology. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/bk9781839169946-00530.

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The brain surpasses traditional von Neumann computers in multiple ways, such as its efficiency in energy consumption and resilience to damage. This has garnered significant attention from researchers in recent years, leading to the development of brain-inspired computing and emerging neuromorphic devices. This includes both transistor-based and memristor devices. The focus of this chapter is on oxide transistor-based neuromorphic devices. The functions of biological synapses and neurons are briefly discussed, followed by an overview of the working mechanism and progress in oxide neuromorphic t
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Chougale, Mahesh Y., Rayyan Ali Shaukat, Swapnil R. Patil, et al. "Resistive Switching-based Neuromorphic Devices for Artificial Neural Networks." In Advanced Memory Technology. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/bk9781839169946-00569.

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The integration of software- and hardware-based brain computing is attracting attention to develop future artificial neural network systems. In this chapter, we have discussed materials modification, device engineering, sensory neuro-electronics, and flexible memristor devices for ANNs. Moreover, the basic properties of brain computing such as potentiation, depression, STDP, and SRDP have been discussed by modulating electrical stimuli like the amplitude and width of the applied pulse as well as sensory effects like optical stimuli and mechanical pressure (tactile stimuli). The effect of activ
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Conference papers on the topic "Ferroelectric memristor"

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Han, Cong, Yixin Wei, Miaocheng Zhang, Yi Liu, Xinpeng Wang, and Yi Tong. "Exploration of Synaptic Plasticity in Memristor Based Wurtzite Ferroelectric Material." In 2024 IEEE International Conference on IC Design and Technology (ICICDT). IEEE, 2024. http://dx.doi.org/10.1109/icicdt63592.2024.10717674.

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Li, Chenlei, Hongyan Yu, Tao Shu, Yueyang Zhang, Feng Qiu, and Daoxin Dai. "PZT Optical Memristors for Integrated Photonics." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.m3g.3.

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We demonstrated first-ever PZT optical memristors capable of unprecedented functional duality by manipulating ferroelectric domains, featuring low loss, high precision, high-efficiency modulation, high stability quasi-continuity and reconfigurability, together with a scalable, CMOS-compatible sol-gel fabrication process.
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Liu, Mingrui, Hang Zang, Shunpeng Lv, et al. "Realization of Wafer-Scale AlScN Ferroelectric Films and the Investigation of AlScN/n-GaN Ferroelectric Memristors." In 2025 9th IEEE Electron Devices Technology & Manufacturing Conference (EDTM). IEEE, 2025. https://doi.org/10.1109/edtm61175.2025.11040912.

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Ueda, M., Y. Kaneko, Y. Nishitani, T. Morie, and E. Fujii. "Biologically-inspired learning device using three-terminal ferroelectric memristor." In 2012 70th Annual Device Research Conference (DRC). IEEE, 2012. http://dx.doi.org/10.1109/drc.2012.6256971.

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Wang, Zhaohao, Weisheng Zhao, Wang Kang, Youguang Zhang, Jacques-Olivier Klein, and Claude Chappert. "Ferroelectric tunnel memristor-based neuromorphic network with 1T1R crossbar architecture." In 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 2014. http://dx.doi.org/10.1109/ijcnn.2014.6889951.

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Kaneko, Y., H. Tanaka, M. Ueda, Y. Kato, and E. Fujii. "A novel ferroelectric memristor enabling NAND-type analog memory characteristics." In 2010 68th Annual Device Research Conference (DRC). IEEE, 2010. http://dx.doi.org/10.1109/drc.2010.5551971.

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Begon-Lours, Laura, Mattia Halter, Youri Popoff, Zhenming Yu, Donato Francesco Falcone, and Bert Jan Offrein. "High-Conductance, Ohmic-like HfZrO4 Ferroelectric Memristor." In ESSCIRC 2021 - IEEE 47th European Solid State Circuits Conference (ESSCIRC). IEEE, 2021. http://dx.doi.org/10.1109/esscirc53450.2021.9567870.

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Begon-Lours, Laura, Mattia Halter, Youri Popoff, Zhenming Yu, Donato Francesco Falcone, and Bert Jan Offrein. "High-Conductance, Ohmic-like HfZrO4 Ferroelectric Memristor." In ESSDERC 2021 - IEEE 51st European Solid-State Device Research Conference (ESSDERC). IEEE, 2021. http://dx.doi.org/10.1109/essderc53440.2021.9631767.

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Chen, Ziyang, Miaocheng Zhang, Zixuan Ding, et al. "BaFe12O19 based Ferroelectric Memristor for Applications of True Random Number Generator." In 2022 IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA). IEEE, 2022. http://dx.doi.org/10.1109/icta56932.2022.9963047.

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Meyer, Charly, Andre Chanthbouala, Soren Boyn, et al. "Verilog-A model of ferroelectric memristors dedicated to neuromorphic design." In 2018 25th IEEE International Conference on Electronics, Circuits and Systems (ICECS). IEEE, 2018. http://dx.doi.org/10.1109/icecs.2018.8618054.

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