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Journal articles on the topic 'Ferroelectric memristor'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

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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2

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Yue, Wenfeng, Yali Cai, Quansheng Guo, Dawei Wang, and Tingting Jia. "Effect of Thickness on Ferroelectric Properties of Bi3.25La0.75Ti3O12 Thin Films." Ceramics 7, no. 1 (2024): 29–38. http://dx.doi.org/10.3390/ceramics7010003.

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The pursuit of low-power/low-voltage operation in devices has prompted a keen interest in the mesoscale effects within ferroelectric thin films. The downsizing of ferroelectrics can significantly influence performance; for instance, the remanent polarization and coercive field are susceptible to alterations based on thickness. In this study, randomly oriented Bi3.25La0.75Ti3O12 thin films were fabricated on Pt/Ti/SiO2/Si substrates using the sol–gel method, and SEM observations revealed rod-like grains in all thin films. The investigation delved into the correlation between dielectric and ferr

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12

Niu, Xuezhong, Bobo Tian, Qiuxiang Zhu, Brahim Dkhil, and Chungang Duan. "Ferroelectric polymers for neuromorphic computing." Applied Physics Reviews 9, no. 2 (2022): 021309. http://dx.doi.org/10.1063/5.0073085.

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The last few decades have witnessed the rapid development of electronic computers relying on von Neumann architecture. However, due to the spatial separation of the memory unit from the computing processor, continuous data movements between them result in intensive time and energy consumptions, which unfortunately hinder the further development of modern computers. Inspired by biological brain, the in situ computing of memristor architectures, which has long been considered to hold unprecedented potential to solve the von Neumann bottleneck, provides an alternative network paradigm for the nex

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13

Zhang, Jianxing, Hangfei Li, Tao Liu, et al. "Voltage modulated long-term plasticity in perovskite heterostructured memristive synaptic devices with high-performance neuromorphic computing." Journal of Applied Physics 133, no. 4 (2023): 044502. http://dx.doi.org/10.1063/5.0133146.

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The development of neuromorphic computing is expected to enable the computer to realize the integration of storage and computation. The development of memristors provides hardware support possibilities for the development of neuromorphic computing. In this work, we have prepared a (La0.67, Sr0.33)MnO3/BaTiO3-based memristor with good forward and reverse memristor function and multilevel resistive tunability, including an increased resistance state at forward voltage and a decreased resistance state at reverse voltage. This is mainly due to the barriers of the ferroelectric dielectric layer and

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14

Parker, Jacob, and Yi Gu. "van der Waals ferroelectrics: Progress and an outlook for future research directions." Journal of Applied Physics 132, no. 16 (2022): 160901. http://dx.doi.org/10.1063/5.0117355.

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The recent discovery of van der Waals (vdW) ferroelectric materials has inspired their incorporation into numerous nonvolatile technologies and shown potential promise for various device applications. Here in this perspective, we evaluate the recent developments in the field of vdW ferroelectric devices, with discussions focusing on vdW heterostructure ferroelectric field-effect transistors and vdW ferroelectric memristor technologies. Additionally, we discuss some of the many open questions that persist in these technologies and possible pathways research can take to answer these questions an

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15

Woo, Dae-Seong, Hyun-Do Choi, Hong-Uk Jin, Jae-Kyeong Kim, Tae-Hun Shim, and Jea-Gun Park. "Multi-Bit Self-Rectifying Synaptic Memristor Having Tri-Layer Structure for Quantization Aware Training of Quantized Neural Network." ECS Meeting Abstracts MA2023-02, no. 30 (2023): 1560. http://dx.doi.org/10.1149/ma2023-02301560mtgabs.

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Recently, a cross-point synaptic memristor arrays have been employed to implement synaptic cores of various ANNs, i.e., deep neural networks (DNNs), spiking neural networks (SNNs), and convolutional neural networks (CNNs). The most of reported studies conducted training and inference of ANNs using analog synaptic weight modulation of memristors such as resistive random access memory (ReRAM), phase change random access memory (PCRAM), and ferroelectric random access memory (FeRAM). In other words, no case has been reported so far in which a synaptic memristor with quantized multi-bit is utilize

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16

Seo, Euncho, Maria Rasheed, and Sungjun Kim. "Associative Learning Emulation in HZO-Based Ferroelectric Memristor Devices." Materials 18, no. 14 (2025): 3210. https://doi.org/10.3390/ma18143210.

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Neuromorphic computing inspired by biological synapses requires memory devices capable of mimicking short-term memory (STM) and associative learning. In this study, we investigate a 15 nm-thick Hafnium zirconium oxide (HZO)-based ferroelectric memristor device, which exhibits robust STM characteristics and successfully replicates Pavlov’s dog experiment. The optimized 15 nm HZO layer demonstrates enhanced ferroelectric properties, including a stable orthorhombic phase and a reliable short-term synaptic response. Furthermore, through a series of conditional learning experiments, the device effe

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17

Du, Junmei, Bai Sun, Chuan Yang, et al. "Ferroelectric memristor and its neuromorphic computing applications." Materials Today Physics 50 (January 2025): 101607. https://doi.org/10.1016/j.mtphys.2024.101607.

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18

Dragoman, Mircea, Adrian Dinescu, Florin Nastase, and Daniela Dragoman. "Memtransistors Based on Nanopatterned Graphene Ferroelectric Field-Effect Transistors." Nanomaterials 10, no. 7 (2020): 1404. http://dx.doi.org/10.3390/nano10071404.

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The ultimate memristor, which acts as resistive memory and an artificial neural synapse, is made from a single atomic layer. In this manuscript, we present experimental evidence of the memristive properties of a nanopatterned ferroelectric graphene field-effect transistor (FET). The graphene FET has, as a channel, a graphene monolayer transferred onto an HfO2-based ferroelectric material, the channel being nanopatterned with an array of holes with a diameter of 20 nm.

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19

Templin, Zoe, and Feng Zhao. "Natural Organic Honey-CNT Memristor Based Artificial Synaptic Devices for Sustainable Neuromorphic System." ECS Meeting Abstracts MA2024-01, no. 57 (2024): 2995. http://dx.doi.org/10.1149/ma2024-01572995mtgabs.

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Neuromorphic computing is considered to have the potential to overcome the limitations of traditional von Neumann architecture due to its high efficiency, low energy consumption, and fault-tolerance. Hardware components that can emulate the synaptic plasticity of neurons, i.e. artificial synaptic devices, are required by neuromorphic systems. New devices have been examined for such components, such as phase-change artificial synapse, ferroelectric artificial synapse, and memristor synapses. Among them, memristor, a two-terminal metal-insulator-metal structure that are analogous to a biological

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20

Hou, Pengfei, Jinbin Wang, Xiangli Zhong, and Yuexian Wu. "A ferroelectric memristor based on the migration of oxygen vacancies." RSC Advances 6, no. 59 (2016): 54113–18. http://dx.doi.org/10.1039/c6ra08257b.

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21

Zhou, He-Chun, Yan-Ping Jiang, Xin-Gui Tang, Qiu-Xiang Liu, Wen-Hua Li, and Zhen-Hua Tang. "Excellent Bipolar Resistive Switching Characteristics of Bi4Ti3O12 Thin Films Prepared via Sol-Gel Process." Nanomaterials 11, no. 10 (2021): 2705. http://dx.doi.org/10.3390/nano11102705.

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Herein, Bi4Ti3O12 (BIT) ferroelectric thin films were fabricated into Au/BIT/LaNiO3/Si structures to demonstrate their memristor properties. Repeatable and stable bipolar resistive switching (RS) characteristics of the device are first reported in this work. The switching ratio of the device annealed in air reached approximately 102 at 0.1 and −0.1 V. The RS performance was not significantly degraded after 100 consecutive cycles of testing. We also explored the factors affecting the RS behavior of the device. By investigating the RS characteristics of the devices annealed in O2, and in combina

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22

Li, Tiancheng, Erping Li, Huali Duan, Zhufei Chu, Jian Wang, and Wenchao Chen. "Artificial neural network models for metal-ferroelectric-insulator-semiconductor ferroelectric tunnel junction memristor." Microelectronics Journal 144 (February 2024): 106083. http://dx.doi.org/10.1016/j.mejo.2023.106083.

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23

Lee, Joonbong, Myeong Seop Song, Woo-Sung Jang, et al. "Artificial nociceptor based on interface engineered ferroelectric volatile memristor." Applied Materials Today 39 (August 2024): 102346. http://dx.doi.org/10.1016/j.apmt.2024.102346.

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24

Wang, Zhaohao, Weisheng Zhao, Wang Kang, et al. "Nonvolatile Boolean Logic Block Based on Ferroelectric Tunnel Memristor." IEEE Transactions on Magnetics 50, no. 11 (2014): 1–4. http://dx.doi.org/10.1109/tmag.2014.2329774.

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25

Raj, P. Michael Preetam, V. Jeffry Louis, Sumit Kumar Chatterjee, Sayan Kanungo, and Souvik Kundu. "Ferroelectric Memristive Networks for Dimensionality Reduction: A Process for Effectively Classifying Cancer Datasets." Integrated Ferroelectrics 201, no. 1 (2019): 126–41. http://dx.doi.org/10.1080/10584587.2019.1668697.

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In this work, a copper-doped (5%) zinc oxide (Cu:ZnO) ferroelectric materials-based memristor model was realized and it was employed to develop principal component analysis (PCA), a data dimension reduction technique. The developed PCA was utilized to efficaciously classify breast cancer datasets, which are considered as complex and big volumes of data. It was found that the controllable memristance variations were analogous to the weight modulations in the implemented neural network-based learning systems. Sanger’s rule was utilized to achieve unsupervised online learning in order to generate

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26

Mikheev, V., A. Chouprik, Yu Lebedinskii, et al. "Memristor with a ferroelectric HfO2 layer: in which case it is a ferroelectric tunnel junction." Nanotechnology 31, no. 21 (2020): 215205. http://dx.doi.org/10.1088/1361-6528/ab746d.

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27

Hu, Zhongqiang, Qian Li, Meiya Li, et al. "Ferroelectric memristor based on Pt/BiFeO3/Nb-doped SrTiO3 heterostructure." Applied Physics Letters 102, no. 10 (2013): 102901. http://dx.doi.org/10.1063/1.4795145.

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28

Yoon, Sung-Min, Shinhyuk Yang, Soon-Won Jung, et al. "Polymeric ferroelectric and oxide semiconductor-based fully transparent memristor cell." Applied Physics A 102, no. 4 (2011): 983–90. http://dx.doi.org/10.1007/s00339-011-6280-9.

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29

Park, Yongjin, Eunjin Lim, Seungjun Lee, Vihar Georgiev, Sungjoon Kim, and Sungjun Kim. "Ferroelectric memristor crossbar arrays for highly integrated neuromorphic computing system." Nano Energy 141 (August 2025): 111137. https://doi.org/10.1016/j.nanoen.2025.111137.

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30

Woo, Dae-Seong, Jae Kyeong Kim, Tae-Hun Shim, Jinsub Park, and Jea-Gun Park. "Ultra-Low-Power Hybrid Synaptic Memristor via Simultaneous-Controlled Mechanism of Cations and Anions for Hyper-Scale Synaptic Cores." ECS Meeting Abstracts MA2024-01, no. 57 (2024): 3001. http://dx.doi.org/10.1149/ma2024-01573001mtgabs.

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Recently, memristive devices such as phase change random access memory (PCRAM), resistive random access memory (ReRAM), and ferroelectric random access memory (FeRAM) have been actively researched to implement hyper-scale synaptic cores of various ANNs, i.e., deep neural networks (DNNs), spiking neural networks (SNNs), convolutional neural networks (CNNs), and binarized neural networks (BNNs). In particular, ReRAM is currently being highlighted as an artificial synaptic device because of its multilevel capability (> 11 bits)1, high switching speed (< 100 ps)2, high endurance (> 1012 c

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31

Zhang, Miaocheng, Qi Qin, Xingyu Chen, et al. "Towards an universal artificial synapse using MXene-PZT based ferroelectric memristor." Ceramics International 48, no. 11 (2022): 16263–72. http://dx.doi.org/10.1016/j.ceramint.2022.02.175.

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32

Michael Preetam Raj, P., Amlan Ranjan Kalita, Mantu K. Hudait, Shashank Priya, and Souvik Kundu. "Nonlinear DC equivalent circuits for ferroelectric memristor and Its FSM application." Integrated Ferroelectrics 192, no. 1 (2018): 16–27. http://dx.doi.org/10.1080/10584587.2018.1521667.

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33

Yan, Z. B., H. M. Yau, Z. W. Li, X. S. Gao, J. Y. Dai, and J. M. Liu. "Self-electroforming and high-performance complementary memristor based on ferroelectric tunnel junctions." Applied Physics Letters 109, no. 5 (2016): 053506. http://dx.doi.org/10.1063/1.4960523.

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34

Wang, Zhaohao, Weisheng Zhao, Wang Kang, et al. "Compact modelling of ferroelectric tunnel memristor and its use for neuromorphic simulation." Applied Physics Letters 104, no. 5 (2014): 053505. http://dx.doi.org/10.1063/1.4864270.

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35

Guo, Rui, Yaxiong Zhou, Lijun Wu, et al. "Control of Synaptic Plasticity Learning of Ferroelectric Tunnel Memristor by Nanoscale Interface Engineering." ACS Applied Materials & Interfaces 10, no. 15 (2018): 12862–69. http://dx.doi.org/10.1021/acsami.8b01469.

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36

Loskutov, Alexander. "Bio-Memristor Based on Peptide and Peptide Composite with Gold Nanoparticles." EPJ Web of Conferences 224 (2019): 03003. http://dx.doi.org/10.1051/epjconf/201922403003.

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The structure, morphology and electrical properties of thin dipeptide hexamethylenediamide bis (N-monosuccinylglutamlysin) (DPT) layers and a DPT composite with gold nanoparticles deposited on gold and HOPG substrates were studied by probe microscopy and spectroscopy. The chemical formula of DPT is: {HOOC–(CH2)2–CO-L-Glu-L-Lys-NH–(CH2)3}2, and it is a mimetic of nerve growth factor. The results demonstrate that the structure and morphology of DPT thin layers depend significantly on the molecule charge (neutral or anion) and the nature of the substrate–layer interface. It was possible to contro

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37

Wen, Yiyang, Yilin Cao, Hongda Ren, et al. "Ferroelectric Optical Memristors Enabled by Non‐Volatile Electro‐Optic Effect." Advanced Materials, January 7, 2025. https://doi.org/10.1002/adma.202417658.

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AbstractMemristors enable non‐volatile memory and neuromorphic computing. Optical memristors are the fundamental element for programmable photonic integrated circuits due to their high‐bandwidth computing, low crosstalk, and minimal power consumption. Here, an optical memristor enabled by a non‐volatile electro‐optic (EO) effect, where refractive index modulation under zero field is realized by deliberate control of domain alignment in the ferroelectric material Pb(Mg1/3Nb2/3)O3‐PbTiO3(PMN‐PT) is proposed. The non‐volatile EO memristor is designed exclusively for the modulation of the optical

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38

Qin, Qi, Miaocheng Zhang, Suhao Yao, et al. "Fabrication and Investigation of Ferroelectric Memristors with Various Synaptic Plasticities." Chinese Physics B, December 1, 2021. http://dx.doi.org/10.1088/1674-1056/ac3ece.

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Abstract In the Post-Moore Era, the neuromorphic computing has been mainly focused on breaking the von Neumann bottlenecks. Memristor has been proposed as a key part for the neuromorphic computing architectures, which can be used to emulate the synaptic plasticities of human brain. Ferroelectric memristor is a breakthrough for memristive devices on account of its reliable-nonvolatile storage, low-write/read latency, and tunable-conductive states. However, among the reported ferroelectric memristors, the mechanisms of resistive-switching are still under debate. In addition, the research of emul

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Xia, Zhipeng, Xiao Sun, Zhenlong Wang, Jialin Meng, Boyan Jin, and Tianyu Wang. "Low-Power Memristor for Neuromorphic Computing: From Materials to Applications." Nano-Micro Letters 17, no. 1 (2025). https://doi.org/10.1007/s40820-025-01705-4.

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Abstract As an emerging memory device, memristor shows great potential in neuromorphic computing applications due to its advantage of low power consumption. This review paper focuses on the application of low-power-based memristors in various aspects. The concept and structure of memristor devices are introduced. The selection of functional materials for low-power memristors is discussed, including ion transport materials, phase change materials, magnetoresistive materials, and ferroelectric materials. Two common types of memristor arrays, 1T1R and 1S1R crossbar arrays are introduced, and phys

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40

Wei, Jiajing, Yanfang He, Ying Yang, et al. "Ferroelectric polarization-driven ionic modulation enabling enhanced self-rectification in NiOx/HfO2−x: Al memristors." Applied Physics Letters 127, no. 3 (2025). https://doi.org/10.1063/5.0273610.

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Self-rectifying memristors, which integrate diode-like behavior to suppress sneak currents in crossbar arrays, are promising for high-density neuromorphic systems. However, conventional designs often suffer from nonlinear weight updates and stochastic switching due to filamentary or Schottky-based mechanisms. Here, we present a NiOx/HfO2−x:Al bilayer memristor utilizing ferroelectric polarization to actively direct oxygen vacancy migration, thereby enhancing self-rectification. The polarization-induced internal field drives oxygen vacancy migration and synergizes with oxygen ion migration to m

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41

Lee, Jung‐Kyu, Yongjin Park, Euncho Seo, Jong‐Ho Lee, Sungjoon Kim, and Sungjun Kim. "Integrated Design of Electrically Configurable Ferroelectric and Redox‐Based Memristors for Hardware‐Implemented Reservoir Computing." Advanced Science, June 10, 2025. https://doi.org/10.1002/advs.202505688.

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AbstractReservoir computing (RC) offers advantages in processing time‐series data with reduced training costs and simpler architectures. This study presents a hardware‐implemented RC system utilizing multifunctional memristors fabricated using a single process. By leveraging a ferroelectric‐based memristor (FM) as a volatile reservoir layer and a redox‐based memristor (RM) as a non‐volatile readout layer, seamless integration without additional fabrication steps is achieved. The dual‐functional memristor structure enables electrical conversion from FM to RM, enhancing system scalability and ve

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42

Wang, Hong, Yusong Tang, Zhisheng Wang, et al. "Efficient and Stable Topological/Ferroelectric Bi₂Te₃/SnSe Hetero‐Memristor for In Situ Bionic‐Visual Semi‐Hardware Systems." Advanced Materials, July 2025. https://doi.org/10.1002/adma.202501066.

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AbstractAs the application of artificial vision systems continues to grow, developing efficient and low‐power visual sensing devices has become a key challenge. Memristors offer tunable conductivity and integrated in‐situ storage and computation functions, making them ideal for low‐cost visual systems. However, most memristors currently face the dual challenges of poor stability and limited optoelectronic synaptic plasticity. Here, a Bi2Te2.7Se0.3/SnSe hetero‐memristor is designed, which combines the advantages of two‐dimensional (2D) topological insulators and 2D ferroelectric materials. The

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43

Wang, Hong, Jialiang Yang, Zhisheng Wang, et al. "Reconfigurable multifunctional neuromorphic memristor fabricated from two-dimensional ReSe2 ferroelectric nanosheet films." Applied Physics Reviews 11, no. 1 (2024). http://dx.doi.org/10.1063/5.0170147.

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Memristor-based neuromorphic computing is beneficial for artificial intelligence to process external information autonomously with high speed and high efficiency. Two-dimensional (2D) layered van der Waals rhenium selenide (ReSe2) has optoelectronic and semiconductor properties, but its ferroelectricity has not been confirmed fully experimentally and the application exploration is currently limited. Here, we experimentally confirmed the room-temperature ferroelectricity of 2D ReSe2 and proposed a reconfigurable ReSe2 memristor that can realize multiple functions. The device can realize the con

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44

Wang, Hong, Runyao Lin, Kangbo Zhao, et al. "Dual‐Way Alternation Sensing Memristors Based on (BA)₂(MA)₃Pb₄Br₁₃ Ferroelectric Quantum Dots with Sub‐0.16 V Switching for Broadband Image‐Recognition Systems." Advanced Functional Materials, June 2, 2025. https://doi.org/10.1002/adfm.202510543.

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AbstractThe traditional research on organic‐inorganic hybrid perovskite can only realize single visible‐light band sensing, which cannot meet the demand of flexible sensing adjustment of distance. Here, the ferroelectric (BA)2(MA)3Pb4Br13 quantum dots (BMP‐QDs) are proposed and the Pd/BMP‐QDs/Nb:SrTiO₃ sensing memristors are designed. The memristor exhibits robust ferroelectric polarization properties with sub‐0.16 V ferroelectric switching and excellent fatigue resistance over 10⁹ cycles, and the 8 × 104 ON/OFF ratio. In addition, a broadband dual‐way alternation optical sensing behavior of t

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45

Jiang, Xingan, Xueyun Wang, Xiaolei Wang, et al. "Manipulation of current rectification in van der Waals ferroionic CuInP2S6." Nature Communications 13, no. 1 (2022). http://dx.doi.org/10.1038/s41467-022-28235-6.

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AbstractDeveloping a single-phase self-rectifying memristor with the continuously tunable feature is structurally desirable and functionally adaptive to dynamic environmental stimuli variations, which is the pursuit of further smart memristors and neuromorphic computing. Herein, we report a van der Waals ferroelectric CuInP2S6 as a single memristor with superior continuous modulation of current and self-rectifying to different bias stimuli (sweeping speed, direction, amplitude, etc.) and external mechanical load. The synergetic contribution of controllable Cu+ ions migration and interfacial Sc

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Quindeau, Andy, Dietrich Hesse, and Marin Alexe. "Programmable ferroelectric tunnel memristor." Frontiers in Physics 2 (2014). http://dx.doi.org/10.3389/fphy.2014.00007.

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Zhou, Lei, Yifei Pei, Changliang Li, et al. "A temperature sensing based Na0.5Bi0.5TiO3 ferroelectric memristor device for artificial neural systems." Applied Physics Letters 124, no. 9 (2024). http://dx.doi.org/10.1063/5.0190861.

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With the development of artificial intelligence technology, it remains a challenge to improve the resistive switching performance of next-generation nonvolatile ferroelectric memristor device (FMD). Here, we report an epitaxial Na0.5Bi0.5TiO3 ferroelectric memristor device (NBT-FMD) with temperature sensing. The NBT epitaxial films with strong polarization strength and suitable oxygen vacancy concentration were obtained by temperature adjustment (700 °C). In addition, the function of the spiking-time-dependent plasticity and paired-pulse facilitation is simulated in ferroelectric memristor dev

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Tang, Zhenxun, Linjie Liu, Jianyuan Zhang, Weijin Chen, and Yue Zheng. "Prediction of neuronal functionality of asymmetric ferroelectric tunneling junction with coupled polarization and thermal dynamics." Journal of Applied Physics 137, no. 2 (2025). https://doi.org/10.1063/5.0239947.

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The performance of neuromorphic computing (NC) in executing data-intensive artificial intelligence tasks relies on hardware network structure and information processing behavior mimicking neural networks in the human brain. The functionalities of synapses and neurons, the key components in neural networks, have been widely pursued in memristor systems. Nevertheless, the realization of neuronal functionalities in a single memristor remains challenging. By theoretical modeling, here we propose asymmetric ferroelectric tunneling junction (AFTJ) as a potential platform to realize neuronal function

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Sharma, Pankaj, Chi-Hou Lei, Yunya Liu, et al. "Ferroelectric Domain Wall Warp Memristor." ACS Applied Materials & Interfaces, December 23, 2024. https://doi.org/10.1021/acsami.4c16347.

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Jeon, Yu‐Rim, Dongyoon Kim, Chandan Biswas, et al. "Enhanced Synaptic Memory Window and Linearity in Planar In₂Se₃ Ferroelectric Junctions." Advanced Materials, December 20, 2024. https://doi.org/10.1002/adma.202413178.

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AbstractA synaptic memristor using 2D ferroelectric junctions is a promising candidate for future neuromorphic computing with ultra‐low power consumption, parallel computing, and adaptive scalable computing technologies. However, its utilization is restricted due to the limited operational voltage memory window and low on/off current (ION/OFF) ratio of the memristor devices. Here, it is demonstrated that synaptic operations of 2D In2Se3 ferroelectric junctions in a planar memristor architecture can reach a voltage memory window as high as 16 V (±8 V) and ION/OFF ratio of 108, significantly hig

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