The DOI matches its Crossref record, with a small difference in title or year; 4 of the 4 references it cites were found in a registry or an index.
Improved Synaptic Behavior Under Identical Pulses Using AlO x /HfO 2 Bilayer RRAM Array for Neuromorphic Systems
Abstract
We analyze the response of identical pulses on a filamentary resistive memory (RRAM) to implement the synapse function in neuromorphic systems. Our findings show that the multilevel states of conductance are achieved by varying the measurement conditions related to the formation and rupture of a conductive filament. Furthermore, abrupt set switching behavior in the RRAM leads to an unchanged conductance state, leading to degradation in the accuracy of pattern recognition. Thus, we demonstrate a linear potentiation (or depression) behavior of conductance under identical pulses using the effect of barrier layer on the switching, which was realized by fabricating an RRAM on top of an Al electrode. As a result, when the range of the conductance is symmetrically controlled at both polarities, a significantly improved accuracy is achieved for pattern recognition using a neural network with a multilayer perceptron.
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BackgroundNeuromorphic electronic systems
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Items 301–369 of 369
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“Comparing Figures 11 and 12, the FGFET-CSB model has good linearity and is therefore suitable for a neural network.”
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“For CMO, there are several established materials that can be found in literature such as TiOx [8], AlOx [5] and TaOx [9].”
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“In this work, our memristors and other state-of-the-art memristors (Ag:SiGe [11] and AlOx/HfO 2 [12]) are incorporated into the SA as shown in Figure 7 (e).”
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Committee Machines - A Universal Method to Deal with Non-Idealities in RRAM-Based Neural Networks
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System-level benchmark of synaptic device characteristics for neuro-inspired computing
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“1 Representative analog synaptic device weight update behaviors from literature: TiOx/TiO2 [6], PCMO [7], Ag:a-Si [8], AlOx/HfO2 [9].”
BackgroundNeuromorphic synaptic behavior in Gd-doped HfO2 memristors with optoelectronic dual-modal modulation
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“Therefore, fully parallel computing enhances the efficiency of a brain sys-tem, and many studies have attempted to implement parallel structures for synapses using cross-point resistive memory arrays [12], [13].”
BackgroundSelf-citationImproving the Performance of Charge Trapping Memtransistor as Synaptic Device by Ti-Doped HfO 2
Chou, Y.-C., Chung, W.-H., Tsai, C.-W., Yi, C.-Y., & Chien, C.-H. (2020) · IEEE Journal of the Electron Devices Society · doi:10.1109/jeds.2020.3045194 · Cited by 3
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“The proposed methods can be generally used for any memristors with pulse stimulation and filament dependence, such as popular memristors based on Ag:a-Si [50], TaO x /TiO 2 [51], and AlO x /HfO 2 [52].”
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“…to its relatively high oxygen ion migration barrier, can effectively suppress the abrupt formation and rupture of CFs, thereby significantly improving the switching characteristics and multi-level resistance controllability of memristors, which has attracted extensive academic attention [17– 19].”
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Hamun: An approximate computing method to prolong the lifespan of ReRAM-based accelerators
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“Studies have found that the behavior of synaptic weight update, specifically the linearity of conductance update with voltage pulse numbers, influences the recognition accuracy of neuromorphic computational chips [19], [20], [21].”
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“Solutions to improving linearity, symmetry, and CW have been reported [2,14-16,18,21-23] ranging from implementing an additional AlOx bilayer for improved oxygen vacancy movement due to the AlOx layer having lower V0 mobility, to periphery circuits, and combined multi-ReRAM cell circuits.”
MethodResistive Switching Characteristics of Alloyed AlSiOx Insulator for Neuromorphic Devices
Lee, Y., Jang, J., Jeon, B., Lee, K., Chung, D., & Kim, S. (2022) · Materials · doi:10.3390/ma15217520 · Cited by 4
“The implementation of various synaptic weights through improved gradualness has the advantage of synaptic characteristics and, furthermore, improves the accuracy performance in the simulations, such as MNIST’s handwritten digit recognition [47].”
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“00% [181] Binary NN TaOx/HfO2 RRAM 2016 96.”
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“The gradual depression is easier to achieve with respect to gradual potentiation in filamentary RRAM [4].”
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“Figure 10 shows change of conductance with the pulse numbers corresponding to increase of weight (potentiation) and decrease (depression) for TiO x / TiO 2 [40], PCMO [41], Ag:a-Si [42], and AlO x / HfO 2 [43] which shows a highly non-linear and asymmetric nature.”
Suppressing Poole--Frenkel Instability in NbOx Memristors Through an Ultrathin SiOx Tunneling Layer
Xu, Z.-Y., Zhang, M., Lu, W.-D., Xu, L.-H., Feng, Z., Zhu, Y.-L., Wu, Z.-H., & Dai, Y.-H. (2026) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2026.3728400
“Introducing an ultrathin dielectric interlayer forms an interfacial tunneling barrier that redistributes the electric field, suppresses parasitic leakage, and improves the controllability of carrier injection [9], [17], [18].”
In-Memory Computing Enabled by 32 × 8 1T1R Memristive Crossbar Arrays
Mikhaylov, A., Shchanikov, S., Koryazhkina, M., Bordanov, I., Lukoyanov, V., Belov, A. I., Zharkov, G., Serov, D. A., Antonov, I., Kotina, A. Y., et al. (2026) · Electronics · doi:10.3390/electronics15184267
Fully Sputtered HfOy/HfOx RRAM: Improved Synaptic Behavior for Neuromorphic Training Accelerators
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“It has also been reported that analog-type switching emerges only when the entire conductive filament (CF) evolves gradually rather than through localized abrupt formation, consistent with previous findings in previous works [8], [16].”
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“Because resistance-based readout is also sensitive to device variability and read-path parasitics, read margin and multi-level stability degrade in scaled architectures [12], [13].”
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“Previous studies have reported that constructing HfO x /AlO γ super-lattice resistive layers can further confine oxygen ion migration and conductive filament evolution, improving the multilevel resistance modulation capability of devices [16].”
BAFTCNet: A model for motor imagery decoding based on EEG principles and attention mechanisms
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An IR drop-robust Mapping Method for Reliable Memristive Accelerators
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ULTRARAM: An Emerging Memory Technology for NVM and Low-Power Neuromorphic Systems
Kumar, A., Hodgson, P. D., Ashforth-Pook, J., Hayne, M., & Dasgupta, A. (2026) · IEEE International Symposium on Quality Electronic Design · doi:10.1109/isqed69900.2026.11534662
Electric Field-Moderated Filament Control for Cryogenic RRAM Synapse
Kim, Y., & Woo, J.-Y. (2026) · IEEE Electron Device Letters · doi:10.1109/led.2025.3632969
Self-citationJia, W., Hu, L., Mu, W., & Liu, W. (2025) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2025.3607913
“Gradual RS is particularly suitable for implementing the multilevel cells in synaptic devices for a neuromorphic system [25], [26].”
Artificial synapse based on ULTRARAM memory device for neuromorphic applications
Kumar, A., Hodgson, P. D., Hayne, M., & Dasgupta, A. (2025) · Journal of Applied Physics · doi:10.1063/5.0314826
“However, PCM experiences a sudden reset transition, whereas oxygen vacancy-based RRAM devices are prone to cycle-to-cycle variability and limited G max /G min ratios, which leads to asymmetric potentiation and depression characteristics [16].”
Vinuesa, G., del Val, T., Kalam, K., García, H., González, M. B., Campabadal, F., Dueñas, S., & Castán, H. (2025) · Solid-State Electronics · doi:10.1016/j.sse.2025.109262
Modeling homosynaptic and heterosynaptic plasticity with a single neuromemristive synapse
Mannan, Z. I., Azam, S., Budhathoki, R. K., Alam, M. N., & Kim, H. (2025) · Journal of Advanced Research · doi:10.1016/j.jare.2025.10.031
“They surpass traditional Von-Neumann architectures because of their high parallel processing, low power consumption, and high adaptability [4] .”
Lahkar, S., Bragaglia, V., Bagheri, B., Falcone, D., Galetta, M., Sousa, M., & Todri-Sanial, A. (2025) · ACS Applied Materials and Interfaces · doi:10.1021/acsami.5c16414
A Hybrid Encryption Framework for Federated Learning Accelerated on RRAM
Li, L., Mou, X., Ren, J., Yang, Q., & Wang, Y. (2025) · International Symposium of Electronics Design Automation (ISEDA) · doi:10.1109/iseda65950.2025.11100980
Hamun: An Approximate Computation Method to Prolong the Lifespan of ReRAM-Based Accelerators
Sabri, M., Riera, M., & González, A. (2025) · arXiv.org · doi:10.48550/arxiv.2502.01502
“The specific mechanism driving these transitions varies depending on the type of ReRAM insulator used [20, 24, 28, 48].”
State-Aware Multibit Write Algorithm for TiOx-Based Resistive Switching Memory Devices
Shi, Y., Sachdev, M., & Miao, G.-X. (2025) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2024.3521653
“Similar dependence on voltage amplitude and pulse time of RESET have also been observed on HfO 2 - [17], [18] and TaO x - based [19] RRAM devices, indicating that this strategy can be generalized to other metal oxide-based RRAM technologies.”
Gupta, S., Yadav, M. S., Tavva, V. K., & Rawat, B. (2024) · International Semiconductor Conference · doi:10.1109/cas62834.2024.10736761
“In particular, hafnium oxide ( HfO X )-based resistive switching device has emerged as a promising candidate due to its robust endurance, high uniformity, long retention capabilities, and compatibility with the current semiconductor fabrication process [7], [8].”
BackgroundUnraveling the Dynamics of HfO2-Based NW-CTT as an Artificial Synapse
Goel, A., Ansari, M. H. R., El-atab, N., & Rawat, B. (2024) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2024.3418726
“Moreover, the inherent nonlinear and nonuniform conductance modulation of these devices can lead to considerable degradation in learning accuracy and energy efficiency, limiting the potential advantages of such synapses [3].”
BackgroundMethodInfluentialAL2O3/ALOX Memristor with Nearly Ideal Synaptic Characteristics
He, Q., Wang, H.-L., Bai, Y., Hu, J., Li, H., Ma, W., Xu, Y., Zhang, Y.-S., & Yu, B. (2024) · China Semiconductor Technology International Conference · doi:10.1109/cstic61820.2024.10531814
“Previous works have proposed various structures to optimize the formation of conductive filament [4-5], but these approaches often complicate the manufacturing process.”
BackgroundResistive Switching of RF Sputtered Tin-dioxide (SnO2) Based Memristor for Non-volatile Memory
Bao, G., Liao, W.-G., Pang, Y., Wang, X.-F., Chen, X., Liu, Z., Zheng, W., & Guo, Z. (2023) · International Conference on Electrical and Control Engineering · doi:10.1109/icece59822.2023.10462310
“7[18].”
Double-Forming Mechanism of TaOx-Based Resistive Memory Device and Its Synaptic Applications
Ju, D., Kim, S., Lee, S., & Kim, S. (2023) · Materials · doi:10.3390/ma16186184
“To overcome these problems, bioinspired neuromorphic computing is attracting great attention because of its high efficiency, low power consumption, and parallel data-processing feature [4,5].”
Self-citationJeon, S., Hong, E., Kang, H., Kim, H., Kim, N., & Woo, J. (2023) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2023.3244761 · Cited by 2
“Through the development of analog RRAM [26], which can be solely controlled by the pulse number, the burden for realizing hybrid precision in w + and w − columns can be alleviated.”
MethodSelf-citationComputing-in-memory circuits and cross-layer integrated design and optimization: from SRAM to FeFET
尹, 勋., 岳, 金., 黄, 庆., 李, 超., 蔡, 嘉., 杨, 泽., 卓, 成., & 刘, 明. (2022) · Scientia Sinica Informationis · doi:10.1360/ssi-2021-0420 · Cited by 1
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Electronegative metal dopants improve switching consistency in Al2O3 resistive switching devicesPreprint
Tan, Z.-J., Somjit, V., Toparli, C., Yildiz, B., & Fang, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.13301
A 35x10 Charge-Trap Synaptic Memory for 57xMatrix Recognition
Park, E.-J., Choi, J.-M., Kwon, D.-W., Woo, J.-J., & Kwon, K.-W. (2020) · International Conference on Artificial Intelligence Circuits and Systems · doi:10.1109/aicas48895.2020.9073813
“Therefore, a lot of studies are focused to improve the recognition performance of non-volatile memories using NeuroSim [3-5].”
BackgroundSelf-citationAn Artificial Bio-Synapse Based on Ag/a-Si:Ag/a-Si/X Memristors With Different Bottom Electrode X
Chen, Y., Li, D.-Y., Liu, C., Jiang, X.-D., & Li, W. (2018) · IOP Conference Series: Materials Science and Engineering · doi:10.1088/1757-899x/452/4/042160
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“In order to resolve this issue, we have (d) identical potentiating pulses, (e) identical depressing pulses, adopted from [21].”
Conductance modulation strategy for Pt/Al2O3/HfO2/TiN memristors
Zhou, J., Liang, H., Wang, Y., Yu, W.-D., Bian, L., Wu, H., Lin, S., Tian, N., Zeng, Z., & Zhang, B. (2026) · Materials Science in Semiconductor Processing · doi:10.1016/j.mssp.2026.110750
Memristors, Spintronics and 2D Materials for Future Computing Systems
Joksas, D., AlMutairi, A., Lee, O., Cubukcu, M., Lombardo, A., Kurebayashi, H., Kenyon, A., & Mehonic, A. (2022) · arXiv.org · doi:10.48550/arxiv.2203.06147
“Approaches for dealing with this include adjusting the fabrication process [139, 140] and using digital electronic devices in conjunction with the analog ones [141].”
MethodSystem-level integration in neuromorphic co-processors
Indiveri, G., Linares-Barranco, B., & Payvand, M. (2020) · Elsevier eBooks · doi:10.1016/b978-0-08-102782-0.00018-6 · Cited by 1
Metadata
- Authors
- Woo, J., Moon, K., Song, J., Lee, S., Kwak, M., Park, J., & Hwang, H.
- Affiliations
- Pohang University of Science and Technology (KR)
- Venue
- IEEE Electron Device Letters
- Published
- 2016-08
- Volume
- 37
- Issue
- 8
- Pages
- 994-997
- ISSN
- 0741-3106 (ISSN-L)
- 1558-0563
- OpenAlex
- W2462963692
- Cited by
- 512
- References
- 15
- Citations by year
- 2026: 22 · 2025: 45 · 2024: 59 · 2023: 80 · 2022: 74 · 2021: 65 · 2020: 70 · 2019: 38 · 2018: 39 · 2017: 19 · 2016: 1
- FWCI
- 14.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Electron Device Letters (publishedVersion)
- Open Access System for Information Sharing (Pohang University of Science and Technology) (submittedVersion)
- Funders
- Ministry of Science, ICT and Future Planning through the Pioneer Research Center Program within the National Research Foundation of Korea (2012-0009460)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Ferroelectric and Negative Capacitance Devices
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Reservoir Computing
- Keywords
- resistive random access memory, synaptic behavior, neuromorphic systems, conductive filaments, set switching, barrier layer, pattern recognition, multilayer perceptron, resistive switching
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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