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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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“FeFETs exhibit a clear accuracy and latency advantage compared to other RRAM synapses, where the training time for one million images from the MNIST database reduces by 2 to 4 orders of magnitude (AlOx/HfO2 [30] and Ag:a-Si [31]).”
BackgroundEmulating Short-Term and Long-Term Plasticity of Bio-Synapse Based on Cu/a-Si/Pt Memristor
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“Recently, memristors with improved symmetry have been developed by employing complex switching media 13–16 , but they still suffer from large variability, and many of them are not compatible with direct back-end-of-line integration 15,16 .”
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“…11 – 14 , and the realization of color-distinguishable mechanism with a two-terminal memristor, competitive to enhancing its integration density even with its excellent electrical characteristics such as low operating voltage and high switching speed 15 – 17 , still remains a challenge.”
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HfO 2 -based resistive switching memory devices for neuromorphic computing
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“For instance Woo et al [53] used a AlO x /HfO 2 bilayer to demonstrate a linear conductance dynamics.”
MethodResistive Memory-Based Analog Synapse: The Pursuit for Linear and Symmetric Weight Update
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“Therefore, as a methodology to prevent the filament evolution in an unwanted direction, additional alOx [27] or TaOx [44] served as a barrier, or a thermal-enhanced layer was introduced to the f ilamentary hfO2 rraM devices.”
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Self-citationCommittee machines—a universal method to deal with non-idealities in memristor-based neural networks
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“potentiation (or depression) behavior of conductance under identical pulses (Woo et al., 2016).”
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“To implement multi-level conductance, Woo et al analyzed the response of identical pulses on a fi lamentary RRAM system with an Al / HfO 2 / Ti / TiN stack to implement the synapse function in neuromorphic computing systems [ 111 ] .”
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“The identical pulse scheme is commonly proposed to evaluate the synaptic plasticity of the memristor synapses [12].”
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To the Issue of the Memristor’s HRS and LRS States Degradation and Data Retention Time
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Memristive, Spintronic, and 2D‐Materials‐Based Devices to Improve and Complement Computing Hardware
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“Approaches for dealing with this include adjusting the fabrication process [139, 140] and using digital electronic devices in conjunction with the analog ones [141].”
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Woo, J., Lee, D.-W., Koo, Y., & Hwang, H. (2017) · Microelectronic Engineering · doi:10.1016/j.mee.2017.09.001 · Cited by 42
Self-citationZinc Tin Oxide Synaptic Device for Neuromorphic Engineering
Ryu, J., Kim, B., Hussain, F., Ismail, M., Mahata, C., Oh, T., Imran, M., Min, K., Kim, T.-H., Yang, B., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3005303 · Cited by 41
“…have frequently reported on material approaches to improve the synaptic characteristics, including using a bilayer structure, such as HfO 2 /Al 2 O 3 [36], layered two-dimensional materials such as graphene, MoS 2 , and h-BN [37], and semiconducting materials such as IGZO and ITO [38], [39].”
BackgroundPadovani, A., Woo, J., Hwang, H., & Larcher, L. (2018) · IEEE Electron Device Letters · doi:10.1109/led.2018.2821707 · Cited by 41
“RRAM device [10].”
Self-citationImproved Synaptic Behavior of CBRAM Using Internal Voltage Divider for Neuromorphic Systems
Lim, S., Kwak, M., & Hwang, H. (2018) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2018.2857494 · Cited by 40
“sumption, have been extensively studied for neuromorphic systems [4]–[7].”
BackgroundSelf-citationShim, W., Luo, Y., Seo, J.-S., & Yu, S. (2020) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2020.2985013 · Cited by 49
Technological Benchmark of Analog Synaptic Devices for Neuroinspired Architectures
Chen, P.-Y., & Yu, S. (2018) · IEEE Design and Test · doi:10.1109/mdat.2018.2890229 · Cited by 50
“, resistive memories [3]–[6] and phase change memory (PCM) [7], [8].”
BackgroundInfluentialNonideality‐Aware Training for Accurate and Robust Low‐Power Memristive Neural Networks
Joksas, D., Wang, E., Barmpatsalos, N., Ng, W. H., Kenyon, A. J., Constantinides, G. A., & Mehonic, A. (2022) · Advanced Science · doi:10.1002/advs.202105784 · Cited by 39
“Other techniques of dealing with memristor nonidealities include • in-situ (re)training of weights (or just a subset of them [14]) to recover from the effects of nonidealities [15–19], including in convolutional neural networks (CNNs) [14, 20], recurrent structures [20], and in neural networks used for reinforcement l…”
BackgroundHwang, H.-G., Pyo, Y., Woo, J., Kim, I., Kim, S., Kim, D., Kim, B., Jeong, J., & Nahm, S. (2022) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2022.163764 · Cited by 48
Self-citationMahata, C., Algadi, H., Ismail, M., Kwon, D., & Kim, S. (2021) · Journal of Material Science and Technology · doi:10.1016/j.jmst.2021.03.079 · Cited by 39
Ismail, M., Mahata, C., Abbas, H., Choi, C., & Kim, S. (2021) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2020.158416 · Cited by 44
Woo, J., & Yu, S. (2019) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2019.2917764 · Cited by 50
“In this paper, the recently reported linear weight update behavior in filamentary RRAM with HfO 2 /AlO x stack [10] was simulated.”
BackgroundMethodSelf-citationAnalog ferroelectric domain-wall memories and synaptic devices integrated with Si substrates
Wang, C., Wang, T., Zhang, W.-D., Jiang, J., Chen, L., & Jiang, A.-Q. (2021) · Nano Reseach · doi:10.1007/s12274-021-3899-5 · Cited by 38
Hadiyawarman, Budiman, F., Hernowo, D. G. O., Pandey, R. R., & Tanaka, H. (2018) · Japanese Journal of Applied Physics · doi:10.7567/jjap.57.03ea06 · Cited by 47
Kwak, H., Kim, N., Jeon, S., Kim, S., & Woo, J. (2024) · Nano Convergence · doi:10.1186/s40580-024-00415-8 · Cited by 47
“The synaptic connection strength between [39]. e Research publications related to ECRAM, which are tracked over the years two neurons is determined by the synaptic weight, which, during operation, is represented by the device’s resistance, corresponding to the channel’s conductance.”
BackgroundSelf-citationTrade‐Off Between Data Retention and Switching Speed in Resistive Switching ReRAM Devices
Siegel, S., Baeumer, C., Gutsche, A., Witzleben, M., Waser, R., Menzel, S., & Dittmann, R. (2020) · Advanced Electronic Materials · doi:10.1002/aelm.202000815 · Cited by 37
Luo, Y., & Yu, S.-M. (2020) · IEEE transactions on computers · doi:10.1109/tc.2020.3000218 · Cited by 37
“devices (TaOx/HfOx [42], AlOx/HfO2 [43], Ag:a-Si [44]).”
Sodium-based nano-ionic synaptic transistor with improved retention characteristics
Lee, K., Lee, J., Nikam, R., Heo, S., & Hwang, H. (2020) · Nanotechnology · doi:10.1088/1361-6528/abaa0e · Cited by 37
Self-citationBae, H., Kim, D., Seo, M., Jin, I.-K., Jeon, S.-B., Lee, H., Jung, S.-H., Jang, B., Son, G., Yu, K., et al. (2019) · Advanced Materials & Technologies · doi:10.1002/admt.201900151 · Cited by 37
Liu, J., Yang, H., Ji, Y., Ma, Z., Chen, K., Zhang, X., Zhang, H., Sun, Y., Huang, X., & Oda, S. (2018) · Nanotechnology · doi:10.1088/1361-6528/aad64d · Cited by 48
Multi-Level Switching of Al-Doped HfO2 RRAM with a Single Voltage Amplitude Set Pulse
Lin, J., Wang, S.-L., & Liu, H.-X. (2021) · Electronics · doi:10.3390/electronics10060731 · Cited by 36
“[27] explained that RRAM devices with an AlOx/HfOx bi-layer structure present 12 distinct analog multi-levels with on/off ratio over 10.”
BackgroundEmerging memory devices for artificial synapses
Park, Y., Kim, M., & Lee, J. (2020) · Journal of Materials Chemistry C · doi:10.1039/d0tc01500h · Cited by 74
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
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