RRAM-based synapse for neuromorphic system with pattern recognition function
Abstract
Feasibility of a high speed pattern recognition system using 1k-bit cross-point synaptic RRAM array and CMOS-based neuron chip has been experimentally demonstrated. Learning capability of a neuromorphic system comprising RRAM synapses and CMOS neurons has been confirmed experimentally, for the first time.
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“3MnO3 [13], Ta2O5 [14], γ-Fe2O3 [15], amorphous InGaZnO [11,16], and perovskite oxide such as LaAlO3/ SrTiO3, BiFeO3/Nb-doped SrTiO3, and amorphous SrTiO3 [7,8,17,18].”
BackgroundSelf-citationITT-RNA: Imperfection Tolerable Training for RRAM-Crossbar-Based Deep Neural-Network Accelerator
Song, Z., Sun, Y., Chen, L., Li, T., Jing, N., Liang, X., & Jiang, L. (2020) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2020.2989373 · Cited by 31 · see citing works
“Inspired from such technologies [19]–[23], we assume one weight is mapped to one memristor cell.”
BackgroundMethodChain of magnetic tunnel junctions as a spintronic memristor
Raymenants, E., Vaysset, A., Wan, D., Manfrini, M., Zografos, O., Bultynck, O., Doevenspeck, J., Heyns, M., Radu, I. P., & Devolder, T. (2018) · Journal of Applied Physics · doi:10.1063/1.5042431 · Cited by 30 · see citing works
Pentecost, L., Donato, M., Reagen, B., Gupta, U., Ma, S., Wei, G.-Y., & Brooks, D. M. (2019) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3352460.3358258 · Cited by 29 · see citing works
Memristor-based perceptron classifier: Increasing complexity and coping with imperfect hardware
Bayat, F. M., Prezioso, M., Chakrabarti, B., Kataeva, I., & Strukov, D. (2017) · 2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD) · doi:10.1109/iccad.2017.8203825 · Cited by 29 · see citing works
“INTRODUCTION Several types of emerging nonvolatile memory devices are now being actively investigated for their use in mixed-signal neuromorphic networks [1-9].”
BackgroundMemristors with Nociceptor Characteristics Using Threshold Switching of Pt/HfO2/TaOx/TaN Devices
Park, M., Jeon, B., Park, J., & Kim, S. (2022) · Nanomaterials · doi:10.3390/nano12234206 · Cited by 29 · see citing works
Self-citationEnhanced analog synaptic behavior of SiNx/a-Si bilayer memristors through Ge implantation
Kim, K., Park, S., Hu, S. M., Song, J., Lim, W., Jeong, Y., Kim, J., Lee, S., Kwak, J. Y., Park, J., et al. (2020) · NPG Asia Materials · doi:10.1038/s41427-020-00261-0 · Cited by 28 · see citing works
Self-citationKang, Y., Jang, J., Cha, D., & Lee, S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3047963 · Cited by 27 · see citing works
Self-citationFumarola, A., Sidler, S., Moon, K., Jang, J., Shelby, R. M., Narayanan, P., Leblebici, Y., Hwang, H., & Burr, G. W. (2017) · IEEE Journal of the Electron Devices Society · doi:10.1109/jeds.2017.2782184 · Cited by 27 · see citing works
“tance and excellent uniformity in a high-density, 1 kbit cross-point array [26].”
BackgroundSelf-citationReRAM Crossbar based Recurrent Neural Network for human activity detection
Long, Y., Jung, E. M., Kung, J., & Mukhopadhyay, S. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727299 · Cited by 26 · see citing works
On-Chip TaOx-Based Non-volatile Resistive Memory for in vitro Neurointerfaces
Zhuk, M. Y., Zarubin, S., Karateev, I. A., Matveyev, Y., Gornev, E. S., Krasnikov, G. Y., Negrov, D. V., & Zenkevich, A. V. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00094 · Cited by 25 · see citing works
ReRAM-based analog synapse and IMT neuron device for neuromorphic system
Moon, K., Cha, E., Lee, D., Jang, J., Park, J., & Hwang, H. (2016) · International Symposium on VLSI Technology, Systems and Applications (VLSI-TSA) · doi:10.1109/vlsi-tsa.2016.7480499 · Cited by 23 · see citing works
Self-citationElectron Doping Effect in the Resistive Switching Properties of Al/Gd1–xCaxMnO3/Au Memristor Devices
Lähteenlahti, V., Schulman, A., Beiranvand, A., Huhtinen, H., & Paturi, P. (2021) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.1c02963 · Cited by 23 · see citing works
Chen, B., Yang, H., Zhuge, F., Li, Y., Chang, T., He, Y., Yang, W., Xu, N., & Miao, X. (2019) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2019.2907541 · Cited by 22 · see citing works
“OWING to their capability of history-dependent conductance tuning, memristors have been intensely studied as artificial synapses used in neuromorphic computing [1]–[3].”
BackgroundTosson, A. M. S., Yu, S., Anis, M. H., & Wei, L. (2017) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2017.2734819 · Cited by 22 · see citing works
Bio-Cellulose Substrate for Fabricating Fully Biodegradable Resistive Random Access Devices
Huang, W., Chang, Y., Sie, Y.-F., Yu, C.-R., Wu, C., & Hsu, Y. (2021) · ACS Applied Polymer Materials · doi:10.1021/acsapm.1c00485 · Cited by 20 · see citing works
Deterministic mechanisms of spiking in diffusive memristors
Ushakov, Y., Akther, A., Borisov, P., Pattnaik, D. P., Savel'ev, S. E., & Balanov, A. G. (2021) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2021.110997 · Cited by 20 · see citing works
Zhou, Z., Liu, C., Shen, W., Dong, Z., Chen, Z., Huang, P. C., Liu, L., Liu, X., & Kang, J. (2017) · Nanoscale Research Letters · doi:10.1186/s11671-017-2023-y · Cited by 19 · see citing works
Efficient Implementation of Max-Pooling Algorithm Exploiting History-Effect in Ferroelectric-FinFETs
Rafiq, M., Parihar, S. S., Chauhan, Y. S., & Sahay, S. (2022) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2022.3207114 · Cited by 19 · see citing works
“Several non-von-Neumann CNN implementations based on hybrid-CMOS nonvolatile memories such as resistive RAMs (RRAMs) [12], [13], [14], [15], phase change memories (PCMs) [11], etc. were also proposed.”
BackgroundInGaZnO-based synaptic transistor with embedded ZnO charge-trapping layer for reservoir computing
Jang, J., Lee, J., Bae, J., Cho, S., & Kim, S. (2024) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2024.115405 · Cited by 19 · see citing works
Self-citationKim, J., Choi, J. H., Kim, S., Choi, C., & Kim, S. (2023) · Carbon · doi:10.1016/j.carbon.2023.118438 · Cited by 18 · see citing works
Self-citationRole of noise in spiking dynamics of diffusive memristor driven by heating-cooling cycles
Ushakov, Y., Balanov, A. G., & Savel'ev, S. E. (2021) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2021.110803 · Cited by 18 · see citing works
Post-silicon nano-electronic device and its application in brain-inspired chips
Lv, Y., Chen, H., Wang, Q., Li, X., Xie, C., & Song, Z. (2022) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2022.948386 · Cited by 17 · see citing works
“In the RRAM device with an interface mechanism, the resistance changes during the set and reset process are relatively gentle (Park et al., 2012, 2013; Gao et al., 2015b; Wang et al., 2015).”
MethodElectrical model of multi-level bipolar Ta2O5/TaOx Bi-layered ReRAM
Chee, H. L., Kumar, T. N., & Almurib, H. A. F. (2019) · Microelectronics Journal · doi:10.1016/j.mejo.2019.104616 · Cited by 17 · see citing works
SPICE Simulation of RRAM-Based Cross-Point Arrays Using the Dynamic Memdiode Model
Aguirre, F., Pazos, S., Palumbo, F., Suñé, J., & Miranda, E. (2021) · Frontiers of Physics · doi:10.3389/fphy.2021.735021 · Cited by 14 · see citing works
Jiang, S., Peng, L., Li, L., Dai, Q., Pei, M., Wu, C., Su, J., Gu, D., Zhang, H., Guo, H., et al. (2024) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.4c00284 · Cited by 16 · see citing works
Tang, S., Shih, Y., Shen, Y., Cyu, R., Chen, C.-T., Yang, T., Chaudhary, M., Peng, Y., Kuo, Y.-R., Miao, W., et al. (2024) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.3c01274 · Cited by 16 · see citing works
Analog high resistance bilayer RRAM device for hardware acceleration of neuromorphic computation
Jacobs-Gedrim, R. B., Agarwal, S., Goeke, R. S., Smith, C., Finnegan, P. S., Niroula, J., Hughart, D. R., Kotula, P. G., James, C. D., & Marinella, M. J. (2018) · Journal of Applied Physics · doi:10.1063/1.5042432 · Cited by 16 · see citing works
- Authors
- Park, S., Kim, H., Choo, M., Noh, J., Sheri, A., Jung, S., Seo, K., Park, J., Kim, S., Lee, W., Shin, J., Lee, D., Choi, G., Woo, J., Cha, E., Jang, J., Park, C., Jeon, M., Lee, B., Lee, B. H., & Hwang, H.
- Affiliations
- Gwangju Institute of Science and Technology (KR)
- Pohang University of Science and Technology (KR)
- Venue
- 2012 International Electron Devices Meeting
- Publisher
- IEEE
- Published
- 2012-12
- Pages
- 10.2.1-10.2.4
- ISSN
- 0163-1918 (ISSN-L)
- OpenAlex
- W1993932725
- Cited by
- 191
- References
- 4
- Citations by year
- 2026: 5 · 2025: 2 · 2024: 11 · 2023: 10 · 2022: 13 · 2021: 21 · 2020: 22 · 2019: 18 · 2018: 24 · 2017: 25 · 2016: 18 · 2015: 10 · 2014: 8 · 2013: 4
- FWCI
- 8.99
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Technical digest - International Electron Devices Meeting/Technical digest (publishedVersion)
- 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, neuromorphic systems, pattern recognition, learning ability
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