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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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Synaptic electronics: materials, devices and applications
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“On the other hand, the interfacial device typically shows both gradual SET and RESET processes, as demonstrated in Ag:a-Si [77], PrCaMnO3 (PCMO) [78], [79], and TaOx/TiO2 [80], [81] devices.”
BackgroundNeuromorphic computing using non-volatile memory
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“Continuously increasing potentiation (depression) behaviors were observed upon iterative programming with identical pulses of negative (positive) bias [169,170].”
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An Artificial Neuron Based on a Threshold Switching Memristor
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“memristors present promising characteristics for developing artificial synapses to realize brain-inspired computing [1]–[3].”
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“1), extending upon earlier work [6].”
Self-citationOxide-based RRAM materials for neuromorphic computing
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“In 2012 and 2013, Park [138, 139] demonstrated 1-kbit cross-point array neuromorphic system based on PCMO synaptic devices.”
BackgroundInfluentialA Compact Model for Metal–Oxide Resistive Random Access Memory With Experiment Verification
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“Beyond the traditional data storage, various RRAM applications have been reported, such as using RRAM devices in neuromorphic systems, ternary content-addressable-memory (TCAM), and nonvolatile SRAM [11]–[16].”
MethodRRAM-based synapse devices for neuromorphic systems
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Self-citationBio-Inspired Stochastic Computing Using Binary CBRAM Synapses
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“memory (CBRAM) and oxide based memory (OXRAM) have been shown as good candidates for emulation of synaptic plasticity and learning rules like spike-timing dependent plasticity (STDP) [5],[6],[7],[8],[9].”
BackgroundIn-memory Learning with Analog Resistive Switching Memory: A Review and Perspective
Xi, Y., Gao, B., Tang, J., Chen, A., Chang, M., Hu, X. S., der Spiegel, J. V., Qian, H., & Wu, H. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2020.3004543 · Cited by 200
“These include the use of set-only two-RRAM per cell, reset-only two-RRAM per cell [51]–[53], multiple-weak-filament RRAM [54], [55], and interfacial switching RRAM [23], [56]–[59].”
MethodNanoionic memristive phenomena in metal oxides: the valence change mechanism
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Characterization and Modeling of Nonfilamentary Ta/TaOx/TiO2/Ti Analog Synaptic Device
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“However, exotic materials, such as praseodymium calcium manganese oxide (PCMO) [12,13], and processes incompatible with semiconductor fabrication, such as sol-gel [14], are often required for fabricating these devices, thus posing additional challenges for high-density integration.”
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High-Performance Mixed-Signal Neurocomputing With Nanoscale Floating-Gate Memory Cell Arrays
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Memristor devices for neural networks
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“Until now, many types of synaptic devices based on RRAM such as GdOx/Cu:MoOx [246], TiOx [232], WOx [247], FeOx/SiOx [248], HfOx [249], AlOx [250], TiOx/HfOx multilayers [251], and PCMO [252] have been reported.”
BackgroundFully parallel write/read in resistive synaptic array for accelerating on-chip learning
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“Compared to the reported devices [ 14, 20 ] , the nonlinearity and temporal variations of the weight update curve ( conductance versus number of pulses ) are signi fi cantly suppressed, which enables us to tune the conductance to any particular state requiring no feedback.”
BackgroundResultResistive Memory-Based Analog Synapse: The Pursuit for Linear and Symmetric Weight Update
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“The reversibly modulated alOx thickness by the field-driven oxygen ions depending on the bias polarity not only enables analogously changed resistance states in the PcMO device during both potentiation and depression, but it also allows for excellent cycle-to-cycle and cell-to-cell uniformities of the multiple states…”
BackgroundSelf-citationRathi, N., Panda, P., & Roy, K. (2018) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2018.2819366 · Cited by 136
A 250 mV Cu/SiO2/W Memristor with Half-Integer Quantum Conductance States
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ReRAM-Based Processing-in-Memory Architecture for Recurrent Neural Network Acceleration
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“A lot of works have been done to explore physical resistive switching mechanism of ReRAM [25]–[27].”
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Wu, H., Wang, X. H., Gao, B., Deng, N., Lu, Z., Haukness, B., Bronner, G. B., & Qian, H. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2684830 · Cited by 117
“Some advanced tasks such as pattern and speech recognitions have been demonstrated based on this method [158], [159], [167], [168].”
MethodTwo-dimensional materials for synaptic electronics and neuromorphic systems
Wang, S., Zhang, D. W., & Zhou, P. (2019) · 中国科学通报：英文版 · doi:10.1016/j.scib.2019.01.016 · Cited by 108
“…are in rapid development, including metal oxide (such as AlO x [6–8], HfO x [9–13], TiO x [14–16], WO x [17,18], TaO x [19], etc.) RRAM [6,20–25], Ag or Cu based conductive bridge synapses [16,26–35], ferroelectric material based synapses [36–39], phase change (PC) material based…”
BackgroundAll Spin Artificial Neural Networks Based on Compound Spintronic Synapse and Neuron
Zhang, D., Zhao, W. S., Zeng, L., Cao, K., Wang, M., Peng, S., Zhang, Y., Zhang, Y., Klein, J., & Wang, Y. (2016) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2016.2533798 · Cited by 105
Neuroinspired unsupervised learning and pruning with subquantum CBRAM arrays
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“Non-volatile memory-based synaptic devices such as phase change synapses (PCM) 16,17 , Ag-based conductive bridging synapses (CBRAM) 18 , and resistive RAM synapses (RRAM) 19 – 21 have been investigated for implementing synaptic weight updates during neural network operation.”
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“phase change memory [3], [4], spin-based memory [5], and resistive switching memory (RRAM) [6]–[11].”
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“It also inspired intriguing applications, such as pattern recognition and auditory processing [4-7].”
BackgroundProgramming Protocol Optimization for Analog Weight Tuning in Resistive Memories
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“In addition, many resistive memories can tune the conductance continuously by applying voltage pulses consecutively [2]–[5].”
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“access memory (RAM) [3], magnetic RAM [4], phase-change memory [5], [6], and resistive memory (RRAM) [7]–[13], have been suggested.”
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“…al., 2011; Bichler et al., 2012; Kuzum et al., 2012), organic-based switches (Bichler et al., 2010), chalcogenide-based switches (Ohno et al., 2011; Suri et al., 2013) and oxide-based resistive switching memories (Seo et al., 2011; Yu et al., 2011, 2013; Park et al., 2012; Ambrogio et al., 2013).”
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“This is essential to evaluate the technology potential and limitations for specific applications such as nonvolatile memory (NVM) [13]–[16], neuromorphic computing [17]–[20], physical unclonable function (PUF), and random number generator (RNG) [21]–[23].”
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“Figure 2 Analog memristors with interface-switching mechanism and b memristor with the interface-switching mechanism [10,11], where the memris memristor with the filamentary-switching mechanism [12-17], where the mem In this paper, we propose a binary memristor crossbar circuit for recognizing five different vowels.”
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“Recently, we demonstrated 1 kbit Al/PCMO-based synaptic arrays for use in neuromorphic systems [25].”
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“However, previous studies on RRAM for neuromorphic applications have demonstrated non-linear conductance characteristics due to intrinsic conduction mechanisms such as Schottky barrier (interface type) [2]–[4] or non-metallic filament (filamentary type) [5]–[7].”
BackgroundSelf-citationOptimization of non-linear conductance modulation based on metal oxide memristors
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“[9, 138] are researchers who have done research in this area.”
BackgroundImplementation of reservoir computing using volatile WO -based memristor
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Self-citationAnalog Synapse Device With 5-b MLC and Improved Data Retention for Neuromorphic System
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“shows excellent uniformity [10], [11], [13].”
MethodInfluentialSelf-citationPrezioso, M., Kataeva, I., Merrikh‐Bayat, F., Hoskins, B. D., Adam, G. C., Sota, T., Likharev, K. K., & Strukov, D. B. (2015) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm.2015.7409719 · Cited by 51
Resistive switching in microscale anodic titanium dioxide-based memristors
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“Furthermore, the ultra-dense crossbar array consisting of memristors have been recognized as a potentially promising path to building neuromorphic computing systems that can mimic the massive parallelism and extremely low-power operations found in the human brain [6].”
BackgroundSelf-citationHigh density neuromorphic system with Mo/Pr0.7Ca0.3MnO3 synapse and NbO2 IMT oscillator neuron
Moon, K., Cha, E., Park, J., Gi, S.-G., Chu, M., Baek, K., Lee, B., Oh, S., & Hwang, H. (2015) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm.2015.7409721 · Cited by 48
“Among the various synapse devices, RRAM based analog synapse device has a strong potential due to the simple 2-terminal device structure, spiketiming dependent plasticity and low power consumption [1].”
BackgroundSelf-citationConvolutional Neural Networks Based on RRAM Devices for Image Recognition and Online Learning Tasks
Dong, Z., Zhou, Z., Li, Z., Liu, C., Huang, P. C., Liu, L., Liu, X., & Kang, J. (2018) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2018.2882779 · Cited by 51
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Oxide based nanoscale analog synapse device for neural signal recognition system
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“For high performance of neuromorphic system, the synapse device should have analog type multi-bit operation and symmetric weight change between both potentiation and depression [5-11].”
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Mohanan, K. U. (2024) · Nanomaterials · doi:10.3390/nano14060527 · Cited by 48
Annealing Effect on the Performance of Copper Oxide Resistive Memory Devices
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“In these applications, RRAM devices with a high conductance ON/OFF ratio are required [22]–[24].”
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Self-citationComplex Learning in Bio-plausible Memristive Networks
Deng, L., Li, G., Deng, N., Wang, D., Zhang, Z., He, W., Li, H., Pei, J., & Shi, L. (2015) · Scientific Reports · doi:10.1038/srep10684 · Cited by 43
“D memrsitor integration technology 25,64 and improve the compatibility with CMOS process 65 .”
An FPGA-Based Hardware Emulator for Neuromorphic Chip With RRAM
Luo, T., Wang, X., Qu, C., Lee, M. K. F., Tang, W. T., Wong, W., & Goh, R. S. M. (2018) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2018.2889670 · Cited by 43
“[33] fabricated a 1 kb crosspoint synaptic RRAM array and CMOS-based neural chip, and validated the chip experimentally.”
BackgroundMethodRandom sparse adaptation for accurate inference with inaccurate multi-level RRAM arrays
Mohanty, A., Du, X., Chen, P.-Y., Seo, J.-S., Yu, S., & Cao, Y. K. (2017) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm.2017.8268339 · Cited by 42
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Evaluating tantalum oxide stoichiometry and oxidation states for optimal memristor performance
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Metal oxide resistive random access memory based synaptic devices for brain-inspired computing
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Improved Synaptic Behavior of CBRAM Using Internal Voltage Divider for Neuromorphic Systems
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Self-citationCompact Neuromorphic System With Four-Terminal Si-Based Synaptic Devices for Spiking Neural Networks
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“The on-chip learning was trained using the adaptive momentum estimation (ADAM) algorithm to perform handwritten digit classification for the MNIST data set [23]–[25].”
MethodSelf-citationAnalog resistive switching behavior of Al/Nb2O5/Al device
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A differential memristive synapse circuit for on-line learning in neuromorphic computing systems
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“Clearly, the resulting synaptic circuit is much larger in area than synapse elements employed in dense 1R or 1T-1R crossbar arrays [16, 38, 40].”
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Self-citationReRAM‐Based Neuromorphic Computing
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Forgetting memristor based neuromorphic system for pattern training and recognition
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Electroforming in Metal-Oxide Memristive Synapses
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Impact of PCM resistance-drift in neuromorphic systems and drift-mitigation strategy
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“These RRAM technologies may include devices such as phase change memory (PCM) [5], [6] conductive-bridge (CBRAM) or programmablemetallization cell (PMC) [4], [7] and oxide based resistive memory (OXRAM) [8], [9], [10].”
MethodPark, S.-O., Park, T., Jeong, H., Hong, S., Seo, S., Kwon, Y., Lee, J., & Choi, S. (2023) · Nanoscale Horizons · doi:10.1039/d3nh00121k · Cited by 31
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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
“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 28
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
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
“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
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
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
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
“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 25
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
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
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
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
“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 23
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
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
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
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
“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
Self-citationKim, J., Choi, J. H., Kim, S., Choi, C., & Kim, S. (2023) · Carbon · doi:10.1016/j.carbon.2023.118438 · Cited by 18
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
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
“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
SPICE Simulation of RRAM-Based Cross-Point Arrays Using the Dynamic Memdiode Model
Aguirre, F. L., Pazos, S. M., Palumbo, F., Suñé, J., & Miranda, E. A. (2021) · Frontiers in Physics · doi:10.3389/fphy.2021.735021 · Cited by 16
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 15
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
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
Metadata
- 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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