Forming-Less Compliance-Free Multistate Memristors as Synaptic Connections for Brain-Inspired Computing
Open accessAbstract
Hardware realization of artificial neural networks (ANNs) requires analogue weights to be encoded into the device conductances via blind update and access operations, leveraging Kirchhoff’s circuit laws. However, most memristive solutions lag behind in this aspect due to numerous device nonidealities, like limited number of addressable states, need for a stringent compliance current control, and an electroforming process. By modulating the oxygen vacancy profile of tin oxide switching elements, here we design and evaluate multistate memristors as synaptic connections for brain-inspired computing. Harnessing the advantages of a forming-less compliance-free operation, our devices display gradual switching transitions across multiple conductance states, sufficing the switching requirements of synaptic connections in an ANN. The soft boundary conditions are analyzed systematically, and spike-based plasticity rules, state-dependent spike-timing-dependent-plasticity (STDP) modulations, ternary digital logic, and analogue updatability schemes are proposed and demonstrated comprehensively to establish the analogue programming window of our memristors.
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The forming-free and compliance current-free properties of gradual TiOx memristors make the device more suitable for neuromorphic hardware in edge devices with simple circuit designs.
“These forming-free and compliance current-free properties of gradual TiOx memristors make the device more suitable for neuromorphic hardware in edge devices with simple circuit designs18.”
From Experimental demonstration of highly reliable dynamic memristor for artificial neuron and neuromorphic computing (2022) · cites this paper as [18]
The Conclusion (page unavailable) reports gradual, forming-free, compliance-current-free switching suitable for brain-inspired hardware; the Abstract says avoiding control transistors reduces peripheral-circuit complexity. But the device studied uses SnOx (tin oxide), not TiOx, and the cited passages do not establish suitability specifically for edge devices. (LLM, conf=0.90)
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Cites
Nanoscale Memristor Device as Synapse in Neuromorphic Systems
Jo, S. H., Chang, T., Ebong, I., Bhadviya, B. B., Mazumder, P., & Lu, W. (2010) · Nano Letters · doi:10.1021/nl904092h · Cited by 4,174 · see citing works
Memristive switching mechanism for metal/oxide/metal nanodevices
Yang, J. J., Pickett, M. D., Li, X., Ohlberg, D. A. A., Stewart, D. R., & Williams, R. S. (2008) · Nature Nanotechnology · doi:10.1038/nnano.2008.160 · Cited by 2,987 · see citing works
Self-citationNeuromorphic computing using non-volatile memory
Burr, G. W., Shelby, R. M., Sebastian, A., Kim, S., Kim, S., Sidler, S., Virwani, K. R., Ishii, M., Narayanan, P., Fumarola, A., et al. (2016) · Advances in Physics X · doi:10.1080/23746149.2016.1259585 · Cited by 1,158 · see citing works
Burr, G. W., Shelby, R. M., Sidler, S., di Nolfo, C., Jang, J., Boybat, I., Shenoy, R. S., Narayanan, P., Virwani, K., Giacometti, E. U., et al. (2015) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2015.2439635 · Cited by 936 · see citing works
Artificial synapse network on inorganic proton conductor for neuromorphic systems
Zhu, L. Q., Wan, C., Guo, L. Q., Shi, Y., & Wan, Q. C. (2014) · Nature Communications · doi:10.1038/ncomms4158 · Cited by 852 · see citing works
Integration of nanoscale memristor synapses in neuromorphic computing architectures
Indiveri, G., Linares-Barranco, B., Legenstein, R. A., Deligeorgis, G., & Prodromakis, T. (2013) · Nanotechnology · doi:10.1088/0957-4484/24/38/384010 · Cited by 608 · see citing works
Long-Term Plasticity of Intrinsic Excitability: Learning Rules and MechanismsPreprint
Daoudal, G., & Debanne, D. (2003) · Learning & Memory · doi:10.1101/lm.64103 · Cited by 537 · see citing works
Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices
Querlioz, D., Bichler, O., Dollfus, P., & Gamrat, C. (2013) · IEEE Transactions on Nanotechnology · doi:10.1109/tnano.2013.2250995 · Cited by 416 · see citing works
Leydecker, T., Herder, M., Pavlica, E., Bratina, G., Hecht, S., Orgiu, E., & Samorì, P. (2016) · Nature Nanotechnology · doi:10.1038/nnano.2016.87 · Cited by 371 · see citing works
Spike-Timing-Dependent Plasticity: A Comprehensive Overview
Markram, H., Gerstner, W., & Sjöström, P. J. (2012) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2012.00002 · Cited by 367 · see citing works
Beyond von‐Neumann Computing with Nanoscale Phase‐Change Memory Devices
Wright, C. D., Hosseini, P., & Diosdado, J. A. V. (2012) · Advanced Functional Materials · doi:10.1002/adfm.201202383 · Cited by 365 · see citing works
John, R. A., Liu, F., Chien, N. A., Kulkarni, M. R., Zhu, C., Fu, Q., Basu, A., Liu, Z., & Mathews, N. (2018) · Advanced Materials · doi:10.1002/adma.201800220 · Cited by 362 · see citing works
Self-citationMemristor-Based Multilayer Neural Networks With Online Gradient Descent Training
Soudry, D., Castro, D. D., Gal, A., Avinoam Kolodny, A., & Kvatinsky, S. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2383395 · Cited by 271 · see citing works
Kim, K. M., Zhang, J., Graves, C., Yang, J. J., Choi, B. J., Hwang, C. S., Li, Z., & Williams, R. S. (2016) · Nano Letters · doi:10.1021/acs.nanolett.6b01781 · Cited by 246 · see citing works
Self-citationSwitching Kinetic of VCM‐Based Memristor: Evolution and Positioning of Nanofilament
Chen, J.-Y., Huang, C., Chiu, C., Huang, Y., & Wu, W. (2015) · Advanced Materials · doi:10.1002/adma.201502758 · Cited by 232 · see citing works
Ionotronic Halide Perovskite Drift‐Diffusive Synapses for Low‐Power Neuromorphic Computation
John, R. A., Yantara, N., Ng, Y. F., Narasimman, G., Mosconi, E., Meggiolaro, D., Kulkarni, M. R., Gopalakrishnan, P. K., Nguyen, A. C., Angelis, F. D., et al. (2018) · Advanced Materials · doi:10.1002/adma.201805454 · Cited by 226 · see citing works
Self-citationLimits on the memory storage capacity of bounded synapses
Fusi, S., & Abbott, L. F. (2007) · Nature Neuroscience · doi:10.1038/nn1859 · Cited by 224 · see citing works
Nanoionics‐Enabled Memristive Devices: Strategies and Materials for Neuromorphic Applications
Wang, Z., Wang, L., Nagai, M., Xie, L., Yi, M., & Huang, W. (2017) · Advanced Electronic Materials · doi:10.1002/aelm.201600510 · Cited by 221 · see citing works
John, R. A., Ko, J., Kulkarni, M. R., Tiwari, N., Chien, N. A., Ng, G. I., Leong, W. L., & Mathews, N. (2017) · Small · doi:10.1002/smll.201701193 · Cited by 187 · see citing works
Self-citationExtending the road beyond CMOS
HUTCHBY, J. A., Bourianoff, G. I., Zhirnov, V. V., & Brewer, J. E. (2002) · IEEE Circuits and Devices Magazine · doi:10.1109/101.994856 · Cited by 185 · see citing works
Physical mechanisms of endurance degradation in TMO-RRAM
Chen, B., Lu, Y., Gao, B., et al. (2011) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm.2011.6131539 · Cited by 172 · see citing works
Burr, G. W., Narayanan, P., Shelby, R. M., Sidler, S., Boybat, I., di Nolfo, C., & Leblebici, Y. (2015) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm.2015.7409625 · Cited by 171 · see citing works
Interfacial Metal–Oxide Interactions in Resistive Switching Memories
Cho, D., Lübben, M., Wiefels, S., Lee, K., & Valov, I. (2017) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.7b02921 · Cited by 129 · see citing works
Abbas, Y., Jeon, Y., Sokolov, A. S., Kim, S., Ku, B., & Choi, C. (2018) · Scientific Reports · doi:10.1038/s41598-018-19575-9 · Cited by 127 · see citing works
John, R. A., Tiwari, N. G., Yaoyi, C., Ankit, A., Tiwari, N., Kulkarni, M. R., Nirmal, A., Nguyen, A. C., Basu, A., & Mathews, N. (2018) · ACS Nano · doi:10.1021/acsnano.8b05903 · Cited by 86 · see citing works
Self-citationEvidence of soft bound behaviour in analogue memristive devices for neuromorphic computing
Frascaroli, J., Brivio, S., Covi, E., & Spiga, S. (2018) · Scientific Reports · doi:10.1038/s41598-018-25376-x · Cited by 83 · see citing works
Multistate Memristive Tantalum Oxide Devices for Ternary Arithmetic
Kim, W., Chattopadhyay, A., Siemon, A., Linn, E., Waser, R. M., & Rana, V. (2016) · Scientific Reports · doi:10.1038/srep36652 · Cited by 72 · see citing works
Coexistence of Multiple Types of Synaptic Plasticity in Individual Hippocampal CA1 Pyramidal Neurons
Edelmann, E., Cepeda-Prado, E., & Leßmann, V. (2017) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2017.00007 · Cited by 48 · see citing works
Memristor Resistance Modulation for Analog Applications
Lee, T.-W., & Nickel, J. H. (2012) · IEEE Electron Device Letters · doi:10.1109/led.2012.2207429 · Cited by 42 · see citing works
Chang, Y., Fowler, B. W., Zhou, F., et al. (2016) · Applied Physics Letters · doi:10.1063/1.4940203 · Cited by 40 · see citing works
Huang, R., Yan, X., Ye, S., Kashtiban, R. J., Beanland, R., Morgan, K. A., Charlton, M. D. B., & de Groot, C. H. (2017) · Nanoscale Research Letters · doi:10.1186/s11671-017-2155-0 · Cited by 37 · see citing works
Nanodevice-based novel computing paradigms and the neuromorphic approach
Zhao, W. S., Querlioz, D., Klein, J., Chabi, D., & Chappert, C. (2012) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2012.6271812 · Cited by 32 · see citing works
Cristiano, G., Giordano, M., Ambrogio, S., Romero, L. P., Cheng, C., Narayanan, P., Tsai, H., Shelby, R., & Burr, G. (2018) · Journal of Applied Physics · doi:10.1063/1.5042462 · Cited by 27 · see citing works
Moraitis, T., Sebastian, A., & Eleftheriou, E. S. (2018) · IEEE Nanotechnology Magazine · doi:10.1109/mnano.2018.2845479 · Cited by 21 · see citing works
RRAM Endurance and Retention: Challenges, Opportunities and Implications on Reliable Design
Swaidan, Z., Kanj, R. N., Hajj, J. E., et al. (2019) · IEEE International Conference on Electronics, Circuits and Systems (ICECS) · doi:10.1109/icecs46596.2019.8964707 · Cited by 19 · see citing works
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Park, S.-O., Jeong, H., Park, J., Bae, J., & Choi, S. (2022) · Nature Communications · doi:10.1038/s41467-022-30539-6 · Cited by 331 · see citing works
“17, 18).”
Method
- Authors
- Ng, S., John, R. A., Yang, J.-T., & Mathews, N.
- Affiliations
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
- Nanyang Technological University (SG)
- Corresponding author
- Mathews, Nripan
- Venue
- ACS Applied Electronic Materials
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-02-25
- Volume
- 2
- Issue
- 3
- Pages
- 817-826
- ISSN
- 2637-6113 (ISSN-L)
- OpenAlex
- W3007191921
- Cited by
- 18
- References
- 35
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 1 · 2023: 1 · 2022: 3 · 2021: 5
- FWCI
- 0.77
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- ACS Applied Electronic Materials (publishedVersion)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- Funders
- Ministry of Education - Singapore (MOE2016-T2-1-100, MOE2018-T2-2-083)
- 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 Applications
- Keywords
- synaptic connectivity, neuromorphic computing, artificial neural networks, spike-timing-dependent plasticity, ternary logic, conductance states
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