High precision tuning of state for memristive devices by adaptable variation-tolerant algorithm
Open accessAbstract
Using memristive properties common for titanium dioxide thin film devices, we designed a simple write algorithm to tune device conductance at a specific bias point to 1% relative accuracy (which is roughly equivalent to seven-bit precision) within its dynamic range even in the presence of large variations in switching behavior. The high precision state is nonvolatile and the results are likely to be sustained for nanoscale memristive devices because of the inherent filamentary nature of the resistive switching. The proposed functionality of memristive devices is especially attractive for analog computing with low precision data. As one representative example we demonstrate hybrid circuitry consisting of an integrated circuit summing amplifier and two memristive devices to perform the analog multiply-and-add (dot-product) computation, which is a typical bottleneck operation in information processing.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
An incremental-pulse write-verify technique is used to program an RRAM cell to its target conductance.
“To program an RRAM cell to its target conductance, we use an incremental-pulse write–verify technique42.”
From A compute-in-memory chip based on resistive random-access memory (2022) · cites this paper as [42]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Redox‐Based Resistive Switching Memories – Nanoionic Mechanisms, Prospects, and Challenges
Waser, R., Dittmann, R., Staikov, G., & Szot, K. (2009) · Advanced Materials · doi:10.1002/adma.200900375 · Cited by 5,080 · see citing works
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
Resistive switching in transition metal oxides
Sawa, A. (2008) · Materials Today · doi:10.1016/s1369-7021(08)70119-6 · Cited by 2,949 · see citing works
Atomic structure of conducting nanofilaments in TiO2 resistive switching memory
Kwon, D.-H., Kim, K. M., Jang, J. H., Jeon, J. M., Lee, M. H., Kim, G. H., Li, X.-S., Park, G.-S., Lee, B., Han, S., et al. (2010) · Nature Nanotechnology · doi:10.1038/nnano.2009.456 · Cited by 2,092 · see citing works
Resistance switching memories are memristors
Chua, L. (2011) · Applied Physics A · doi:10.1007/s00339-011-6264-9 · Cited by 1,434 · see citing works
Reproducible switching effect in thin oxide films for memory applications
Beck, A., Bednorz, J. G., Gerber, C., Rossel, C., & Widmer, D. (2000) · Applied Physics Letters · doi:10.1063/1.126902 · Cited by 1,122 · see citing works
Memory effects in complex materials and nanoscale systems
Pershin, Y. V., & Di Ventra, M. (2011) · Advances In Physics · doi:10.1080/00018732.2010.544961 · Cited by 823 · see citing works
Yu, S., Wu, Y., Jeyasingh, R., Kuzum, D., & Wong, H.-S. P. (2011) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2011.2147791 · Cited by 805 · see citing works
Switching dynamics in titanium dioxide memristive devices
Pickett, M. D., Strukov, D. B., Borghetti, J. L., Yang, J. J., Snider, G. S., Stewart, D. R., & Williams, R. S. (2009) · Journal of Applied Physics · doi:10.1063/1.3236506 · Cited by 760 · see citing works
Self-citationSub-nanosecond switching of a tantalum oxide memristor
Torrezan, A. C., Strachan, J. P., Medeiros-Ribeiro, G., & Williams, R. S. (2011) · Nanotechnology · doi:10.1088/0957-4484/22/48/485203 · Cited by 701 · see citing works
Practical Approach to Programmable Analog Circuits With Memristors
Pershin, Y. V., & Di Ventra, M. (2010) · IEEE Transactions on Circuits and Systems I: Regular Papers · doi:10.1109/tcsi.2009.2038539 · Cited by 639 · see citing works
Analog Versus Digital: Extrapolating from Electronics to Neurobiology
Sarpeshkar, R. (1998) · Neural Computation · doi:10.1162/089976698300017052 · Cited by 496 · see citing works
Exponential ionic drift: fast switching and low volatility of thin-film memristors
Strukov, D. B., & Williams, R. S. (2008) · Applied Physics A · doi:10.1007/s00339-008-4975-3 · Cited by 494 · see citing works
Self-citationSynaptic behaviors and modeling of a metal oxide memristive device
Chang, T., Jo, S.-H., Kim, K.-H., Sheridan, P., Gaba, S., & Lu, W. (2011) · Applied Physics A · doi:10.1007/s00339-011-6296-1 · Cited by 432 · cited 1× · see citing works
“In some applications such variations can be tolerated by the structure of the circuits, such as the case with some versions of artificial neural networks in which memristive devices are implementing synapses [Lik11, Sni07, Yu11b, Seo11, Cha11, Jo10a].”
BackgroundCMOS Compatible Nanoscale Nonvolatile Resistance Switching Memory
Jo, S. H., & Lu, W. (2008) · Nano Letters · doi:10.1021/nl073225h · Cited by 406 · see citing works
Memristor Applications for Programmable Analog ICs
Shin, S., Kim, K., & Kang, S.-M. S. (2010) · IEEE Transactions on Nanotechnology · doi:10.1109/tnano.2009.2038610 · Cited by 385 · see citing works
Direct Identification of the Conducting Channels in a Functioning Memristive Device
Strachan, J. P., Pickett, M. D., Yang, J. J., Aloni, S., Kilcoyne, A. L. D., Medeiros‐Ribeiro, G., & Williams, R. S. (2010) · Advanced Materials · doi:10.1002/adma.201000186 · Cited by 327 · see citing works
Yu, S., Wu, Y., & Wong, H. ‐. P. (2011) · Applied Physics Letters · doi:10.1063/1.3564883 · Cited by 287 · see citing works
A Bipolar-Selected Phase Change Memory Featuring Multi-Level Cell Storage
Bedeschi, F., Fackenthal, R., Resta, C., Donze, E. M., Jagasivamani, M., Buda, E., Pellizzer, F., Chow, D. W., Cabrini, A., Calvi, G. M. A., et al. (2009) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2008.2006439 · Cited by 277 · see citing works
Ielmini, D., Nardi, F., & Cagli, C. (2011) · Nanotechnology · doi:10.1088/0957-4484/22/25/254022 · Cited by 231 · see citing works
Driscoll, T., Quinn, J., Klein, S., Kim, H. T., Kim, B.-S., Pershin, Y. V., Ventra, M. D., & Basov, D. N. (2010) · Applied Physics Letters · doi:10.1063/1.3485060 · Cited by 198 · see citing works
Hybrid CMOS/Nanoelectronic Circuits: Opportunities and Challenges
Likharev, K. K. (2008) · Journal of Nanoelectronics and Optoelectronics · doi:10.1166/jno.2008.301 · Cited by 129 · see citing works
Reversible resistive switching of SrTiOx thin films for nonvolatile memory applications
Choi, D., Lee, D., Sim, H., Chang, M., & Hwang, H. (2006) · Applied Physics Letters · doi:10.1063/1.2178405 · Cited by 129 · see citing works
CrossNets: Neuromorphic Hybrid CMOS/Nanoelectronic Networks
Likharev, K. K. (2011) · Science of Advanced Materials · doi:10.1166/sam.2011.1177 · Cited by 115 · see citing works
Diffusion of Adhesion Layer Metals Controls Nanoscale Memristive Switching
Yang, J. J., Strachan, J. P., Xia, Q., Ohlberg, D. A. A., Kuekes, P. J., Kelley, R. D., Stickle, W. F., Stewart, D. R., Medeiros‐Ribeiro, G., & Williams, R. S. (2010) · Advanced Materials · doi:10.1002/adma.201000663 · Cited by 111 · see citing works
Electrical transport and thermometry of electroformed titanium dioxide memristive switches
Borghetti, J. L., Strukov, D. B., Pickett, M. D., Yang, J. J., Stewart, D. R., & Williams, R. S. (2009) · Journal of Applied Physics · doi:10.1063/1.3264621 · Cited by 100 · see citing works
Self-citationProbing Cu doped Ge0.3Se0.7 based resistance switching memory devices with random telegraph noise
Soni, R., Meuffels, P., Petraru, A., Weides, M. P., Kügeler, C., Waser, R. M., & Kohlstedt, H. (2010) · Journal of Applied Physics · doi:10.1063/1.3291132 · Cited by 98 · see citing works
Feedback write scheme for memristive switching devices
Yi, W., Perner, F., Qureshi, M. S., Abdalla, H., Pickett, M. D., Yang, J. J., Zhang, M. M., Medeiros‐Ribeiro, G., & Williams, R. S. (2011) · Applied Physics A · doi:10.1007/s00339-011-6279-2 · Cited by 80 · see citing works
Variable gain amplifier circuit using titanium dioxide memristors
Wey, T. A., & Jemison, W. D. (2011) · IET Circuits Devices & Systems · doi:10.1049/iet-cds.2010.0210 · Cited by 57 · see citing works
Reconfigurable Hybrid CMOS/Nanodevice Circuits for Image Processing
Strukov, D. B., & Likharev, K. K. (2007) · IEEE Transactions on Nanotechnology · doi:10.1109/tnano.2007.907841 · Cited by 51 · see citing works
Self-citationLarge 1/f noise of unipolar resistance switching and its percolating nature
Lee, S. B., Park, S., Lee, J. S., Chae, S. C., Chang, S. H., Jung, M. H., Jo, Y., Kahng, B., Kang, B. S., Lee, M.-J., et al. (2009) · Applied Physics Letters · doi:10.1063/1.3237167 · Cited by 47 · see citing works
Shkabko, A., Aguirre, M. H., Marozau, I. P., Lippert, T. K., & Weidenkaff, A. (2009) · Applied Physics Letters · doi:10.1063/1.3238563 · Cited by 38 · see citing works
Arithmetic operations within memristor-based analog memory
Laiho, M., & Lehtonen, E. (2010) · International Workshop on Cellular Nanoscale Networks and their Applications (CNNA) · doi:10.1109/cnna.2010.5430319 · Cited by 37 · see citing works
Intrinsic constrains on thermally-assisted memristive switching
Strukov, D. B., & Williams, R. S. (2011) · Applied Physics A · doi:10.1007/s00339-011-6269-4 · Cited by 28 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 5
- [12]12. Kim K M, Jeong D S and Hwang C S 2011 Nanofilamentary resistive switching in binary oxide systems: a review on the present status and outlook Nanotechnology 22 254002
- [16]16. Lee J, Park J, Jung S and Hwang H 2011 Scaling effect of device area and film thickness on electrical and reliability characteristics of RRAM Proc. Int. Technology Conf. and Materials for Advanced Metallization (Dresden, May 2011) pp 1-3
- [19]19. Papandreou N, Pozidis H, Pantazi A, Sebastian A, Breitwisch M, Lam C and Eleftheriou E 2011 Programming algorithm for multilevel phase-change memory Proc. IEEE Int. Symp. on Circuits and Systems (Rio de Janeiro, May 2011) pp 329-32
- [23]23. Prodromakis T and Toumazou C 2010 A review on memristive devices and applications Proc. IEEE Int. Conf. on Electronics, Circuits, and Systems (Athens, Dec. 2010) pp 934-7
- [31]31. Strukov D B and Likharev K K 2007 Hybrid CMOS/nanodevice circuits for digital signal processing IEEE Trans. Nanotechnol. 6 696-710
Cited by
Sorted by citations
Memristive devices for computing
Yang, J. J., Strukov, D. B., & Stewart, D. R. (2013) · Nature Nanotechnology · doi:10.1038/nnano.2012.240 · Cited by 3,885 · see citing works
Self-citationTowards spike-based machine intelligence with neuromorphic computing
Roy, K., Jaiswal, A., & Panda, P. (2019) · Nature · doi:10.1038/s41586-019-1677-2 · Cited by 2,267 · see citing works
The future of electronics based on memristive systems
Zidan, M. A., Strachan, J. P., & Lu, W. D. (2018) · Nature Electronics · doi:10.1038/s41928-017-0006-8 · Cited by 2,138 · see citing works
Memristive crossbar arrays for brain-inspired computing
Xia, Q., & Yang, J. J. (2019) · Nature Materials · doi:10.1038/s41563-019-0291-x · Cited by 1,800 · see citing works
Synaptic electronics: materials, devices and applications
Kuzum, D., Yu, S., & Wong, H. ‐. P. (2013) · Nanotechnology · doi:10.1088/0957-4484/24/38/382001 · Cited by 1,257 · see citing works
Chi, P., Li, S., Xu, C., Zhang, T., Zhao, J., Liu, Y., Wang, Y., & Xie, Y. (2016) · ACM SIGARCH Computer Architecture News · doi:10.1145/3007787.3001140 · Cited by 1,226 · see citing works
Neuro-Inspired Computing With Emerging Nonvolatile Memorys
Yu, S. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2790840 · Cited by 1,170 · see citing works
Neuromorphic 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
Equivalent-accuracy accelerated neural-network training using analogue memory
Ambrogio, S., Narayanan, P., Tsai, H., Shelby, R. M., Boybat, I., di Nolfo, C., Sidler, S., Giordano, M., Bodini, M., Farinha, N. C. P., et al. (2018) · Nature · doi:10.1038/s41586-018-0180-5 · Cited by 1,151 · see citing works
A compute-in-memory chip based on resistive random-access memory
Wan, W., Kubendran, R., Schaefer, C., Eryilmaz, S. B., Zhang, W., Wu, D., Deiss, S., Raina, P., Qian, H., Gao, B., et al. (2022) · Nature · doi:10.1038/s41586-022-04992-8 · Cited by 943 · see citing works
PipeLayer: A Pipelined ReRAM-Based Accelerator for Deep Learning
Song, L., Qian, X., Li, H. H., & Chen, Y. (2017) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca.2017.55 · Cited by 841 · see citing works
Dot-product engine for neuromorphic computing
Hu, M., Strachan, J. P., Li, Z., Grafals, E. M., Dávila, N., Graves, C. E., Lam, S., Ge, N., Yang, J. J., & Williams, R. S. (2016) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/2897937.2898010 · Cited by 685 · see citing works
Pattern classification by memristive crossbar circuits using ex situ and in situ training
Alibart, F., Zamanidoost, E., & Strukov, D. B. (2013) · Nature Communications · doi:10.1038/ncomms3072 · Cited by 642 · see citing works
Self-citationChi, P., Li, S., Xu, C., Zhang, T., Zhao, J., Liu, Y., Wang, Y., & Xie, Y. (2016) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca.2016.13 · Cited by 612 · see citing works
The Future of Memristors: Materials Engineering and Neural Networks
Sun, K., Chen, J., & Yan, X. (2020) · Advanced Functional Materials · doi:10.1002/adfm.202006773 · Cited by 486 · see citing works
Resistive switching phenomena: A review of statistical physics approaches
Lee, J. S., Lee, S., & Noh, T. W. (2015) · Applied Physics Reviews · doi:10.1063/1.4929512 · Cited by 462 · see citing works
Chen, S., Mahmoodi, M. R., Shi, Y., Mahata, C., Yuan, B., Liang, X., Wen, C., Hui, F., Akinwande, D., Strukov, D. B., et al. (2020) · Nature Electronics · doi:10.1038/s41928-020-00473-w · Cited by 447 · see citing works
Self-citationUltrafast Synaptic Events in a Chalcogenide Memristor
Li, Y., Zhong, Y., Xu, L., Zhang, J., Xu, X., Sun, H., & Miao, X. (2013) · Scientific Reports · doi:10.1038/srep01619 · Cited by 423 · 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 420 · see citing works
Compute-in-Memory Chips for Deep Learning: Recent Trends and Prospects
Yu, S., Jiang, H., Huang, S., Peng, X., & Lu, A. (2021) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2021.3092533 · Cited by 399 · see citing works
Bayat, F. M., Prezioso, M., Chakrabarti, B., Nili, H., Kataeva, I., & Strukov, D. (2018) · Nature Communications · doi:10.1038/s41467-018-04482-4 · Cited by 377 · see citing works
Self-citationSemiconductor Quantum Dots for Memories and Neuromorphic Computing Systems
Lv, Z., Wang, Y., Chen, J., Wang, J., Zhou, Y., & Han, S. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.9b00730 · Cited by 367 · see citing works
Reliability of analog resistive switching memory for neuromorphic computing
Zhao, M., Gao, B., Tang, J., Qian, H., & Wu, H. (2020) · Applied Physics Reviews · doi:10.1063/1.5124915 · Cited by 349 · see citing works
Neuromemristive Circuits for Edge Computing: A Review
Krestinskaya, O., James, A. P., & Chua, L. O. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2899262 · Cited by 321 · see citing works
A Carbon Nanotube Synapse with Dynamic Logic and Learning
Kim, K.-H., Chen, C., Truong, Q., Shen, A. M., & Chen, Y. (2012) · Advanced Materials · doi:10.1002/adma.201203116 · Cited by 310 · see citing works
Memristor-Based Cellular Nonlinear/Neural Network: Design, Analysis, and Applications
Duan, S., Hu, X., Dong, Z., Wang, L., & Mazumder, P. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2334701 · Cited by 308 · see citing works
GraphR: Accelerating Graph Processing Using ReRAM
Song, L., Zhuo, Y., Qian, X., Li, H. H., & Chen, Y. (2018) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca.2018.00052 · Cited by 303 · see citing works
Physics of the Switching Kinetics in Resistive Memories
Menzel, S., Böttger, U., Wimmer, M., & Salinga, M. (2015) · Advanced Functional Materials · doi:10.1002/adfm.201500825 · Cited by 298 · see citing works
Yan, X., Zhang, L., Chen, H., Li, X., Wang, J., Liu, Q., Lü, C., Chen, J., Wu, H., & Zhou, P. (2018) · Advanced Functional Materials · doi:10.1002/adfm.201803728 · Cited by 299 · see citing works
Memristor-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
From biomaterial-based data storage to bio-inspired artificial synapse
Lv, Z., Zhou, Y., Han, S., & Roy, V. A. L. (2018) · Materials Today · doi:10.1016/j.mattod.2017.12.001 · Cited by 263 · see citing works
A general memristor-based partial differential equation solver
Zidan, M. A., Jeong, Y., Lee, J., Chen, B., Huang, S., Kushner, M. J., & Lu, W. D. (2018) · Nature Electronics · doi:10.1038/s41928-018-0100-6 · Cited by 262 · see citing works
4K-memristor analog-grade passive crossbar circuit
Kim, H., Mahmoodi, M. R., Nili, H., & Strukov, D. B. (2021) · Nature Communications · doi:10.1038/s41467-021-25455-0 · Cited by 265 · see citing works
Self-citation3-D Memristor Crossbars for Analog and Neuromorphic Computing Applications
Adam, G. C., Hoskins, B. D., Prezioso, M., Merrikh-Bayat, F., Chakrabarti, B., & Strukov, D. B. (2017) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2016.2630925 · Cited by 234 · see citing works
Self-citationEmerging Memristor-Based Logic Circuit Design Approaches: A Review
Vourkas, I., & Sirakoulis, G. C. (2016) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2016.2583673 · Cited by 225 · see citing works
Nili, H., Adam, G. C., Hoskins, B. D., Prezioso, M., Kim, J., Mahmoodi, M. R., Bayat, F. M., Kavehei, O., & Strukov, D. B. (2018) · Nature Electronics · doi:10.1038/s41928-018-0039-7 · Cited by 227 · see citing works
Self-citationAmirsoleimani, A., Alibart, F., Yon, V., Xu, J., Pazhouhandeh, M. R., Ecoffey, S., Beilliard, Y., Genov, R., & Drouin, D. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.202000115 · Cited by 216 · see citing works
Self-citationPrezioso, M., Mahmoodi, M. R., Bayat, F. M., Nili, H., Kim, H., Vincent, A., & Strukov, D. B. (2018) · Nature Communications · doi:10.1038/s41467-018-07757-y · Cited by 215 · see citing works
Self-citationSelf-Adaptive Spike-Time-Dependent Plasticity of Metal-Oxide Memristors
Prezioso, M., Bayat, F. M., Hoskins, B. D., Likharev, K. K., & Strukov, D. B. (2016) · Scientific Reports · doi:10.1038/srep21331 · Cited by 212 · see citing works
Self-citationHe, K., Wang, C., He, Y., Su, J., & Chen, X. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00527 · Cited by 202 · see citing works
Nanoionic memristive phenomena in metal oxides: the valence change mechanism
Dittmann, R., Menzel, S., & Waser, R. M. (2021) · Advances In Physics · doi:10.1080/00018732.2022.2084006 · Cited by 199 · see citing works
Synaptic plasticity functions in an organic electrochemical transistor
Gkoupidenis, P., Schaefer, N., Strakosas, X., Fairfield, J. A., & Malliaras, G. G. (2015) · Applied Physics Letters · doi:10.1063/1.4938553 · Cited by 195 · see citing works
Analog architectures for neural network acceleration based on non-volatile memory
Xiao, T. P., Bennett, C. H., Feinberg, B., Agarwal, S., & Marinella, M. J. (2020) · Applied Physics Reviews · doi:10.1063/1.5143815 · Cited by 194 · see citing works
Programmable CMOS/Memristor Threshold Logic
Gao, L., Alibart, F., & Strukov, D. B. (2013) · IEEE Transactions on Nanotechnology · doi:10.1109/tnano.2013.2241075 · Cited by 188 · see citing works
Self-citationHam, S., Kang, M., Jang, S., Jang, J., Choi, S., Kim, T.-W., & Wang, G. (2020) · Science Advances · doi:10.1126/sciadv.aba1178 · Cited by 184 · see citing works
MNSIM: Simulation Platform for Memristor-based Neuromorphic Computing System
Xia, L., Li, B., Tang, T., Gu, P., Chen, P.-Y., Yu, S., Cao, Y. K., Wang, Y., Xie, Y., & Yang, H. (2017) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2017.2729466 · Cited by 186 · see citing works
Grafals, E. M., Dávila, N., Ge, N., Williams, R. S., & Strachan, J. P. (2016) · Nanotechnology · doi:10.1088/0957-4484/27/36/365202 · Cited by 183 · see citing works
Binary convolutional neural network on RRAM
Tang, T., Xia, L., Li, B., Wang, Y., & Yang, H. (2017) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1109/aspdac.2017.7858419 · Cited by 179 · see citing works
Nonvolatile Multistates Memories for High-Density Data Storage
Cao, Q., Lü, W., Wang, X. R., Guan, X., Wang, L., Yan, S., Wu, T., & Wang, X. (2020) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.0c10184 · Cited by 173 · see citing works
A Memristive Multilayer Cellular Neural Network With Applications to Image Processing
Hu, X., Feng, G., Duan, S., & Liu, L. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2552640 · Cited by 173 · see citing works
Bulk mixed ion electron conduction in amorphous gallium oxide causes memristive behaviour
Aoki, Y., Wiemann, C., Feyer, V., Kim, H., Schneider, C. M., Yoo, H., & Martin, M. (2014) · Nature Communications · doi:10.1038/ncomms4473 · Cited by 171 · see citing works
Multi-level control of conductive nano-filament evolution in HfO 2 ReRAM by pulse-train operations
Zhao, L., Chen, H.-Y., Wu, S., Jiang, Z., Yu, S., Hou, T., Wong, H. ‐. P., & Nishi, Y. (2014) · Nanoscale · doi:10.1039/c4nr00500g · Cited by 163 · see citing works
Emerging perovskite materials for high density data storage and artificial synapses
Wang, Y., Lv, Z., Zhou, L., Chen, X., Chen, J., Zhou, Y., Roy, V. A. L., & Han, S. (2018) · Journal of Materials Chemistry C · doi:10.1039/c7tc05326f · Cited by 161 · see citing works
He, Z., Lin, J., Ewetz, R., Yuan, J. S., & Fan, D. (2019) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/3316781.3317870 · Cited by 150 · see citing works
Stochastic resonance in a metal-oxide memristive device
Mikhaylov, A. N., Guseinov, D. V., Belov, A. I., Korolev, D. S., Shishmakova, V. A., Koryazhkina, M. N., Filatov, D. O., Gorshkov, O. N., Maldonado, D., Alonso, F., et al. (2021) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2021.110723 · Cited by 143 · see citing works
Hänsch, W., Raghunathan, A., Roy, K., Chakrabarti, B., Phatak, C., Wang, C., & Guha, S. (2022) · Advanced Materials · doi:10.1002/adma.202204944 · Cited by 145 · see citing works
Liu, X., Mao, M., Liu, B., Li, H. H., Chen, Y., Li, B., Wang, Y., Jiang, H., Barnell, M. D., Wu, Q., et al. (2015) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/2744769.2744900 · Cited by 142 · see citing works
Fully parallel write/read in resistive synaptic array for accelerating on-chip learning
Gao, L., Wang, I., Chen, P.-Y., Vrudhula, S. B. K., Seo, J.-S., Cao, Y. K., Hou, T., & Yu, S. (2015) · Nanotechnology · doi:10.1088/0957-4484/26/45/455204 · Cited by 138 · see citing works
Self-citationResistive Memory-Based Analog Synapse: The Pursuit for Linear and Symmetric Weight Update
Woo, J., & Yu, S.-M. (2018) · IEEE Nanotechnology Magazine · doi:10.1109/mnano.2018.2844902 · Cited by 88 · see citing works
RRAM-Based Analog Approximate Computing
Li, B., Gu, P., Shan, Y., Wang, Y., Chen, Y., & Yang, H. (2015) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2015.2445741 · Cited by 135 · see citing works
Neuromorphic Computing Based on Emerging Memory Technologies
Rajendran, B., & Alibart, F. (2016) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2016.2533298 · Cited by 135 · see citing works
Self-citationMaking Memristive Neural Network Accelerators Reliable
Feinberg, B., Wang, S., & İpek, E. (2018) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca.2018.00015 · Cited by 130 · see citing works
Choi, S., Jang, S., Moon, J., Kim, J. C., Jeong, H. Y., Jang, P.-H., Lee, K., & Wang, G. (2018) · NPG Asia Materials · doi:10.1038/s41427-018-0101-y · Cited by 129 · see citing works
Continuous Electrical Tuning of the Chemical Composition of TaOx-Based Memristors
Miao, F., Yi, W., Goldfarb, I., Yang, J. J., Zhang, M.-X., Pickett, M. D., Strachan, J. P., Medeiros‐Ribeiro, G., & Williams, R. S. (2012) · ACS Nano · doi:10.1021/nn2044577 · Cited by 127 · see citing works
Memristor crossbar deep network implementation based on a Convolutional neural network
Yakopcic, C., Alom, M. Z., & Taha, T. M. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727302 · Cited by 128 · see citing works
Advances in Memristor-Based Neural Networks
Xu, W., Wang, J., & Yan, X. (2021) · Frontiers in Nanotechnology · doi:10.3389/fnano.2021.645995 · Cited by 128 · see citing works
ReRAM-Based Processing-in-Memory Architecture for Recurrent Neural Network Acceleration
Long, Y., Na, T., & Mukhopadhyay, S. (2018) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2018.2819190 · Cited by 123 · see citing works
Xcel-RAM: Accelerating Binary Neural Networks in High-Throughput SRAM Compute Arrays
Agrawal, A., Jaiswal, A., Roy, D., Han, B., Srinivasan, G., Ankit, A., & Roy, K. (2019) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2019.2907488 · Cited by 121 · see citing works
Multilayer Metal‐Oxide Memristive Device with Stabilized Resistive Switching
Mikhaylov, A. N., Belov, A. I., Korolev, D. S., Antonov, I. N., Kotomina, V. E., Kotina, A., Gryaznov, E. G., Шарапов, А. Н., Koryazhkina, M. N., Kryukov, R. N., et al. (2019) · Advanced Materials Technologies · doi:10.1002/admt.201900607 · Cited by 118 · see citing works
Enabling Scientific Computing on Memristive Accelerators
Feinberg, B., Vengalam, U. K. R., Whitehair, N., Wang, S., & İpek, E. (2018) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca.2018.00039 · Cited by 112 · see citing works
Wijesinghe, P., Ankit, A., Sengupta, A., & Roy, K. (2018) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2018.2829924 · Cited by 111 · see citing works
Resistive random-access memory based on ratioed memristors
Lastras-Montaño, M. Á., & Cheng, K.-T. T. (2018) · Nature Electronics · doi:10.1038/s41928-018-0115-z · Cited by 108 · see citing works
Applicability of Well-Established Memristive Models for Simulations of Resistive Switching Devices
Linn, E., Siemon, A., Waser, R. M., & Menzel, S. (2014) · RWTH Publications (RWTH Aachen) · Cited by 108 · see citing works
Mahmoodi, M. R., Prezioso, M., & Strukov, D. B. (2019) · Nature Communications · doi:10.1038/s41467-019-13103-7 · Cited by 101 · see citing works
Self-citationEfficient training algorithms for neural networks based on memristive crossbar circuits
Kataeva, I., Merrikh-Bayat, F., Zamanidoost, E., & Strukov, D. B. (2015) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280785 · Cited by 101 · see citing works
Self-citationElectrochemical Ionic Synapses: Progress and Perspectives
Huang, M., Schwacke, M., Onen, M., del Alamo, J. A., Li, J., & Yildiz, B. (2022) · Advanced Materials · doi:10.1002/adma.202205169 · Cited by 105 · see citing works
Applicability of Well-Established Memristive Models for Simulations of Resistive Switching Devices
Linn, E., Siemon, A., Waser, R., & Menzel, S. (2014) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2014.2332261 · Cited by 102 · see citing works
Extremely parallel memristor crossbar architecture for convolutional neural network implementation
Yakopcic, C., Alom, M. Z., & Taha, T. M. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966055 · Cited by 102 · see citing works
Programming Protocol Optimization for Analog Weight Tuning in Resistive Memories
Gao, L., Chen, P.-Y., & Yu, S. (2015) · IEEE Electron Device Letters · doi:10.1109/led.2015.2481819 · Cited by 101 · see citing works
Self-citationXia, L., Tang, T., Huangfu, W., Cheng, M., Yin, X., Li, B., Wang, Y., & Yang, H. (2016) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/2897937.2898101 · Cited by 100 · see citing works
In-Memory Computing with Resistive Memory Circuits: Status and Outlook
Pedretti, G., & Ielmini, D. (2021) · Electronics · doi:10.3390/electronics10091063 · Cited by 99 · see citing works
Stromatias, E., Neil, D., Pfeiffer, M., Galluppi, F., Furber, S. B., & Liu, S. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00222 · Cited by 98 · see citing works
Modeling affections with memristor-based associative memory neural networks
Hu, X., Duan, S., Chen, G., & Chen, L. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.10.028 · Cited by 92 · see citing works
Interfacial versus filamentary resistive switching in TiO2 and HfO2 devices
Sassine, G., Barbera, S. L., Najjari, N., Minvielle, M., Dubourdieu, C., & Alibart, F. (2016) · Journal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · doi:10.1116/1.4940129 · Cited by 91 · see citing works
Self-citationBellanthudawa, B. K. A., Nawalage, N. M. S. K., Handapangoda, H. M. A. K., Suvendran, S., Wijayasenarathne, K. A. S. H., Rathnasuriya, M. L. D., Wickramasinghe, P. G. M. U., Aberathna, A. A. A. U., Tennakoon, A., & Perera, I. J. J. U. N. (2023) · Nanotechnology for Environmental Engineering · doi:10.1007/s41204-023-00344-7 · Cited by 99 · see citing works
AgInSbTe memristor with gradual resistance tuning
Zhang, J. J., Sun, H., Li, Y., Wang, Q., Xu, X. H., & Miao, X. (2013) · Applied Physics Letters · doi:10.1063/1.4804983 · Cited by 88 · see citing works
Chou, T., Tang, W., Botimer, J., & Zhang, Z. (2019) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3352460.3358328 · Cited by 87 · see citing works
Memristors for Hardware Security Applications
Pang, Y., Gao, B., Lin, B., Qian, H., & Wu, H. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201800872 · Cited by 89 · see citing works
An associative memory circuit based on physical memristors
Guo, M., Zhu, Y., Liu, R.-Y., Zhao, K.-X., & Dou, G. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.11.034 · Cited by 68 · see citing works
Boolean Logic Operations and Computing Circuits Based on Memristors
Papandroulidakis, G., Vourkas, I., Vasileiadis, N., & Sirakoulis, G. C. (2014) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2014.2357351 · Cited by 84 · see citing works
Schmitt, R., Kubicek, M., Sediva, E., Trassin, M., Weber, M. C., Rossi, A. M., Hutter, H., Kreisel, J., Fiebig, M., & Rupp, J. L. M. (2018) · Advanced Functional Materials · doi:10.1002/adfm.201804782 · Cited by 83 · see citing works
Uniting Gradual and Abrupt set Processes in Resistive Switching Oxides
Fleck, K., Torre, C. L., Aslam, N., Hoffmann‐Eifert, S., Böttger, U., & Menzel, S. (2016) · Physical Review Applied · doi:10.1103/physrevapplied.6.064015 · Cited by 81 · see citing works
Emerging memory technologies for neuromorphic computing
Kim, C.-H., Lim, S., Woo, S. Y., Kang, W.-M., Seo, Y.-T., Lee, S., Lee, S., Kwon, D., Oh, S., Noh, Y., et al. (2018) · Nanotechnology · doi:10.1088/1361-6528/aae975 · Cited by 82 · see citing works
Ismail, M., Rasheed, M., Mahata, C., Kang, M., & Kim, S. (2023) · Nano Convergence · doi:10.1186/s40580-023-00380-8 · Cited by 80 · see citing works
Novel charm of 2D materials engineering in memristor: when electronics encounter layered morphology
Batool, S. A., Idrees, M., Zhang, S., Han, S., & Zhou, Y. (2022) · Nanoscale Horizons · doi:10.1039/d2nh00031h · Cited by 79 · see citing works
Khot, A. C., Dongale, T. D., Nirmal, K. A., Sung, J. H., Lee, H. J., Nikam, R. D., & Kim, T. G. (2022) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.1c23268 · Cited by 78 · see citing works
Yttria-stabilized zirconia cross-point memristive devices for neuromorphic applications
Emelyanov, A. V., Nikiruy, K. E., Demin, V. A., Rylkov, V. V., Belov, A. I., Korolev, D. S., Gryaznov, E. G., Павлов, Д. А., Gorshkov, O. N., Mikhaylov, A. N., et al. (2019) · Microelectronic Engineering · doi:10.1016/j.mee.2019.110988 · Cited by 75 · see citing works
Applications and Techniques for Fast Machine Learning in Science
Deiana, A. M., Tran, N. V., Agar, J., Blott, M., Guglielmo, G. D., Duarte, J. M., Harris, P. C., Hauck, S., Liu, M., Neubauer, M. S., et al. (2022) · Frontiers in Big Data · doi:10.3389/fdata.2022.787421 · Cited by 81 · see citing works
Self-citationTemperature-insensitive analog vector-by-matrix multiplier based on 55 nm NOR flash memory cells
Guo, X., Bayat, F. M., Prezioso, M., Chen, Y., Nguyen, B. Q., Do, N., & Strukov, D. B. (2017) · Custom Integrated Circuits Conference · doi:10.1109/cicc.2017.7993628 · Cited by 77 · see citing works
Self-citationLiu, X., Mao, M., Liu, B., Li, B., Wang, Y., Jiang, H., Barnell, M. D., Wu, Q., Yang, J. J., Li, H. H., et al. (2016) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2016.2529279 · Cited by 72 · see citing works
- Authors
- Alibart, F., Gao, L., Hoskins, B. D., & Strukov, D. B.
- Affiliations
- University of California, Santa Barbara (US)
- Venue
- Nanotechnology
- Publisher
- IOP Publishing
- Published
- 2012-01-20
- Volume
- 23
- Issue
- 7
- Pages
- 075201
- ISSN
- 0957-4484 (ISSN-L)
- 1361-6528
- OpenAlex
- W2091777687
- Cited by
- 557
- References
- 35
- Citations by year
- 2026: 9 · 2025: 26 · 2024: 21 · 2023: 37 · 2022: 37 · 2021: 45 · 2020: 56 · 2019: 73 · 2018: 58 · 2017: 43 · 2016: 47 · 2015: 39 · 2014: 27 · 2013: 22 · 2012: 16
- FWCI
- 21.27
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nanotechnology (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1028336, 1121053)
- Division of Materials Research (DMR 1121053, 1121053)
- 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 › Engineering › Electrical and Electronic Engineering › Advancements in Semiconductor Devices and Circuit Design
- Keywords
- memristors, TiO2 thin films, resistive switching, conductance tuning, variation tolerance, filamentary switching, non-volatile memory, analog computing, dot product computation, summing amplifier
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 ›
Are you an author, or do you see a problem with this page? Request a review
