Pattern classification by memristive crossbar circuits using ex situ and in situ training
Open accessAbstract
Memristors are memory resistors that promise the efficient implementation of synaptic weights in artificial neural networks. Whereas demonstrations of the synaptic operation of memristors already exist, the implementation of even simple networks is more challenging and has yet to be reported. Here we demonstrate pattern classification using a single-layer perceptron network implemented with a memrisitive crossbar circuit and trained using the perceptron learning rule by ex situ and in situ methods. In the first case, synaptic weights, which are realized as conductances of titanium dioxide memristors, are calculated on a precursor software-based network and then imported sequentially into the crossbar circuit. In the second case, training is implemented in situ, so the weights are adjusted in parallel. Both methods work satisfactorily despite significant variations in the switching behaviour of the memristors. These results give hope for the anticipated efficient implementation of artificial neuromorphic networks and pave the way for dense, high-performance information processing systems.
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?
Ex situ training requires little/no circuit overhead to perform quick tests of classification performance.
“Ex situ training has the advantage of requiring little/no circuit overhead to perform quick tests of the classification performance of the network”
From Hardware implementation of memristor-based artificial neural networks (2024) · cites this paper as [188]
The Discussion (page unavailable) says ex situ training is easier to implement, supporting the claim of reduced implementation burden. It does not say that it requires little or no circuit overhead or enables quick tests; it notes that ex situ training may take longer for large networks. (LLM, conf=0.83)
Experimental — accuracy not yet measuredThe NeuRRAM chip achieves 1.6-times to 2.3-times lower EDP and 7-times to 13-times higher computational density at various MVM input and output bit-precisions than previous state-of-the-art RRAM-based CIM chips, despite being fabricated at an older technology node.
“the NeuRRAM chip achieves 1.6-times to 2.3-times lower EDP and 7-times to 13-times higher computational density (measured by throughput per million of RRAMs) at various MVM input and output bit-precisions than previous state-of-the-art RRAM-based CIM chips, despite being fabricated at an older technology node17–27,36.”
From A compute-in-memory chip based on resistive random-access memory (2022) · cites this paper as [22]
Ex situ training has made possible to evaluate the performance of home-made memristive crossbar-arrays.
“and has made possible to evaluate the performance of home-made memristive crossbar-arrays93,188.”
From Hardware implementation of memristor-based artificial neural networks (2024) · cites this paper as [188]
Memristive crossbars can be fabricated with very small areal footprint.
“Memristive crossbars can also be fabricated with very small areal footprint27,29,60.”
From Neuromorphic computing with multi-memristive synapses (2018) · cites this paper as [27]
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
Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,744 · see citing works
Self-citationIntroduction to the theory of neural computation
Hertz, J., Krogh, A., & Palmer, R. G. (1994) · Neural Networks · doi:10.1016/0893-6080(94)90014-0 · Cited by 6,431 · see citing works
Nanoionics-based resistive switching memories
Waser, R., & Aono, M. (2007) · Nature Materials · doi:10.1038/nmat2023 · Cited by 4,923 · 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 · cited 1× · see citing works
“These milestones, in turn, revived interest in the development of memristor-based ANNs and led to numerous few-or single-memristor demonstrations of synaptic functionality and simple associative memory 21–28 , as well as the theoretical modelling of large-scale networks 10,29–33 .”
BackgroundMemristive 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-citationWong, H.-S. P., Lee, H.-Y., Yu, S., Chen, Y.-S., Wu, Y., Chen, P.-S., Lee, B., Chen, F. T., & Tsai, M.-J. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2190369 · Cited by 2,757 · see citing works
Lee, M.-J., Lee, C. B., Lee, D., Lee, S. R., Chang, M., Hur, J. H., Kim, Y.-B., Kim, C.-J., Seo, D. H., Seo, S., et al. (2011) · Nature Materials · doi:10.1038/nmat3070 · Cited by 2,228 · see citing works
Neuromorphic Silicon Neuron Circuits
Indiveri, G., Linares-Barranco, B., Hamilton, T. J., André, V. S., Etienne‐Cummings, R., Delbrück, T., Liu, S., Dudek, P., Hafliger, P. D., Renaud, S., et al. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00073 · Cited by 1,836 · see citing works
Short-term plasticity and long-term potentiation mimicked in single inorganic synapses
Ohno, T., Hasegawa, T., Tsuruoka, T., Terabe, K., Gimzewski, J. K., & Aono, M. (2011) · Nature Materials · doi:10.1038/nmat3054 · Cited by 1,793 · 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
Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing
Kuzum, D., Jeyasingh, R. G. D., Lee, B., & Wong, H.-S. P. (2012) · Nano Letters · doi:10.1021/nl201040y · Cited by 1,267 · see citing works
A 160-kilobit molecular electronic memory patterned at 1011 bits per square centimetre
Green, J. E., Choi, J. W., Boukai, A. I., Bunimovich, Y. L., Johnston‐Halperin, E., DeIonno, E., Luo, Y., Sheriff, B. A., Xu, K., Shin, Y. S., et al. (2007) · Nature · doi:10.1038/nature05462 · Cited by 1,230 · see citing works
<i>Introduction to the Theory of Neural Computation</i>
Hertz, J., Krogh, A., Palmer, R. G., & Horner, H. (1991) · Physics Today · doi:10.1063/1.2810360 · Cited by 1,079 · see citing works
Experimental demonstration of associative memory with memristive neural networks
Pershin, Y. V., & Di Ventra, M. (2010) · Neural Networks · doi:10.1016/j.neunet.2010.05.001 · Cited by 1,041 · see citing works
Kim, K.-H., Gaba, S., Wheeler, D., Cruz-Albrecht, J. M., Hussain, T., Srinivasa, N., & Lu, W. (2012) · Nano Letters · doi:10.1021/nl203687n · Cited by 924 · see citing works
Electrochemical metallization memories—fundamentals, applications, prospects
Valov, I., Waser, R., Jameson, J. R., & Kozicki, M. N. (2011) · Nanotechnology · doi:10.1088/0957-4484/22/25/254003 · Cited by 921 · see citing works
TEAM: ThrEshold Adaptive Memristor Model
Kvatinsky, S., Friedman, E. G., Avinoam Kolodny, A., & Weiser, U. C. (2012) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2012.2215714 · Cited by 849 · 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
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 765 · 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
Zhang, H., & Maloney, L. T. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00001 · Cited by 679 · see citing works
Govoreanu, B., Kar, G. S., Chen, Y., Paraschiv, V., Kubicek, S., Fantini, A., Radu, I. P., Goux, L., Clima, S., Degraeve, R., et al. (2011) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm.2011.6131652 · Cited by 623 · see citing works
On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex
Zamarreño-Ramos, C., Camuñas-Mesa, L. A., Pérez‐Carrasco, J. A., Masquelier, T., Serrano‐Gotarredona, T., & Linares-Barranco, B. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00026 · Cited by 558 · see citing works
High precision tuning of state for memristive devices by adaptable variation-tolerant algorithm
Alibart, F., Gao, L., Hoskins, B. D., & Strukov, D. B. (2012) · Nanotechnology · doi:10.1088/0957-4484/23/7/075201 · Cited by 557 · see citing works
Self-citationExponential 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-citationA single-transistor silicon synapse
Diorio, C., Hasler, P., Minch, A., & Mead, C. A. (1996) · IEEE Transactions on Electron Devices · doi:10.1109/16.543035 · Cited by 360 · cited 1× · see citing works
“Another prominent artificial synapse candidate with analogue memory functionality is based on a floating gate transistor and was pioneered in the 1980s by Carver Mead and his students 11,12 .”
BackgroundAn 8 Mb Multi-Layered Cross-Point ReRAM Macro With 443 MB/s Write Throughput
Kawahara, A., Azuma, R., Ikeda, Y., Kawai, K., Katoh, Y., Hayakawa, Y., Tsuji, K., Yoneda, S., 姫野, 敦., Shimakawa, K., et al. (2012) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2012.2215121 · Cited by 310 · see citing works
Adaptive oxide electronics: A review
Ha, S. D., & Ramanathan, S. (2011) · Journal of Applied Physics · doi:10.1063/1.3640806 · Cited by 305 · see citing works
Seo, K., Kim, I., Jung, S., Jo, M., Park, S., Park, J., Shin, J., Biju, K. P., Kong, J., Lee, K., et al. (2011) · Nanotechnology · doi:10.1088/0957-4484/22/25/254023 · Cited by 286 · see citing works
Self-organized computation with unreliable, memristive nanodevices
Snider, G. S. (2007) · Nanotechnology · doi:10.1088/0957-4484/18/36/365202 · Cited by 264 · see citing works
An Electronic Version of Pavlov's Dog
Ziegler, M., Soni, R., Patelczyk, T., Ignatov, M., Bartsch, T., Meuffels, P., & Kohlstedt, H. (2012) · Advanced Functional Materials · doi:10.1002/adfm.201200244 · Cited by 206 · see citing works
Simulation of a memristor-based spiking neural network immune to device variations
Querlioz, D., Bichler, O., & Gamrat, C. (2011) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2011.6033439 · Cited by 203 · see citing works
Kim, G. H., Lee, J.-H., Ahn, Y., Jeon, W., Song, S. J., Seok, J. Y., Yoon, J. H., Yoon, K. J., Park, T. J., & Hwang, C. S. (2012) · Advanced Functional Materials · doi:10.1002/adfm.201202170 · Cited by 174 · see citing works
Four-dimensional address topology for circuits with stacked multilayer crossbar arrays
Strukov, D. B., & Williams, R. S. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0906949106 · Cited by 153 · see citing works
Self-citationAn electrically modifiable synapse array of resistive switching memory
Choi, H., Jung, H., Lee, J., Yoon, J., Park, J., Seong, D.-J., Lee, W., Hasan, M., Jung, G. Y., & Hwang, H. (2009) · Nanotechnology · doi:10.1088/0957-4484/20/34/345201 · Cited by 147 · see citing works
Russo, U., Ielmini, D., Cagli, C., Lacaita, A. L., Spiga, S., Wiemer, C., Perego, M., & Fanciulli, M. (2007) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm.2007.4419062 · Cited by 147 · 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
CrossNets: High‐Performance Neuromorphic Architectures for CMOL Circuits
LIKHAREV, K., MAYR, A., MUCKRA, I., & TÜREL, Ö. (2003) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1292.010 · Cited by 121 · 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
Measuring the switching dynamics and energy efficiency of tantalum oxide memristors
Strachan, J. P., Torrezan, A. C., Medeiros-Ribeiro, G., & Williams, R. S. (2011) · Nanotechnology · doi:10.1088/0957-4484/22/50/505402 · Cited by 114 · 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
Pfeil, T., Potjans, T. C., Schrader, S., Potjans, W., Schemmel, J., Diesmann, M., & Meier, K. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00090 · Cited by 106 · see citing works
Solid-state thin-film memistor for electronic neural networks
Thakoor, S., Moopenn, A., Daud, T., & Thakoor, A. P. (1990) · Journal of Applied Physics · doi:10.1063/1.345390 · Cited by 88 · see citing works
Pavlov's dog associative learning demonstrated on synaptic-like organic transistors
Bichler, O., Zhao, W. S., Alibart, F., Pleutin, S., Lenfant, S., Vuillaume, D., & Gamrat, C. (2013) · LillOA (Université de Lille (University Of Lille)) · Cited by 84 · see citing works
Self-citationPavlov's Dog Associative Learning Demonstrated on Synaptic-Like Organic Transistors
Bichler, O., Zhao, W. S., Alibart, F., Pleutin, S., Lenfant, S., Vuillaume, D., & Gamrat, C. (2012) · Neural Computation · doi:10.1162/neco_a_00377 · Cited by 82 · see citing works
Self-citationStrukov, D. B. (2011) · Nature · doi:10.1038/476403a · Cited by 76 · see citing works
Self-citationTransition-metal-oxide-based resistance-change memories
Karg, S., Meijer, G. I., Bednorz, J. G., Rettner, C. T., Schrott, A. G., Joseph, E. A., Lam, C., Janousch, M., Staub, U., Mattina, F. L., et al. (2008) · IBM Journal of Research and Development · doi:10.1147/rd.524.0481 · Cited by 73 · see citing works
Kuzum, D., Jeyasingh, R. G. D., & Wong, H. ‐. P. (2011) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm.2011.6131643 · Cited by 63 · 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-citationMeuffels, P., & Schroeder, H. (2011) · Applied Physics A · doi:10.1007/s00339-011-6578-7 · Cited by 44 · see citing works
Stack friendly all-oxide 3D RRAM using GaInZnO peripheral TFT realized over glass substrates
Lee, M.-J., Lee, C. B., Kim, S., Yin, H., Park, J., Ahn, S. E., Kang, B. S., Kim, K. H., Stefanovich, G., Song, I., et al. (2008) · 2008 IEEE International Electron Devices Meeting · doi:10.1109/iedm.2008.4796620 · Cited by 36 · see citing works
Performance/price estimates for cortex-scale hardware: A design space exploration
Zaveri, M., & Hammerstrom, D. W. (2010) · Neural Networks · doi:10.1016/j.neunet.2010.12.003 · Cited by 19 · see citing works
Use of a-Si:H memory devices for non-volatile weight storage in artificial neural networks
Holmes, A. J., Gibson, R. A. G., Hajtó, J., Murray, A. F., Owen, A. E., Rose, M. J., & Snell, A. J. (1993) · Journal of Non-Crystalline Solids · doi:10.1016/0022-3093(93)91122-j · Cited by 12 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [20]20. Kawahara, A. et al. An 8Mb multi-layered cross-point ReRAM macro with 443MB/s write throughput. IEEE Int. Solid-State Circuits Conf. 2012, 432-434 (2012).
- [45]45. Farabet, C. et al. Machine Learning on Very Large Data Sets. (eds Bekkerman, R. et al.) 399-419 (Cambridge Press, Cambridge, MA, 2011).
- [48]48. Pickett, M. et al. Switching dynamics in a titanium dioxed memristive device. J. Appl. Phys. 106, 074508 (2009).
Cited by
Sorted by citations
Training and operation of an integrated neuromorphic network based on metal-oxide memristors
Prezioso, M., Merrikh-Bayat, F., Hoskins, B. D., Adam, G. C., Likharev, K. K., & Strukov, D. B. (2015) · Nature · doi:10.1038/nature14441 · Cited by 3,001 · see citing works
Self-citationMemory devices and applications for in-memory computing
Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R., & Eleftheriou, E. (2020) · Nature Nanotechnology · doi:10.1038/s41565-020-0655-z · Cited by 2,289 · 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
Shafiee, A. R., Nag, A., Muralimanohar, N., Balasubramonian, R., Strachan, J. P., Hu, M., Williams, R. S., & Srikumar, V. (2016) · ACM SIGARCH Computer Architecture News · doi:10.1145/3007787.3001139 · Cited by 1,538 · 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
Resistive switching materials for information processing
Wang, Z., Wu, H., Burr, G. W., Hwang, C. S., Wang, K. L., Xia, Q., & Yang, J. J. (2020) · Nature Reviews Materials · doi:10.1038/s41578-019-0159-3 · Cited by 1,214 · 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
Organic electronics for neuromorphic computing
van de Burgt, Y., Melianas, A., Keene, S. T., Malliaras, G. G., & Salleo, A. (2018) · Nature Electronics · doi:10.1038/s41928-018-0103-3 · Cited by 1,112 · see citing works
Reservoir computing using dynamic memristors for temporal information processing
Du, C., Cai, F., Zidan, M. A., Ma, W., Lee, S. H., & Lu, W. D. (2017) · Nature Communications · doi:10.1038/s41467-017-02337-y · Cited by 1,028 · see citing works
Efficient and self-adaptive in-situ learning in multilayer memristor neural networks
Li, C., Belkin, D., Li, Y., Yan, P., Hu, M., Ge, N., Jiang, H., Montgomery, E., Lin, P., Wang, Z., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04484-2 · Cited by 931 · 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
Face classification using electronic synapses
Yao, P., Wu, H., Gao, B., Eryilmaz, S. B., Huang, X., Zhang, W., Zhang, Q., Deng, N., Shi, L., Wong, H.-S. P., et al. (2017) · Nature Communications · doi:10.1038/ncomms15199 · Cited by 909 · see citing works
Neuromorphic computing with multi-memristive synapses
Boybat, I., Le Gallo, M., Nandakumar, S. R., Moraitis, T., Parnell, T., Tuma, T., Rajendran, B., Leblebici, Y., Sebastian, A., & Eleftheriou, E. (2018) · Nature Communications · doi:10.1038/s41467-018-04933-y · Cited by 903 · see citing works
A comprehensive review on emerging artificial neuromorphic devices
Zhu, J., Zhang, T., Yang, Y., & Huang, R. (2020) · Applied Physics Reviews · doi:10.1063/1.5118217 · Cited by 763 · see citing works
ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars
Shafiee, A., Nag, A., Muralimanohar, N., Balasubramonian, R., Strachan, J. P., Hu, M., Williams, R. S., & Srikumar, V. (2016) · 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA) · doi:10.1109/isca.2016.12 · Cited by 775 · see citing works
A fully integrated reprogrammable memristor–CMOS system for efficient multiply–accumulate operations
Cai, F., Correll, J. M., Lee, S. H., Lim, Y., Bothra, V., Zhang, Z., Flynn, M. P., & Lu, W. D. (2019) · Nature Electronics · doi:10.1038/s41928-019-0270-x · Cited by 728 · see citing works
Choi, S., Tan, S. H., Li, Z., Kim, Y., Choi, C., Chen, P.-Y., Yeon, H., Yu, S., & Kim, J. (2018) · Nature Materials · doi:10.1038/s41563-017-0001-5 · Cited by 690 · see citing works
Sparse coding with memristor networks
Sheridan, P. M., Cai, F., Du, C., Ma, W., Zhang, Z., & Lu, W. D. (2017) · Nature Nanotechnology · doi:10.1038/nnano.2017.83 · Cited by 619 · see citing works
Chi, 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
Review of memristor devices in neuromorphic computing: materials sciences and device challenges
Li, Y., Wang, Z., Midya, R., Xia, Q., & Yang, J. J. (2018) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/aade3f · Cited by 572 · see citing works
Lee, M., Lee, W., Choi, S., Jo, J., Kim, J., Park, S. K., & Kim, Y. (2017) · Advanced Materials · doi:10.1002/adma.201700951 · Cited by 569 · see citing works
Training of photonic neural networks through in situ backpropagation and gradient measurement
Hughes, T. W., Minkov, M., Shi, Y., & Fan, S. (2018) · Optica · doi:10.1364/optica.5.000864 · Cited by 554 · see citing works
Hardware implementation of memristor-based artificial neural networks
Aguirre, F., Sebastian, A., Le Gallo, M., Song, W., Wang, T., Yang, J. J., Lu, W., Chang, M.-F., Ielmini, D., Yang, Y., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45670-9 · Cited by 499 · see citing works
“Ex situ training has the advantage of requiring little/no circuit overhead to perform quick tests of the classi fi cation performance of the network, and hasmade possible to evaluate the performanceof home-made memristive crossbar-arrays 93,188 .”
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
Comprehensive Physical Model of Dynamic Resistive Switching in an Oxide Memristor
Kim, S., Choi, S., & Lu, W. D. (2014) · ACS Nano · doi:10.1021/nn405827t · Cited by 463 · see citing works
Spintronic Nanodevices for Bioinspired Computing
Grollier, J., Querlioz, D., & Stiles, M. D. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2016.2597152 · Cited by 432 · see citing works
Emerging Memristive Artificial Synapses and Neurons for Energy‐Efficient Neuromorphic Computing
Choi, S., Yang, J., & Wang, G. (2020) · Advanced Materials · doi:10.1002/adma.202004659 · Cited by 429 · see citing works
PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference
Ankit, A., Hajj, I. E., Chalamalasetti, S. R., Ndu, G., Foltin, M., Williams, R. S., Faraboschi, P., Hwu, W.-M. W., Strachan, J. P., Roy, K., et al. (2019) · Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems · doi:10.1145/3297858.3304049 · Cited by 424 · see citing works
Memristors for Energy‐Efficient New Computing Paradigms
Jeong, D. S., Kim, K. M., Kim, S., Choi, B. J., & Hwang, C. S. (2016) · Advanced Electronic Materials · doi:10.1002/aelm.201600090 · Cited by 414 · see citing works
Brain-inspired computing with memristors: Challenges in devices, circuits, and systems
Zhang, Y., Wang, Z., Zhu, J., Yang, Y., Rao, M., Song, W., Zhuo, Y., Zhang, X., Cui, M., Shen, L., et al. (2020) · Applied Physics Reviews · doi:10.1063/1.5124027 · Cited by 382 · 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-citationMott Memory and Neuromorphic Devices
Zhou, Y., & Ramanathan, S. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2431914 · Cited by 373 · 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
Activity-Dependent Synaptic Plasticity of a Chalcogenide Electronic Synapse for Neuromorphic Systems
Li, Y., Zhong, Y., Zhang, J., Xu, L., Wang, Q., Sun, H., Tong, H., Cheng, X., & Miao, X. (2014) · Scientific Reports · doi:10.1038/srep04906 · Cited by 344 · see citing works
Mehonic, A., Sebastian, A., Rajendran, B., Simeone, O., Vasilaki, E., & Kenyon, A. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.202000085 · Cited by 293 · see citing works
Wu, C., Kim, T. W., Choi, H. Y., Strukov, D. B., & Yang, J. J. (2017) · Nature Communications · doi:10.1038/s41467-017-00803-1 · Cited by 322 · see citing works
Self-citationNeuromemristive 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
Towards Oxide Electronics: a Roadmap
Coll, M., Fontcuberta, J., Althammer, M., Bibès, M., Boschker, H., Calleja, A., Cheng, G., Cuoco, M., Dittmann, R., Dkhil, B., et al. (2019) · Applied Surface Science · doi:10.1016/j.apsusc.2019.03.312 · Cited by 315 · see citing works
Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems
Song, M., Kang, J., Zhang, X., Ji, W., Ascoli, A., Messaris, I., Demirkol, A. Ş., Dong, B., Aggarwal, S., Wan, W., et al. (2023) · ACS Nano · doi:10.1021/acsnano.3c03505 · Cited by 314 · see citing works
Self-citationMemristive Artificial Synapses for Neuromorphic Computing
Huang, W., Xia, X., Zhu, C., Steichen, P., Quan, W., Mao, W., Yang, J., Chu, L., & Li, X. (2021) · Nano-Micro Letters · doi:10.1007/s40820-021-00618-2 · Cited by 290 · see citing works
Reservoir Computing Using Diffusive Memristors
Midya, R., Wang, Z., Asapu, S., Zhang, X., Rao, M., Song, W., Zhuo, Y., Upadhyay, N., Xia, Q., & Yang, J. J. (2019) · Advanced Intelligent Systems · doi:10.1002/aisy.201900084 · Cited by 279 · 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
Learning of spatiotemporal patterns in a spiking neural network with resistive switching synapses
Wang, W. J., Pedretti, G., Milo, V., Carboni, R., Calderoni, A., Ramaswamy, N., Spinelli, A. S., & Ielmini, D. (2018) · Science Advances · doi:10.1126/sciadv.aat4752 · Cited by 266 · see citing works
Associative memory realized by a reconfigurable memristive Hopfield neural network
Hu, S. G., Liu, Y., Liu, Z., Chen, T. P., Wang, J., Yu, Q., Deng, L. J., Yin, Y., & Hosaka, S. (2015) · Nature Communications · doi:10.1038/ncomms8522 · Cited by 255 · see citing works
Nonvolatile Memory Materials for Neuromorphic Intelligent Machines
Jeong, D. S., & Hwang, C. S. (2018) · Advanced Materials · doi:10.1002/adma.201704729 · Cited by 244 · see citing works
3-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-citationRecent progress in analog memory-based accelerators for deep learning
Tsai, H.-Y. S., Ambrogio, S., Narayanan, P., Shelby, R. M., & Burr, G. W. (2018) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/aac8a5 · Cited by 223 · see citing works
Neuromorphic electronics based on copying and pasting the brain
Ham, D., Park, H., Hwang, S., & Kim, K. (2021) · Nature Electronics · doi:10.1038/s41928-021-00646-1 · Cited by 231 · see citing works
Filamentary Switching: Synaptic Plasticity through Device Volatility
Barbera, S. L., Vuillaume, D., & Alibart, F. (2015) · ACS Nano · doi:10.1021/nn506735m · Cited by 221 · 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-citationA New Memristor with 2D Ti3C2Tx MXene Flakes as an Artificial Bio‐Synapse
Yan, X., Wang, K., Zhao, J., Zhou, Z., Wang, H., Wang, J., Zhang, L., Li, X., Xiao, Z., Zhao, Q., et al. (2019) · Small · doi:10.1002/smll.201900107 · Cited by 215 · see citing works
Organic Memory and Memristors: From Mechanisms, Materials to Devices
Yuan, L., Liu, S., Chen, W., Fan, F., & Liu, G. (2021) · Advanced Electronic Materials · doi:10.1002/aelm.202100432 · Cited by 215 · see citing works
Ferroelectric photosensor network: an advanced hardware solution to real-time machine vision
Cui, B., Fan, Z. H., Li, W., Chen, Y., Dong, S., Tan, Z., Cheng, S., Tian, B., Tao, R., Tian, G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29364-8 · Cited by 210 · see citing works
Memristive Crossbar Arrays for Storage and Computing Applications
Li, H., Wang, S., Zhang, X., Wang, W., Yang, R., Sun, Z., Feng, W., Lin, P., Wang, Z., Sun, L., et al. (2021) · Advanced Intelligent Systems · doi:10.1002/aisy.202100017 · Cited by 205 · see citing works
Vortex: variation-aware training for memristor X-bar
Liu, B., Li, H., Chen, Y., Li, X., Wu, Q., & Huang, T. (2015) · Proceedings of the 52nd Annual Design Automation Conference · doi:10.1145/2744769.2744930 · Cited by 197 · see citing works
Variability in Resistive Memories
Roldán, J. B., Miranda, E. A., Maldonado, D., Mikhaylov, A. N., Викторович, А. Н., Dubkov, A. A., Koryazhkina, M. N., González, M. B., Villena, M. A., Poblador, S., et al. (2023) · Advanced Intelligent Systems · doi:10.1002/aisy.202200338 · Cited by 199 · 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
Choi, S., Shin, J. H., Lee, J., Sheridan, P. M., & Lu, W. D. (2017) · Nano Letters · doi:10.1021/acs.nanolett.7b00552 · Cited by 188 · see citing works
Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array
Eryilmaz, S. B., Kuzum, D., Jeyasingh, R., Kim, S., BrightSky, M., Lam, C., & Wong, H.-S. P. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00205 · Cited by 205 · 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
Memristive Synapses with Photoelectric Plasticity Realized in ZnO1–x/AlOyHeterojunction
Hu, D.-C., Yang, R., Jiang, L., & Guo, X. (2018) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.8b01036 · Cited by 182 · see citing works
Yin, J., Zeng, F., Wan, Q., Li, F., Sun, Y., Hu, Y., Liu, J., Li, G., & Pan, F. (2018) · Advanced Functional Materials · doi:10.1002/adfm.201706927 · Cited by 182 · see citing works
Electronic system with memristive synapses for pattern recognition
Park, S., Chu, M., Kim, J., Noh, J., Jeon, M., Hun Lee, B., Hwang, H., Lee, B., & Lee, B.-G. (2015) · Scientific Reports · doi:10.1038/srep10123 · Cited by 181 · see citing works
Adaptive Extreme Edge Computing for Wearable Devices
Covi, E., Donati, E., Liang, X., Kappel, D., Heidari, H., Payvand, M., & Wang, W. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.611300 · Cited by 182 · 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
Pedretti, G., Milo, V., Ambrogio, S., Carboni, R., Bianchi, S., Calderoni, A., Ramaswamy, N., Spinelli, A. S., & Ielmini, D. (2017) · Scientific Reports · doi:10.1038/s41598-017-05480-0 · Cited by 165 · see citing works
Memristive LSTM Network for Sentiment Analysis
Wen, S., Wei, H., Yang, Y., Guo, Z., Zeng, Z., Huang, T., & Chen, Y. (2019) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2019.2906098 · Cited by 158 · see citing works
Mimicking Classical Conditioning Based on a Single Flexible Memristor
Wu, C., Kim, T. W., Guo, T., Li, F., Lee, D. U., & Yang, J. J. (2016) · Advanced Materials · doi:10.1002/adma.201602890 · Cited by 150 · see citing works
Recent Developments and Perspectives for Memristive Devices Based on Metal Oxide Nanowires
Milano, G., Porro, S., Valov, I., & Ricciardi, C. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201800909 · Cited by 150 · see citing works
Ferroelectric Artificial Synapses for Recognition of a Multishaded Image
Kaneko, Y., Nishitani, Y., & Ueda, M. (2014) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2014.2331707 · Cited by 149 · see citing works
Nonlinearity in memristors for neuromorphic dynamic systems
Yang, K., Joshua Yang, J., Huang, R., & Yang, Y. (2022) · Small Science · doi:10.1002/smsc.202100049 · Cited by 151 · see citing works
Xiang, S., Zhang, Y., Gong, J., Guo, X., Lin, L., & Hao, Y. (2019) · IEEE Journal of Selected Topics in Quantum Electronics · doi:10.1109/jstqe.2019.2911565 · Cited by 148 · see citing works
A Survey on Neuromorphic Computing: Models and Hardware
Shrestha, A., Fang, H., Mei, Z., Rider, D. P., Wu, Q., & Qiu, Q. (2022) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2022.3166331 · Cited by 147 · see citing works
Emulating short-term synaptic dynamics with memristive devices
Berdan, R., Vasilaki, E., Khiat, A., Indiveri, G., Serb, A., & Prodromakis, T. (2016) · Scientific Reports · doi:10.1038/srep18639 · Cited by 143 · see citing works
Essential Characteristics of Memristors for Neuromorphic Computing
Chen, W., Song, L., Wang, S., Zhang, Z., Wang, G., Hu, G., & Gao, S. (2022) · Advanced Electronic Materials · doi:10.1002/aelm.202200833 · Cited by 144 · see citing works
Perspective on photonic memristive neuromorphic computing
Goi, E., Zhang, Q., Chen, X., Luan, H., & Gu, M. (2020) · PhotoniX · doi:10.1186/s43074-020-0001-6 · 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
Bioinspired Programming of Memory Devices for Implementing an Inference Engine
Querlioz, D., Bichler, O., Vincent, A. F., & Gamrat, C. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2437616 · Cited by 138 · see citing works
Roadmap to neuromorphic computing with emerging technologies
Mehonić, A., Ielmini, D., Roy, K., Mutlu, O., Kvatinsky, S., Serrano‐Gotarredona, T., Linares-Barranco, B., Spiga, S., Savel'ev, S. E., Balanov, A. G., et al. (2024) · APL Materials · doi:10.1063/5.0179424 · Cited by 146 · see citing works
Self-citationAmorphous InGaZnO (a-IGZO) Synaptic Transistor for Neuromorphic Computing
Jang, Y., Park, J., Kang, J., & Lee, S. (2022) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.1c01088 · Cited by 136 · see citing works
Carbon Nanotube Synaptic Transistor Network for Pattern Recognition
Kim, S., Yoon, J., Kim, H., & Choi, S. (2015) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.5b08541 · 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-citationFlexible artificial nociceptor using a biopolymer-based forming-free memristor
Ge, J., Zhang, S., Liu, Z., Xie, Z., & Pan, S. (2019) · Nanoscale · doi:10.1039/c8nr08721k · Cited by 133 · see citing works
Midya, R., Wang, Z., Asapu, S., Joshi, S., Li, Y., Zhuo, Y., Song, W., Jiang, H., Upadhyay, N. K., Rao, M., et al. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201900060 · Cited by 132 · see citing works
Data Clustering using Memristor Networks
Choi, S., Sheridan, P., & Lu, W. D. (2015) · Scientific Reports · doi:10.1038/srep10492 · Cited by 132 · see citing works
Adjusting Learning Rate of Memristor-Based Multilayer Neural Networks via Fuzzy Method
Wen, S., Xiao, S., Yang, Y., Yan, Z., Zeng, Z., & Huang, T. (2018) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2018.2834436 · Cited by 130 · see citing works
Memristors Empower Spiking Neurons With Stochasticity
Al-Shedivat, M., Naous, R., Cauwenberghs, G., & Salama, K. N. (2015) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2015.2435512 · Cited by 128 · 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
A Theoretical Approach to Memristor Devices
Corinto, F., Civalleri, P. P., & Chua, L. O. (2015) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2015.2426494 · Cited by 126 · see citing works
Synaptic Characteristics of Ag/AgInSbTe/Ta-Based Memristor for Pattern Recognition Applications
Zhang, Y., Li, Y., Wang, X., & Friedman, E. G. (2017) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2017.2671433 · Cited by 125 · see citing works
Nano Meets Security: Exploring Nanoelectronic Devices for Security Applications
Rajendran, J., Karri, R., Wendt, J. B., Potkonjak, M., McDonald, N., Rose, G. S., & Wysocki, B. T. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2387353 · Cited by 125 · 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
Memristor for computing: Myth or reality?
Hamdioui, S., Kvatinsky, S., Cauwenberghs, G., Xie, L., Wald, N., Joshi, S., Mostafa, H., Corporaal, H., & Bertels, K. (2017) · Design, Automation, and Test in Europe · doi:10.23919/date.2017.7927083 · Cited by 122 · see citing works
Organic synaptic devices for neuromorphic systems
Sun, J., Fu, Y., & Wan, Q. C. (2018) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/aacd99 · Cited by 122 · see citing works
Memristor‐Based Intelligent Human‐Like Neural Computing
Wang, S., Song, L., Chen, W., Wang, G., Hao, E., Li, C., Hu, Y., Pan, Y., Nathan, A., Hu, G., et al. (2022) · Advanced Electronic Materials · doi:10.1002/aelm.202200877 · Cited by 121 · see citing works
Memristor-based analogue computing for brain-inspired sound localization with in situ training
Gao, B., Zhou, Y., Zhang, Q., Zhang, S., Yao, P., Xi, Y., Liu, Q., Zhao, M., Zhang, W., Liu, Z., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29712-8 · Cited by 122 · see citing works
The strategies of filament control for improving the resistive switching performance
Li, T., Yu, H., Chen, S. H. Y., Zhou, Y., & Han, S. (2020) · Journal of Materials Chemistry C · doi:10.1039/d0tc03639k · Cited by 110 · see citing works
Mimicking Synaptic Plasticity and Neural Network Using Memtranstors
Shen, J., Shang, D., Chai, Y., Wang, S., Shen, B., & Sun, Y. (2018) · Advanced Materials · doi:10.1002/adma.201706717 · Cited by 110 · see citing works
General memristor with applications in multilayer neural networks
Wen, S.-P., Xie, X., Yan, Z., Huang, T.-W., & Zeng, Z.-G. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.03.015 · Cited by 103 · see citing works
Memristive Synapses for Brain‐Inspired Computing
Wang, J., & Zhuge, F. (2019) · Advanced Materials Technologies · doi:10.1002/admt.201800544 · Cited by 108 · see citing works
- Authors
- Alibart, F., Zamanidoost, E., & Strukov, D. B.
- Affiliations
- University of California, Santa Barbara (US)
- Corresponding author
- Strukov, Dmitri B.
- Venue
- Nature Communications
- Published
- 2013-06-25
- Volume
- 4
- Issue
- 1
- Pages
- 2072-2072
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2155954834
- Cited by
- 642
- References
- 53
- Citations by year
- 2026: 22 · 2025: 32 · 2024: 36 · 2023: 52 · 2022: 63 · 2021: 64 · 2020: 72 · 2019: 60 · 2018: 65 · 2017: 50 · 2016: 45 · 2015: 60 · 2014: 17 · 2013: 3
- FWCI
- 25.00
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature Communications (publishedVersion)
- PubMed (publishedVersion)
- LillOA (Université de Lille (University Of Lille)) (submittedVersion, other-oa)
- HAL (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Multidisciplinary University Research Initiative
- Air Force Office of Scientific Research (FA9550-, FA9550-12, FA9550‐12‐1‐0038, FA9550‐12‐1)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Ferroelectric and Negative Capacitance Devices
- Keywords
- memristors, pattern classification, single-layer perceptron, perceptron learning rule, in situ training, synaptic weights, neuromorphic hardware
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
