The DOI matches its Crossref record; 103 of the 103 references it cites were found in a registry or an index.
Memristive technologies for data storage, computation, encryption, and radio-frequency communication
Open accessAbstract
Memristive devices, which combine a resistor with memory functions such that voltage pulses can change their resistance (and hence their memory state) in a nonvolatile manner, are beginning to be implemented in integrated circuits for memory applications. However, memristive devices could have applications in many other technologies, such as non–von Neumann in-memory computing in crossbar arrays, random number generation for data security, and radio-frequency switches for mobile communications. Progress toward the integration of memristive devices in commercial solid-state electronic circuits and other potential applications will depend on performance and reliability challenges that still need to be addressed, as described here.
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Cites
Memristor—The Missing Circuit Element
Chua, L. (1971) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1971.1083337 · Cited by 10,268 · cited 1×
“Several scientists think that the memristor, as initially defined by Chua in 1971 (5), has still never been realized (3).”
BackgroundIn-memory computing with resistive switching devices
Ielmini, D., & Wong, H.-S. P. (2018) · Nature Electronics · doi:10.1038/s41928-018-0092-2 · Cited by 2,298 · cited 2×
“This IMC computational paradigm is finding a range of applications including scientific computing and deep learning ( 11 , 12 ).”
BackgroundMemory 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
Self-citationFully hardware-implemented memristor convolutional neural network
Yao, P., Wu, H., Gao, B., Tang, J., Zhang, Q., Zhang, W., Yang, J. J., & Qian, H. (2020) · Nature · doi:10.1038/s41586-020-1942-4 · Cited by 2,179
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) · ACM SIGARCH Computer Architecture News · doi:10.1145/3007787.3001139 · Cited by 1,536
Neuro-Inspired Computing With Emerging Nonvolatile Memorys
Yu, S. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2790840 · Cited by 1,170
Spintronics based random access memory: a review
Bhatti, S., Sbiaa, R., Hirohata, A., Ohno, H., Fukami, S., & Piramanayagam, S. N. (2017) · Materials Today · doi:10.1016/j.mattod.2017.07.007 · Cited by 1,167
Multi-terminal memtransistors from polycrystalline monolayer molybdenum disulfide
Sangwan, V. K., Lee, H.-S., Bergeron, H., Balla, I., Beck, M. E., Chen, K.-S., & Hersam, M. C. (2018) · Nature · doi:10.1038/nature25747 · Cited by 1,023
Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
Diehl, P. U., Neil, D., Binas, J., Cook, M., Liu, S.-C., & Pfeiffer, M. (2015) · 2015 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280696 · Cited by 991
Ferroelectric tunnel junctions for information storage and processing
Garcia, V., & Bibes, M. (2014) · Nature Communications · doi:10.1038/ncomms5289 · Cited by 896
Cited by
Thousands of conductance levels in memristors integrated on CMOS
(2023) · doi:10.1038/s41586-023-05759-5 · Cited by 503
Le Gallo, M., Khaddam-Aljameh, R., Stanisavljevic, M., Vasilopoulos, A., Kersting, B., Dazzi, M., Karunaratne, G., Brändli, M., Singh, A., Müller, S. M., et al. (2023) · Nature Electronics · doi:10.1038/s41928-023-01010-1 · Cited by 310
Self-citationHuang, J., Yang, S., Tang, X., Yang, L., Chen, W., Chen, Z., Li, X., Zeng, Z., Tang, Z., & Gui, X. (2023) · Advanced Materials · doi:10.1002/adma.202303737 · Cited by 101
Yoon, J. H., Song, Y.-W., Ham, W., Park, J.-M., & Kwon, J.-Y. (2023) · APL Materials · doi:10.1063/5.0149393 · Cited by 74
Thousands of conductance levels in memristors monolithically integrated on CMOS
Yang, J., Rao, M., Tang, H., Wu, J.-B., Song, W., Zhang, M., Yin, W., Zhuo, Y., Kiani, F., Chen, B., et al. (2022) · doi:10.21203/rs.3.rs-1939455/v1 · Cited by 504
Metadata
- Authors
- Lanza, M., Sebastian, A., Lu, W. D., Le Gallo, M., Chang, M.-F., Akinwande, D., Puglisi, F. M., Alshareef, H. N., Liu, M., & Roldan, J. B.
- Venue
- Science
- Published
- 2022-06-03
- Volume
- 376
- Issue
- 6597
- Pages
- eabj9979-eabj9979
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W4281874271
- Cited by
- 708
- References
- 103
- FWCI
- 38.41
- Type
- journal article
- Language
- en
- Open access
- green
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- Lanza, M., Sebastian, A., Lu, W. D., Le Gallo, M., Chang, M.-F., Akinwande, D., Puglisi, F. M., Alshareef, H. N., Liu, M., & Roldan, J. B. (2022). Memristive technologies for data storage, computation, encryption, and radio-frequency communication. Science. https://doi.org/10.1126/science.abj9979
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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