The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
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.
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“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]
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Citations
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“However, the degree of realistic device variation can be estimated from results presented in [2].”
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Self-citationTowards memristive crossbar-based neuromorphic HW accelerators for signal processing
Vourkas, I., Hoffman, A. C. A., Vasileiadis, N., Sirakoulis, G. C., & Papamarkos, N. (2017) · International Conference on Modern Circuits and Systems Technologies · doi:10.1109/mocast.2017.7937678
“However, in this work memristance programming was carried out using the enhanced closed loop voltage-divider approach, proposed in [23], which properly addresses the threshold voltage variability and achieves equally high precision with [22] on average 3× faster.”
MethodEmulations of synaptic plasticity in the planar transistor configuration for neuromorphic computing
Chen, Y., Zhang, C., LaiYuan, W. A. N. G., Li, T., Ying, Z. H. U., MingDong, Y. I., & Huang, W. (2017) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/n972016-01359
A Memristor-Based Optimization Framework for AI ApplicationsPreprint
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“In addition to circuit-level techniques [19], [27], [28], [48], we will show that the non-ideal effects caused by hardware variations can also be mitigated by optimizing the algorithm prior to mapping to memristor crossbars.”
BackgroundMethodApplications of Emerging Memory in Modern Computer Systems: Storage and Acceleration
Liu, X. (2017) · D-Scholarship@Pitt (University of Pittsburgh)
Neuromorphic device specifications for unsupervised learning in robots
Sarim, M., Jha, R., & Kumar, M. (2017) · IEEE National Aerospace and Electronics Conference (NAECON) · doi:10.1109/naecon.2017.8268743
“, resistive Random Access Memory (ReRAM) devices and memristors [5], [6], [7], [8].”
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“(as floating point) is mapped onto an MCA with limited bit accuracy [1].”
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A Statistical View of Architecture Design
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Approximate In-memory computing on RERAMs
Khokhar, S. A. (2019) · Journal of International Crisis and Risk Communication Research
Тихов, С. В., Белов, А. И., Королев, Д. С., Антонов, И. Н., Sushkov, A. A., Павлов, Д. А., Тетельбаум, Д. И., Горшков, О. Н., & Михайлов, А. Н. (2020) · Журнал технической физики · doi:10.21883/jtf.2020.02.48824.211-19
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An Atomic Force Microscopic Study of Resistive Switching Resonance Activation in ZrO2(Y) Films
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Redox-Based Bi-Layer Metal Oxide Memristive Devices
Zahari, F., Park, S., Mahadevaiah, M. K., Wenger, C., Kohlstedt, H., & Ziegler, M. (2023) · Springer series on bio- and neurosystems · doi:10.1007/978-3-031-36705-2_3
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Zhao, H., Cheng, Z., Han, C., Guo, H., & Sun, L. (2023) · Analog Integrated Circuits and Signal Processing · doi:10.1007/s10470-023-02214-3
Towards Scalable Cryogenic Quantum Dot Biasing Using Memristor-Based Dc SourcesPreprint
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Huang, Y., Zheng, L. G., Liu, H., Qihang, Q. I. U., Jie, X., Liao, X., & Jin, H. L. (2024) · Scientia Sinica Informationis · doi:10.1360/ssi-2023-0345
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“3(a) and (c) shows that the model calculation results well agree with the experimental data during the LTP and LTD phases for the titanium dioxide resistive device [10].”
BackgroundResultPrecise control method of resistance of memristor
Yi, Q., Zhang, K.-M., Wang, Z., Wang, W., & Li, N. (2024) · International Conference on Cloud Computing and Communication Engineering (CCCE) · doi:10.1117/12.3050016
“After successful electroforming, the device is SET and RESET [4] .”
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Messaris, I., Ntinas, V. G., Prousalis, D. A., Demirkol, A. Ş., Tetzlaff, R., Zhang, S., Rana, V., Menzel, S., & Ascoli, A. (2025) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas56072.2025.11043454
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Schroedter, R., Demirkol, A. Ş., Messaris, I., Bruchatz, C., Mgeladze, E., Slesazeck, S., Mikolajick, T., & Tetzlaff, R. (2025) · International Conference on Modern Circuits and Systems Technologies · doi:10.1109/mocast65744.2025.11083963
“The conventional write-and-verify tuning algorithm (WVTA) [5] fails to define an optimal write time, which could be determined a priori from the dynamics based on potentiation and depression characteristics or a compact model.”
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“These practical constraints affect the precision with which weights can be represented in hardware, impacting overall system performance [35].”
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Self-citationDevelopment of filamentary Memristive devices for synaptic plasticity implementation
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Local learning with highly analog memory devices
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“matrix-vector multiplications, can be naturally performed on-chip in the crossbar configuration [219, 220].”
BackgroundEmbracing Imperfections : Hardware-compatible Neural Networks for Neuromorphic Computing
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Mehra, S., Sateesh, & Sahay, S. (2026) · International Conference on Machine Learning and Autonomous Systems (ICMLAS) · doi:10.1109/icmlas67792.2026.11483998
“Nevertheless, previous research demonstrated the feasibility of achieving precise tuning of RRAM conductance states exceeding 7-bits using program-verify algorithm [15].”
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Xiong, Y., Xiao, C., Yang, Z., (6656), L. W., & Zhao, M. (2026) · Design, Automation, and Test in Europe · doi:10.23919/date69613.2026.11539562
“Neurons use the LIF model [41] with 7-bit quantized weights [42].”
Bang, H., & Ko, J. H. (2026) · International Symposium on Low Power Electronics and Design (ISLPED) · doi:10.1145/3816440.3818513
“Notably, multi-level cell (MLC) ReRAM [2, 18, 34] further enhances storage density, making it particularly well-suited for efficient DNN acceleration [4, 9, 28].”
VeRA+: Vector-Based Lightweight Compensation for Drift-Resilient RRAM In-Memory Computing
Dong, W.-K., Zhou, K., Kong, Z., Yang, Z., Cheng, Q., Li, H., Huang, J., Lan, J., Li, Y., Hashimoto, M., et al. (2026) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/3770743.3804309 · Cited by 1
“On-chip retraining [8], [9], though conceptually appealing, is throttled by RRAM’s slow write-and-verify cycle [10], limited endurance [11] and large training dataset.”
Metadata
- 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
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