Abstract
The advancement of neuromorphic computing hardware requires energy-efficient operation, scalable device integration, and reliable conduction. These challenges can be effectively addressed by employing functional ferroelectric material as an active layer in memristors, leveraging their electrostatically modulated conduction for reliable switching. In this study, we present memristor devices that achieve a rectifying ratio exceeding 10⁶ and an off-state current below 10⁻¹² A based on epitaxial heterostructures consisting of Pt/Ba0.2Bi0.8FeO3 (BBFO)/SrRuO3/SrTiO3 stacks. Substitution of 20% Ba in BiFeO₃ induces a coupled interaction between ferroelectric polarization and oxygen vacancy migration, which under pulsed bias governs vacancy transport and ensures reliable memristive synaptic behavior with near-zero nonlinearity and endurance beyond 10⁷ cycles. Owing to the demonstrated linear synaptic performance and strong memristive rectification, a selector-free crossbar array (CBA) was implemented. By mitigating key CBA challenges such as sneak currents and cell-to-cell variability while maintaining high synaptic performance, BBFO provides a robust material platform for CBA-based neuromorphic systems.
Similar content being viewed by others
Data availability
The data that support the conclusions of this study are available from the corresponding authors upon request. Source data are provided with this paper.
Code availability
The code that used for the software simulation for this study is available from the corresponding authors upon request.
References
Merolla, P. A. et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345, 668–673 (2014).
Mayberry, M. et al. Intel’s new self-learning chip promises to accelerate artificial intelligence. Intel (2017).
Imam, N. & Cleland, T. A. Rapid online learning and robust recall in a neuromorphic olfactory circuit. Nat. Mach. Intell. 2, 181–191 (2020).
Kim, Y., C. W. Lee, and H. W. Jang, Neuromorphic Hardware for Artificial Sensory Systems: A Review. J. Electron. Mater. 1-42 (2025).
Yang, J. J. et al. Memristive switching mechanism for metal/oxide/metal nanodevices. Nat. Nanotechnol. 3, 429–433 (2008).
Rao, M. et al. Thousands of conductance levels in memristors integrated on CMOS. Nature 615, 823–829 (2023).
Song, W. et al. Programming memristor arrays with arbitrarily high precision for analog computing. Science 383, 903–910 (2024).
Yang, J. J., Strukov, D. B. & Stewart, D. R. Memristive devices for computing. Nat. Nanotechnol. 8, 13–24 (2013).
Chen, S. et al. Electrochemical-memristor-based artificial neurons and synapses—fundamentals, applications, and challenges. Adv. Mater. 35, 2301924 (2023).
International Roadmap For Devices And Systems 2023 Update Beyond Cmos The Irds Is Devised And Intended For Technology Assessment Only And Is Without Regard To Any. (2023).
Kim, S. J. et al. Vertically aligned two-dimensional halide perovskites for reliably operable artificial synapses. Mater. Today 52, 19–30 (2022).
Kwak, K. J. et al. Ambient stable all inorganic CsCu2I3 artificial synapses for neurocomputing. Nano Lett. 22, 6010–6017 (2022).
Im, I. H. et al. Halide perovskites-based diffusive memristors for artificial mechano-nociceptive system. Adv. Mater. 36, 2307334 (2024).
Lee, Y. J. et al. Nanoelectronics using metal–insulator transition. Adv. Mater. 36, 2305353 (2024).
Lee, Y. J. et al. Memristive artificial synapses based on brownmillerite for endurable weight modulation. Small 21, 2405749 (2025).
Khasanov, D. and O. Primqulov. Gradient Descent In Machine Learning. in 2021 International Conference on Information Science and Communications Technologies (ICISCT). IEEE. (2021).
Van Doremaele, E. R. et al. Hardware implementation of backpropagation using progressive gradient descent for in situ training of multilayer neural networks. Sci. Adv. 10, eado8999 (2024).
Kim, K. S. et al. The future of two-dimensional semiconductors beyond Moore’s law. Nat. Nanotechnol. 19, 895–906 (2024).
Kim, Y. and H. W. Jang, Designing memristive materials for artificial dynamic intelligence. Adv. Intell. Discov. 202500020 (2025).
Wang, S. et al. Memristor-based intelligent human-like neural computing. Adv. Electron. Mater. 9, 2200877 (2023).
Xiao, Z. et al. Adapting magnetoresistive memory devices for accurate and on-chip-training-free in-memory computing. Sci. Adv. 10, eadp3710 (2024).
Jacobs-Gedrim, R. B., et al. Impact of linearity and write noise of analog resistive memory devices in a neural algorithm accelerator. in 2017 IEEE International Conference on Rebooting Computing (ICRC). IEEE. (2017).
Ren, S. G. et al. Self-rectifying memristors for three-dimensional in-memory computing. Adv. Mater. 36, 2307218 (2024).
Song, M.-K. et al. Recent advances and future prospects for memristive materials, devices, and systems. ACS nano 17, 11994–12039 (2023).
Wang, Z. et al. High-performance CMOS-compatible self-rectifying memristor for passive array integration. Phys. Rev. Appl. 22, 064003 (2024).
Zhang, G. et al. Self-rectifying memristors with high rectification ratio for attack-resilient autonomous driving systems. Nat. Commun. 16, 5759 (2025).
Jeong, H. et al. Self-supervised video processing with self-calibration on an analogue computing platform based on a selector-less memristor array. Nat. Electron. 8, 168–178 (2025).
Zhang, G. et al. Self-rectifying memristor-based reservoir computing for real-time intrusion detection in cybersecurity. Nano Lett. 24, 15707–15715 (2024).
Zhang, G. et al. Self-rectifying memristors with high rectification ratio and dynamic linearity for in-memory computing. Appl. Phys. Lett. 125, 133501 (2024).
Zhang, G., et al. Stochastic self-rectifying memristor crossbar array as physical unclonable function for authentication and tamper-evident image security. Device 4, 100988(2025).
Fan, X. et al., Superior rectification self-rectifying memristors with self-recovery capabilities enabled by GaOx/InOx heterostructures. Appl. Phys. Lett. 127, 19 (2025).
Nian, Y. et al. Evidence for an oxygen diffusion model for the electric pulse induced resistance change effect in transition-metal oxides. Phys. Rev. Lett. 98, 146403 (2007).
Yang, Q. et al. Strain effects on formation and migration energies of oxygen vacancy in perovskite ferroelectrics: a first-principles study. J. Appl. Phys.113, 184110 (2013).
Xiang, X. et al. Manipulating the resistive switching in epitaxial SrCoO2.5 thin-film-based memristors by strain engineering. ACS Appl. Electron. Mater. 4, 2729–2738 (2022).
Miot, C. et al. X-ray photoelectron spectroscopy characterization of barium titanate ceramics prepared by the citric route. Residual carbon study. J. Mater. Res. 12, 2388–2392 (1997).
Yaakob, M. et al. Self-interaction corrected LDA+U investigations of BiFeO3 properties: plane-wave pseudopotential method. Mater. Res. Express 2, 116101 (2015).
Lee, D. et al. Polarity control of carrier injection at ferroelectric/metal interfaces for electrically switchable diode and photovoltaic effects. Phys. Rev. B—Condens. Matter Mater. Phys. 84, 125305 (2011).
Liang, N. et al. Structure evolution and energy band modulation in Ba-doped BiFeO3 thin films. J. Appl. Phys. 135, 045304 (2024).
Kim, S. J. et al. Linearly programmable two-dimensional halide perovskite memristor arrays for neuromorphic computing. Nat. Nanotechnol. 20, 83–92 (2025).
Lee, J. et al. Role of oxygen vacancies in ferroelectric or resistive switching hafnium oxide. Nano Convergence 10, 55 (2023).
Noguchi, Y. et al. Ferroelectrics with a controlled oxygen-vacancy distribution by design. Sci. Rep. 9, 4225 (2019).
Kelley, K. P. et al. Oxygen vacancy injection as a pathway to enhancing electromechanical response in ferroelectrics. Adv. Mater. 34, 2106426 (2022).
Scott, J. & Dawber, M. Oxygen-vacancy ordering as a fatigue mechanism in perovskite ferroelectrics. Appl. Phys. Lett. 76, 3801 (2000).
Li, W. et al. Correlation among oxygen vacancies in bismuth titanate ferroelectric ceramics. Appl. Phys. Lett. 85, 4717–4719 (2004).
Mikheev, V. et al. Ferroelectric second-order memristor. ACS Appl. Mater. interfaces 11, 32108–32114 (2019).
Kim, S. et al. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett. 15, 2203–2211 (2015).
Kumar, S. et al. Dynamical memristors for higher-complexity neuromorphic computing. Nat. Rev. Mater. 7, 575–591 (2022).
Zhong, Y. N. et al. Synapse-like organic thin film memristors. Adv. Funct. Mater. 28, 1800854 (2018).
Zhou, J. et al. A monochloro copper phthalocyanine memristor with high-temperature resilience for electronic synapse applications. Adv. Mater. 33, 2006201 (2021).
Li, J. et al. Polymeric memristor based artificial synapses with ultra-wide operating temperature. Adv. Mater. 35, 2209728 (2023).
Lao, J. et al. An air-stable artificial synapse based on a lead-free double perovskite Cs2AgBiBr6 film for neuromorphic computing. J. Mater. Chem. C. 9, 5706–5712 (2021).
Tang, J. et al. A reliable all-2D materials artificial synapse for high energy-efficient neuromorphic computing. Adv. Funct. Mater. 31, 2011083 (2021).
Shi, Y. et al. Electronic synapses made of layered two-dimensional materials. Nat. Electron. 1, 458–465 (2018).
Yang, J. et al. Wafer-scale memristor array based on aligned grain boundaries of 2d molybdenum ditelluride for application to artificial synapses. Adv. Funct. Mater. 34, 2309455 (2024).
Choi, S. et al. Experimental demonstration of feature extraction and dimensionality reduction using memristor networks. Nano Lett. 17, 3113–3118 (2017).
Chen, J. et al. Reconfigurable Ag/HfO2/NiO/Pt memristors with stable synchronous synaptic and neuronal functions for renewable homogeneous neuromorphic computing system. Nano Lett. 24, 5371–5378 (2024).
Peng, Z. et al. HfO2-based memristor as an artificial synapse for neuromorphic computing with tri-layer HfO2/BiFeO3/HfO2 design. Adv. Funct. Mater. 31, 2107131 (2021).
Park, T. et al. Au-Nanodots Embedded Self-Rectifying Analog Charge Trap Memristor with Modified Bias Voltage Application Method for Stable Multi-Bit Hardware-Based Neural Network. Adv. Mater. Technol. 10, 2400965 (2025).
Sun, Y. et al. A Ti/AlOx/TaOx/Pt analog synapse for memristive neural network. IEEE Electron Device Lett. 39, 1298–1301 (2018).
Deng, S. et al. Selective area doping for Mott neuromorphic electronics. Sci. Adv. 9, eade4838 (2023).
Luo, Z. et al. High-precision and linear weight updates by subnanosecond pulses in ferroelectric tunnel junction for neuro-inspired computing. Nat. Commun. 13, 699 (2022).
Liu, G. et al. Silicon based Bi0.9La0.1FeO3 ferroelectric tunnel junction memristor for convolutional neural network application. Nanoscale 15, 13009–13017 (2023).
Moon, K. et al. Bidirectional non-filamentary RRAM as an analog neuromorphic synapse, Part I: Al/Mo/Pr0.7Ca0.3MnO3 material improvements and device measurements. IEEE J. Electron Devices Soc. 6, 146–155 (2017).
Acknowledgements
This research was supported by the National Research Council of Science & Technology(NST) grant by the Korea government (MSIT) (No. GTL25021-000). This work was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT), South Korea (RS-2024-00421181). This work was supported by the InnoCORE program of the Ministry of Science and ICT(1.250021.01). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2025-23963262). This research was supported by Creative Materials Discovery Program (No. 2017M3D1A1040834) through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT. The Inter-University Semiconductor Research Center, Institute of Engineering Research, and SOFT Foundry Institute at Seoul National University provided research facilities for this work.
Author information
Authors and Affiliations
Contributions
Y.K., Y.J.L., and J.Y. equally contributed this work. J. J. Y., S.L., and H.W.J. supervised this work. B.K. helped phi-scan measurement. S.J.K., J.K., I.I, J.Y.K., H.R., and H.K. helped with device characterization. D.S. measured PFM image. C.W.B., M.H.P., D.H.K. and S.-J.C. revised this manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Communications thanks Yishu Zhang (eRef) who co-reviewed with Guobin Zhang (ECR); Cheng Li and the other anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Source data
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Kim, Y., Lee, Y.J., Yang, J. et al. Programmable ferroelectric rectifier for reliable and efficient neuromorphic crossbar array. Nat Commun (2026). https://doi.org/10.1038/s41467-026-70727-2
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41467-026-70727-2
