Abstract
Memristors have emerged as a promising hardware platform for in-memory computing, but many current devices suffer from channel material degradation during repeated resistive switching. This leads to high energy consumption and limited endurance. Here we introduce a molecular crystal memristor, of which the representative channel material, Sb2O3, possesses a molecular crystal structure where molecular cages are interconnected via van der Waals forces. This unique configuration allows ions to migrate through intermolecular spaces with relatively low energy input, preserving the integrity of the crystal structure even after extensive switching cycles. Our molecular crystal memristor thus exhibits low energy consumption of 26 zJ per operation, with prominent endurance surpassing 109 switching cycles. The device delivers both reconfigurable non-volatile and volatile resistive switching behaviours over a broad range of device scales, from micrometres down to nanometres. Furthermore, we establish the scalability of this technology by fabricating large crossbar arrays on an 8 inch wafer. This enables the successful implementation of reservoir computing on a single CMOS-integrated chip using these memristors, achieving 100% accuracy in dynamic vision recognition.
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Data availability
The data that support the findings of this study are available from the corresponding authors upon reasonable request.
Code availability
The code that is used for the software simulation for this study is available from the corresponding authors upon reasonable request.
References
Lanza, M. et al. Memristive technologies for data storage, computation, encryption, and radio-frequency communication. Science 376, eabj9979 (2022).
Wang, C. et al. Parallel in-memory wireless computing. Nat. Electron. 6, 381–389 (2023).
Langenegger, J. et al. In-memory factorization of holographic perceptual representations. Nat. Nanotechnol. 18, 479–485 (2023).
Yi, S. I., Kendall, J. D., Williams, R. S. & Kumar, S. Activity-difference training of deep neural networks using memristor crossbars. Nat. Electron. 6, 45–51 (2023).
Zidan, M. A., Strachan, J. P. & Lu, W. D. The future of electronics based on memristive systems. Nat. Electron. 1, 22–29 (2018).
Zhu, K. et al. Hybrid 2D-CMOS microchips for memristive applications. Nature 618, 57–62 (2023).
Rao, M. et al. Thousands of conductance levels in memristors integrated on CMOS. Nature 615, 823–829 (2023).
Xiong, T. et al. Neuromorphic functions with a polyelectrolyte-confined fluidic memristor. Science 379, 156–161 (2023).
Milano, G. et al. In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks. Nat. Mater. 21, 195–202 (2022).
Lin, P. et al. Three-dimensional memristor circuits as complex neural networks. Nat. Electron. 3, 225–232 (2020).
Strukov, D. B., Snider, G. S., Stewart, D. R. & Williams, R. S. The missing memristor found. Nature 453, 80–83 (2008).
Wang, X. et al. Vertically integrated spiking cone photoreceptor arrays for color perception. Nat. Commun. 14, 3444 (2023).
Zhou, X. et al. Thermally stable threshold selector based on CuAg alloy for energy-efficient memory and neuromorphic computing applications. Nat. Commun. 14, 3285 (2023).
Zhao, H. et al. Energy-efficient high-fidelity image reconstruction with memristor arrays for medical diagnosis. Nat. Commun. 14, 2276 (2023).
Wang, C. et al. Scalable massively parallel computing using continuous-time data representation in nanoscale crossbar array. Nat. Nanotechnol. 16, 1079–1085 (2021).
Chen, S. et al. Wafer-scale integration of two-dimensional materials in high-density memristive crossbar arrays for artificial neural networks. Nat. Electron. 3, 638–645 (2020).
Wang, M. et al. Robust memristors based on layered two-dimensional materials. Nat. Electron. 1, 130–136 (2018).
Li, Y. et al. Anomalous resistive switching in memristors based on two-dimensional palladium diselenide using heterophase grain boundaries. Nat. Electron. 4, 348–356 (2021).
Liu, K., Liu, L. & Zhai, T. Emerging two-dimensional inorganic molecular crystals: the concept and beyond. J. Phys. Chem. Lett. 13, 2173–2179 (2022).
Li, M. et al. Imperfection-enabled memristive switching in van der Waals materials. Nat. Electron. 6, 491–505 (2023).
Kang, J. et al. Cluster-type analogue memristor by engineering redox dynamics for high-performance neuromorphic computing. Nat. Commun. 13, 4040 (2022).
Zhang, Z. et al. In-sensor reservoir computing system for latent fingerprint recognition with deep ultraviolet photo-synapses and memristor array. Nat. Commun. 13, 6590 (2022).
Sun, W. et al. Understanding memristive switching via in situ characterization and device modeling. Nat. Commun. 10, 3453 (2019).
Wei, T. et al. Three-dimensional reconstruction of conductive filaments in HfOx-based memristor. Adv. Mater. 35, 2209925 (2023).
Yang, Y. et al. Electrochemical dynamics of nanoscale metallic inclusions in dielectrics. Nat. Commun. 5, 4232 (2014).
Yang, Y. et al. Observation of conducting filament growth in nanoscale resistive memories. Nat. Commun. 3, 732 (2012).
Zhang, B. et al. 90% yield production of polymer nano-memristor for in-memory computing. Nat. Commun. 12, 1984 (2021).
Lanza, M. et al. Standards for the characterization of endurance in resistive switching devices. ACS Nano 15, 17214–17231 (2021).
Liu, S. et al. Eliminating negative-SET behavior by suppressing nanofilament overgrowth in cation-based memory. Adv. Mater. 28, 10623–10629 (2016).
Liu, K. et al. A wafer-scale van der Waals dielectric made from an inorganic molecular crystal film. Nat. Electron. 4, 906–913 (2021).
Han, W. et al. Two-dimensional inorganic molecular crystals. Nat. Commun. 10, 4728 (2019).
Ryu, H. et al. Van der Waals epitaxially grown molecular crystal dielectric Sb2O3 for 2D electronics. ACS Nano 18, 13098–13105 (2024).
Xu, Y. et al. Scalable integration of hybrid high-dielectric materials on two-dimensional semiconductors. Nat. Mater. 22, 1078–1084 (2023).
Kopperberg, N., Wouters, D. J., Waser, R., Menzel, S. & Wiefels, S. Accurate evaluation method for HRS retention of VCM ReRAM. APL Mater. 12, 031112 (2024).
Wang, Z. et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat. Mater. 16, 101–108 (2017).
Liu, L. et al. Low-power memristive logic device enabled by controllable oxidation of 2D HfSe2 for in-memory computing. Adv. Sci. 8, 2005038 (2021).
Cao, R. et al. Improvement of device reliability by introducing a BEOL-compatible TiN barrier layer in CBRAM. IEEE Electron Device Lett. 38, 1371–1374 (2017).
Tian, H. et al. Extremely low operating current resistive memory based on exfoliated 2D perovskite single crystals for neuromorphic computing. ACS Nano 11, 12247–12256 (2017).
Chekol, S. A., Menzel, S., Ahmad, R. W., Waser, R. & Hoffmann-Eifert, S. Effect of the threshold kinetics on the filament relaxation behavior of Ag-based diffusive memristors. Adv. Funct. Mater. 32, 2111242 (2022).
Lei, P. et al. High-performance memristor based on 2D layered BiOI nanosheet for low-power artificial optoelectronic synapses. Adv. Funct. Mater. 32, 2201276 (2022).
Li, Y. et al. In-memory computing using memristor arrays with ultrathin 2D PdSeOx/PdSe2 heterostructure. Adv. Mater. 34, 2201488 (2022).
Chen, C. et al. A photoelectric spiking neuron for visual depth perception. Adv. Mater. 34, 2201895 (2022).
Sun, Y. et al. Performance-enhancing selector via symmetrical multilayer design. Adv. Funct. Mater. 29, 1808376 (2019).
Lu, Y. et al. An electronic silicon-based memristor with a high switching uniformity. Nat. Electron. 2, 66–74 (2019).
Goswami, S. et al. Decision trees within a molecular memristor. Nature 597, 51–56 (2021).
Wang, T. et al. Reconfigurable neuromorphic memristor network for ultralow-power smart textile electronics. Nat. Commun. 13, 7432 (2022).
Shi, Y. et al. Electronic synapses made of layered two-dimensional materials. Nat. Electron. 1, 458–465 (2018).
Du, C. et al. Reservoir computing using dynamic memristors for temporal information processing. Nat. Commun. 8, 2204 (2017).
Appeltant, L. et al. Information processing using a single dynamical node as complex system. Nat. Commun. 2, 468 (2011).
Zhong, Y. et al. A memristor-based analogue reservoir computing system for real-time and power-efficient signal processing. Nat. Electron. 5, 672–681 (2022).
Hennen, T. et al. Current-limiting amplifier for high speed measurement of resistive switching data. Rev. Sci. Instrum. 92, 054701 (2021).
Kresse, G. & Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. Rev. B 47, 558–561 (1993).
Kresse, G. & Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal–amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251–14269 (1994).
Kresse, G. & Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. Mater. Sci. 6, 15–50 (1996).
Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B 54, 11169–11186 (1996).
Perdew, J. P., Burke, K. & Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 77, 3865–3868 (1996).
Grimme, S., Antony, J., Ehrlich, S. & Krieg, H. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J. Chem. Phys. 132, 154104 (2010).
Wang, V., Xu, N., Liu, J.-C., Tang, G. & Geng, W.-T. VASPKIT: a user-friendly interface facilitating high-throughput computing and analysis using VASP code. Comput. Phys. Commun. 267, 108033 (2021).
Henkelman, G., Uberuaga, B. P. & Jónsson, H. A climbing image nudged elastic band method for finding saddle points and minimum energy paths. J. Chem. Phys. 113, 9901–9904 (2000).
Blöchl, P. E. Projector augmented-wave method. Phys. Rev. B 50, 17953–17979 (1994).
Perdew, J. P., Ernzerhof, M. & Burke, K. Rationale for mixing exact exchange with density functional approximations. J. Chem. Phys. 105, 9982–9985 (1996).
Chan, M. K. Y., Wolverton, C. & Greeley, J. P. First principles simulations of the electrochemical lithiation and delithiation of faceted crystalline silicon. J. Am. Chem. Soc. 134, 14362–14374 (2012).
Wolverton, C. & Zunger, A. First-principles prediction of vacancy order-disorder and intercalation battery voltages in LixCoO2. Phys. Rev. Lett. 81, 606–609 (1998).
Acknowledgements
This work was supported by the National Natural Science Foundation of China (22350003, T.Z.; 22535004, T.Z.; U22A20137, Yuan Li; U21A2069, T.Z.) and the National Key R&D Program of China (2021YFA1200501, Yuan Li) and the Interdisciplinary Research Program of HUST (2024JCYJ008, T.Z.) and the Open Research Fund of Suzhou Laboratory (SZLAB-1508-2024-ZD013, T.Z.). We also acknowledge the support of the HPC platform of Huazhong University of Science and Technology.
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Yuan Li and T.Z. conceived the ideas. L.Q., P.G. and Jiefan Shao designed and conducted most of the experiments under Yuan Li and T.Z.’s supervision. L.Q., P.G. and Jiefan Shao contributed equally to this work. Pengyu Li and Y.Z. contributed to the DFT calculation of the migration barrier. Jie Su and Z.Y. carried out the works of the DFT calculation of structural evolution. Jiefan Shao, Y.Y. and J.W. prepared the cross-sectional samples and performed the TEM characterizations. L.Q., C.Z., Yanyong Li and F.H. conducted the CAFM characterizations. L.Q. and P.G. contributed to crossbar array fabrication. L.Q., P.G., Y.X., F.L., Peng Lin, K.Z. and M.L. fabricated the 1T1R chip and demonstrated the dynamic vision recognition. Jiefan Shao, D.O., L.Q., P.G., W.H., S.L., K.L., M.L. and H.L. worked on the figure preparation with assistance from all the others. L.Q., P.G., Jiefan Shao and M.L. wrote and revised the paper with inputs from all authors.
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Extended data
Extended Data Fig. 1 Convolutional image processing in the molecular crystal memristor crossbar array.
a, Schematic of convolution operation between voltage vectors and device conductance matrix in hardware. b, Prewitt kernels for different convolution image processing. The symbol of each matrix element mapped from the conductance difference of HRS and LRS. c, The original input image. d-k, Hardware- and software-processed images with different kernels, including vertical, horizontal and soft edges. The combinations of vertical and horizontal edges are shown in f and j.
Extended Data Fig. 2 In-situ TEM demonstration of the switching process in molecular crystal memristor.
a-b, Schematic (a) and TEM image (b) of the cross-section TEM sample with vertical structure. The scale bar in b is 2 µm. c, Cross sectional HAADF image and corresponding EDS mapping images, the scale bar is 40 nm. d-h, HRTEM images of Sb2O3 when the applied voltage is 0 V (d), 2 V (e), 0 V (f), −2 V (g) and 0 V (h), respectively. The lattice spacings are obtained by averaging the fast Fourier transform spots distances in Supplementary Fig. 27. All the scale bars are 2 nm. i-m, Corresponding fast Fourier transform patterns when the applied voltage is 0 V (i), 2 V (j), 0 V (k), −2 V (l) and 0 V (m), respectively.
Extended Data Fig. 3 Electrical characterization of nanosized molecular crystal memristor.
a, Schematic of the memristor constructed using CAFM. The tip/sample junction forms a nanosized Pt/Sb2O3/Ag memristor. b-c, Non-volatile (b) and volatile (c) switching curves using various compliance currents. d, Non-volatile switching curves of one spot. e-f, Statistical analysis of VSET/VRESET (e) and RHRS/RLRS (f) for non-volatile switching from 5 spots in Supplementary Fig. 28. g, Volatile threshold-switching curves of one spot. h, Statistical histogram of Vth/Vhold for volatile threshold-switching from 5 spots in Supplementary Fig. 28.
Extended Data Fig. 4 Wafer-scale 1024×1024 molecular crystal memristor crossbar array.
a-b, Image of wafer (a) and die (b) of crossbar array. c, Layout details of TE and BE for crossbar array. d, SEM characterization of the crossbar array. e-f, SEM (e) and AFM (f) image of 3 × 3 devices in the crossbar array. The size of high-density device is 400 nm × 400 nm. g-h, Volatile (g) and non-volatile (h) switching curves of 1024 tested cells in Supplementary Fig. 29. i, On/off ratio mapping of 1024 tested cells in Supplementary Fig. 29 extracted from h with VREAD = 0.1 V.
Extended Data Fig. 5 Characterizations of the 1T1R chip corresponding to Fig. 4.
a-b, Schematic (a) and STEM image (b) of the 1T1R cell cross-section. The inset in (b) is the HR-TEM image of Sb2O3 memristor. c, Volatile switching curves of 1T1R cell with different VG. d, Volatile response to the 4-bit sequential electrical pulse in the reservoir, with each state tested 3 times. e, Non-volatile switching curves of 1T1R cell with different VG. f, Multi-conductance states of 1T1R cell. g, Comparison between the directly measured output current and the arithmetic output current of 8 columns in the readout layer.
Supplementary information
Supplementary Information
Supplementary Figs. 1–38 and Tables 1 and 2.
Supplementary Video 1
Real-time in situ HRTEM observation of the resistive switching process in the Sb2O3 memristor under a programmed voltage sweep.
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Qin, L., Guan, P., Shao, J. et al. Molecular crystal memristors. Nat. Nanotechnol. 20, 1641–1647 (2025). https://doi.org/10.1038/s41565-025-02013-z
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DOI: https://doi.org/10.1038/s41565-025-02013-z
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