Abstract
Photonic memristors based on two-dimensional materials are emerging as critical components for ultrascalable, energy-efficient artificial vision systems, integrating opto-sensing, data storage and processing capabilities. However, existing devices typically exhibit narrow spectral response ranges and operate in a single mode (for example, non-volatility), limiting their applications in complex computing scenarios. Here we introduce photonic memristor arrays based on a wafer-scale hexagonal boron nitride (hBN)/silicon (Si) heterostructure. These memristors are developed via in situ, low-temperature (250 °C), large-area growth of highly homogeneous hBN films on Si-based substrates. The devices exhibit opto-reconfigurability across a broad spectral range from ultraviolet to near infrared. By adjusting the incident laser power, the device can be reconfigured between non-resistive-switching, volatile and non-volatile modes. This light-induced reconfigurability is attributed to the formation of conductive filaments through interactions between hydrogen ions and photogenerated electrons within the engineered hBN/Si heterostructures. Furthermore, the photonic memristor features a switching ratio exceeding 109, retention time surpassing 40,000 s, endurance over 106 cycles and thermal stability up to 300 °C. These findings provide a scalable solution for developing integrated sensing–storage–computation artificial vision systems, fully compatible with sophisticated Si-based semiconductor technologies.
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Data availability
The data that support the findings of this study are presented in the article and the Supplementary Information. Additional relevant data are available from the corresponding authors upon reasonable request.
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Acknowledgements
This work is supported by the King Abdullah University of Science and Technology, Office of Sponsored Research (OSR), under award numbers ORA-CRG10-2021-4665 and ORA-CRG11-2022-5031 and the Semiconductor Initiative.
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Contributions
M.C. conceptualized the study, designed the experimental framework and coordinated the overall research progress. M.C., L.L., Xiaowen Zhang, K.M. and T.D.A. performed the optical test characterization. M.C., Y.M., K.Z., O.A., X.X., H.N.A. and M.L. executed the electrical test characterization. M.C., N.A., B.T. and J.L. optimized the wafer-scale hBN growth conditions. C.L., Y.C. and D.Z. prepared the focused ion beam samples and collected cross-sectional STEM-EDS data. H.L., Y.Y., Heng Wang, Z.Z., Y.W. and A.S. conducted circuit simulations for system-level validation. Xixiang Zhang proposed the physics model/mechanism to interpret the memory effect, while H.X., F.X., X.H. and K.C. performed theoretical calculations and simulation analysis. Hanwen Wang, Y.M., F.X. and Z.H. co-designed the three-dimensional schematic illustrations and contributed to paper drafting. All authors contributed to data interpretation, critically revised the paper and approved the final version. F.X. and Xixiang Zhang supervised the project.
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Extended data
Extended Data Fig. 1 Raman mapping image of the E2g peak intensity on the 4-inch hBN film.
(a) Photograph of a 4-inch wafer of hBN film deposited on SiO₂ (290 nm)/Si. (b) Raman intensity mapping images of the E2g peak at the 9 marked positions on (a). Each mapped region measures 90 μm × 70 μm, indicating the spatial uniformity of the film. Scale bars: 20 μm.
Extended Data Fig. 2 Structural and functional characterization of hBN synthesized at 250 °C.
(a) AFM image of the hBN surface over a 2 × 2 μm area, revealing an morphology with RMS roughness = 0.56 ± 0.03 nm. (b) Raman spectrum of hBN, showing the characteristic E2g vibrational mode at ~1362 cm−1, confirming hBN formation. (c) Cross-sectional TEM image of hBN, showcasing its atomic-scale layered structure and nanocrystallinity. (d) I–V curves under white LED illumination (red curve) and dark conditions (black curve), demonstrating pronounced hysteresis and nonvolatile memristive switching behavior.
Extended Data Fig. 3 High-Resolution TEM Imaging of the hBN Interface.
(a) High-resolution TEM (HRTEM) image of the complete interface, illustrating the structural integrity and layering of the materials. (b) Magnified HRTEM view of the hBN resistive switching layer, corresponding to the highlighted region in (a), providing detailed insight into the atomic arrangement and crystallinity.
Extended Data Fig. 4 Nanoscale structural mapping of hBN via 4D-STEM.
(a) Schematic of the 4D-STEM selective area mapping technique, where a quasi-parallel electron probe (convergence angle: 0.5 mrad) is scanned across the TEM lamella, recording spatially resolved diffraction patterns recorded at each probe position. Scale bar: 10 nm. (b) High-resolution TEM (HRTEM) image of the selected hBN region, showing lattice fringes corresponding to the (002) basal plane spacing (~0.34 nm). Scale bar: 10 nm. (c) Atomic-resolution HAADF-STEM image of the same region from (b). Scale bar: 10 nm. (d, e) Representative nano-beam electron diffraction (NBED) patterns from two distinct hBN grains in the selected hBN region. The diffraction rings indicate a nanocrystalline structure. Reciprocal space scale: 5 nm⁻¹ (Q-space).
Extended Data Fig. 5 Structural and elemental characterization of the Si/hBN heterostructure interface.
(a) Cross-sectional high-resolution TEM image of the Si/hBN interface, demonstrating sharp atomic alignment. Scale bar: 50 nm. (b) Magnified lattice-resolved view of hBN layers marked in (a). High-angle annular dark-field (HAADF) STEM image confirming uniform hBN layer thickness (~1.7 nm) and continuity across the Si substrate. Scale bar: 5 nm. (c) Composite electron energy-loss spectroscopy (EELS) elemental map showing boron (B, purple), nitrogen (N, red), oxygen (O, blue), and silicon (Si, green) at the interface. (d) Corresponding intensity line profiles of B, N, O, and Si across the dashed line in (c), revealing minimal elemental intermixing and a sharp chemically interface. (e–h) Individual EELS elemental maps for B, N, O, and Si, spatially resolved at the heterojunction. (i–l) Element-specific intensity line profiles derived from (e–h), quantifying the distribution of each element. Profiles confirm negligible oxygen diffusion and sharp Si/hBN interfacial boundaries. All data were acquired using a probe-corrected Titan Themis Z TEM operated at 200 kV. Scale bars for elemental maps: 10 nm. Error margins for line profiles reflect a 5% signal-to-noise ratio.
Extended Data Fig. 6 Temperature-dependent volatile and nonvolatile resistive-switching under varying current compliance.
(a–c) Volatile current–voltage (I–V) characteristics measured under white LED illumination with low current compliance (CC = 100 nA). Grey lines represent ten consecutive switching cycles; a representative cycle is highlighted in blue. Measurements were carried out at (a) 20 °C, (b) 125 °C and (c) 300 °C. The absolute current |I| is shown on a logarithmic scale against applied voltage V; the dashed line marks the 100 nA compliance. (d–f) Nonvolatile I–V characteristics recorded under identical illumination conditions with high current compliance (CC = 100 µA). Grey lines depict cycle-to-cycle variability; a typical switching curve is shown in red. Data were acquired at (d) 20 °C, (e) 125 °C and (f) 300 °C. The dashed line indicates the 100 µA compliance.
Extended Data Fig. 7 Cycling endurance and thermal robustness of resistive switching in the memristor device.
(a) Evolution of resistance for the high-resistance state (HRS) and low-resistance state (LRS) over approximately 2 million cycles at 20 °C, with no significant degradation observed. The HRS remains stable at around 109 Ω, with slight fluctuations due to measurement precision, while the LRS is stabilized at approximately 7 × 107 Ω. The switching ratio remains ~10, demonstrating a stable and robust high/low resistance window throughout the endurance test. (b) Endurance testing at 125 °C, showing that the device maintains stable switching behavior over millions of cycles at elevated temperatures. The HRS and LRS values remain relatively stable, confirming excellent thermal stability and consistent resistive switching performance.
Extended Data Fig. 8 Humidity-controlled resistive switching behavior.
Devices show enhanced ion migration and filament formation at higher humidity levels. The set voltage decreased linearly with increasing humidity (31–85%).
Extended Data Fig. 9 Grain size-dependent resistive switching behavior in hBN-based memristors.
(a–d) TEM images of hBN films with progressively increasing grain sizes, synthesized under different growth conditions. (e–h) Non-volatile resistive switching characteristics of memristors fabricated using the hBN films from (a–d), respectively. All devices exhibit stable bipolar switching with low variability. (i) Statistical comparison of set/reset voltages across grain sizes (n = 5 independent devices). Data are presented as mean ± standard deviation (SD), with each data point representing a measurement from a separate device. Error bars represent ± SD. The box plots show the minimum, maximum, median, and interquartile range (25th and 75th percentiles), with whiskers extending to the minimum and maximum data points. The groups compared include devices fabricated from hBN films with different grain sizes.
Extended Data Fig. 10 Wavelength and power dependence of the memristive characteristics.
(a–e) I–V characteristics of the memristor under various laser wavelengths: (a) 375 nm, (b) 490 nm, (c) 595 nm, (d) 700 nm, and (e) 850 nm, with each panel plotting the I–V curves at different laser power densities (1%, 5%, 10%, 50%, and 100% of maximum power). (f) I–V characteristics of the memristor under 1064 nm laser wavelength.
Supplementary information
Supplementary Information
Supplementary Figs. 1–23 and Tables 1–5.
Supplementary Video 1
Dynamic 4D-STEM scan of nanocrystalline hBN film. Comprising 4,500 spectrum images acquired over a 70 nm × 70 nm region, this video captures a dynamic 4D-STEM/high-resolution STEM (HRSTEM) raster scan of hBN film grains. The scan reveals uniform nanocrystallinity without evidence of amorphous phases, corroborating NBED data in Fig. 1n. Grain-dependent atomic-resolution contrast highlights beam-orientation sensitivity: only grains aligned along zone axes exhibit clear lattice fringes, while tilted grains show attenuated contrast due to HRSTEM’s inherent angular sensitivity. This orientation-dependent variability resolves ambiguities in conventional diffraction analyses and aligns with the structural schematic and representative data in Extended Data Fig. 4. The dataset further validates crystallinity consistency across the film, excluding amorphous contributions as detailed in Extended Data Fig. 3.
Supplementary Video 2
AIMD simulation of proton migration through hBN grain boundaries. AIMD trajectory (0–90 ps) showing proton migration in nanocrystalline hBN, initiated with 0.002 a.u. kinetic energy. The proton (H) traverses the lattice, undergoing three distinct phases: (i) transient trapping (~30 ps) via interaction with intralayer N atoms; (ii) normal diffusion within the crystal (~50 ps); (iii) grain-boundary-driven transfer to a lower-energy minimum (~90 ps). Atomic-configuration insets correlate with these energy–time transitions. The simulation confirms that grain boundaries provide a thermodynamically favoured pathway for proton transport, consistent with reduced activation energy in DFT calculations (Supplementary Fig. 18). This mechanistic visualization aligns with the energy profiles in Supplementary Fig. 19.
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Chen, M., Ma, Y., Aslam, N. et al. Ultrawide-bandwidth boron nitride photonic memristors. Nat. Nanotechnol. (2025). https://doi.org/10.1038/s41565-025-01991-4
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DOI: https://doi.org/10.1038/s41565-025-01991-4
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