Romero, F. J. et al. Resistive switching in graphene oxide. Front. Mater. 7, 1–5 (2020).
Moazzeni, A., Riyahi Madvar, H., Hamedi, S. & Kordrostami, Z. Fabrication of graphene oxide-based resistive switching memory by the spray pyrolysis technique for neuromorphic computing. ACS Appl. Nano Mater. 6, 2236–2248 (2023).
Ahmed, T. et al. Mixed ionic-electronic charge transport in layered black-phosphorus for low-power memory. Adv. Funct. Mater. 32, 2202923 (2022).
Ge, R. et al. Atomristor: nonvolatile resistance switching in atomic sheets of transition metal dichalcogenides. Nano Lett. 18, 434–441 (2018).
Lee, H. S. et al. Dual-gated MoS2 memtransistor crossbar array. Adv. Funct. Mater. 30, 2001339 (2020).
Wu, Q. T. et al. Two-dimensional hexagonal boron nitride based memristor. Acta Phys. Sin. 66, 228504 (2017).
Zhu, K. et al. Graphene-boron nitride-graphene cross-point memristors with three stable resistive states. ACS Appl. Mater. Interfaces 11, 37999–38005 (2019).
Shi, Y. et al. Electronic synapses made of layered two-dimensional materials. Nat. Electron. 1, 458–465 (2018).
Xie, J., Afshari, S. & Sanchez Esqueda, I. Hexagonal boron nitride (h-BN) memristor arrays for analog-based machine learning hardware. npj 2D Mater. Appl. 6, 71 (2022).
Wu, X. et al. Thinnest nonvolatile memory based on monolayer h-BN. Adv. Mater. 31, 1900175 (2019).
Zhao, H. et al. Atomically thin femtojoule memristive device. Adv. Mater. 29, 1703232 (2017).
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).
Teja Nibhanupudi, S. S. et al. Ultra-fast switching memristors based on two-dimensional materials. Nat. Commun. 15, 2334 (2024).
Afshari, S. et al. Dot-product computation and logistic regression with 2D hexagonal-boron nitride (h-BN) memristor arrays. 2D Mater. 10, 35031 (2023).
Xie, J. et al. Quantum conductance in vertical hexagonal boron nitride memristors with graphene-edge contacts. Nano Lett. 24, 2473–2480 (2024).
Chen, H. et al. Introduction of defects in hexagonal boron nitride for vacancy-based 2D memristors. Nanoscale 15, 4309–4316 (2023).
Wen, C. et al. Advanced data encryption using 2D materials. Adv. Mater. 33, 2100187 (2021).
Shen, Y. et al. Variability and yield in h-BN-based memristive circuits: the role of each type of defect. Adv. Mater. 33, e2103656 (2021).
Lanza, M. et al. Back-end-of-line integration of 2D materials on silicon microchips. In Proc. International Electron Devices Meeting 7–10 (IEEE, 2023).
Merenkov, I. S. et al. Orientation-controlled, low-temperature plasma growth and applications of h-BN nanosheets. Nano Res. 12, 91–99 (2019).
Hoang, D. Q. et al. Growth mechanisms of hBN crystalline nanostructures with RF sputtering deposition: challenges, opportunities, and future perspectives. Phys. Scr. 98, 085003 (2023).
Hoang, D. Q. et al. Elucidation of the growth mechanism of sputtered 2D hexagonal boron nitride nanowalls. Cryst. Growth Des. 16, 3699–3708 (2016).
Yu, J. et al. Vertically aligned boron nitride nanosheets: chemical vapor synthesis, ultraviolet light emission, and superhydrophobicity. ACS Nano 4, 414–422 (2010).
Andújar, J. L., Bertran, E. & Maniette, Y. Microstructure of highly oriented, hexagonal, boron nitride thin films grown on crystalline silicon by radio frequency plasma-assisted chemical vapor deposition. J. Appl. Phys. 80, 6553–6555 (1996).
Toth, P. Nanostructure quantification of turbostratic carbon by HRTEM image analysis: state of the art, biases, sensitivity and best practices. Carbon 178, 688–707 (2021).
Yamamoto, M. et al. Low-temperature direct synthesis of multilayered h-BN without catalysts by inductively coupled plasma-enhanced chemical vapor deposition. ACS Omega 8, 5497–5505 (2023).
Liu, D. et al. Conformal hexagonal-boron nitride dielectric interface for tungsten diselenide devices with improved mobility and thermal dissipation. Nat. Commun. 10, 5140 (2019).
Yuan, Y. et al. On the quality of commercial chemical vapour deposited hexagonal boron nitride. Nat. Commun. 15, 2651 (2024).
Lee, S. H. et al. Improvements in structural and optical properties of wafer-scale hexagonal boron nitride film by post-growth annealing. Sci. Rep. 9, 14420 (2019).
Radhakrishnan, S. et al. Fluorinated h-BN as a magnetic semiconductor. Sci. Adv. 3, e1700842 (2017).
Yang, S. J. et al. Volatile and nonvolatile resistive switching coexistence in conductive point hexagonal boron nitride monolayer. ACS Nano 17, 13457–13466 (2023).
Lim, E. W. & Ismail, R. Conduction mechanism of valence change resistive switching memory: a survey. Electron 4, 586–613 (2015).
Li, Y. et al. Resistive switching properties of monolayer h-BN atomristors with different electrodes. Appl. Phys. Lett. 120, 183504 (2022).
Pan, C. et al. Coexistence of grain-boundaries-assisted bipolar and threshold resistive switching in multilayer hexagonal boron nitride. Adv. Funct. Mater. 27, 1703703 (2017).
Cai, Z., Liu, L. & Zhou, P. The development of transfer technologies for advanced 2D circuits integration. InfoMat 6, 304–322 (2024).
Nakatani, M. et al. Ready-to-transfer two-dimensional materials using tunable adhesive force tapes. Nat. Electron. 7, 119–130 (2024).
Pham, P. V. et al. Transfer of 2D films: from imperfection to perfection. ACS Nano 18, 14841–14876 (2024).
Lupina, G. et al. Residual metallic contamination of transferred chemical vapor deposited graphene. ACS Nano 9, 4776–4785 (2015).
Wan, W. et al. A compute-in-memory chip based on resistive random-access memory. Nature 608, 504–512 (2022).
Rao, M. et al. Thousands of conductance levels in memristors integrated on CMOS. Nature 615, 823–829 (2023).
Kim, H., Mahmoodi, M. R., Nili, H. & Strukov, D. B. 4K-memristor analog-grade passive crossbar circuit. Nat. Commun. 12, 5191 (2021).
Haensch, W. et al. Compute in-memory with non-volatile elements for neural networks: a review from a co-design perspective. Adv. Mater. 35, e2204944 (2023).
Song, W. et al. Programming memristor arrays with arbitrarily high precision for analog computing. Science 383, 903–910 (2024).
Park, H., Mastro, M. A., Tadjer, M. J. & Kim, J. Programmable multilevel memtransistors based on van der Waals heterostructures. Adv. Electron. Mater. 5, 1800720 (2019).
Khot, A. C. et al. Amorphous boron nitride memristive device for high-density memory and neuromorphic computing applications. ACS Appl. Mater. Interfaces 14, 10546–10557 (2022).
Zhu, K. et al. Hybrid 2D–CMOS microchips for memristive applications. Nature 618, 57–62 (2023).
Yu, S. Neuro-inspired computing with emerging nonvolatile memorys. Proc. IEEE 106, 260–285 (2018).
Kim, H., Kim, T., Kim, J. & Kim, J. J. Neural network optimized to resistive memory with nonlinear current-voltage characteristics. ACM J. Emerg. Technol. Comput. Syst. 14, 46 (2018).
Jiang, Z. et al. COPS: an efficient and reliability-enhanced programming scheme for analog RRAM and on-chip implementation of denoising diffusion probabilistic model. In Proc. 60th ACM/IEEE Design Automation Conference 8–11 (ACM, 2023).
Yao, P. et al. Fully hardware-implemented memristor convolutional neural network. Nature 577, 641–646 (2020).
Ambrogio, S. et al. Statistical fluctuations in HfOx resistive-switching memory: part II—random telegraph noise. IEEE Trans. Electron Devices 61, 2920–2927 (2014).
Puglisi, F. M. in Noise in Nanoscale Semiconductor Devices (ed. Grasser, T.) 87–133 (Springer, 2020).
Becker, T. et al. Resistive switching devices producing giant random telegraph noise. IEEE Electron Device Lett. 43, 146–149 (2022).
Pazos, S. et al. Hardware implementation of a true random number generator integrating a hexagonal boron nitride memristor with a commercial microcontroller. Nanoscale 15, 2171–2180 (2022).
Maestro, M. et al. New high resolution random telegraph noise (RTN) characterization method for resistive RAM. Solid. State Electron. 115, 140–145 (2016).
Martin-Martinez, J., Diaz, J., Rodriguez, R., Nafria, M. & Aymerich, X. New weighted time lag method for the analysis of random telegraph signals. IEEE Electron Device Lett. 35, 479–481 (2014).
Pazos, S. et al. High-temporal-resolution characterization reveals outstanding random telegraph noise and the origin of dielectric breakdown in h-BN memristors. Adv. Funct. Mater. 2213816, 2213816 (2023).
Huang, Y. C. et al. 15.7 A 32Mb RRAM in a 12nm FinFET technology with a 0.0249 μm2 bit-cell, a 3.2 GB/s read throughput, a 10K cycle write endurance and a 10-year retention at 105 °C. In Proc. IEEE International Solid-State Circuits Conference 288–290 (IEEE, 2024).
Krishnan, G. et al. Robust RRAM-based in-memory computing in light of model stability. In Proc. IEEE International Reliability Physics Symposium 1–5 (IEEE, 2021).
