May 2, 2024
Thousands of conductance levels in memristors integrated on CMOS – Nature

Thousands of conductance levels in memristors integrated on CMOS – Nature

  • Chua, L. O. Memristor—the missing circuit element. IEEE Trans. Circuit Theory 18, 507–519 (1971).

    Article 

    Google Scholar
     

  • Valov, I., Waser, R., Jameson, J. R. & Kozicki, M. N. Electrochemical metallization memories—fundamentals, applications, prospects. Nanotechnology 22, 254003 (2011).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Yang, Y. & Huang, R. Probing memristive switching in nanoionic devices. Nat. Electron. 1, 274–287 (2018).

    Article 

    Google Scholar
     

  • Wen, W., Wu, C., Wang, Y., Chen, Y. & Li, H. Learning structured sparsity in deep neural networks. In Advances in Neural Information Processing Systems 29 (eds Lee, D. D., et al.), 2082–2090 (Curan Associates, 2016).

  • Wan, W. et al. A compute-in-memory chip based on resistive random-access memory. Nature 608, 504–512 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kumar, S., Wang, X., Strachan, J. P., Yang, Y. & Lu, W. D. Dynamical memristors for higher-complexity neuromorphic computing.Nat. Rev. Mater. 7, 575–591 (2022).

    Article 
    ADS 

    Google Scholar
     

  • Xue, C.-X. et al. A CMOS-integrated compute-in-memory macro based on resistive random-access memory for AI edge devices. Nat. Electron. 4, 81–90 (2021).

    Article 
    CAS 

    Google Scholar
     

  • Lanza, M. et al. Memristive technologies for data storage, computation, encryption, and radio-frequency communication. Science 376, eabj9979 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Yao, P. et al. Fully hardware-implemented memristor convolutional neural network. Nature 577, 641–646 (2020).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhang, W. et al. Neuro-inspired computing chips. Nat. Electron. 3, 371–382 (2020).

    Article 
    ADS 

    Google Scholar
     

  • Ielmini, D. & Wong, H.-S. P. In-memory computing with resistive switching devices. Nat. Electron. 1, 333–343 (2018).

    Article 

    Google Scholar
     

  • Zidan, M. A., Strachan, J. P. & Lu, W. D. The future of electronics based on memristive systems. Nat. Electron. 1, 22–29 (2018).

    Article 

    Google Scholar
     

  • Yu, S. Neuro-inspired computing with emerging nonvolatile memorys. Proc. IEEE 106, 260–285 (2018).

    Article 
    CAS 

    Google Scholar
     

  • Jung, S. et al. A crossbar array of magnetoresistive memory devices for in-memory computing. Nature 601, 211–216 (2022).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Sangwan, V. K. & Hersam, M. C. Neuromorphic nanoelectronic materials. Nat. Nanotechnol. 15, 517–528 (2020).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Burr, G. W. A role for analogue memory in AI hardware. Nat. Mach. Intell. 1, 10–11 (2019).

    Article 

    Google Scholar
     

  • 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).

    Article 
    CAS 

    Google Scholar
     

  • Fuller, E. J. et al. Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing. Science 364, 570–574 (2019).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Choi, C. et al. Reconfigurable heterogeneous integration using stackable chips with embedded artificial intelligence.Nat. Electron. 5, 386–393 (2022).

    Article 

    Google Scholar
     

  • Lim, D.-H. et al. Spontaneous sparse learning for PCM-based memristor neural networks. Nat. Commun. 12, 319 (2021).

    Article 
    MathSciNet 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Xu, X. et al. Scaling for edge inference of deep neural networks. Nat. Electron. 1, 216–222 (2018).

    Article 

    Google Scholar
     

  • Sun, Y. et al. A Ti/AlOx/TaOx/Pt analog synapse for memristive neural network. IEEE Electron Device Lett. 39, 1298–1301 (2018).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Stathopoulos, S. et al. Multibit memory operation of metal-oxide bi-layer memristors. Sci. Rep. 7, 17532 (2017).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kim, H., Mahmoodi, M. R., Nili, H. & Strukov, D. B. 4K-memristor analog-grade passive crossbar circuit. Nat. Commun. 12, 5198 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zidan, M. A. et al. A general memristor-based partial differential equation solver. Nat. Electron. 1, 411–420 (2018).

    Article 

    Google Scholar
     

  • Li, C. et al. Analogue signal and image processing with large memristor crossbars. Nat. Electron. 1, 52–59 (2018).

    Article 

    Google Scholar
     

  • Mackin, C. et al. Optimised weight programming for analogue memory-based deep neural networks. Nat. Commun. 13, 3765 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Choi, S. et al. SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations. Nat. Mater. 17, 335–340 (2018).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Yao, P. et al. Face classification using electronic synapses. Nat. Commun. 8, 15199 (2017).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hinton, G. The forward–forward algorithm: some preliminary investigations. Preprint at https://arxiv.org/abs/2212.13345 (2022).

  • Yan, Z., Hu, X. S. & Shi, Y. SWIM: Selective write-verify for computing-in-memory neural accelerators. Preprint at https://arxiv.org/abs/2202.08395 (2022).

  • Chen, B. et al. A memristor-based hybrid analog-digital computing platform for mobile robotics. Sci. Robot. 5, eabb6938 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Choi, S., Yang, Y. & Lu, W. Random telegraph noise and resistance switching analysis of oxide based resistive memory. Nanoscale 6, 400–404 (2014).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Ielmini, D., Nardi, F. & Cagli, C. Resistance-dependent amplitude of random telegraph-signal noise in resistive switching memories. Appl. Phys. Lett. 96, 053503 (2010).

    Article 
    ADS 

    Google Scholar
     

  • Puglisi, F. M., Pavan, P., Padovani, A., Larcher, L. & Bersuker, G. Random telegraph signal noise properties of HfOx RRAM in high resistive state. In 2012 Proc. European Solid-State Device Research Conference (ESSDERC), 274–277 (IEEE, 2012).

  • Lee, J.-K. et al. Extraction of trap location and energy from random telegraph noise in amorphous TiOx resistance random access memories. Appl. Phys. Lett. 98, 143502 (2011).

    Article 
    ADS 

    Google Scholar
     

  • Puglisi, F. M., Padovani, A., Larcher, L. & Pavan, P. Random telegraph noise: measurement, data analysis, and interpretation. In 2017 IEEE 24th International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA), 1–9 (IEEE, 2017).

  • Puglisi, F. M., Zagni, N., Larcher, L. & Pavan, P. Random telegraph noise in resistive random access memories: compact modeling and advanced circuit design. IEEE Trans. Electron Devices 65, 2964–2972 (2018).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Yang, Y. et al. Probing nanoscale oxygen ion motion in memristive systems. Nat. Commun. 8, 15173 (2017).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Puglisi, F. M. Noise in Nanoscale Semiconductor Devices (ed. Grassor, T.), 87–133 (Springer, 2020).

  • Hui, F. & Lanza, M. Scanning probe microscopy for advanced nanoelectronics. Nat. Electron. 2, 221–229 (2019).

    Article 

    Google Scholar
     

  • Celano, U. et al. Three-dimensional observation of the conductive filament in nanoscaled resistive memory devices. Nano Lett. 14, 2401–2406 (2014).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Du, H. et al. Nanosized conducting filaments formed by atomic-scale defects in redox-based resistive switching memories. Chem. Mater. 29, 3164–3173 (2017).

    Article 
    CAS 

    Google Scholar
     

  • Puglisi, F. M., Larcher, L., Padovani, A. & Pavan, P. A complete statistical investigation of RTN in HfO2-based RRAM in high resistive state. IEEE Trans. Electron Devices 62, 2606–2613 (2015).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Ambrogio, S. et al. Statistical fluctuations in HfOx resistive-switching memory: part II—random telegraph noise. IEEE Trans. Electron Devices 61, 2920–2927 (2014).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Becker, T. et al. An electrical model for trap coupling effects on random telegraph noise. IEEE Electron Device Lett. 41, 1596–1599 (2020).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Brivio, S., Frascaroli, J., Covi, E. & Spiga, S. Stimulated ionic telegraph noise in filamentary memristive devices. Sci. Rep. 9, 6310 (2019).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Miao, F. et al. Anatomy of a nanoscale conduction channel reveals the mechanism of a high‐performance memristor. Adv. Mater. 23, 5633–5640 (2011).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhou, Y. et al. The effects of oxygen vacancies on ferroelectric phase transition of HfO2-based thin film from first-principle. Comput. Mater. Sci. 167, 143–150 (2019).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. Rev. B 59, 1758–1775 (1999).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • 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).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Perdew, J. P., Burke, K. & Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 77, 3865–3868 (1996).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Monkhorst, H. J. & Pack, J. D. Special points for Brillouin-zone integrations. Phys. Rev. B 13, 5188–5192 (1976).

    Article 
    ADS 
    MathSciNet 

    Google Scholar
     

  • Lyons, J. L., Janotti, A. & Van de Walle, C. G. The role of oxygen-related defects and hydrogen impurities in HfO2 and ZrO2. Microelectron. Eng. 88, 1452–1456 (2011).

    Article 
    CAS 

    Google Scholar
     

  • Monaghan, S., Hurley, P. K., Cherkaoui, K., Negara, M. A. & Schenk, A. Determination of electron effective mass and electron affinity in HfO2 using MOS and MOSFET structures. Solid State Electron. 53, 438–444 (2009).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Zhao, X. & Vanderbilt, D. First-principles study of structural, vibrational, and lattice dielectric properties of hafnium oxide. Phys. Rev. B 65, 233106 (2002).

    Article 
    ADS 

    Google Scholar
     

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