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Vol. 17 (2025)

Wafer-Scale Ag2S-Based Memristive Crossbar Arrays with Ultra-Low Switching-Energies Reaching Biological Synapses

https://doi.org/10.1007/s40820-024-01559-2
Yuan Zhu
Division of Solid‑State Electronics, Department of Electrical Engineering, Uppsala University, 75121 Uppsala, Sweden
Tomas Nyberg
Division of Solid‑State Electronics, Department of Electrical Engineering, Uppsala University, 75121 Uppsala, Sweden
Leif Nyholm
Department of Chemistry, Uppsala University, Uppsala, Sweden
Daniel Primetzhofer
Department of Physics and Astronomy, Uppsala University, Uppsala, Sweden
Xun Shi
State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai 200050, People’s Republic of China
Zhen Zhang
Division of Solid‑State Electronics, Department of Electrical Engineering, Uppsala University, 75121 Uppsala, Sweden

Corresponding Author: Zhen Zhang

zhen.zhang@angstrom.uu.se

Nano-Micro Letters, Vol. 17 (2025), Article Number: 69

Abstract

Memristive crossbar arrays (MCAs) offer parallel data storage and processing for energy-efficient neuromorphic computing. However, most wafer-scale MCAs that are compatible with complementary metal-oxide-semiconductor (CMOS) technology still suffer from substantially larger energy consumption than biological synapses, due to the slow kinetics of forming conductive paths inside the memristive units. Here we report wafer-scale Ag2S-based MCAs realized using CMOS-compatible processes at temperatures below 160 °C. Ag2S electrolytes supply highly mobile Ag+ ions, and provide the Ag/Ag2S interface with low silver nucleation barrier to form silver filaments at low energy costs. By further enhancing Ag+ migration in Ag2S electrolytes via microstructure modulation, the integrated memristors exhibit a record low threshold of approximately − 0.1 V, and demonstrate ultra-low switching-energies reaching femtojoule values as observed in biological synapses. The low-temperature process also enables MCA integration on polyimide substrates for applications in flexible electronics. Moreover, the intrinsic nonidealities of the memristive units for deep learning can be compensated by employing an advanced training algorithm. An impressive accuracy of 92.6% in image recognition simulations is demonstrated with the MCAs after the compensation. The demonstrated MCAs provide a promising device option for neuromorphic computing with ultra-high energy-efficiency.


Highlights:
1 Wafer-scale integration of Ag2S-based memristive crossbar arrays was demonstrated using complementary metal–oxide–semiconductor (CMOS) compatible processes below 160 °C.
2 A record-low threshold voltage for filament formation and an ultra-low switching-energy reaching that of biological synapses in wafer-scale CMOS-compatible memristive units were achieved.
3 The energy-efficient resistance switching was enabled by self-supply of mobile Ag+ ions in Ag2S electrolytes and low silver-nucleation barrier at Ag/Ag2S interface.

Keywords

Wafer-scale Ag2S films Reactive sputter Silver nucleation Ag migration Energy-efficient neuromorphic computing
Zhu, Yuan, Tomas Nyberg, Leif Nyholm, Daniel Primetzhofer, Xun Shi, and Zhen Zhang. 2024. “Wafer-Scale Ag2S-Based Memristive Crossbar Arrays With Ultra-Low Switching-Energies Reaching Biological Synapses”. Nano-Micro Letters 17 (November):69. https://doi.org/10.1007/s40820-024-01559-2.
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References
  1. T.B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan et al., Language models are few-shot learners, arXiv:2005.14165v4 (2020). https://doi.org/10.48550/arXiv.2005.14165
  2. H.-S.P. Wong, S. Salahuddin, Memory leads the way to better computing. Nat. Nanotechnol. 10, 191–194 (2015). https://doi.org/10.1038/nnano.2015.29
  3. D. Ielmini, H.-S.P. Wong, In-memory computing with resistive switching devices. Nat. Electron. 1, 333–343 (2018). https://doi.org/10.1038/s41928-018-0092-2
  4. G.W. Burr, M.J. Breitwisch, M. Franceschini, D. Garetto, K. Gopalakrishnan et al., Phase change memory technology. J. Vac. Sci. Technol. B 28, 223–262 (2010). https://doi.org/10.1116/1.3301579
  5. B.C. Jang, S. Kim, S.Y. Yang, J. Park, J.H. Cha et al., Polymer analog memristive synapse with atomic-scale conductive filament for flexible neuromorphic computing system. Nano Lett. 19, 839–849 (2019). https://doi.org/10.1021/acs.nanolett.8b04023
  6. M.J. Lee, C.B. Lee, D. Lee, S.R. Lee, M. Chang et al., A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O(5–x)/TaO(2–x) bilayer structures. Nat. Mater. 10, 625–630 (2011). https://doi.org/10.1038/nmat3070
  7. K. Kamiya, M.Y. Yang, S.-G. Park, B. Magyari-Köpe, Y. Nishi et al., ON-OFF switching mechanism of resistive–random–access–memories based on the formation and disruption of oxygen vacancy conducting channels. Appl. Phys. Lett. 100, 073502 (2012). https://doi.org/10.1063/1.3685222
  8. E.J. Fuller, S.T. Keene, A. Melianas, Z. Wang, S. Agarwal et al., Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing. Science 364, 570–574 (2019). https://doi.org/10.1126/science.aaw5581
  9. P. Gkoupidenis, N. Schaefer, B. Garlan, G.G. Malliaras, Neuromorphic functions in PEDOT:PSS organic electrochemical transistors. Adv. Mater. 27, 7176–7180 (2015). https://doi.org/10.1002/adma.201503674
  10. Y. van de Burgt, E. Lubberman, E.J. Fuller, S.T. Keene, G.C. Faria et al., A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat. Mater. 16, 414–418 (2017). https://doi.org/10.1038/nmat4856
  11. H. Bian, Y.Y. Goh, Y. Liu, H. Ling, L. Xie et al., Stimuli-responsive memristive materials for artificial synapses and neuromorphic computing. Adv. Mater. 33, e2006469 (2021). https://doi.org/10.1002/adma.202006469
  12. Z. Wang, L. Wang, Y. Wu, L. Bian, M. Nagai et al., Signal filtering enabled by spike voltage-dependent plasticity in metalloporphyrin-based memristors. Adv. Mater. 33, e2104370 (2021). https://doi.org/10.1002/adma.202104370
  13. Y. Yu, L. Bian, Y. Zhang, Z. Liu, Y. Li et al., Synthesis of donor–acceptor gridarenes with tunable electronic structures for synaptic learning memristor. ACS Omega 4, 5863–5869 (2019). https://doi.org/10.1021/acsomega.9b00172
  14. H. Shao, Y. Li, W. Yang, X. He, L. Wang et al., A reconfigurable optoelectronic synaptic transistor with stable Zr-CsPbI3 nanocrystals for visuomorphic computing. Adv. Mater. 35, e2208497 (2023). https://doi.org/10.1002/adma.202208497
  15. J. Huang, J. Feng, Z. Chen, Z. Dai, S. Yang et al., A bioinspired MXene-based flexible sensory neuron for tactile near-sensor computing. Nano Energy 126, 109684 (2024). https://doi.org/10.1016/j.nanoen.2024.109684
  16. J. Huang, S. Yang, X. Tang, L. Yang, W. Chen et al., Flexible, transparent, and wafer-scale artificial synapse array based on TiOx/Ti3C2Tx film for neuromorphic computing. Adv. Mater. 35, e2303737 (2023). https://doi.org/10.1002/adma.202303737
  17. X. Tang, L. Yang, J. Huang, W. Chen, B. Li et al., Controlling sulfurization of 2D Mo2C crystal for Mo2C/MoS2-based memristor and artificial synapse. npj Flex. Electron. 6, 93 (2022). https://doi.org/10.1038/s41528-022-00227-y
  18. T. Gokmen, W. Haensch, Algorithm for training neural networks on resistive device arrays. Front. Neurosci. 14, 103 (2020). https://doi.org/10.3389/fnins.2020.00103
  19. T. Gokmen, Enabling training of neural networks on noisy hardware. Front. Artif. Intell. 4, 699148 (2021). https://doi.org/10.3389/frai.2021.699148
  20. W. Huh, D. Lee, C.-H. Lee, Memristors based on 2D materials as an artificial synapse for neuromorphic electronics. Adv. Mater. 32, e2002092 (2020). https://doi.org/10.1002/adma.202002092
  21. C. Liu, H. Chen, S. Wang, Q. Liu, Y.-G. Jiang et al., Two-dimensional materials for next-generation computing technologies. Nat. Nanotechnol. 15, 545–557 (2020). https://doi.org/10.1038/s41565-020-0724-3
  22. J. Jadwiszczak, D. Keane, P. Maguire, C.P. Cullen, Y. Zhou et al., MoS2 memtransistors fabricated by localized helium ion beam irradiation. ACS Nano 13, 14262–14273 (2019). https://doi.org/10.1021/acsnano.9b07421
  23. M. Wang, S. Cai, C. Pan, C. Wang, X. Lian et al., Robust memristors based on layered two-dimensional materials. Nat. Electron. 1, 130–136 (2018). https://doi.org/10.1038/s41928-018-0021-4
  24. X. Shi, H. Chen, F. Hao, R. Liu, T. Wang et al., Room-temperature ductile inorganic semiconductor. Nat. Mater. 17, 421–426 (2018). https://doi.org/10.1038/s41563-018-0047-z
  25. A. Geresdi, M. Csontos, A. Gubicza, A. Halbritter, G. Mihály, A fast operation of nanometer-scale metallic memristors: highly transparent conductance channels in Ag2S devices. Nanoscale 6, 2613–2617 (2014). https://doi.org/10.1039/c3nr05682a
  26. A. Gubicza, M. Csontos, A. Halbritter, G. Mihály, Non-exponential resistive switching in Ag2S memristors: a key to nanometer-scale non-volatile memory devices. Nanoscale 7, 4394–4399 (2015). https://doi.org/10.1039/c5nr00399g
  27. A. Gubicza, M. Csontos, A. Halbritter, G. Mihály, Resistive switching in metallic Ag2S memristors due to a local overheating induced phase transition. Nanoscale 7, 11248–11254 (2015). https://doi.org/10.1039/C5NR02536B
  28. Y. Zhu, J.-S. Liang, V. Mathayan, T. Nyberg, D. Primetzhofer et al., High performance full-inorganic flexible memristor with combined resistance-switching. ACS Appl. Mater. Interfaces 14, 21173–21180 (2022). https://doi.org/10.1021/acsami.2c02264
  29. Y. Zhu, J.-S. Liang, X. Shi, Z. Zhang, Full-inorganic flexible Ag2S memristor with interface resistance-switching for energy-efficient computing. ACS Appl. Mater. Interfaces 14, 43482–43489 (2022). https://doi.org/10.1021/acsami.2c11183
  30. S. Li, M.-E. Pam, Y. Li, L. Chen, Y.-C. Chien et al., Wafer-scale 2D hafnium diselenide based memristor crossbar array for energy-efficient neural network hardware. Adv. Mater. 34, e2103376 (2022). https://doi.org/10.1002/adma.202103376
  31. J. Cui, F. An, J. Qian, Y. Wu, L.L. Sloan et al., CMOS-compatible electrochemical synaptic transistor arrays for deep learning accelerators. Nat. Electron. 6, 292–300 (2023). https://doi.org/10.1038/s41928-023-00939-7
  32. H.-S. Lee, V. Sangwan, W.A.G. Rojas, H. Bergeron, H.Y. Jeong et al., Dual-gated MoS2 memtransistor crossbar array. Adv. Funct. Mater. 30, 2003683 (2020). https://doi.org/10.1002/adfm.202003683
  33. L. Sun, Y. Zhang, G. Han, G. Hwang, J. Jiang et al., Self-selective van der Waals heterostructures for large scale memory array. Nat. Commun. 10, 3161 (2019). https://doi.org/10.1038/s41467-019-11187-9
  34. M. Sivan, Y. Li, H. Veluri, Y. Zhao, B. Tang et al., All WSe2 1T1R resistive RAM cell for future monolithic 3D embedded memory integration. Nat. Commun. 10, 5201 (2019). https://doi.org/10.1038/s41467-019-13176-4
  35. B. Govoreanu, G.S. Kar, Y.-Y. Chen, V. Paraschiv, S. Kubicek et al., 10 × 10 nm2 Hf/HfOx crossbar resistive RAM with excellent performance, reliability and low-energy operation. In 2011 International Electron Devices Meeting. December 5–7, 2011, Washington, DC, USA. IEEE, (2011). 31.6.1–31.6.4.
  36. G.S. Park, Y.B. Kim, S.Y. Park, X.S. Li, S. Heo et al., In situ observation of filamentary conducting channels in an asymmetric Ta2O5-x/TaO2-x bilayer structure. Nat. Commun. 4, 2382 (2013). https://doi.org/10.1038/ncomms3382
  37. K. Shibuya, R. Dittmann, S. Mi, R. Waser, Impact of defect distribution on resistive switching characteristics of Sr2TiO4 thin films. Adv. Mater. 22, 411–414 (2010). https://doi.org/10.1002/adma.200901493
  38. Y. Yang, P. Gao, L. Li, X. Pan, S. Tappertzhofen et al., Electrochemical dynamics of nanoscale metallic inclusions in dielectrics. Nat. Commun. 5, 4232 (2014). https://doi.org/10.1038/ncomms5232
  39. M. Lanza, H.-S.P. Wong, E. Pop, D. Ielmini, D. Strukov et al., Recommended Methods to Study Resistive Switching Devices. Adv. Electron. Mater. 5, 1800143 (2019). https://doi.org/10.1002/aelm.201800143
  40. Y. Li, L. Loh, S. Li, L. Chen, B. Li et al., Anomalous resistive switching in memristors based on two-dimensional palladium diselenide using heterophase grain boundaries. Nat. Electron. 4, 348–356 (2021). https://doi.org/10.1038/s41928-021-00573-1
  41. H. Yeon, P. Lin, C. Choi, S.H. Tan, Y. Park et al., Alloying conducting channels for reliable neuromorphic computing. Nat. Nanotechnol. 15, 574–579 (2020). https://doi.org/10.1038/s41565-020-0694-5
  42. H. Schmalzried, Ag2S—the physical chemistry of an inorganic material. Prog. Solid State Chem. 13, 119–157 (1980). https://doi.org/10.1016/0079-6786(80)90002-3
  43. W. Carl, Investigations on silver sulfide. J. Chem. Phys. 21, 1819–1827 (1953). https://doi.org/10.1063/1.1698670
  44. P. Yang, S. Zhang, S. Pan, B. Tang, Y. Liang et al., Epitaxial growth of centimeter-scale single-crystal MoS2 monolayer on Au(111). ACS Nano 14, 5036–5045 (2020). https://doi.org/10.1021/acsnano.0c01478
  45. R. Yue, A.T. Barton, H. Zhu, A. Azcatl, L.F. Pena et al., HfSe2 thin films: 2D transition metal dichalcogenides grown by molecular beam epitaxy. ACS Nano 9, 474–480 (2015). https://doi.org/10.1021/nn5056496
  46. A. Jana, R.E. García, Lithium dendrite growth mechanisms in liquid electrolytes. Nano Energy 41, 552–565 (2017). https://doi.org/10.1016/j.nanoen.2017.08.056
  47. H. Liu, X.-B. Cheng, J.-Q. Huang, H. Yuan, Y. Lu et al., Controlling dendrite growth in solid-state electrolytes. ACS Energy Lett. 5, 833–843 (2020). https://doi.org/10.1021/acsenergylett.9b02660
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