Wafer-Scale Ag2S-Based Memristive Crossbar Arrays with Ultra-Low Switching-Energies Reaching Biological Synapses
Corresponding Author: Zhen Zhang
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
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References
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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
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
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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
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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
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
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
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
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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
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
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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
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
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
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
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
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
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
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
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
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
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
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
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.
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
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
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
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
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
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
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
W. Carl, Investigations on silver sulfide. J. Chem. Phys. 21, 1819–1827 (1953). https://doi.org/10.1063/1.1698670
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
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
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
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