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Jan, 2025;21 (1): e2405749.

Memristive Artificial Synapses Based on Brownmillerite for Endurable Weight Modulation.

Yoon Jung Lee, Eun Seok Choi, Ji Hyun Baek, Jiwoong Yang, Jaehyun Kim, Jae Young Kim, Byungsoo Kim, Donghoon Shin, Sung Hyuk Park, In Hyuk Im, Hyeonji Lee, Youngmin Kim, Deokjae Choi, Sanghan Lee, Ho Won Jang
Author Information
  1. Yoon Jung Lee: Department of Material Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea. ORCID
  2. Eun Seok Choi: School of Materials Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea.
  3. Ji Hyun Baek: Department of Material Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea. ORCID
  4. Jiwoong Yang: School of Materials Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea.
  5. Jaehyun Kim: Department of Material Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea. ORCID
  6. Jae Young Kim: Department of Material Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea. ORCID
  7. Byungsoo Kim: Department of Material Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea.
  8. Donghoon Shin: Department of Chemistry, Northwestern University, Evanston, IL, 60208, USA. ORCID
  9. Sung Hyuk Park: Department of Material Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea. ORCID
  10. In Hyuk Im: Department of Material Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea.
  11. Hyeonji Lee: Department of Material Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea. ORCID
  12. Youngmin Kim: Department of Material Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea. ORCID
  13. Deokjae Choi: Department of Chemistry, Northwestern University, Evanston, IL, 60208, USA.
  14. Sanghan Lee: School of Materials Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea. ORCID
  15. Ho Won Jang: Department of Material Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea. ORCID
PMID: 39468890 DOI: 10.1002/smll.202405749

Abstract

Exploring a computing paradigm that blends memory and computation functions is essential for artificial synapses. While memristors for artificial synapses are widely studied due to their energy-efficient structures, random filament conduction in general memristors makes them less preferred for endurability in long-term synaptic modulation. Herein, the topotactic phase transition (TPT) in brownmillerite-phased (110)-SrCoO (SCO) is harnessed to enhance the reversibility of oxygen ion migration through 1-D oxygen vacancy channels. By employing a heteroepitaxial structured 2-terminal configuration of Au/SCO/SrRuO/SrTiO, the brownmillerite SCO-based synapse artificial synapses are exploited. Demonstration of the TPT behavior is corroborated by comparing oxygen migration energy by density-functional theory calculations and experimental results, and by monitoring the voltage pulse-induced peak shift in the Raman spectra of SCO. With the voltage pulse-driven TPT behaviors, it is reliably characterized by linear, symmetric, and endurable long-term potentiation and depression performances. Notably, the durability of the TPT-based weight control mechanism is demonstrated by achieving consistent and noise-free weight updates over 32 000 iterations across 640 cycles. Furthermore, learning performances based on deep neural networks and convolutional neural networks on various image datasets yielded very high recognition accuracy. The work offers valuable insights into designing memristive synapses that enable reliable weight updates in neural networks.

Keywords

1‐D Oxygen vacancy channels artificial synapse brownmillerite SrCoO2.5 neuromorphic Hardware topotactic phase transition

References

  1. Q. Huo, Y. Yang, T. Wang, D. Lei, X. Fu, Q. Ren, X. Xu, Q. Luo, G. Xing, C. Chen, X. Si, H. Wu, Y. Yuan, Q. Li, X. Li, X. Wang, M.‐F. Chang, F. Zhang, M. Liu, Nat. Electron. 2022, 5, 469.
  2. P. Chi, S. Li, C. Xu, T. Zhang, J. Zhao, Y. Liu, Y. Wang, Y. Xie, ACM SIGARCH Comput. Archit. News 2016, 44, 27.
  3. Y. J. Lee, Y. Kim, Y. Gim, K. Hong, H. W. Jang, Adv. Mater. 2024, 36, 2305353.
  4. S. J. Kim, J. Yang, J. W. Yang, Y. J. Lee, M. Choi, S. A. Lee, J. M. Suh, K. J. Kwak, J. H. Baek, I. H. Im, D. E. Lee, J. Y. Kim, J. Kim, J. S. Han, S. Y. Kim, D. Lee, N.‐G. Park, H. W. Jang, Mater. Today 2022, 52, 19.
  5. J. H. Baek, K. J. Kwak, S. J. Kim, J. Kim, I. H. Im, S. Lee, K. Kang, H. W. Jang, Nano‐Micro Lett. 2023, 15, 69.
  6. K. J. Kwak, J. H. Baek, D. E. Lee, I. H. Im, J. Kim, S. J. Kim, Y. J. Lee, J. Y. Kim, H. W. Jang, Nano Lett. 2022, 22, 6010.
  7. H. Shim, F. Ershad, S. Patel, Y. Zhang, B. Wang, Z. Chen, T. J. Marks, A. Facchetti, C. Yu, Nat. Electron. 2022, 5, 660.
  8. X. Le, Y. Liu, J. Zhang, F. Wu, M. Hu, H. Yang, Adv. Intell. Syst. 2022, 4, 2200015.
  9. J. J. Yang, D. B. Strukov, D. R. Stewart, Nat. Nanotechnol. 2013, 8, 13.
  10. B. Cramer, S. Billaudelle, S. Kanya, A. Leibfried, A. Grubl, V. Karasenko, C. Pehle, K. Schreiber, Y. Stradmann, J. Weis, J. Schemmel, F. Zenke, Proc. Natl. Acad. Sci. USA 2022, 119, 2109194119.
  11. T. Chang, S.‐H. Jo, W. Lu, presented at 13th Int. Workshop on Cellular Nanoscale Networks and their Appl., Turin, Italy, August 2012.
  12. S. Wang, L. Song, W. Chen, G. Wang, E. Hao, C. Li, Y. Hu, Y. Pan, A. Nathan, G. Hu, S. Gao, Adv. Electron, Mater. 2023, 9, 2200877.
  13. M. S. Islam, A. M. Nolan, S. Wang, Q. Bai, Y. Mo, Chem. Mater. 2020, 32, 5028.
  14. H. Jeen, W. S. Choi, J. W. Freeland, H. Ohta, C. U. Jung, H. N. Lee, Arxiv 2013, 1307.3932.
  15. Y. Wang, K.‐M. Kang, M. Kim, H. Lee, R. Waser, D. Wouters, R. Dittmann, J. J. Yang, H.‐H. Park, Mater. Today 2019, 28, 63.
  16. H. Han, A. Sharma, H. L. Meyerheim, J. Yoon, H. Deniz, K.‐R. Jeon, A. K. Sharma, K. Mohseni, C. Guillemard, M. Valvidares, P. Gargiani, S. S. P. Parkin, ACS nano 2022, 16, 6206.
  17. J. Zhao, H. Guo, X. He, Q. Zhang, L. Gu, X. Li, K.‐J. Jin, T. Yang, C. Ge, Y. Luo, M. He, Y. Long, J.‐O. Wang, H. Qian, C. Wang, H. Lu, G. Yang, K. Ibrahim, ACS. Appl. Mater. Interfaces 2018, 10, 10211.
  18. M. S. Irshad, N. Arshad, J. Zhang, C. Song, N. Mushtaq, M. Alomar, T. Shamim, V.‐D. Dao, H. Wang, X. Wang, H. Zhang, Adv. Energy Sustain. Res. 2023, 4, 2200158.
  19. A. Aguadero, D. Perez‐Coll, J. A. Alonso, S. J. Skinner, J. Kilner, Chem. Mater. 2012, 24, 2655.
  20. X. Mou, J. Tan, Y. Lyu, Q. Zhang, S. Yang, F. Xu, W. Liu, M. Xu, Y. Zhou, W. Sun, Y. Zhong, B. Gao, P. Yu, H. Qian, H. Wu, Sci. Adv. 2021, 7, eabh0648.
  21. J. C. Magee, C. Grienberger, Annu. Rev. Neurosci. 2020, 43, 95.
  22. A. Thomas, A. N. Resmi, A. Ganguly, K. B. Jinesh, Sci. Rep. 2020, 10, 12450.
  23. P. B. Pillai, M. M. D. Souza, ACS Appl. Mater. Interfaces 2017, 9, 1609.
  24. H. Harkram, J. Lubke, M. Frotscher, B. Sakmann, Science 1997, 275, 213.
  25. G. Li, D. W. McLaughlin, C. S. Peskin, Proc. Natl. Acad. Sci. USA 2024, 121, 2311709121.
  26. T. Miao, B. Cui, C. Huang, D. Wang, L. Liu, W. Liu, Y. Li, R. Chu, X. Ren, L. Liu, B. Cheng, G. Zhou, H. Qin, G. Xing, J. Lu, Adv. Intell. Sys. 2023, 5, 2200287.
  27. N. Siannas, C. Zacharaki, P. Tsipas, D. J. Kim, W. Hamouda, C. Istrate, L. Pintilie, M. Schmidbauer, C. Dubourdieu, A. Dimoulas, Adv. Funct. Mater. 2023, 34, 2311767.
  28. S. Seo, B.‐S. Kang, J.‐J. Lee, H.‐J. Ryu, S. Kim, H. Kim, S. Oh, J. Shim, K. Heo, S. Oh, J.‐H. Park, Nat. Commun. 2020, 11, 3936.
  29. S. H. Jo, T. Chang, I. Ebong, B. B. Bhadviya, P. Mazumder, W. Lu, Nano Lett. 2010, 10, 1297.
  30. N. Gong, T. Ide, S. Kim, I. Boydat, A. Sebastian, V. Narayanan, T. Ando, Nat. Commun. 2018, 9, 2102.
  31. T. P. Xiao, C. H. Bennett, B. Feinberg, M. J. Marinella, S. Agarwal, CrossSim Inference Manual v2.0, Sandia National Lab, Livermore, CA, USA 2022.
  32. S. Roy, S. Sridharan, S. Jain, A. Raghunathan, IEEE Trans. Very Large Scale Integr. (VLSI) Sys. 2021, 29, 730.
  33. B. Jeong, P. H. Chung, J. Han, T. Noh, T.‐S. Yoon, Nanoscale 2024, 16, 5737.
  34. F. Aguirre, A. Sebastian, M. L. Gallo, W. Song, T. Wang, J. J. Yang, W. Lu, M.‐F. Chang, D. lelmini, Y. Yang, A. Mehonic, A. Kenyon, M. A. Villena, J. B. Roldan, Y. Wu, H.‐H. Hsu, N. Raghavan, J. Sune, E. Miranda, A. Eltawil, G. Setti, K. Smagulova, K. N. Salama, O. Krestinskaya, K.‐W. Ang, S. Jain, S. Li, O. Alharbi, S. Pazos, M. Lanza, Nat. Commun. 2024, 15, 1974.
  35. S. Hu, J. Seidel, Nanotechnology 2016, 27, 325301.
  36. J. Zhang, D. Doutt, T. Merz, J. Chakhalian, M. Kareev, J. Liu, L. J. Brillson, Appl. Phys. Lett. 2009, 94, 092904.
  37. J. B. Giesbers, M. W. J. Prins, J. F. f M. Cillessen, H. A. van Esch, Microelectron. Eng. 1997, 35, 71.
  38. T. Harada, A. Tsukazaki, Appl. Phys. Lett. 2020, 116, 232104.
  39. L. G. Wright, T. Onodera, M. M. Stein, T. Wang, D. T. Schachter, Z. Hu, P. L. McMahon, Nature 2022, 601, 549.
  40. D. Kaushik, J. Sharda, D. Bhowmik, Nanotechnology 2020, 31, 364004.
  41. R. Ge, X. Wu, L. Laing, S. M. Hus, Y. Gu, E. Okogbue, H. Chou, J. Shi, Y. Zhang, S. K. Banerjee, Y. Jung, J. C. Lee, D. Akinwande, Adv. Mater. 2021, 33, 2007792.
  42. V. Joshi, M. L. Gallo, S. Haefeli, I. Boybat, S. R. Nandakumar, C. Piveteau, M. Dazzi, B. Rajendran, A. Sebastian, E. Eleftheriou, Nat. Commun. 2020, 11, 2473.
  43. S. J. Kim, S. Kim, H. W. Jang, iScience 2021, 24, 101889.
  44. T. P. Xiao, B. Feinberg, C. H. Bennett, V. Agrawal, P. Saxena, V. Prabhakar, IEEE Trans. Circuits. Syst. I: Regular Papers 2022, 69, 1480.
  45. S. Kunwar, Z. Jernigan, Z. Hughes, C. Somodi, M. D. Saccone, F. Caravelli, P. Roy, D. Zhang, H. Wang, Q. Jia, J. L. MacManus‐Driscoll, G. Kenyon, A. Sornborger, W. Nie, A. Chen, Adv. Intell. Syst. 2023, 5, 2300035.
  46. H. Xiao, K. Rasul, R. Vollgraf, Arxiv 2017, 1708.07747.
  47. S. Ham, J. Jang, D. Koo, S. Gi, D. Kim, S. Jang, N. D. Kim, S. Bae, B. Lee, C.‐H. Lee, G. Wang, Nano Energy 2024, 124, 109435.
  48. Y. Zhang, L. Chu, W. Li, Nano Micro‐Lett. 2024, 16, 166.
  49. K. He, X. Zhang, S. Ren, J. Sun, in Proc. 2016 IEEE Conf. Comput. Vision and Pattern Recognition (CVPR), IEEE, Piscataway, NJ 2016, p. 770.
  50. S. Singla, S. Singla, S. Feizi, Arxiv 2022, 2108.04062.
  51. V. J. Reddi, C. Cheng, D. Kanter, P. Mattson, G. Schmuelling, C.‐J. Wu, presented at 47th ACM/IEEE Annual Int. Symp. Comput. Architecture (ISCA), Valencia, Spain, June 2020.
  52. P. Mattson, V. J. Reddi, C. Cheng, C. Coleman, G. Diamos, D. Kanter, IEEE. Micro 2020, 40, 8.
  53. M. Ismail, C. Mahata, S. Kim, J. Alloys Compd. 2022, 892, 162141.
  54. S. Rajasekaran, F. M. Simanjuntak, S. Chandrasekaran, D. Panda, A. Saleem, T.‐Y. Tseng, IEEE Electron Device Lett. 2022, 43, 9.
  55. A. Liang, J. Zhang, F. Wang, Y. Jiang, K. Hu, X. Shan, Q. Liu, Z. Song, K. Zhang, Nanotechnology 2021, 32, 145202.
  56. S.‐U. Lee, S.‐Y. Kim, J. Lee, J. H. Baek, J.‐W. Lee, H. W. Jang, N.‐G. Park, Nano Lett. 2024, 24, 4869.
  57. M. Ismail, M. Rasheed, C. Mahata, M. Kang, S. Kim, Nano Converg. 2023, 10, 33.
  58. J. Chen, X. Liu, C. Liu, L. Tang, T. Bu, B. Jiang, Y. Qing, Y. Xie, Y. Wang, Y. Shan, R. Li, C. Ye, L. Liao, Nano Lett. 2024, 24, 5371.
  59. Z. Wang, W. Zhu, J. Li, Y. Shao, X. Li, H. Shi, J. Zhao, Z. Zhou, Y. Wang, X. Yan, ACS Appl. Mater. Interfaces 2023, 15, 49390.
  60. H. Chen, Z. Shen, W.‐T. Guo, Y.‐P. Jiang, W. Li, D. Zhang, Z. Tang, Q.‐J. Sun, X.‐G. Tang, J. Materiomics 2024, 10, 1308.
  61. J. Yang, A. Yoon, D. Lee, S. Song, I. J. Jung, D.‐H. Lim, H. Jeong, Z. Lee, M. Lanza, S.‐Y. Kwon, Adv. Funct. Mater. 2024, 34, 2309455.

Grants

  1. /Cooperative Research Program for Agriculture Science and Technology Development Rural Development Administration, Republic of Korea
  2. /Korea Research Institute of Standards and Science
  3. 2021M3H4A3A02086430/National R&D Program
  4. 2022M3H4A1A01011993/Nano Material Technology Development Program
  5. /Ministry of Science and ICT, South Korea
  6. RS-2024-00405016/Ministry of Science and ICT (MSIT)
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