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Lead-free perovskites-based photonic synaptic devices with zero electric energy consumption

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Abstract

The von Neumann bottleneck is a critical limitation in synaptic devices. Therefore, artificial synaptic devices resembling biological neuromorphic synapses have been developed to overcome the von Neumann bottleneck. However, synaptic devices require voltages, which results in considerable energy consumption. Here, photonic synaptic devices with the vertical structure of indium tin oxide (ITO)/SnO2/Al2O3/CsBi3I10/Au are fabricated, which can work in the self-powered mode owing to the photovoltaic effect endowed by a vertical multilayer structure. Several fundamental synaptic functions, such as excitatory postsynaptic current, paired-pulse facilitation, short-term plasticity (STP), long-term plasticity (LTP), pulse-frequency-dependent plasticity, transition of STP to LTP, and the learning experience are emulated. Moreover, Morse-coded external light information is decoded by self-powered photonic synaptic devices. The results indicate that self-powered photonic synaptic devices based on lead-free perovskites exhibit great potential for efficient neuromorphic computing and optical wireless communication.

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References

  1. Yu R, Li E, Wu X, et al. Electret-based organic synaptic transistor for neuromorphic computing. ACS Appl Mater Interface, 2020, 12: 15446–15455

    Article  CAS  Google Scholar 

  2. Gong Y, Wang Y, Li R, et al. Tailoring synaptic plasticity in a perovskite QD-based asymmetric memristor. J Mater Chem C, 2020, 8: 2985–2992

    Article  CAS  Google Scholar 

  3. Wang T Y, Meng J L, He Z, et al. Ultralow power wearable heterosynapse with photoelectric synergistic modulation. Adv Sci, 2020, 7: 1903480

    Article  CAS  Google Scholar 

  4. Liu Y, Zhong J, Li E, et al. Self-powered artificial synapses actuated by triboelectric nanogenerator. Nano Energy, 2019, 60: 377–384

    Article  CAS  Google Scholar 

  5. Wang W, Gao S, Wang Y, et al. Advances in emerging photonic memristive and memristive-like devices. Adv Sci, 2022, 9: 2105577

    Article  CAS  Google Scholar 

  6. Zhang Y, Wang Z, Zhu J, et al. Brain-inspired computing with memristors: challenges in devices, circuits, and systems. Appl Phys Rev, 2020, 7: 011308

    Article  CAS  Google Scholar 

  7. Mao H, Zhu Y, Zhu Y, et al. Amorphous indium-gallium-zinc-oxide memristor arrays for parallel true random number generators. Appl Phys Lett, 2023, 122: 053503

    Article  ADS  CAS  Google Scholar 

  8. Li E, Lin W, Yan Y, et al. Synaptic transistor capable of accelerated learning induced by temperature-facilitated modulation of synaptic plasticity. ACS Appl Mater Interface, 2019, 11: 46008–46016

    Article  CAS  Google Scholar 

  9. Wang Z, Joshi S, Savel’ev S E, et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat Mater, 2017, 16: 101–108

    Article  ADS  CAS  PubMed  Google Scholar 

  10. Cao F, Tian W, Deng K, et al. Self-powered UV-Vis-NIR photodetector based on conjugated-polymer/CsPbBr3 nanowire array. Adv Funct Mater, 2019, 29: 1906756

    Article  CAS  Google Scholar 

  11. Hu L, Zhao Q, Huang S, et al. Flexible and efficient perovskite quantum dot solar cells via hybrid interfacial architecture. Nat Commun, 2021, 12: 466

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  12. Dong Y, Wang Y K, Yuan F, et al. Bipolar-shell resurfacing for blue LEDs based on strongly confined perovskite quantum dots. Nat Nanotechnol, 2020, 15: 668–674

    Article  ADS  CAS  PubMed  Google Scholar 

  13. Zhang Z X, Li C, Lu Y, et al. Sensitive deep ultraviolet photodetector and image sensor composed of inorganic lead-free Cs3Cu2I5 perovskite with wide bandgap. J Phys Chem Lett, 2019, 10: 5343–5350

    Article  CAS  PubMed  Google Scholar 

  14. Wang Y, Lv Z, Chen J, et al. Photonic synapses based on inorganic perovskite quantum dots for neuromorphic computing. Adv Mater, 2018, 30: 1802883

    Article  Google Scholar 

  15. Hao D D, Yang Z Y, Huang J, et al. Recent developments of optoelectronic synaptic devices based on metal halide perovskites. Adv Funct Mater, 2023, 33: 2211467

    Article  CAS  Google Scholar 

  16. Kumar M, Abbas S, Lee J H, et al. Controllable digital resistive switching for artificial synapses and Pavlovian learning algorithm. Nanoscale, 2019, 11: 15596–15604

    Article  CAS  PubMed  Google Scholar 

  17. Li Y, Wang Y, Yin L, et al. Silicon-based inorganic-organic hybrid optoelectronic synaptic devices simulating cross-modal learning. Sci China Inf Sci, 2021, 64: 162401

    Article  Google Scholar 

  18. Hao D, Liu D, Shen Y, et al. Air-stable self-powered photodetectors based on lead-free CsBi3I10/SnO2 heterojunction for weak light detection. Adv Funct Mater, 2021, 31: 2100773

    Article  CAS  Google Scholar 

  19. Liu Z, Dai S, Wang Y, et al. Photoresponsive transistors based on lead-free perovskite and carbon nanotubes. Adv Funct Mater, 2020, 30: 1906335

    Article  CAS  Google Scholar 

  20. Ji F, Huang Y, Wang F, et al. Near-infrared light-responsive Cu-doped Cs2 AgBiBr6. Adv Funct Mater, 2020, 30: 2005521

    Article  CAS  Google Scholar 

  21. Yuan Y, Zhang L, Yan G, et al. Significantly enhanced detectivity of CIGS broadband high-speed photodetectors by grain size control and ALD-Al2O3 interfacial-layer modification. ACS Appl Mater Interface, 2019, 11: 20157–20166

    Article  CAS  Google Scholar 

  22. Meng Y, Li F Z, Lan C Y, et al. Artificial visual systems enabled by quasi-two-dimensional electron gases in oxide superlattice nanowires. Sci Adv, 2020, 6: 6389

    Article  ADS  Google Scholar 

  23. Park J S, Jeong J K, Chung H J, et al. Electronic transport properties of amorphous indium-gallium-zinc oxide semiconductor upon exposure to water. Appl Phys Lett, 2008, 92: 072104

    Article  ADS  Google Scholar 

  24. Jeong J K, Yang H W, Jeong J H, et al. Origin of threshold voltage instability in indium-gallium-zinc oxide thin film transistors. Appl Phys Lett, 2008, 93: 123508

    Article  ADS  Google Scholar 

  25. Guo Z, Liu J, Han X, et al. High-performance artificial synapse based on CVD-grown WSe2 flakes with intrinsic defects. ACS Appl Mater Interface, 2023, 15: 19152–19162

    Article  CAS  Google Scholar 

  26. Hao D, Zhang J, Dai S, et al. Perovskite/organic semiconductor-based photonic synaptic transistor for artificial visual system. ACS Appl Mater Interface, 2020, 12: 39487–39495

    Article  CAS  Google Scholar 

  27. Yang Y, Lisberger S G. Purkinje-cell plasticity and cerebellar motor learning are graded by complex-spike duration. Nature, 2014, 510: 529–532

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  28. Huang W, Hang P, Wang Y, et al. Zero-power optoelectronic synaptic devices. Nano Energy, 2020, 73: 104790

    Article  CAS  Google Scholar 

  29. Dai S, Wu X, Liu D, et al. Light-stimulated synaptic devices utilizing interfacial effect of organic field-effect transistors. ACS Appl Mater Interface, 2018, 10: 21472–21480

    Article  CAS  Google Scholar 

  30. Sun Y, Qian L, Xie D, et al. Photoelectric synaptic plasticity realized by 2D perovskite. Adv Funct Mater, 2019, 29: 1902538

    Article  Google Scholar 

  31. Ahmed T, Kuriakose S, Mayes E L H, et al. Optically stimulated artificial synapse based on layered black phosphorus. Small, 2019, 15: 1900966

    Article  Google Scholar 

  32. Ma F, Zhu Y, Xu Z, et al. Optoelectronic perovskite synapses for neuromorphic computing. Adv Funct Mater, 2020, 30: 1908901

    Article  CAS  Google Scholar 

  33. Wang T Y, Meng J L, He Z Y, et al. Fully transparent, flexible and waterproof synapses with pattern recognition in organic environments. Nanoscale Horiz, 2019, 4: 1293–1301

    Article  ADS  CAS  Google Scholar 

  34. Yu F, Cai J C, Zhu L Q, et al. Artificial tactile perceptual neuron with nociceptive and pressure decoding abilities. ACS Appl Mater Interface, 2020, 12: 26258–26266

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by National Key Research and Development Program of China (Grant Nos. 2021YFA1101303, 2019YFE0121800), Science & Technology Foundation of Shanghai (Grant No. 20JC1415600), National Natural Science Foundation of China (Grant Nos. 62074111, 62088101), Innovation Program of Shanghai Municipal Education Commission (Grant No. 2021-01-07-00-07-E00096), Shanghai Municipal Science and Technology Major Project (Grant No. 2021SHZDZX0100), and Natural Science Foundation of Shandong Province (Grant Nos. ZR2022QB009, ZR2022MF246).

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Author notes

  1. Hao D D and Yang D have the same contribution to this work.

Authors and Affiliations

  1. College of Electronics and Information, Qingdao University, Qingdao, 266071, China

    Dandan Hao & Fukai Shan

  2. Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, 266101, China

    Di Yang

  3. School of Materials Science and Engineering, Tongji University, Shanghai, 201804, China

    Haixia Liang & Jia Huang

  4. National Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University, Shanghai, 201804, China

    Jia Huang

Authors
  1. Dandan Hao
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  2. Di Yang
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  3. Haixia Liang
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  4. Jia Huang
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  5. Fukai Shan
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Corresponding authors

Correspondence to Jia Huang or Fukai Shan.

Additional information

Supporting information Figures S1–S11, Table S1. The supporting information is available online at info.scichina.com and link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.

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Hao, D., Yang, D., Liang, H. et al. Lead-free perovskites-based photonic synaptic devices with zero electric energy consumption. Sci. China Inf. Sci. 67, 162401 (2024). https://doi.org/10.1007/s11432-023-3835-4

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  • DOI: https://doi.org/10.1007/s11432-023-3835-4

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