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Reconfigurable, non-volatile neuromorphic photovoltaics

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Abstract

The neural network image sensor—which mimics neurobiological functions of the human retina—has recently been demonstrated to simultaneously sense and process optical images. However, highly tunable responsivity concurrent with non-volatile storage of image data in the neural network would allow a transformative leap in compactness and function of these artificial neural networks. Here, we demonstrate a reconfigurable and non-volatile neuromorphic device based on two-dimensional semiconducting metal sulfides that is concurrently a photovoltaic detector. The device is based on a metal–semiconductor–metal (MSM) two-terminal structure with pulse-tunable sulfur vacancies at the M–S junctions. By modulating sulfur vacancy concentrations, the polarities of short-circuit photocurrent can be changed with multiple stable magnitudes. The bias-induced motion of sulfur vacancies leads to highly reconfigurable responsivities by dynamically modulating the Schottky barriers. A convolutional neuromorphic network is finally designed for image processing and object detection using the same device. The results demonstrated that neuromorphic photodetectors can be the key components of visual perception hardware.

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Fig. 1: Device characterizations of the plasma-treated MoS2 MSM photovoltaic detectors.
Fig. 2: Photocurrent map** and pulse programmable characteristics of plasma-treated and pristine MoS2 MSM devices.
Fig. 3: Sulfur vacancies migration and potential profile in the plasma-treated MoS2 MSM devices.
Fig. 4: Object detection using MoS2 MSM devices with reconfigurable and non-volatile responsivities.

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Data availability

The data that support the conclusions of this study are available from the corresponding authors upon reasonable request. Source data are provided with this paper.

Code availability

The codes used for simulation and data plotting are available from the corresponding authors upon reasonable request.

References

  1. Sze, V., Chen, Y.-H., Emer, J., Suleiman, A. & Zhang, Z. Hardware for machine learning: challenges and opportunities. In 2017 IEEE Custom Integrated Circuits Conference (CICC) 179–186 (IEEE, 2017).

  2. Zhou, F. C. & Chai, Y. Near-sensor and in-sensor computing. Nat. Electron. 3, 664–671 (2020).

    Google Scholar 

  3. Gollisch, T. & Meister, M. Even smarter than scientists believed: neural computations in circuits of the retina. Neuron 65, 150–164 (2010).

    CAS  Google Scholar 

  4. Kyuma, K. et al. Artificial retinas—fast, versatile image processors. Nature 372, 197–198 (1994).

    CAS  Google Scholar 

  5. Kolb, H. How the retina works—much of the construction of an image takes place in the retina itself through the use of specialized neural circuits. Am. Scientist 91, 28–35 (2003).

    Google Scholar 

  6. Funatsu, E. et al. An artificial retina chip with current-mode focal plane image processing functions. IEEE Trans. Electron Devices 44, 1777–1782 (1997).

    Google Scholar 

  7. Nitta, Y., Ohta, J., Tai, S. & Kyuma, K. Variable-sensitivity photodetector that uses a metal-semiconductor-metal structure for optical neural networks. Opt. Lett. 16, 611–613 (1991).

    CAS  Google Scholar 

  8. Jang, H. et al. In-sensor optoelectronic computing using electrostatically doped silicon. Nat. Electron. 5, 519–525 (2022).

    Google Scholar 

  9. Mennel, L. et al. Ultrafast machine vision with 2D material neural network image sensors. Nature 579, 62–66 (2020).

    CAS  Google Scholar 

  10. Chen, S., Lou, Z., Chen, D. & Shen, G. Z. An artificial flexible visual memory system based on an UV-motivated memristor. Adv. Mater. 30, 1705400 (2018).

    Google Scholar 

  11. Cui, B. Y. et al. Ferroelectric photosensor network: an advanced hardware solution to real-time machine vision. Nat. Commun. 13, 1707 (2022).

    CAS  Google Scholar 

  12. Sun, L. F. et al. In-sensor reservoir computing for language learning via two-dimensional memristors. Sci. Adv. 7, eabg1455 (2021).

    CAS  Google Scholar 

  13. Zhou, F. C. et al. Optoelectronic resistive random access memory for neuromorphic vision sensors. Nat. Nanotechnol. 14, 776–782 (2019).

    CAS  Google Scholar 

  14. Ahmed, T. et al. Optically stimulated artificial synapse based on layered black phosphorus. Small 15, 1900966 (2019).

    Google Scholar 

  15. Fu, X. et al. Graphene/MoS2−xOx/graphene photomemristor with tunable non-volatile responsivities for neuromorphic vision processing. Light.: Sci. Appl. 12, 39 (2023).

    CAS  Google Scholar 

  16. Lee, S. H., Peng, R. M., Wu, C. M. & Li, M. Programmable black phosphorus image sensor for broadband optoelectronic edge computing. Nat. Commun. 13, 1485 (2022).

    CAS  Google Scholar 

  17. Liao, F. Y. et al. Bioinspired in-sensor visual adaptation for accurate perception. Nat. Electron. 5, 84–91 (2022).

    Google Scholar 

  18. Liu, K. Q. et al. An optoelectronic synapse based on α-In2Se3 with controllable temporal dynamics for multimode and multiscale reservoir computing. Nat. Electron. 5, 761–773 (2022).

    CAS  Google Scholar 

  19. Pi, L. J. et al. Broadband convolutional processing using band-alignment-tunable heterostructures. Nat. Electron. 5, 248–254 (2022).

    CAS  Google Scholar 

  20. Seo, S. H. et al. Artificial optic-neural synapse for colored and color-mixed pattern recognition. Nat. Commun. 9, 5106 (2018).

    Google Scholar 

  21. Wang, C.-Y. et al. Gate-tunable van der Waals heterostructure for reconfigurable neural network vision sensor. Sci. Adv. 6, eaba6173 (2020).

    Google Scholar 

  22. Yu, J. R. et al. Bioinspired mechano-photonic artificial synapse based on graphene/MoS2 heterostructure. Sci. Adv. 7, eabd9117 (2021).

    CAS  Google Scholar 

  23. Zhang, Z. H. et al. All-in-one two-dimensional retinomorphic hardware device for motion detection and recognition. Nat. Nanotechnol. 17, 27–32 (2022).

    Google Scholar 

  24. Lien, D.-H. et al. Engineering light outcoupling in 2D materials. Nano Lett. 15, 1356–1361 (2015).

    CAS  Google Scholar 

  25. Wang, Q. H., Kalantar-Zadeh, K., Kis, A., Coleman, J. N. & Strano, M. S. Electronics and optoelectronics of two-dimensional transition metal dichalcogenides. Nat. Nanotechnol. 7, 699–712 (2012).

    CAS  Google Scholar 

  26. Shim, J. et al. Controlled crack propagation for atomic precision handling of wafer-scale two-dimensional materials. Science 362, 665–670 (2018).

    CAS  Google Scholar 

  27. Khan, M. A. et al. The non-volatile electrostatic do** effect in MoTe2 field-effect transistors controlled by hexagonal boron nitride and a metal gate. Sci. Rep. 12, 12085 (2022).

    CAS  Google Scholar 

  28. Wang, M. et al. Robust memristors based on layered two-dimensional materials. Nat. Electron. 1, 130–136 (2018).

    CAS  Google Scholar 

  29. Zhu, X. J., Li, D., Liang, X. & Lu, W. D. Ionic modulation and ionic coupling effects in MoS2 devices for neuromorphic computing. Nat. Mater. 18, 141–148 (2019).

    CAS  Google Scholar 

  30. Lee, H. S. et al. MoS2 nanosheets for top‐gate nonvolatile memory transistor channel. Small 8, 3111–3115 (2012).

    CAS  Google Scholar 

  31. Chen, H. W. et al. Logic gates based on neuristors made from two-dimensional materials. Nat. Electron. 4, 399–404 (2021).

    CAS  Google Scholar 

  32. Liu, W. et al. Graphene charge-injection photodetectors. Nat. Electron. 5, 281–288 (2022).

    CAS  Google Scholar 

  33. Tong, L. et al. 2D materials-based homogeneous transistor-memory architecture for neuromorphic hardware. Science 373, 1353–1358 (2021).

    CAS  Google Scholar 

  34. Wang, Y. et al. An in-memory computing architecture based on two-dimensional semiconductors for multiply-accumulate operations. Nat. Commun. 12, 3347 (2021).

    CAS  Google Scholar 

  35. Miao, J. S. et al. Heterojunction tunnel triodes based on two-dimensional metal selenide and three-dimensional silicon. Nat. Electron. 5, 744–751 (2022).

    CAS  Google Scholar 

  36. Choi, C. S. et al. Human eye-inspired soft optoelectronic device using high-density MoS2-graphene curved image sensor array. Nat. Commun. 8, 1664 (2017).

    Google Scholar 

  37. Cao, R. R. et al. Compact artificial neuron based on anti-ferroelectric transistor. Nat. Commun. 13, 7018 (2022).

    CAS  Google Scholar 

  38. Kim, S. H. et al. Effects of plasma treatment on surface properties of ultrathin layered MoS2. 2D Mater. 3, 035002 (2016).

    Google Scholar 

  39. Kang, N., Paudel, H. P., Leuenberger, M. N., Tetard, L. & Khondaker, S. I. Photoluminescence quenching in single-layer MoS2 via oxygen plasma treatment. J. Phys. Chem. C 118, 21258–21263 (2014).

    CAS  Google Scholar 

  40. Duy, L., Rawal, T. B. & Rahman, T. S. Single-layer MoS2 with sulfur vacancies: structure and catalytic application. J. Phys. Chem. C 118, 5346–5351 (2014).

    Google Scholar 

  41. Komsa, H.-P., Kurasch, S., Lehtinen, O., Kaiser, U. & Krasheninnikov, A. V. From point to extended defects in two-dimensional MoS2: evolution of atomic structure under electron irradiation. Phys. Rev. B 88, 035301 (2013).

    Google Scholar 

  42. Li, D. et al. MoS2 memristors exhibiting variable switching characteristics toward biorealistic synaptic emulation. ACS Nano 12, 9240–9252 (2018).

    CAS  Google Scholar 

  43. Sangwan, V. K. et al. Multi-terminal memtransistors from polycrystalline monolayer molybdenum disulfide. Nature 554, 500–504 (2018).

    CAS  Google Scholar 

  44. Chen, Q. et al. Ultralong 1D vacancy channels for rapid atomic migration during 2D void formation in monolayer MoS2. ACS Nano 12, 7721–7730 (2018).

    CAS  Google Scholar 

  45. Zhou, X. Y., Koltun, V. & Krähenbühl, P. Tracking objects as points. In 16th European Conference on Computer Vision (ECCV) 474–490 (Springer, 2020).

  46. Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. You only look once: unified, real-time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 779–788 (IEEE, 2016).

  47. Ren, S., He, K., Girshick, R. & Sun, J. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137–1149 (2017).

    Google Scholar 

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

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National Key Research and Development Program of China (grant no. 2021YFA0715602), the National Natural Science Foundation of China (grant nos. 62261136552, 62005303 and 62134001), the International Partnership Program of Chinese Academy of Sciences (grant no. 181331KYSB20200012), the Science and Technology Commission of Shanghai Municipality (grant no. 21JC1406100), the Alfred P. Sloan Foundation (Sloan Fellowship in Chemistry) (D.J.) and the Open Research Projects of Zhejiang Laboratory (grant no. 2022NK0AB01). We thank X. Zhang and L. Ma from the Core Facility of Wuhan University for their assistance with WDS measurement.

Author information

Author notes

  1. These authors contributed equally: Tangxin Li, **shui Miao, **ao Fu, Bo Song.

Authors and Affiliations

  1. State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China

    Tangxin Li, **shui Miao, **ao Fu, Xun Ge, **aohao Zhou & Weida Hu

  2. University of Chinese Academy of Sciences, Bei**g, China

    Tangxin Li, **shui Miao, **ao Fu, **aohao Zhou & Weida Hu

  3. Institute of Intelligent Machines, HFIPS, Chinese Academy of Sciences, Hefei, China

    Bo Song & Bin Cai

  4. School of Microelectronics, Fudan University, Shanghai, China

    Peng Zhou

  5. School of Electronic Science and Engineering, Nan**g University, Nan**g, China

    **nran Wang

  6. Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA

    Deep Jariwala

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Contributions

J.M., D.J. and W.H. conceived the idea and directed the collaboration and execution. T.L. and X.F. fabricated the devices and performed the measurements. T.L., J.M., P.Z., X.W., D.J. and W.H. analysed the experimental data. T.L., X.Z. and J.M. did the TCAD device simulation. X.Z. and X.G. did the first-principles theory. B.S. and B.C. performed the Artificial Intelligence object detection. T.L., J.M., W.H. and D.J. cowrote the manuscript with contributions from all the authors. All authors discussed the results and implications and commented on the manuscript at all stages.

Corresponding authors

Correspondence to **shui Miao, Deep Jariwala or Weida Hu.

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Nature Nanotechnology thanks Lincoln Lauhon and Yu-Jung Lu for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–32 and Tables 1–3.

Source data

Source Data Fig. 1

Device characterizations of the plasma-treated MoS2 MSM photovoltaic detectors.

Source Data Fig. 2

Photocurrent map** and pulse programmable characteristics of plasma-treated and pristine MoS2 MSM devices.

Source Data Fig. 3

Sulfur vacancies migration and potential profile in the plasma-treated MoS2 MSM devices.

Source Data Fig. 4

Object detection using MoS2 MSM devices with reconfigurable and non-volatile responsivities.

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Li, T., Miao, J., Fu, X. et al. Reconfigurable, non-volatile neuromorphic photovoltaics. Nat. Nanotechnol. 18, 1303–1310 (2023). https://doi.org/10.1038/s41565-023-01446-8

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