journal article·2020-12Open access

Photoreduced nanocomposites of graphene oxide/N-doped carbon dots toward all-carbon memristive synapses

Ya Lin, Zhongqiang Wang, Xue Zhang, Tao Zeng, Liang Bai, Zhenhui Kang, Changhua Wang, Xiaoning Zhao, Haiyang Xu, Yichun Liu

NPG Asia Materials· Volume 12· Issue 1

Abstract

Abstract An all-carbon memristive synapse is highly desirable for hardware implementation in future wearable neuromorphic computing systems. Graphene oxide (GO) can exhibit resistive switching (RS) and may be a feasible candidate to achieve this objective. However, the digital-type RS often occurring in GO-based memristors restricts the biorealistic emulation of synaptic functions. Here, an all-carbon memristive synapse with analog-type RS behavior was demonstrated through photoreduction of GO and N-doped carbon quantum dot (NCQD) nanocomposites. Ultraviolet light irradiation induced the local reduction of GO near the NCQDs, therefore forming multiple weak conductive filaments and demonstrating analog RS with a continuous conductance change. This analog RS enabled the close emulation of several essential synaptic plasticity behaviors; more importantly, the high linearity of the conductance change also facilitated the implementation of pattern recognition with high accuracy. Furthermore, the all-carbon memristive synapse can be transferred onto diverse substrates, showing good flexibility and 3D conformality. Memristive potentiation/depression was stably performed at 450 K, indicating the resistance of the synapse to high temperature. The photoreduction method provides a new path for the fabrication of all-carbon memristive synapses, which supports the development of wearable neuromorphic electronics.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Neuroscience and Neural Engineering · Photoreceptor and optogenetics research

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (69)

Show source-provided references
1. Wang, Y. et al. Photonic synapses based on inorganic perovskite quantum dots for neuromorphic computing. Adv. Mater. 30, 1802883 (2018).10.1002/adma.201802883 ↗
2. Sun, L. et al. Synaptic computation enabled by joule heating of single-layered semiconductors for sound localization. Nano Lett. 18, 3229–3234 (2018).10.1021/acs.nanolett.8b00994 ↗
3. Zidan, M. A., Strachan, J. P. & Lu, W. D. The future of electronics based on memristive systems. Nat. Electron. 1, 22 (2018).10.1038/s41928-017-0006-8 ↗
4. Wang, Z. et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat. Mater. 16, 101 (2017).10.1038/nmat4756 ↗
5. Wang, Z. Q. et al. Synaptic learning and memory functions achieved using oxygen ion migration/diffusion in an amorphous InGaZnO memristor. Adv. Funct. Mater. 22, 2759–2765 (2012).10.1002/adfm.201103148 ↗
6. Liu, G. et al. Organic biomimicking memristor for information storage and processing applications. Adv. Electron. Mater. 2, 1500298 (2016).10.1002/aelm.201500298 ↗
7. Park, Y. & Lee, J.-S. Artificial synapseFs with short-and long-term memory for spiking neural networks based on renewable materials. ACS Nano 11, 8962–8969 (2017).10.1021/acsnano.7b03347 ↗
8. Yang, R. et al. On-demand nanodevice with electrical and neuromorphic multifunction realized by local ion migration. ACS Nano 6, 9515–9521 (2012).10.1021/nn302510e ↗
9. Yin, J. et al. Adaptive crystallite kinetics in homogenous bilayer oxide memristor for emulating diverse synaptic plasticity. Adv. Funct. Mater. 28, 1706927 (2018).10.1002/adfm.201706927 ↗
10. Zhou, F. et al. Optoelectronic resistive random access memory for neuromorphic vision sensors. Nat. Nanotechnol. 14, 776–782 (2019).10.1038/s41565-019-0501-3 ↗
11. Wu, C., Kim, T. W., Choi, H. Y., Strukov, D. B. & Yang, J. J. Flexible three-dimensional artificial synapse networks with correlated learning and trainable memory capability. Nat. Commun. 8, 752 (2017).
12. Van de Burgt, Y. et al. A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat. Mater. 16, 414–418 (2017).10.1038/nmat4856 ↗
13. Ohno, T. et al. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat. Mater. 10, 591 (2011).10.1038/nmat3054 ↗
14. Zhao, X., Xu, H., Wang, Z., Lin, Y. & Liu, Y. Memristors with organic‐inorganic halide perovskites. InfoMat 1, 183–210 (2019).10.1002/inf2.12032 ↗
15. Liu, C. et al. Small footprint transistor architecture for photoswitching logic and in situ memory. Nat. Nanotechnol. 14, 662–667 (2019).10.1038/s41565-019-0462-6 ↗
16. Li, B. et al. All-carbon electronic devices fabricated by directly grown single-walled carbon nanotubes on reduced graphene oxide electrodes. Adv. Mater. 22, 3058–3061 (2010).10.1002/adma.201000736 ↗
17. Cai, Y. et al. Extraordinarily stretchable all-carbon collaborative nanoarchitectures for epidermal sensors. Adv. Mater. 29, 1606411 (2017).10.1002/adma.201606411 ↗
18. Das, C., Kang, L. X., Ouyang, Q. L. & Yong, K. T. Advanced low-dimensional carbon materials for flexible devices. InfoMat. 2, 698–714 (2020).10.1002/inf2.12073 ↗
19. Amirsardari, Z., Aghdam, R. M., Salavati-Niasari, M. & Shakhesi, S. Enhanced thermal resistance of GO/C/phenolic nanocomposite by introducing ZrB2 nanoparticles. Compos. Part B Eng. 76, 174–179 (2015).10.1016/j.compositesb.2015.01.011 ↗
20. Morteza Najarian, A., Szeto, B., Tefashe, U. M. & McCreery, R. L. Robust all-carbon molecular junctions on flexible or semi-transparent substrates using “Process-Friendly” fabrication. ACS Nano 10, 8918–8928 (2016).10.1021/acsnano.6b04900 ↗
21. Shen, A. M. et al. Analog neuromorphic module based on carbon nanotube synapses. ACS Nano 7, 6117–6122 (2013).10.1021/nn401946s ↗
22. Kim, K., Chen, C. L., Truong, Q., Shen, A. M. & Chen, Y. A carbon nanotube synapse with dynamic logic and learning. Adv. Mater. 25, 1693–1698 (2013).10.1002/adma.201203116 ↗
23. Tian, H. et al. Graphene dynamic synapse with modulatable plasticity. Nano Lett. 15, 8013–8019 (2015).10.1021/acs.nanolett.5b03283 ↗
24. Sharbati, M. T. et al. Low-power, electrochemically tunable graphene synapses for neuromorphic computing. Adv. Mater. 30, 1802353 (2018).10.1002/adma.201802353 ↗
25. Sanchez Esqueda, I. et al. Aligned carbon nanotube synaptic transistors for large-scale neuromorphic computing. ACS Nano 12, 7352–7361 (2018).10.1021/acsnano.8b03831 ↗
26. Jeong, H. Y. et al. Graphene oxide thin films for flexible nonvolatile memory applications. Nano Lett. 10, 4381–4386 (2010).10.1021/nl101902k ↗
27. Porro, S., Accornero, E., Pirri, C. F. & Ricciardi, C. Memristive devices based on graphene oxide. Carbon 85, 383–396 (2015).10.1016/j.carbon.2015.01.011 ↗
28. Kim, S. K. et al. Conductive graphitic channel in graphene oxide-based memristive devices. Adv. Funct. Mater. 26, 7406–7414 (2016).10.1002/adfm.201602748 ↗
29. Liu, J. et al. Fabrication of flexible, all‐reduced graphene oxide non‐volatile memory devices. Adv. Mater. 25, 233–238 (2013).10.1002/adma.201203349 ↗
30. Raeber, T. J. et al. Resistive switching and transport characteristics of an all-carbon memristor. Carbon 136, 280–285 (2018).10.1016/j.carbon.2018.04.045 ↗
31. Garbin, D. et al. HfO2-based OxRAM devices as synapses for convolutional neural networks. IEEE Trans. Electron. Dev. 62, 2494–2501 (2015).10.1109/TED.2015.2440102 ↗
32. Chen, Z. et al. Optimized learning scheme for grayscale image recognition in a RRAM based analog neuromorphic system. 2015 International Electron Devices Meeting (IEDM), p. 17.7.1–17.7.4 (IEEE, Conference Location: Washington, DC, USA 2015).10.1109/IEDM.2015.7409722 ↗
33. Wu, W. et al. Improving analog switching in HfOx-based resistive memory with a thermal enhanced layer. IEEE Electron Device Lett. 38, 1019–1022 (2017).10.1109/LED.2017.2719161 ↗
34. Du, C., Ma, W., Chang, T., Sheridan, P. & Lu, W. D. Biorealistic implementation of synaptic functions with oxide memristors through internal ionic dynamics. Adv. Funct. Mater. 25, 4290–4299 (2015).10.1002/adfm.201501427 ↗
35. Pei, S. & Cheng, H. M. The reduction of graphene oxide. Carbon 50, 3210–3228 (2012).10.1016/j.carbon.2011.11.010 ↗
36. Stankovich, S. et al. Graphene-based composite materials. Nature 442, 282 (2006).10.1038/nature04969 ↗
37. Li, H. et al. Fluorescent N-doped carbon dots for both cellular imaging and highly-sensitive catechol detection. Carbon 91, 66–75 (2015).10.1016/j.carbon.2015.04.032 ↗
38. Ma, Z., Ming, H., Huang, H., Liu, Y. & Kang, Z. One-step ultrasonic synthesis of fluorescent N-doped carbon dots from glucose and their visible-light sensitive photocatalytic ability. N. J. Chem. 36, 861–864 (2012).10.1039/c2nj20942j ↗
39. Zhang, Z., Zheng, T., Li, X., Xu, J. & Zeng, H. Progress of carbon quantum dots in photocatalysis applications. Part. Part. Syst. Char. 33, 457–472 (2016).10.1002/ppsc.201500243 ↗
40. Yang, Y. et al. Fluorescent N-doped carbon dots as in vitro and in vivo nanothermometer. ACS Appl. Mater. Int. 7, 27324–27330 (2015).10.1021/acsami.5b08782 ↗
41. Liu, J. et al. Metal-free efficient photocatalyst for stable visible water splitting via a two-electron pathway. Science 347, 970–974 (2015).10.1126/science.aaa3145 ↗
42. Zhang, W. et al. Enhanced photovoltaic effect of ruthenium complex-modified graphene oxide with P-type conductivity. Mater. Chem. Phys. 147, 1140–1145 (2014).10.1016/j.matchemphys.2014.06.070 ↗
43. Wang, H. et al. Photovoltaic properties of graphene oxide sheets beaded with ZnO nanoparticles. J. Solid State Chem. 184, 881–887 (2011).10.1016/j.jssc.2011.02.025 ↗
44. Jiang, B. et al. Enhanced photocatalytic activity and electron transfer mechanisms of graphene/TiO2 with exposed {001} facets. J. Phys. Chem. C 115, 23718–23725 (2011).10.1021/jp207624x ↗
45. Meng, N., Priestley, R. C. E., Zhang, Y., Wang, H. & Zhang, X. The effect of reduction degree of GO nanosheets on microstructure and performance of PVDF/GO hybrid membranes. J. Membr. Sci. 501, 169–178 (2016).10.1016/j.memsci.2015.12.004 ↗
46. Moon, I. K., Lee, J., Ruoff, R. S. & Lee, H. Reduced graphene oxide by chemical graphitization. Nat. Commun. 1, 73 (2010).
47. Díez-Betriu, X. et al. Raman spectroscopy for the study of reduction mechanisms and optimization of conductivity in graphene oxide thin films. J. Mater. Chem. C 1, 6905–6912 (2013).10.1039/c3tc31124d ↗
48. Yang, J. J., Strukov, D. B. & Stewart, D. R. Memristive devices for computing. Nat. Nanotechnol. 8, 13–24 (2013).10.1038/nnano.2012.240 ↗
49. Zhao, X. et al. Photocatalytic reduction of graphene oxide–TiO2 nanocomposites for improving resistive-switching memory behaviors. Small 14, 1801325 (2018).10.1002/smll.201801325 ↗
50. Wang, Z., Ambrogio, S., Balatti, S. & Ielmini, D. A 2-transistor/1-resistor artificial synapse capable of communication and stochastic learning in neuromorphic systems. Front. Neurosci. 8, 438 (2014).
51. Kim, S. et al. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett. 15, 2203–2211 (2015).10.1021/acs.nanolett.5b00697 ↗
52. Yang, R. et al. Synaptic suppression triplet-STDP learning rule realized in second-order memristors. Adv. Funct. Mater. 28, 1704455 (2018).10.1002/adfm.201704455 ↗
53. Zucker, R. S. & Regehr, W. G. Short-term synaptic plasticity. Annu. Rev. Physiol. 64, 355–405 (2002).10.1146/annurev.physiol.64.092501.114547 ↗
54. Voglis, G. & Tavernarakis, N. The role of synaptic ion channels in synaptic plasticity. EMBO Rep. 7, 1104–1110 (2006).10.1038/sj.embor.7400830 ↗
55. Atluri, P. P. & Regehr, W. G. Determinants of the time course of facilitation at the granule cell to Purkinje cell synapse. J. Neurosci. 16, 5661–5671 (1996).10.1523/JNEUROSCI.16-18-05661.1996 ↗
56. Kirkwood, A., Rioult, M. G. & Bear, M. F. Experience-dependent modification of synaptic plasticity in visual cortex. Nature 381, 526 (1996).10.1038/381526a0 ↗
57. Bear, M. F., Cooper, L. N. & Ebner, F. F. A physiological basis for a theory of synapse modification. Science 237, 42–48 (1987).10.1126/science.3037696 ↗
58. Cooper, L. N. & Bear, M. F. The BCM theory of synapse modification at 30: interaction of theory with experiment. Nat. Rev. Neurosci. 13, 798 (2012).10.1038/nrn3353 ↗
59. Suk, J. W. et al. Transfer of CVD-grown monolayer graphene onto arbitrary substrates. ACS Nano 5, 6916–6924 (2011).10.1021/nn201207c ↗
60. Li, X. et al. Transfer of large-area graphene films for high-performance transparent conductive electrodes. Nano Lett. 9, 4359–4363 (2009).10.1021/nl902623y ↗
61. Tang, Q. et al. Micrometer- and nanometer-sized organic single-crystalline transistors. Adv. Mater. 20, 2947–2951 (2008).10.1002/adma.200800669 ↗
62. Yablonovitch, E., Hwang, D., Gmitter, T., Florez, L. & Harbison, J. Van der Waals bonding of GaAs epitaxial liftoff films onto arbitrary substrates. Appl. Phys. Lett. 56, 2419–2421 (1990).10.1063/1.102896 ↗
63. Kumar, P. et al. Large-area reduced graphene oxide thin film with excellent thermal conductivity and electromagnetic interference shielding effectiveness. Carbon 94, 494–500 (2015).10.1016/j.carbon.2015.07.032 ↗
64. Tian, H. et al. A novel solid-state thermal rectifier based on reduced graphene oxide. Sci. Rep. 2, 1–7 (2012).
65. Wang, M. et al. Robust memristors based on layered two-dimensional materials. Nat. Electron. 1, 130–136 (2018).10.1038/s41928-018-0021-4 ↗
66. Belhumeur, P. N., Hespanha, J. P. & Kriegman, D. J. Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. 19, 711–720 (1997).10.1109/34.598228 ↗
67. Gao, B. et al. Ultra-low-energy three-dimensional oxide-based electronic synapses for implementation of robust high-accuracy neuromorphic computation systems. ACS Nano 8, 6998–7004 (2014).10.1021/nn501824r ↗
68. Lin, Y. et al. Analog-digital hybrid memristive devices for image pattern recognition with tunable learning accuracy and speed. Small Methods 3, 1900160 (2019).10.1002/smtd.201900160 ↗
69. Sheikh, H. R., Wang, Z., Bovik, A. & Simoncelli, E. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004).10.1109/TIP.2003.819861 ↗