AI Chat Paper
Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Carbon quantum dots (CQDs) have been used in memristors due to their attractive optical and electronic properties, which are considered candidates for brain-inspired computing devices. In this work, the performance of CQDs-based memristors is improved by utilizing nitrogen-doping. In contrast, nitrogen-doped CQDs (N-CQDs)-based optoelectronic memristors can be driven with smaller programming voltages (–0.6 to 0.7 V) and exhibit lower powers (78 nW/0.29 μW). The physical mechanism can be attributed to the reversible transition between C–N and C=N with lower binding energy induced by the electric field and the generation of photogenerated carriers by ultraviolet light irradiation, which adjusts the conductivity of the initial N-CQDs to implement resistance switching. Importantly, the convolutional image processing based on various cross kernels is efficiently demonstrated by stable multi-level storage properties. An N-CQDs-based optoelectronic reservoir computing implements impressively high accuracy in both no noise and various noise modes when recognizing the Modified National Institute of Standards and Technology (MNIST) dataset. It illustrates that N-CQDs-based memristors provide a novel strategy for developing artificial vision system with integrated in-memory sensor and computing.
Silver, D.; Huang, A.; Maddison, C. J.; Guez, A.; Sifre, L.; van den Driessche, G.; Schrittwieser, J.; Antonoglou, I.; Panneershelvam, V.; Lanctot, M. et al. Mastering the game of Go with deep neural networks and tree search. Nature 2016, 529, 484–489.
Lei, P. X.; Duan, H.; Qin, L.; Wei, X. H.; Tao, R.; Wang, Z. G.; Guo, F.; Song, M. L.; Jie, W. J.; Hao, J. H. High-performance memristor based on 2D layered BiOI nanosheet for low-power artificial optoelectronic synapses. Adv. Funct. Mater. 2022, 32, 2201276.
Wang, Y.; Gong, Y.; Yang, L.; Xiong, Z. Y.; Lv, Z. Y.; Xing, X. C.; Zhou, Y.; Zhang, B.; Su, C. L.; Liao, Q. F. et al. MXene-ZnO memristor for multimodal in-sensor computing. Adv. Funct. Mater. 2021, 31, 2100144.
Wan, Q.; Jiang, X. Y.; Negroiu, A. M.; Lu, S. G.; McKay, K. S.; Abrams, T. W. Protein kinase C acts as a molecular detector of firing patterns to mediate sensory gating in Aplysia. Nat. Neurosci. 2012, 15, 1144–1152.
Wang, T. Y.; Meng, J. L.; Li, Q. X.; He, Z. Y.; Zhu, H.; Ji, L.; Sun, Q. Q.; Chen, L.; Zhang, D. W. Reconfigurable optoelectronic memristor for in-sensor computing applications. Nano Energy 2021, 89, 106291.
Zhou, F. C.; Zhou, Z.; Chen, J. W.; Choy, T. H.; Wang, J. L.; Zhang, N.; Lin, Z. Y.; Yu, S. M.; Kang, J. F.; Wong, H. S. P. et al. Optoelectronic resistive random access memory for neuromorphic vision sensors. Nat. Nanotechnol. 2019, 14, 776–782.
Tan, H. W.; Zhou, Y. F.; Tao, Q. Z.; Rosen, J.; van Dijken, S. Bioinspired multisensory neural network with crossmodal integration and recognition. Nat. Commun. 2021, 12, 1120.
Chen, Y. F.; Wang, Y.; Wang, Z.; Gu, Y.; Ye, Y.; Chai, X. L.; Ye, J. F.; Chen, Y.; Xie, R. Z.; Zhou, Y. et al. Unipolar barrier photodetectors based on van der Waals heterostructures. Nat. Electron. 2021, 4, 357–363.
Krishnaprasad, A.; Dev, D.; Han, S. S.; Shen, Y. Q.; Chung, H. S.; Bae, T. S.; Yoo, C.; Jung, Y.; Lanza, M.; Roy, T. MoS2 synapses with ultra-low variability and their implementation in boolean logic. ACS Nano 2022, 16, 2866–2876.
Park, Y.; Lee, J. S. Artificial synapses with short-and long-term memory for spiking neural networks based on renewable materials. ACS Nano 2017, 11, 8962–8969.
Esqueda, I. S.; Yan, X. D.; Rutherglen, C.; Kane, A.; Cain, T.; Marsh, P.; Liu, Q. Z.; Galatsis, K.; Wang, H.; Zhou, C. W. Aligned carbon nanotube synaptic transistors for large-scale neuromorphic computing. ACS Nano 2018, 12, 7352–7361.
Yang, J. Q.; Zhang, F.; Xiao, H. M.; Wang, Z. P.; Xie, P.; Feng, Z. H.; Wang, J. J.; Mao, J. Y.; Zhou, Y.; Han, S. T. A perovskite memristor with large dynamic space for analog-encoded image recognition. ACS Nano 2022, 16, 21324–21333.
Huh, W.; Lee, D.; Lee, C. H. Memristors based on 2D materials as an artificial synapse for neuromorphic electronics. Adv. Mater. 2020, 32, 2002092.
Datye, I. M.; Rojo, M. M.; Yalon, E.; Deshmukh, S.; Mleczko, M. J.; Pop, E. Localized heating and switching in MoTe2-based resistive memory devices. Nano Lett. 2020, 20, 1461–1467.
Sun, F. Q.; Lu, Q. F.; Feng, S. M.; Zhang, T. Flexible artificial sensory systems based on neuromorphic devices. ACS Nano 2021, 15, 3875–3899.
Wang, J. Y.; Teng, C. J.; Zhang, Z. Y.; Chen, W. J.; Tan, J. Y.; Pan, Y. K.; Zhang, R. J.; Zhou, H. Y.; Ding, B. F.; Cheng, H. M. et al. A scalable artificial neuron based on ultrathin two-dimensional titanium oxide. ACS Nano 2021, 15, 15123–15131.
Bian, H. Y.; Goh, Y. Y.; Liu, Y. X.; Ling, H. F.; Xie, L. H.; Liu, X. G. Stimuli-responsive memristive materials for artificial synapses and neuromorphic computing. Adv. Mater. 2021, 33, 2006469.
Wang, R.; Lu, K. Q.; Tang, Z. R.; Xu, Y. J. Recent progress in carbon quantum dots: Synthesis, properties and applications in photocatalysis. J. Mater. Chem. A 2017, 5, 3717–3734.
Zhou, Z. Y.; Zhao, J. H.; Chen, A. P.; Pei, Y. F.; Xiao, Z. A.; Wang, G.; Chen, J. S.; Fu, G. S.; Yan, X. B. Designing carbon conductive filament memristor devices for memory and electronic synapse applications. Mater. Horiz. 2020, 7, 1106–1114.
Wu, Z. L.; Zhang, P.; Gao, M. X.; Liu, C. F.; Wang, W.; Leng, F.; Huang, C. Z. One-pot hydrothermal synthesis of highly luminescent nitrogen-doped amphoteric carbon dots for bioimaging from Bombyx mori silk-natural proteins. J. Mater. Chem. B 2013, 1, 2868–2873.
Ye, Y. W.; Yang, D. P.; Chen, H.; Guo, S. D.; Yang, Q. M.; Chen, L. Y.; Zhao, H. C.; Wang, L. P. A high-efficiency corrosion inhibitor of N-doped citric acid-based carbon dots for mild steel in hydrochloric acid environment. J Hazard. Mater. 2020, 381, 121019.
Atchudan, R.; Edison, T. N. J. I.; Lee, Y. R. Nitrogen-doped carbon dots originating from unripe peach for fluorescent bioimaging and electrocatalytic oxygen reduction reaction. J. Colloid Interface Sci. 2016, 482, 8–18.
Yu, T. Q.; Fang, Y.; Chen, X. Y.; Liu, M.; Wang, D.; Liu, S. L.; Lei, W.; Jiang, H. L.; Shafie, S.; Mohtar, M. N. et al. Hybridization state transition-driven carbon quantum dot (CQD)-based resistive switches for bionic synapses. Mater. Horiz. 2023, 10, 2181–2190.
Li, Y. S.; Chen, S.; Yu, Z. G.; Li, S. F.; Xiong, Y.; Pam, M. E.; Zhang, Y. W.; Ang, K. W. In-memory computing using memristor arrays with ultrathin 2D PdSeO x /PdSe2 heterostructure. Adv. Mater. 2022, 34, 2201488.
Yan, X. B.; Zhang, L.; Chen, H. W.; Li, X. Y.; Wang, J. J.; Liu, Q.; Lu, C.; Chen, J. S.; Wu, H. Q.; Zhou, P. Graphene oxide quantum dots based memristors with progressive conduction tuning for artificial synaptic learning. Adv. Funct. Mater. 2018, 28, 1803728.
Ray, S. C.; Saha, A.; Jana, N. R.; Sarkar, R. Fluorescent carbon nanoparticles: Synthesis, characterization, and bioimaging application. J. Phys. Chem. C 2009, 113, 18546–18551.
Huang, Q. C.; Sun, H. C.; Lu, C. G.; Wang, C. L.; Xu, S. H. Post-synthetic regulation of the fluorescence of CDs: Insights into the fluorescence mechanism. Anal. Methods 2023, 15, 353–360.
Kuila, T.; Bose, S.; Mishra, A. K.; Khanra, P.; Kim, N. H.; Lee, J. H. Chemical functionalization of graphene and its applications. Prog. Mater. Sci. 2012, 57, 1061–1105.
Qin, S. J.; Zhang, J. Y.; Fu, D.; Xie, D.; Wang, Y.; Qian, H.; Liu, L. T.; Yu, Z. P. A physics/circuit-based switching model for carbon-based resistive memory with sp2/sp3 cluster conversion. Nanoscale 2012, 4, 6658–6663.
Mercer, T. W.; DiNardo, N. J.; Rothman, J. B.; Siegal, M. P.; Friedmann, T. A.; Martinez-Miranda, L. J. Electron emission induced modifications in amorphous tetrahedral diamondlike carbon. Appl. Phys. Lett. 1998, 72, 2244–2246.
Li, C.; Hu, M.; Li, Y. N.; Jiang, H.; Ge, N.; Montgomery, E.; Zhang, J. M.; Song, W. H.; Dávila, N.; Graves, C. E. et al. Analogue signal and image processing with large memristor crossbars. Nat. Electron. 2017, 1, 52–59.
Lim, D. H.; Wu, S.; Zhao, R.; Lee, J. H.; Jeong, H.; Shi, L. P. Spontaneous sparse learning for PCM-based memristor neural networks. Nat. Commun. 2021, 12, 319.
Midya, R.; Wang, Z. R.; Asapu, S.; Zhang, X. M.; Rao, M. Y.; Song, W. H.; Zhuo, Y.; Upadhyay, N.; Xia, Q. F.; Yang, J. J. Reservoir computing using diffusive memristors. Adv. Intell. Syst. 2019, 1, 1900084.
Wang, P.; Li, J.; Xue, W. H.; Ci, W. J.; Jiang, F. X.; Shi, L.; Zhou, F. C.; Zhou, P.; Xu, X. H. Integrated in-memory sensor and computing of artificial vision based on full-vdW optoelectronic ferroelectric field-effect transistor. Adv. Sci. 2024, 11, 2305679.
Milano, G.; Pedretti, G.; Montano, K.; Ricci, S.; Hashemkhani, S.; Boarino, L.; Ielmini, D.; Ricciardi, C. In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks. Nat. Mater. 2022, 21, 195–202.
