Abstract
The quantum-dot light-emitting diodes (QLEDs) that emit near-infrared (NIR) light may be important optoelectronic synaptic devices for the realization of artificial neural networks with complete optoelectronic integration. To improve the performance of NIR QLEDs, we take advantage of their low-energy light emission to explore the use of poly(3-hexylthiophene) (P3HT) as the hole transport layer (HTL). P3HT has one of the highest hole mobilities among organic semiconductors and essentially does not absorb NIR light. The usage of P3HT as the HTL indeed significantly mitigates the imbalance of carrier injection in NIR QLEDs. With the additional incorporation of an interlayer of poly [9,9-bis(3′-(N, N-dimethylamino)propyl)-2,7-flourene]-alt-2,7-(9,9-dioctylfluorene)], P3HT obviously improves the performance of NIR QLEDs. As electroluminescent synaptic devices, these NIR QLEDs exhibit important synaptic functionalities such as short- and long-term plasticity, and may be employed for image recognition.
摘要
为了提高近红外发光二极管的性能, 我们利用聚3-己基噻吩(P3HT)空穴迁移率高和对近红外光没有吸收的特点, 将其作为器件的空穴传输层. 实验发现, P3HT改善了基于硅量子点的近红外发光二极管的空穴/电子传输不平衡的现象. 进一步地, 将聚[9,9-二(3′-(N, N-二甲胺基)丙基)-2,7-芴-交-2,7-(9,9-二辛基芴)](PFN)作为中间层修饰P3HT, 近红外硅量子点发光二极管的性能得到了更大改善, 其外量子效率和功率效率分别达到了3.4%和4.4%. 性能改善后的近红外硅量子点发光二极管可以用于模拟神经突触的可塑性, 如短时程可塑性和长时程可塑性.
Article PDF
Similar content being viewed by others
Change history
17 September 2021
An Erratum to this paper has been published: https://doi.org/10.1007/s40843-021-1788-1
References
Zidan MA, Strachan JP, Lu WD. The future of electronics based on memristive systems. Nat Electron, 2018, 1: 22–29
Kuzum D, Yu S, Philip Wong HS. Synaptic electronics: Materials, devices and applications. Nanotechnology, 2013, 24: 382001
Abbott LF, Regehr WG. Synaptic computation. Nature, 2004, 431: 796–803
Prezioso M, Merrikh-Bayat F, Hoskins BD, et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature, 2015, 521: 61–64
Tuma T, Pantazi A, Le Gallo M, et al. Stochastic phase-change neurons. Nat Nanotech, 2016, 11: 693–699
Jiang J, Guo J, Wan X, et al. 2D MoS2 neuromorphic devices for brain-like computational systems. Small, 2017, 13: 1700933
Sangwan VK, Lee HS, Bergeron H, et al. Multi-terminal memtransistors from polycrystalline monolayer molybdenum disulfide. Nature, 2018, 554: 500–504
Wang Z, Joshi S, Savel’ev SE, et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat Mater, 2017, 16: 101–108
van de Burgt Y, Lubberman E, Fuller EJ, et al. A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat Mater, 2017, 16: 414–418
Shi Y, Liang X, Yuan B, et al. Electronic synapses made of layered two-dimensional materials. Nat Electron, 2018, 1: 458–465
Tian H, Mi W, Zhao H, et al. A novel artificial synapse with dual modes using bilayer graphene as the bottom electrode. Nanoscale, 2017, 9: 9275–9283
Zhu J, Yang Y, Jia R, et al. Ion gated synaptic transistors based on 2D van der Waals crystals with tunable diffusive dynamics. Adv Mater, 2018, 30: 1800195
Wang Y, Lv Z, Chen J, et al. Photonic synapses based on inorganic perovskite quantum dots for neuromorphic computing. Adv Mater, 2018, 30: 1802883
John RA, Liu F, Chien NA, et al. Synergistic gating of electro-ionophotoactive 2D chalcogenide neuristors: Coexistence of hebbian and homeostatic synaptic metaplasticity. Adv Mater, 2018, 30: 1800220
Cheng Z, Rios C, Pernice WHP, et al. On-chip photonic synapse. Sci Adv, 2017, 3: e1700160
Akemann W, Song C, Mutoh H, et al. Route to genetically targeted optical electrophysiology: Development and applications of voltage-sensitive fluorescent proteins. Neurophotonics, 2015, 0210081
Qin S, Wang F, Liu Y, et al. A light-stimulated synaptic device based on graphene hybrid phototransistor. 2D Mater, 2017, 4: 035022
Wang S, Chen C, Yu Z, et al. A MoS2/PTCDA hybrid heterojunction synapse with efficient photoelectric dual modulation and versatility. Adv Mater, 2019, 31: 1806227
Kim J, Lee HC, Kim KH, et al. Photon-triggered nanowire transistors. Nat Nanotech, 2017, 12: 963–968
Lee M, Lee W, Choi S, et al. Brain-inspired photonic neuromorphic devices using photodynamic amorphous oxide semi-conductors and their persistent photoconductivity. Adv Mater, 2017, 29: 1700951
Tan H, Liu G, Yang H, et al. Light-gated memristor with integrated logic and memory functions. ACS Nano, 2017, 11: 11298–11305
Tan H, Ni Z, Peng W, et al. Broadband optoelectronic synaptic devices based on silicon nanocrystals for neuromorphic computing. Nano Energy, 2018, 52: 422–430
Zhao S, Ni Z, Tan H, et al. Electroluminescent synaptic devices with logic functions. Nano Energy, 2018, 54: 383–389
Peng HT, Nahmias MA, de Lima TF, et al. Neuromorphic photonic integrated circuits. IEEE J Sel Top Quantum Electron, 2018, 24: 1–15
Gong X, Yang Z, Walters G, et al. Highly efficient quantum dot near-infrared light-emitting diodes. Nat Photon, 2016, 10: 253–257
Shen H, Zheng Y, Wang H, et al. Highly efficient near-infrared light-emitting diodes by using type-II CdTe/CdSe core/shell quantum dots as a phosphor. Nanotechnology, 2013, 24: 475603
Dai X, Deng Y, Peng X, et al. Quantum-dot light-emitting diodes for large-area displays: Towards the dawn of commercialization. Adv Mater, 2017, 29: 1607022
Pan J, Quan LN, Zhao Y, et al. Highly efficient perovskite-quantum-dot light-emitting diodes by surface engineering. Adv Mater, 2016, 28: 8718–8725
Dai X, Zhang Z, Jin Y, et al. Solution-processed, high-performance light-emitting diodes based on quantum dots. Nature, 2014, 515: 96–99
Shi Z, Li Y, Zhang Y, et al. High-efficiency and air-stable perovskite quantum dots light-emitting diodes with an all-inorganic heterostructure. Nano Lett, 2017, 17: 313–321
Zhang X, Lin H, Huang H, et al. Enhancing the brightness of cesium lead halide perovskite nanocrystal based green light-emitting devices through the interface engineering with perfluorinated ionomer. Nano Lett, 2016, 16: 1415–1420
Tanase C, Meijer EJ, Blom PWM, et al. Unification of the hole transport in polymeric field-effect transistors and light-emitting diodes. Phys Rev Lett, 2003, 91: 216601
Xiao J, Shi J, Liu H, et al. Efficient CH3NH3PbI3 perovskite solar cells based on graphdiyne (GD)-modified P3HT hole-transporting material. Adv Energy Mater, 2015, 5: 1401943
Song B, He Y. Fluorescent silicon nanomaterials: From synthesis to functionalization and application. Nano Today, 2019
He Z, Zhong C, Su S, et al. Enhanced power-conversion efficiency in polymer solar cells using an inverted device structure. Nat Photon, 2012, 6: 591–595
Abbott LF, Nelson SB. Synaptic plasticity: Taming the beast. Nat Neurosci, 2000, 3: 1178–1183
Zhu LQ, Wan CJ, Guo LQ, et al. Artificial synapse network on inorganic proton conductor for neuromorphic systems. Nat Commun, 2014, 5: 3158–3165
Yu T, Wang F, Xu Y, et al. Graphene coupled with Silicon quantum dots for high-performance bulk-silicon-based Schottky-junction photodetectors. Adv Mater, 2016, 28: 4912–4919
Liu X, Zhang Y, Yu T, et al. Optimum quantum yield of the light emission from 2 to 10 nm hydrosilylated silicon quantum dots. Part Part Syst Charact, 2016, 33: 44–52
Li G, Shrotriya V, Huang J, et al. High-efficiency solution processable polymer photovoltaic cells by self-organization of polymer blends. Nat Mater, 2005, 4: 864–868
Gu W, Liu X, Pi X, et al. Silicon-quantum-dot light-emitting diodes with interlayer-enhanced hole transport. IEEE Photonics J, 2017, 9: 1–10
van Buuren T, Dinh LN, Chase LL, et al. Changes in the electronic properties of Si nanocrystals as a function of particle size. Phys Rev Lett, 1998, 80: 3803–3806
Xu T, Qiao Q. Conjugated polymer-inorganic semiconductor hybrid solar cells. Energy Environ Sci, 2011, 4: 2700–2720
Itskos G, Othonos A, Rauch T, et al. Optical properties of organic semiconductor blends with near-infrared quantum-dot sensitizers for light harvesting applications. Adv Energy Mater, 2011, 1: 802–812
Shastry TA, Clark SC, Rowberg AJE, et al. Enhanced uniformity and area scaling in carbon nanotube-fullerene bulk-heterojunction solar cells enabled by solvent additives. Adv Energy Mater, 2016, 6: 1501466
Liu X, Zhao S, Gu W, et al. Light-emitting diodes based on colloidal silicon quantum dots with octyl and phenylpropyl ligands. ACS Appl Mater Interfaces, 2018, 10: 5959–5966
Huang F, Wu H, Cao Y. Water/alcohol soluble conjugated polymers as highly efficient electron transporting/injection layer in optoelectronic devices. Chem Soc Rev, 2010, 39: 2500–2521
Kim YH, Han TH, Cho H, et al. Polyethylene imine as an ideal interlayer for highly efficient inverted polymer light-emitting diodes. Adv Funct Mater, 2014, 24: 3808–3814
Zhao S, Liu X, Gu W, et al. Al2O3-interlayer-enhanced performance of all-inorganic silicon-quantum-dot near-infrared light-emitting diodes. IEEE Trans Electron Devices, 2018, 65: 577–583
Kwak J, Bae WK, Lee D, et al. Bright and efficient full-color colloidal quantum dot light-emitting diodes using an inverted device structure. Nano Lett, 2012, 12: 2362–2366
Bozyigit D, Yarema O, Wood V. Origins of low quantum efficiencies in quantum dot leds. Adv Funct Mater, 2013, 23: 3024–3029
Markram H. A history of spike-timing-dependent plasticity. Front Syn Neurosci, 2011, 3: 4
Liu YH, Zhu LQ, Feng P, et al. Freestanding artificial synapses based on laterally proton-coupled transistors on chitosan membranes. Adv Mater, 2015, 27: 5599–5604
Yan X, Zhao J, Liu S, et al. Memristor with Ag-cluster-doped TiO2 films as artificial synapse for neuroinspired computing. Adv Funct Mater, 2018, 28: 1705320
Feng P, Xu W, Yang Y, et al. Printed neuromorphic devices based on printed carbon nanotube thin-film transistors. Adv Funct Mater, 2017, 27: 1604447
Du C, Ma W, Chang T, et al. Biorealistic implementation of synaptic functions with oxide memristors through internal ionic dynamics. Adv Funct Mater, 2015, 25: 4290–4299
Chang T, Jo SH, Lu W. Short-term memory to long-term memory transition in a nanoscale memristor. ACS Nano, 2011, 5: 7669–7676
Yang CS, Shang DS, Liu N, et al. A synaptic transistor based on quasi-2D molybdenum oxide. Adv Mater, 2017, 29: 1700906
Fu YM, Wan CJ, Zhu LQ, et al. Hodgkin-huxley artificial synaptic membrane based on protonic/electronic hybrid neuromorphic transistors. Adv Biosys, 2018, 17001981
Xu W, Nguyen TL, Kim YT, et al. Ultrasensitive artificial synapse based on conjugated polyelectrolyte. Nano Energy, 2018, 48: 575–581
Yang CS, Shang DS, Chai YS, et al. Electrochemical-reaction-induced synaptic plasticity in MoOx-based solid state electrochemical cells. Phys Chem Chem Phys, 2017, 19: 4190–4198
Tian H, Mi W, Wang XF, et al. Graphene dynamic synapse with modulatable plasticity. Nano Lett, 2015, 15: 8013–8019
Nayak A, Ohno T, Tsuruoka T, et al. Controlling the synaptic plasticity of a Cu2S gap-type atomic switch. Adv Funct Mater, 2012, 22: 3606–3613
Ohno T, Hasegawa T, Tsuruoka T, et al. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat Mater, 2011, 10: 591–595
Jo SH, Chang T, Ebong I, et al. Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett, 2010, 10: 1297–1301
Sarkar D, Tao J, Wang W, et al. Mimicking biological synaptic functionality with an indium phosphide synaptic device on silicon for scalable neuromorphic computing. ACS Nano, 2018, 12: 1656–1663
Yan X, Zhang L, Chen H, et al. Graphene oxide quantum dots based memristors with progressive conduction tuning for artificial synaptic learning. Adv Funct Mater, 2018, 28: 1803728
Zhu X, Lu WD. Optogenetics-inspired tunable synaptic functions in memristors. ACS Nano, 2018, 12: 1242–1249
Yin J, Zeng F, Wan Q, et al. Adaptive crystallite kinetics in homogenous bilayer oxide memristor for emulating diverse synaptic plasticity. Adv Funct Mater, 2018, 28: 1706927
Jeong DS, Hwang CS. Nonvolatile memory materials for neuromorphic intelligent machines. Adv Mater, 2018, 30: 1704729
Choi S, Tan SH, Li Z, et al. SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations. Nat Mater, 2018, 17: 335–340
Hu L, Fu S, Chen Y, et al. Ultrasensitive memristive synapses based on lightly oxidized sulfide films. Adv Mater, 2017, 29: 1606927
Xiao Z, Huang J. Energy-efficient hybrid perovskite memristors and synaptic devices. Adv Electron Mater, 2016, 16001001
Acknowledgements
The current work was mainly supported by the National Key Research and Development Program of China (2017YFA0205700) and the National Natural Science Foundation of China (NSFC, 61774133 and 6147409). Partial support from the NSFC for Innovative Research Groups (61721005) was also acknowledged.
Author information
Authors and Affiliations
Contributions
Author contributions Pi X conceived the project. Zhao S, Wang Y, Jin H, Huang P, Wang H and Wang K performed the experiments. Huang W simulated the image recognition. Zhao S and Pi X wrote the manuscript. All authors discussed the results and contributed to the writing.
Corresponding author
Additional information
Conflict of interest The authors declare that they have no conflict of interest.
Shuangyi Zhao received his PhD degree in the School of Materials Science and Engineering at Zhejiang University, China, in 2018. He worked on the fabrication of silicon nanocrystals and their applications in optoelectronic devices such as solar cells, light-emitting devices, and synaptic devices.
Xiaodong Pi received his PhD degree in the Department of Physics at the University of Bath, UK, in 2004. Further, he performed research in the Department of Engineering Physics at McMaster University and in the Department of Mechanical Engineering at the University of Minnesota, Twin Cities. He joined Zhejiang University as an associate professor in 2008. Currently, he is a professor in the State Key Laboratory of Silicon Materials and the School of Materials Science and Engineering at Zhejiang University. His research mainly concerns silicon-based optoelectronic materials and devices.
Electronic supplementary material
Rights and permissions
About this article
Cite this article
Zhao, S., Wang, Y., Huang, W. et al. Developing near-infrared quantum-dot light-emitting diodes to mimic synaptic plasticity. Sci. China Mater. 62, 1470–1478 (2019). https://doi.org/10.1007/s40843-019-9437-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40843-019-9437-9