Hybrid C8-BTBT/InGaAs nanowire heterojunction for artificial photosynaptic transistors

Yiling Nie; Pengshan Xie; Xu Chen; Chenxing Jin; Wanrong Liu; Xiaofang Shi; Yunchao Xu; Yongyi Peng; Johnny C. Ho; Jia Sun; Junliang Yang

doi:10.1088/1674-4926/43/11/112201

J. Semicond. > 2022, Volume 43 > Issue 11 > 112201

ARTICLES

Hybrid C8-BTBT/InGaAs nanowire heterojunction for artificial photosynaptic transistors

Yiling Nie^{1, §}, Pengshan Xie^{2, §}, Xu Chen^{1, §}, Chenxing Jin¹, Wanrong Liu¹, Xiaofang Shi¹, Yunchao Xu¹, Yongyi Peng¹, Johnny C. Ho^{2, 3, 4,}, Jia Sun^1, and Junliang Yang^1,

+ Author Affiliations

1. Hunan Key Laboratory for Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha 410083, China
2. Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong SAR, China
3. State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Kowloon 999077, Hong Kong SAR, China
4. Key Laboratory of Advanced Materials Processing & Mold (Zhengzhou University), Ministry of Education, Zhengzhou 450002, China
^§Yiling Nie, Pengshan Xie and Xu Chen contributed equally to this work.

Author Bio:

is currently a postgraduate student in the Department of Physics and Electronics at the Central South University. She received her BS degree from Central South University in 2019. Her research interests mainly focus on organic thin film devices, optical synapse biomimetic devices, and so on.

is currently a PhD student in the Department of Materials Science and Engineering at the City University of Hong Kong. He received his BS and MS degrees from Central South University in 2017 and 2020, respectively. His research interests mainly focus on fabrication of nanomaterials including III–V semiconductor nanowires, novel neuromorphic electronics and field-effect transistors, and so on.

graduated from Anhui University institute of Electronic and Information Engineering with a Bachelor's degree in 2018. He then began his M.S. study in the School of Physics and Electronics at Central South University in September 2020. His research focuses on neural network based on memristor.

is a professor of materials science and engineering at City University of Hong Kong. He received his BS degree in chemical engineering, and his MS and PhD degrees in materials science and engineering from the University of California, Berkeley, in 2002, 2005, and 2009, respectively. From 2009 to 2010, he was a postdoctoral research fellow at Lawrence Livermore National Laboratory. His research interests focus on synthesis, characterization, integration, and device applications of nanoscale materials for various technological applications, including nanoelectronics, sensors, and energy harvesting.

received his PhD degree from Hunan University in 2012. He was a postdoctoral researcher in Central South University (2012-2014) and Sungkyunkwan University (2017–2018). In 2014, he joined the faculty at Central South University and is currently a professor in the School of Physics and Electronics. His research interests focus on novel photoelectronic devices and neuromorphic devices.

received his Ph.D. degree in 2008 from Changchun Institute of Applied Chemistry Chinese, Chinese Academy of Sciences. He joined the University of Warwick as a postdoctoral research fellow. He then moved to the University of Melbourne and the Commonwealth Scientific and Industrial Research Organization to continue the research works. In 2012, he was appointed as a professor in the School of Physics and Electronics at Central South University. His research focuses on flexible and printed electronics, as well as organic and perovskite solar cells.

Corresponding author: Johnny C. Ho, johnnyho@cityu.edu.hk; Jia Sun, jiasun@csu.edu.cn; Junliang Yang, junliang.yang@csu.edu.cn

DOI: 10.1088/1674-4926/43/11/112201

Abstract: The emergence of light-tunable synaptic transistors provides opportunities to break through the von Neumann bottleneck and enable neuromorphic computing. Herein, a multifunctional synaptic transistor is constructed by using 2,7-dioctyl[1]benzothieno[3,2-b][1]benzothiophene (C8-BTBT) and indium gallium arsenide (InGaAs) nanowires (NWs) hybrid heterojunction thin film as the active layer. Under illumination, the Type-I C8-BTBT/InGaAs NWs heterojunction would make the dissociated photogenerated excitons more difficult to recombine. The persistent photoconductivity caused by charge trapping can then be used to mimic photosynaptic behaviors, including excitatory postsynaptic current, long/short-term memory and Pavlovian learning. Furthermore, a high classification accuracy of 89.72% can be achieved through the single-layer-perceptron hardware-based neural network built from C8-BTBT/InGaAs NWs synaptic transistors. Thus, this work could provide new insights into the fabrication of high-performance optoelectronic synaptic devices.

Key words: photonic synaptic transistor, C8-BTBT, InGaAs, heterojunction

References

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Fig. 1. (Color online) (a) Schematic illustration of the CVD setup for the NWs synthesis. (b) SEM image and (c) HRTEM image of the InGaAs NWs. (d) Device schematic of the C8-BTBT/InGaAs NWs phototransistors. (e) I_light/I_dark curve from the devices under light with different intensities. (f) Output characteristics in dark and under light (0.4 mW/cm²).

DownLoad: Full-Size Img PowerPoint

Fig. 2. (Color online) (a) Schematic illustration on the workings of brain connections and biological synapses. (b, c) EPSC stimulated by a light pulse for the phototransistors from pure C8-BTBT and C8-BTBT/InGaAs NWs transistors, respectively. (d) ΔW as a function of time collected from EPSC. (e, f) PPF measured from the phototransistors with pure C8-BTBT and C8-BTBT/InGaAs NWs transistors, respectively. (g) PPF index decay and fitting curves.

DownLoad: Full-Size Img PowerPoint

Fig. 3. (Color online) (a) Light intensity dependence of the EPSC with different light pulse stimulation. (b) Light pulse width dependence of the EPSC with different width stimulation. (c) The EPSC of 10 consistent light pulses measured from the different light intensity. (d) EPSC change extracted from (c). (e) Photonic potentiation and electric depression by 30 times of light pulses and negative electric pulses. (f) Stability testing of photoresponse of C8-BTBT/InGaAs NWs phototransistors.

DownLoad: Full-Size Img PowerPoint

Fig. 4. (Color online) Schematic illustrations of (a) the existence of electrons in the device after light illumination and (b) the recombination process of electrons and holes with voltage applied to the gate. (c) The complete association learning process. After training, CS can trigger UR (canine saliva secretion, unconditional response).

DownLoad: Full-Size Img PowerPoint

Fig. 5. (Color online) (a) LTP/LTD characteristics of the C8-BTBT transistors. (b) LTP/LTD characteristics of the C8-BTBT/InGaAs NWs transistors. (c) Simulated neural network accuracy for MNIST handwritten digit classification for the C8-BTBT transistors (red) and the C8-BTBT/InGaAs NWs transistors (black). (d) Schematic of SLP HW-NN. (e) Corresponding hardware implementation composed of PST crossbar array. (f) Change of the recognition rate with epochs of training.

DownLoad: Full-Size Img PowerPoint

[1]	Ahmed T, Tahir M, Low M X, et al. Fully Light-controlled memory and neuromorphic computation in layered black phosphorus. Adv Mater, 2021, 33, 2004207 doi: 10.1002/adma.202004207
[2]	Hou Y X, Li Y, Zhang Z C, et al. Large-scale and flexible optical synapses for neuromorphic computing and integrated visible information sensing memory processing. ACS Nano, 2021, 15, 1497 doi: 10.1021/acsnano.0c08921
[3]	Lee M, Kim M, Jo J W, et al. Suppression of persistent photo-conductance in solution-processed amorphous oxide thin-film transistors. Appl Phys Lett, 2018, 112, 052103 doi: 10.1063/1.4999934
[4]	Lee M, Lee W, Choi S, et al. Brain-inspired photonic neuromorphic devices using photodynamic amorphous oxide semiconductors and their persistent photoconductivity. Adv Mater, 2017, 29, 1700951 doi: 10.1002/adma.201700951
[5]	Lv Z, Chen M, Qian F, et al. Mimicking neuroplasticity in a hybrid biopolymer transistor by dual modes modulation. Adv Funct Mater, 2019, 29, 1902374 doi: 10.1002/adfm.201902374
[6]	Wang H, Zhao Q, Ni Z, et al. A ferroelectric/electrochemical modulated organic synapse for ultraflexible, artificial visual-perception system. Adv Mater, 2018, 30, 1803961 doi: 10.1002/adma.201803961
[7]	Wang X, Lu Y, Zhang J, et al. Highly sensitive artificial visual array using transistors based on porphyrins and semiconductors. Small, 2021, 17, 2005491 doi: 10.1002/smll.202005491
[8]	Xie D, Wei L, Xie M, et al. Photoelectric visual adaptation based on 0D-CsPbBr₃-quantum-dots/2D-MoS₂ mixed-dimensional heterojunction transistor. Adv Funct Mater, 2021, 31, 2010655 doi: 10.1002/adfm.202010655
[9]	Zhu L Q, Wan C J, Guo L Q, et al. Artificial synapse network on inorganic proton conductor for neuromorphic systems. Nat Commun, 2014, 5, 1 doi: 10.1038/ncomms4158
[10]	Abbott L F, Regehr W G. Synaptic computation. Nature, 2004, 431, 796 doi: 10.1038/nature03010
[11]	Sung S H, Kim T J, Shin H, et al. Memory-centric neuromorphic computing for unstructured data processing. Nano Res, 2021, 14, 3126 doi: 10.1007/s12274-021-3452-6
[12]	Qian C, Kong L A, Yang J, et al. Multi-gate organic neuron transistors for spatiotemporal information processing. Appl Phys Lett, 2017, 110, 083302 doi: 10.1063/1.4977069
[13]	Wan C J, Zhu L Q, Liu Y H, et al. Proton-conducting graphene oxide-coupled neuron transistors for brain-inspired cognitive systems. Adv Mater, 2016, 28, 3557 doi: 10.1002/adma.201505898
[14]	Han C, Han X, Han J, et al. Light-stimulated synaptic transistor with high PPF feature for artificial visual perception system application. Adv Funct Mater, 2022, 32, 2113053 doi: 10.1002/adfm.202113053
[15]	Qian C, Choi Y, Choi Y J, et al. Oxygen-detecting synaptic device for realization of artificial autonomic nervous system for maintaining oxygen homeostasis. Adv Mater, 2020, 32, 2002653 doi: 10.1002/adma.202002653
[16]	Jang Y, Park J, Kang J, et al. Amorphous InGaZnO (a-IGZO) synaptic transistor for neuromorphic computing. ACS Appl Electron Mater, 2022, 4, 1427 doi: 10.1021/acsaelm.1c01088
[17]	Yan X, Qian J H, Sangwan V K, et al. Progress and challenges for memtransistors in neuromorphic circuits and systems. Adv Mater, 2022, 2108025 doi: 10.1002/adma.202108025
[18]	Huh W, Lee D, Lee C H. Memristors based on 2D materials as an artificial synapse for neuromorphic electronics. Adv Mater, 2020, 32, 2002092 doi: 10.1002/adma.202002092
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[22]	Kuramochi E, Nozaki K, Shinya A, et al. Large-scale integration of wavelength-addressable all-optical memories on a photonic crystal chip. Nat Photonics, 2014, 8, 474 doi: 10.1038/nphoton.2014.93
[23]	Luo Z, Xie Y, Li Z, et al. Plasmonically engineered light-matter interactions in Au-nanoparticle/MoS₂ heterostructures for artificial optoelectronic synapse. Nano Res, 2022, 15, 3539 doi: 10.1007/s12274-021-3875-0
[24]	Qian L, Sun Y, Wu M, et al. A lead-free two-dimensional perovskite for a high-performance flexible photoconductor and a light-stimulated synaptic device. Nanoscale, 2018, 10(15), 6837 doi: 10.1039/C8NR00914G
[25]	Sun Y, Qian L, Xie D, et al. Photoelectric synaptic plasticity realized by 2D perovskite. Adv Funct Mater, 2019, 29(28), 1902538 doi: 10.1002/adfm.201902538
[26]	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 doi: 10.1002/adma.201806227
[27]	Qin S, Wang F, Liu Y, et al. A light-stimulated synaptic device based on graphene hybrid phototransistor. 2D Mater, 2017, 4, 035022 doi: 10.1088/2053-1583/aa805e
[28]	Wang Y, Yang J, Wang Z, et al. Near-infrared annihilation of conductive filaments in quasiplane MoSe₂/Bi₂Se₃ nanosheets for mimicking heterosynaptic plasticity. Small, 2019, 15, 1805431 doi: 10.1002/smll.201805431
[29]	Yang B, Lu Y, Jiang D, et al. Bioinspired multifunctional organic transistors based on natural chlorophyll/organic semiconductors. Adv Mater, 2020, 32, 2001227 doi: 10.1002/adma.202001227
[30]	Hou J J, Wang F, Han N, et al. Stoichiometric effect on electrical, optical, and structural properties of composition-tunable In_xGa_{1– x}As nanowires. ACS Nano, 2012, 6, 9320 doi: 10.1021/nn304174g
[31]	Huang Y, Sun J, Zhang J, et al. Controllable thin-film morphology and structure for 2,7-dioctyl[1]benzothieno[3,2-b][1] benzothiophene (C8-BTBT) based organic field-effect transistors. Org Electron, 2016, 36, 73 doi: 10.1016/j.orgel.2016.05.019
[32]	Xie P, Liu T, Sun J, et al. Solution-processed ultra-flexible C8-BTBT organic thin-film transistors with the corrected mobility over 18 cm²/(V·s). Sci Bull, 2020, 65, 791 doi: 10.1016/j.scib.2020.03.013
[33]	Tong S, Sun J, Wang C, et al. High-performance broadband perovskite photodetectors based on CH₃NH₃PbI₃/C8-BTBT heterojunction. Adv Electron Mater, 2017, 3, 1700058 doi: 10.1002/aelm.201700058
[34]	Yuan Y, Huang J. Ultrahigh gain, low noise, ultraviolet photodetectors with highly aligned organic crystals. Adv Opt Mater, 2016, 4, 264 doi: 10.1002/adom.201500560
[35]	Mukherjee A, Sagar S, Parveen S, et al. Superionic rubidium silver iodide gated low voltage synaptic transistor. Appl Phys Lett, 2021, 119, 253502 doi: 10.1063/5.0069478
[36]	Kim S, Choi B, Lim M, et al. Pattern recognition using carbon nanotube synaptic transistors with an adjustable weight update protocol. ACS Nano, 2017, 11, 2814 doi: 10.1021/acsnano.6b07894
[37]	Dai S, Wu X, Liu D, et al. Light-stimulated synaptic devices utilizing interfacial effect of organic field-effect transistors. ACS Appl Mater Interfaces, 2018, 10, 21472 doi: 10.1021/acsami.8b05036
[38]	Han J, Wang J, Yang M, et al. Graphene/organic semiconductor heterojunction phototransistors with broadband and bi-directional photoresponse. Adv Mater, 2018, 30, 1804020 doi: 10.1002/adma.201804020
[39]	Xia H, Tong S, Zhang C, et al. Flexible and air-stable perovskite network photodetectors based on CH₃NH₃PbI₃/C8-BTBT bulk heterojunction. Appl Phys Lett, 2018, 112, 233301 doi: 10.1063/1.5024330
[40]	Yang D, Zhang X, Wang K, et al. Stable efficiency exceeding 20.6% for inverted perovskite solar cells through polymer-optimized PCBM electron-transport layers. Nano Lett, 2019, 19, 3313 doi: 10.1021/acs.nanolett.9b00936
[41]	Xu L, Xiong H, Fu Z, et al. High conductance margin for efficient neuromorphic computing enabled by stacking nonvolatile van der waals transistors. Phys Rev Appl, 2021, 16, 044049 doi: 10.1103/PhysRevApplied.16.044049
[42]	Yu F, Zhu L Q, Xiao H, et al. Restickable oxide neuromorphic transistors with spike-timing-dependent plasticity and pavlovian associative learning activities. Adv Funct Mater, 2018, 28, 1804025 doi: 10.1002/adfm.201804025
[43]	Guo Y B, Zhu L Q, Long T Y, et al. Bio-polysaccharide electrolyte gated photoelectric synergic coupled oxide neuromorphic transistor with Pavlovian activities. J Mater Chem C, 2020, 8, 2780 doi: 10.1039/C9TC06749C
[44]	Qian C, Oh S, Choi Y, et al. Solar-stimulated optoelectronic synapse based on organic heterojunction with linearly potentiated synaptic weight for neuromorphic computing. Nano Energy, 2019, 66, 104095 doi: 10.1016/j.nanoen.2019.104095
[45]	Kim S, Heo K, Lee S, et al. Ferroelectric polymer-based artificial synapse for neuromorphic computing. Nanoscale Horiz, 2021, 6, 139 doi: 10.1039/D0NH00559B
[46]	Li E, Wu X, Chen Q, et al. Nanoscale channel organic ferroelectric synaptic transistor array for high recognition accuracy neuromorphic computing. Nano Energy, 2021, 85, 106010 doi: 10.1016/j.nanoen.2021.106010
[47]	Prezioso M, Merrikh-Bayat F, Hoskins B, et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature, 2015, 521, 61 doi: 10.1038/nature14441

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Received: 19 May 2022 Revised: 27 June 2022 Online: Accepted Manuscript: 12 August 2022Uncorrected proof: 12 August 2022Published: 01 November 2022

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Yiling Nie, Pengshan Xie, Xu Chen, Chenxing Jin, Wanrong Liu, Xiaofang Shi, Yunchao Xu, Yongyi Peng, Johnny C. Ho, Jia Sun, Junliang Yang. Hybrid C8-BTBT/InGaAs nanowire heterojunction for artificial photosynaptic transistors[J]. Journal of Semiconductors, 2022, 43(11): 112201. doi: 10.1088/1674-4926/43/11/112201 ****Yiling Nie, Pengshan Xie, Xu Chen, Chenxing Jin, Wanrong Liu, Xiaofang Shi, Yunchao Xu, Yongyi Peng, Johnny C. Ho, Jia Sun, Junliang Yang. 2022: Hybrid C8-BTBT/InGaAs nanowire heterojunction for artificial photosynaptic transistors. Journal of Semiconductors, 43(11): 112201. doi: 10.1088/1674-4926/43/11/112201

Citation:

Yiling Nie, Pengshan Xie, Xu Chen, Chenxing Jin, Wanrong Liu, Xiaofang Shi, Yunchao Xu, Yongyi Peng, Johnny C. Ho, Jia Sun, Junliang Yang. 2022: Hybrid C8-BTBT/InGaAs nanowire heterojunction for artificial photosynaptic transistors. Journal of Semiconductors, 43(11): 112201. doi: 10.1088/1674-4926/43/11/112201

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Hybrid C8-BTBT/InGaAs nanowire heterojunction for artificial photosynaptic transistors

DOI: 10.1088/1674-4926/43/11/112201

Yiling Nie^{1, §},
Pengshan Xie^{2, §},
Xu Chen^{1, §},
Chenxing Jin¹,
Wanrong Liu¹,
Xiaofang Shi¹,
Yunchao Xu¹,
Yongyi Peng¹,
Johnny C. Ho^{2, 3, 4,},
Jia Sun^1,,
Junliang Yang^1,

1.
Hunan Key Laboratory for Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha 410083, China
2.
Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong SAR, China
3.
State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Kowloon 999077, Hong Kong SAR, China
4.
Key Laboratory of Advanced Materials Processing & Mold (Zhengzhou University), Ministry of Education, Zhengzhou 450002, China

More Information

Yiling Nie：is currently a postgraduate student in the Department of Physics and Electronics at the Central South University. She received her BS degree from Central South University in 2019. Her research interests mainly focus on organic thin film devices, optical synapse biomimetic devices, and so on
Pengshan Xie：is currently a PhD student in the Department of Materials Science and Engineering at the City University of Hong Kong. He received his BS and MS degrees from Central South University in 2017 and 2020, respectively. His research interests mainly focus on fabrication of nanomaterials including III–V semiconductor nanowires, novel neuromorphic electronics and field-effect transistors, and so on
Xu Chen：graduated from Anhui University institute of Electronic and Information Engineering with a Bachelor's degree in 2018. He then began his M.S. study in the School of Physics and Electronics at Central South University in September 2020. His research focuses on neural network based on memristor
Johnny C. Ho：is a professor of materials science and engineering at City University of Hong Kong. He received his BS degree in chemical engineering, and his MS and PhD degrees in materials science and engineering from the University of California, Berkeley, in 2002, 2005, and 2009, respectively. From 2009 to 2010, he was a postdoctoral research fellow at Lawrence Livermore National Laboratory. His research interests focus on synthesis, characterization, integration, and device applications of nanoscale materials for various technological applications, including nanoelectronics, sensors, and energy harvesting
Jia Sun：received his PhD degree from Hunan University in 2012. He was a postdoctoral researcher in Central South University (2012-2014) and Sungkyunkwan University (2017–2018). In 2014, he joined the faculty at Central South University and is currently a professor in the School of Physics and Electronics. His research interests focus on novel photoelectronic devices and neuromorphic devices
Junliang Yang：received his Ph.D. degree in 2008 from Changchun Institute of Applied Chemistry Chinese, Chinese Academy of Sciences. He joined the University of Warwick as a postdoctoral research fellow. He then moved to the University of Melbourne and the Commonwealth Scientific and Industrial Research Organization to continue the research works. In 2012, he was appointed as a professor in the School of Physics and Electronics at Central South University. His research focuses on flexible and printed electronics, as well as organic and perovskite solar cells
Corresponding author: johnnyho@cityu.edu.hk; jiasun@csu.edu.cn; junliang.yang@csu.edu.cn
Received Date: 2022-05-19
Revised Date: 2022-06-27

Available Online: 2022-08-12

Abstract

Abstract

The emergence of light-tunable synaptic transistors provides opportunities to break through the von Neumann bottleneck and enable neuromorphic computing. Herein, a multifunctional synaptic transistor is constructed by using 2,7-dioctyl[1]benzothieno[3,2-b][1]benzothiophene (C8-BTBT) and indium gallium arsenide (InGaAs) nanowires (NWs) hybrid heterojunction thin film as the active layer. Under illumination, the Type-I C8-BTBT/InGaAs NWs heterojunction would make the dissociated photogenerated excitons more difficult to recombine. The persistent photoconductivity caused by charge trapping can then be used to mimic photosynaptic behaviors, including excitatory postsynaptic current, long/short-term memory and Pavlovian learning. Furthermore, a high classification accuracy of 89.72% can be achieved through the single-layer-perceptron hardware-based neural network built from C8-BTBT/InGaAs NWs synaptic transistors. Thus, this work could provide new insights into the fabrication of high-performance optoelectronic synaptic devices.
- photonic synaptic transistor,
- C8-BTBT,
- InGaAs,
- heterojunction

Supplementary materials to this article can be found online at https://doi.org/10.1088/1674-4926/43/11/112201.
^§Yiling Nie, Pengshan Xie and Xu Chen contributed equally to this work.

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References(47)

References

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Hybrid C8-BTBT/InGaAs nanowire heterojunction for artificial photosynaptic transistors

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