AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Article Link
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article

Reconfigurable photovoltaic effect for optoelectronic artificial synapse based on ferroelectric p–n junction

Yanrong Wang1,4,5,§Feng Wang1,3,§Zhenxing Wang1,3,4( )Junjun Wang1,3Jia Yang1,3Yuyu Yao1,4,5Ningning Li1,4,5Marshet Getaye Sendeku1Xueying Zhan1Congxin Shan6Jun He2( )
CAS Center for Excellence in Nanoscience, CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology, Beijing 100190, China
Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
Sino-Danish college, University of Chinese Academy of Sciences, Beijing 100049, China
Sino-Danish Centre for Education and Research, Beijing 100049, China
School of Physics and Engineering, Zhengzhou University, Zhengzhou 450001, China

Show Author Information

Abstract

Neuromorphic machine vision has attracted extensive attention on wide fields. However, both current and emerging strategies still suffer from power/time inefficiency, and/or low compatibility, complex device structure. Here we demonstrate a driving-voltage-free optoelectronic synaptic device using non-volatile reconfigurable photovoltaic effect based on MoTe2/α-In2Se3 ferroelectric p–n junctions. This function comes from the non-volatile reconfigurable built-in potential in the p–n junction that is related to the ferroelectric polarization in α-In2Se3. Reconfigurable rectification behavior and photovoltaic effect are demonstrated firstly. Notably, the figure-of-merits for photovoltaic effect like photoelectrical conversion efficiency non-volatilely increases more than one order. Based on this, retina synapse-like vision functions are mimicked. Optoelectronic short-term and long-term plasticity, as well as basic neuromorphic learning and memory rule are achieved without applying driving voltage. Our work highlights the potential of ferroelectric p–n junctions for enhanced solar cell and low-power optoelectronic synaptic device for neuromorphic machine vision.

Electronic Supplementary Material

Download File(s)
12274_2021_3833_MOESM1_ESM.pdf (812.5 KB)

References

1

Krizhevsky, A.; Sutskever, I.; Hinton, G. E. ImageNet classification with deep convolutional neural networks. Commun. ACM 2017, 60, 84–90.

2

Hinton, G.; Deng, L.; Yu, D.; Dahl, G. E.; Mohamed, A. R.; Jaitly, N.; Senior, A.; Vanhoucke, V.; Nguyen, P.; Sainath, T. N. et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Proc. Mag. 2012, 29, 82–97.

3

LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444.

4

Lichtsteiner, P.; Posch, C.; Delbruck, T. A 128 × 128 120 dB 15 μs latency asynchronous temporal contrast vision sensor. IEEE J. Solid-State Circuits 2008, 43, 566–576.

5

Cottini, N.; Gottardi, M.; Massari, N.; Passerone, R.; Smilansky, Z. A 33 μW 64 × 64 pixel vision sensor embedding robust dynamic background subtraction for event detection and scene interpretation. IEEE J. Solid-State Circuits 2013, 48, 850–863.

6

Mennel, L.; Symonowicz, J.; Wachter, S.; Polyushkin, D. K.; Molina-Mendoza, A. J.; Mueller, T. Ultrafast machine vision with 2D material neural network image sensors. Nature 2020, 579, 62–66.

7

Chai, Y. In-sensor computing for machine vision. Nature 2020, 579, 32–33.

8

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. Sci. 2003, 91, 28–35.

9

Kyuma, K.; Lange, E.; Ohta, J.; Hermanns, A.; Banish, B.; Oita, M. Artificial retinas-fast, versatile image processors. Nature 1994, 372, 197–198.

10

Seo, S.; Jo, S. H.; Kim, S.; Shim, J.; Oh, S.; Kim, J. H.; Heo, K.; Choi, J. W.; Choi, C.; Oh, S. et al. Artificial optic-neural synapse for colored and color-mixed pattern recognition. Nat. Commun. 2018, 9, 5106.

11

Hong, S.; Choi, S. H.; Park, J.; Yoo, H.; Oh, J. Y.; Hwang, E.; Yoon, D. H.; Kim, S. Sensory adaptation and neuromorphic phototransistors based on CsPb(Br1−xIx)3 perovskite and MoS2 hybrid structure. ACS Nano 2020, 14, 9796–9806.

12

Sun, J.; Oh, S.; Choi, Y.; Seo, S.; Oh, M. J.; Lee, M.; Lee, W. B.; Yoo, P. J.; Cho, J. H.; Park, J. H. Optoelectronic synapse based on IGZO-alkylated graphene oxide hybrid structure. Adv. Funct. Mater. 2018, 28, 1804397.

13

Pradhan, B.; Das, S.; Li, J. X.; Chowdhury, F.; Cherusseri, J.; Pandey, D.; Dev, D.; Krishnaprasad, A.; Barrios, E.; Towers, A. et al. Ultrasensitive and ultrathin phototransistors and photonic synapses using perovskite quantum dots grown from graphene lattice. Sci. Adv. 2020, 6, eaay5225.

14

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.

15
Donati, S. Photodetectors: Devices, Circuits and Applications; Prentice Hall PTR: Upper Saddle River, 1999.
16

Meyer, R.; Waser, R. Hysteretic resistance concepts in ferroelectric thin films. J. Appl. Phys. 2006, 100, 051611.

17

Scott, J. F. Applications of modern ferroelectrics. Science 2007, 315, 954–959.

18
Jerry, M.; Chen, P. Y.; Zhang, J. C.; Sharma, P.; Ni, K.; Yu, S. M.; Datta, S. Ferroelectric FET analog synapse for acceleration of deep neural network training. In Proceedings of 2017 IEEE International Electron Devices Meeting (IEDM), San Francisco, USA, 2017, pp 6.2.1–6.2.4.https://doi.org/10.1109/IEDM.2017.8268338
19

Seo, M.; Kang, M. H.; Jeon, S. B.; Bae, H.; Hur, J.; Jang, B. C.; Yun, S.; Cho, S.; Kim, W. K.; Kim, M. S. et al. First demonstration of a logic-process compatible junctionless ferroelectric FinFET synapse for neuromorphic applications. IEEE Electron Device Lett. 2018, 39, 1445–1448.

20

Berdan, R.; Marukame, T.; Ota, K.; Yamaguchi, M.; Saitoh, M.; Fujii, S.; Deguchi, J.; Nishi, Y. Low-power linear computation using nonlinear ferroelectric tunnel junction memristors. Nat. Electron. 2020, 3, 259–266.

21

Khan, A. I.; Keshavarzi, A.; Datta, S. The future of ferroelectric field-effect transistor technology. Nat. Electron. 2020, 3, 588–597.

22

Böscke, T. S.; Müller, J.; Bräuhaus, D.; Schröder, U.; Böttger, U. Ferroelectricity in hafnium oxide thin films. Appl. Phys. Lett. 2011, 99, 102903.

23

Zhou, Y.; Wu, D.; Zhu, Y. H.; Cho, Y.; He, Q.; Yang, X.; Herrera, K.; Chu, Z. D.; Han, Y.; Downer, M. C. et al. Out-of-plane piezoelectricity and ferroelectricity in layered α-In2Se3 nanoflakes. Nano Lett. 2017, 17, 5508–5513.

24

Ding, W. J.; Zhu, J. B.; Wang, Z.; Gao, Y. F.; Xiao, D.; Gu, Y.; Zhang, Z. Y.; Zhu, W. G. Prediction of intrinsic two-dimensional ferroelectrics in In2Se3 and other III2-VI3 van der Waals materials. Nat. Commun. 2017, 8, 14956.

25

Xiao, J.; Zhu, H. Y.; Wang, Y.; Feng, W.; Hu, Y. X.; Dasgupta, A.; Han, Y. M.; Wang, Y.; Muller, D. A.; Martin, L. W. et al. Intrinsic two-dimensional ferroelectricity with dipole locking. Phys. Rev. Lett. 2018, 120, 227601.

26

Si, M. W.; Saha, A. K.; Gao, S. J.; Qiu, G.; Qin, J. K.; Duan, Y. Q.; Jian, J.; Niu, C.; Wang, H. Y.; Wu, W. Z. et al. A ferroelectric semiconductor field-effect transistor. Nat. Electron. 2019, 2, 580–586.

27

Zheng, C. X.; Yu, L.; Zhu, L.; Collins, J. L.; Kim, D.; Lou, Y. D.; Xu, C.; Li, M.; Wei, Z.; Zhang, Y. P. et al. Room temperature in-plane ferroelectricity in van der Waals In2Se3. Sci. Adv. 2018, 4, eaar7720.

28

Wang, L.; Wang, X. J.; Zhang, Y. S.; Li, R. L.; Ma, T.; Leng, K.; Chen, Z.; Abdelwahab, I.; Loh, K. P. Exploring ferroelectric switching in α-In2Se3 for neuromorphic computing. Adv. Funct. Mater. 2020, 30, 2004609.

29

Xue, F.; He, X.; Liu, W. H.; Periyanagounder, D.; Zhang, C. H.; Chen, M. G.; Lin, C. H.; Luo, L. Q.; Yengel, E.; Tung, V. et al. Optoelectronic ferroelectric domain-wall memories made from a single van der Waals ferroelectric. Adv. Funct. Mater. 2020, 30, 2004206.

30

Yamamoto, M.; Wang, S. T.; Ni, M. Y.; Lin, Y. F.; Li, S. L.; Aikawa, S.; Jian, W. B.; Ueno, K.; Wakabayashi, K.; Tsukagoshi, K. Strong enhancement of Raman scattering from a bulk-inactive vibrational mode in few-layer MoTe2. ACS Nano 2014, 8, 3895–3903.

31

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. 2012, 7, 699–712.

32

Liu, Y.; Weiss, N. O.; Duan, X. D.; Cheng, H. C.; Huang, Y.; Duan, X. F. Van der Waals heterostructures and devices. Nat. Rev. Mater. 2016, 1, 16042.

33

Wang, F.; Wang, Z. X.; Xu, K.; Wang, F. M.; Wang, Q. S.; Huang, Y.; Yin, L.; He, J. Tunable GaTe-MoS2 van der Waals p-n junctions with novel optoelectronic performance. Nano Lett. 2015, 15, 7558–7566.

34

Lee, C. H.; Lee, G. H.; van der Zande, A. M.; Chen, W. C.; Li, Y. L.; Han, M. Y.; Cui, X.; Arefe, G.; Nuckolls, C.; Heinz, T. F. et al. Atomically thin p–n junctions with van der Waals heterointerfaces. Nat. Nanotechnol. 2014, 9, 676–681.

35

Cheng, R. Q.; Wang, F.; Yin, L.; Wang, Z. X.; Wen, Y.; Shifa, T. A.; He, J. High-performance, multifunctional devices based on asymmetric van der Waals heterostructures. Nat. Electron. 2018, 1, 356–361.

36

Xu, K.; Jiang, W.; Gao, X. S.; Zhao, Z. J.; Low, T.; Zhu, W. J. Optical control of ferroelectric switching and multifunctional devices based on van der Waals ferroelectric semiconductors. Nanoscale 2020, 12, 23488–23496.

37

Cui, C. J.; Hu, W. J.; Yan, X. X.; Addiego, C.; Gao, W. P.; Wang, Y.; Wang, Z.; Li, L. Z.; Cheng, Y. C.; Li, P. et al. Intercorrelated in-plane and out-of-plane ferroelectricity in ultrathin two-dimensional layered semiconductor In2Se3. Nano Lett. 2018, 18, 1253–1258.

38

Blom, P. W. M.; Wolf, R. M.; Cillessen, J. F. M.; Krijn, M. P. C. M. Ferroelectric schottky diode. Phys. Rev. Lett. 1994, 73, 2107–2110.

39

Zhu, L. Q.; Wan, C. J.; Guo, L. Q.; Shi, Y.; Wan, Q. Artificial synapse network on inorganic proton conductor for neuromorphic systems. Nat. Commun. 2014, 5, 3158.

40

Chang, T.; Jo, S. H.; Lu, W. Short-term memory to long-term memory transition in a nanoscale memristor. ACS Nano 2011, 5, 7669–7676.

41

Jo, S. H.; Chang, T.; Ebong, I.; Bhadviya, B. B.; Mazumder, P.; Lu, W. Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett. 2010, 10, 1297–1301.

Nano Research
Pages 4328-4335
Cite this article:
Wang Y, Wang F, Wang Z, et al. Reconfigurable photovoltaic effect for optoelectronic artificial synapse based on ferroelectric p–n junction. Nano Research, 2021, 14(11): 4328-4335. https://doi.org/10.1007/s12274-021-3833-x
Topics:

798

Views

39

Crossref

40

Web of Science

39

Scopus

5

CSCD

Altmetrics

Received: 22 May 2021
Revised: 17 August 2021
Accepted: 21 August 2021
Published: 06 September 2021
© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021
Return