Visual synapse based on reconfigurable organic photovoltaic cell

Xiangrong Pu; Fan Shu; Qifan Wang; Gang Liu; Zhang Zhang

doi:10.1088/1674-4926/24080018

J. Semicond. > 2025, Volume 46 > Issue 2 > 022403

SPECIAL ISSUE ARTICLES

Visual synapse based on reconfigurable organic photovoltaic cell

Xiangrong Pu^{1, §}, Fan Shu^{2, §}, Qifan Wang¹, Gang Liu^2, and Zhang Zhang^1,

+ Author Affiliations

1. School of Microelectronics, Hefei University of Technology, Hefei 230009, China
2. National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai 200240, China
^§Xiangrong Pu and Fan Shu contributed equally to this work and should be considered as co-first authors.

Author Bio:

Xiangrong Pu is currently a Ph.D. student with the School of Microelectronics, Hefei University of Technology. His research interests include deep learning and mixed signal IC design.

Gang Liu is currently a professor at Shanghai Jiao Tong University. After receiving his Ph.D. degree from the National University of Singapore in 2010, he worked at the Nanyang Technological University, the National University of Singapore and the Chinese Academy of Sciences between January 2010 and November 2018. In December 2018, he joined the School of Electronic Information and Electrical Engineering, SJTU. His research interests focus on memristor devices and neuromrphic computing.

Zhang Zhang received the B.S. degree in electronic science and technology from the Hefei University of Technology, Hefei, China, in 2004, and the Ph.D. degree in microelectronics from Fudan University, Shanghai, China, in 2010. He was a Visiting Scholar with the Georgia Institute of Technology from 2016 to 2017. He is currently a Professor with the School of Microelectronics, Hefei University of Technology. His research interests include deep learning and mixed signal IC design.

Corresponding author: Gang Liu, gang.liu@sjtu.edu.cn; Zhang Zhang, zhanzhang@hfut.edu.cn

DOI: 10.1088/1674-4926/24080018CSTR: 32376.14.1674-4926.24080018

Abstract: The hierarchical and coordinated processing of visual information by the brain demonstrates its superior ability to minimize energy consumption and maximize signal transmission efficiency. Therefore, it is crucial to develop artificial visual synapses that integrate optical sensing and synaptic functions. This study fully leverages the excellent photoresponsivity properties of the PM6 : Y6 system to construct a vertical photo-tunable organic memristor and conducts in-depth research on its resistive switching performance, photodetection capability, and simulation of photo-synaptic behavior, showcasing its excellent performance in processing visual information and simulating neuromorphic behaviors. The device achieves stable and gradual resistance change, successfully simulating voltage-controlled long-term potentiation/depression (LTP/LTD), and exhibits various photo-electric synergistic regulation of synaptic plasticity. Moreover, the device has successfully simulated the image perception and recognition functions of the human visual nervous system. The non-volatile Au/PM6 : Y6/ITO memristor is used as an artificial synapse and neuron modeling, building a hierarchical coordinated processing SLP-CNN cascade neural network for visual image recognition training, its linear tunable photoconductivity characteristic serves as the weight update of the network, achieving a recognition accuracy of up to 93.4%. Compared with the single-layer visual target recognition model, this scheme has improved the recognition accuracy by 19.2%.

Key words: organic memristor, visual synapse, neuromorphic computing, PM6 : Y6, image recognition

References

[1]	Shastri B J, Tait A N, Ferreira de Lima T, et al. Photonics for artificial intelligence and neuromorphic computing. Nat Photonics, 2021, 15, 102 doi: 10.1038/s41566-020-00754-y
[2]	Schuman C D, Kulkarni S R, Parsa M, et al. Opportunities for neuromorphic computing algorithms and applications. Nat Comput Sci, 2022, 2, 10 doi: 10.1038/s43588-021-00184-y
[3]	Choi C, Leem J, Kim M, et al. Curved neuromorphic image sensor array using a MoS₂-organic heterostructure inspired by the human visual recognition system. Nat Commun, 2020, 11, 5934 doi: 10.1038/s41467-020-19806-6
[4]	Gollisch T, Meister M. Eye smarter than scientists believed: Neural computations in circuits of the retina. Neuron, 2010, 65, 150 doi: 10.1016/j.neuron.2009.12.009
[5]	Eldred K C, Hadyniak S E, Hussey K A, et al. Thyroid hormone signaling specifies cone subtypes in human retinal organoids. Science, 2018, 362, eaau6348 doi: 10.1126/science.aau6348
[6]	Tong F. Primary visual cortex and visual awareness. Nat Rev Neurosci, 2003, 4, 219 doi: 10.1038/nrn1055
[7]	Han X, Xu Z S, Wu W Q, et al. Recent progress in optoelectronic synapses for artificial visual-perception system. Small Struct, 2020, 1, 2000029 doi: 10.1002/sstr.202000029
[8]	Wang Y, Lv Z Y, Chen J R, et al. Photonic synapses based on inorganic perovskite quantum dots for neuromorphic computing. Adv Mater, 2018, 30, 1802883 doi: 10.1002/adma.201802883
[9]	Radovic A, Williams M, Rousseau D, et al. Machine learning at the energy and intensity frontiers of particle physics. Nature, 2018, 560, 41 doi: 10.1038/s41586-018-0361-2
[10]	Xia Y, Zhang C, Xu Z, et al. Organic iontronic memristors for artificial synapses and bionic neuromorphic computing. Nanoscale, 2024, 16, 1471 doi: 10.1039/D3NR06057H
[11]	Sun H L, Liu T, Yu J W, et al. A monothiophene unit incorporating both fluoro and ester substitution enabling high-performance donor polymers for non-fullerene solar cells with 16.4% efficiency. Energy Environ Sci, 2019, 12, 3328 doi: 10.1039/C9EE01890E
[12]	Perdigón-Toro L, Zhang H T, Markina A, et al. Barrierless free charge generation in the high-performance PM6 : Y6 bulk heterojunction non-fullerene solar cell. Adv Mater, 2020, 32, 1906763 doi: 10.1002/adma.201906763
[13]	Zeng L J, Liu G, Zhang B, et al. Synthesis and memory performance of a conjugated polymer with an integrated fluorene, carbazole and oxadiazole backbone. Polym J, 2012, 44, 257 doi: 10.1038/pj.2011.114
[14]	Röhr J A, Shi X Y, Haque S A, et al. Charge transport in spiro-OMeTAD investigated through space-charge-limited current measurements. Phys Rev Applied, 2018, 9, 044017 doi: 10.1103/PhysRevApplied.9.044017
[15]	Lv Z Y, Hu Q K, Xu Z X, et al. Organic memristor utilizing copper phthalocyanine nanowires with infrared response and cation regulating properties. Adv Electron Mater, 2019, 5, 1800793 doi: 10.1002/aelm.201800793
[16]	Lv Z Y, Wang Y, Chen Z H, et al. Phototunable biomemory based on light-mediated charge trap. Adv Sci, 2018, 5, 1800714 doi: 10.1002/advs.201800714
[17]	Xing B H, Gao R Z, Wu M, et al. Differentiation on crystallographic orientation dependence of hydrogen diffusion in α-Fe and γ-Fe: DFT calculation combined with SKPFM analysis. Appl Surf Sci, 2023, 615, 156395 doi: 10.1016/j.apsusc.2023.156395
[18]	Xie D D, Gao G, Tian B B, et al. Porous metal–organic framework/ReS₂ heterojunction phototransistor for polarization-sensitive visual adaptation emulation. Adv Mater, 2023, 35, 2212118 doi: 10.1002/adma.202212118
[19]	Xie D D, Li Y Z, He J, et al. 0D-carbon-quantum-dots/2D-MoS₂ mixed-dimensional heterojunction transistor for emulating pulsatile photoelectric therapy of visual amnesic behaviors. Sci China Mater, 2023, 66, 4814 doi: 10.1007/s40843-023-2638-y
[20]	Xie P S, Huang Y L, Wang W, et al. Ferroelectric P(VDF-TrFE) wrapped InGaAs nanowires for ultralow-power artificial synapses. Nano Energy, 2022, 91, 106654 doi: 10.1016/j.nanoen.2021.106654

Fig. 1. (Color online) The device schematic and electrical properties of Au/PM6 : Y6/ITO. (a) Schematic diagram of device structure and local magnification of active layer. (b) Molecular formula of PM6 : Y6. (c) Current−voltage (I−V) characteristics of Blends/ITO structures based on Au/PM6 : Y6, where positive bias is the SET (ON) process and negative bias is the RESET (OFF) process. (d) 50 groups of switching voltage profiles of I−V curves are randomly selected. (e) Weibull distribution statistics of the high resistance state (HRS) and low resistance state (LRS) of 50 I−V curves corresponding to (d). (f) Time retention of high and low resistance states of the device at a reading voltage of 0.1 V. (g) Pulse retention of the device under 5 × 10⁵ pulse stimuli (base = 0.1 V, width = 1 μs, delay = 1.2 μs). (h) Endurance of the device under more than 230 switching cycles (3→0.1→(−2)→0.1→ 3 V).

DownLoad: Full-Size Img PowerPoint

Fig. 2. (Color online) The mechanism of Au/PM6 : Y6/ITO device. (a) Fitting curve of SCLC model for PM6 : Y6 blend based device. (b) KPFM spectra of PM6 : Y6 blend based device. (c) The sectional potential curves under different states. (d)−(i) Schematic diagram of resistive memory mechanism, which is divided into six parts.

DownLoad: Full-Size Img PowerPoint

Fig. 3. (Color online) Photoelectric detection performance of the device. (a) I−V curves of the device under red light (L) and in the dark (D) under different intensities of negative pulse voltage. (b) I−V curves of the device under red light and in the absence of light under different intensities of positive pulse voltage. (c) Working mechanism of PM6 : Y6 blends based photodetector.

DownLoad: Full-Size Img PowerPoint

Fig. 4. (Color online) The photoelectric modulation and synaptic behavior of PM6 : Y6 device. The photocurrent of the device varies under light pulses of different (a) wavelengths and (b) light intensity; under maximum light intensity of red (level 255 light intensity), the I−t curve of the device was stimulated by increasing (c) negative bias voltage and (d) forward bias voltage (Since there is no current in the device at 0 V, we used 0.01 V instead of 0 V in the experiment to represent the initial state of the device). (e) LTP under red light irradiation with positive voltage stimulation. (f) LTD under red light irradiation with negative voltage stimulation.

DownLoad: Full-Size Img PowerPoint

Fig. 5. (Color online) Image perception of the Au/PM6 : Y6/ITO. (a) Modulation of device photoresponsivity for self-adaptive image formation. (b) SLP-CNN cascaded neural network. (c) Confusion matrices of front-end single-layer perceptron SLP and back-end CNN network. (d) The accuracy obtained by directly identifying unknown visual targets with SLP.

DownLoad: Full-Size Img PowerPoint

[1]	Shastri B J, Tait A N, Ferreira de Lima T, et al. Photonics for artificial intelligence and neuromorphic computing. Nat Photonics, 2021, 15, 102 doi: 10.1038/s41566-020-00754-y
[2]	Schuman C D, Kulkarni S R, Parsa M, et al. Opportunities for neuromorphic computing algorithms and applications. Nat Comput Sci, 2022, 2, 10 doi: 10.1038/s43588-021-00184-y
[3]	Choi C, Leem J, Kim M, et al. Curved neuromorphic image sensor array using a MoS₂-organic heterostructure inspired by the human visual recognition system. Nat Commun, 2020, 11, 5934 doi: 10.1038/s41467-020-19806-6
[4]	Gollisch T, Meister M. Eye smarter than scientists believed: Neural computations in circuits of the retina. Neuron, 2010, 65, 150 doi: 10.1016/j.neuron.2009.12.009
[5]	Eldred K C, Hadyniak S E, Hussey K A, et al. Thyroid hormone signaling specifies cone subtypes in human retinal organoids. Science, 2018, 362, eaau6348 doi: 10.1126/science.aau6348
[6]	Tong F. Primary visual cortex and visual awareness. Nat Rev Neurosci, 2003, 4, 219 doi: 10.1038/nrn1055
[7]	Han X, Xu Z S, Wu W Q, et al. Recent progress in optoelectronic synapses for artificial visual-perception system. Small Struct, 2020, 1, 2000029 doi: 10.1002/sstr.202000029
[8]	Wang Y, Lv Z Y, Chen J R, et al. Photonic synapses based on inorganic perovskite quantum dots for neuromorphic computing. Adv Mater, 2018, 30, 1802883 doi: 10.1002/adma.201802883
[9]	Radovic A, Williams M, Rousseau D, et al. Machine learning at the energy and intensity frontiers of particle physics. Nature, 2018, 560, 41 doi: 10.1038/s41586-018-0361-2
[10]	Xia Y, Zhang C, Xu Z, et al. Organic iontronic memristors for artificial synapses and bionic neuromorphic computing. Nanoscale, 2024, 16, 1471 doi: 10.1039/D3NR06057H
[11]	Sun H L, Liu T, Yu J W, et al. A monothiophene unit incorporating both fluoro and ester substitution enabling high-performance donor polymers for non-fullerene solar cells with 16.4% efficiency. Energy Environ Sci, 2019, 12, 3328 doi: 10.1039/C9EE01890E
[12]	Perdigón-Toro L, Zhang H T, Markina A, et al. Barrierless free charge generation in the high-performance PM6 : Y6 bulk heterojunction non-fullerene solar cell. Adv Mater, 2020, 32, 1906763 doi: 10.1002/adma.201906763
[13]	Zeng L J, Liu G, Zhang B, et al. Synthesis and memory performance of a conjugated polymer with an integrated fluorene, carbazole and oxadiazole backbone. Polym J, 2012, 44, 257 doi: 10.1038/pj.2011.114
[14]	Röhr J A, Shi X Y, Haque S A, et al. Charge transport in spiro-OMeTAD investigated through space-charge-limited current measurements. Phys Rev Applied, 2018, 9, 044017 doi: 10.1103/PhysRevApplied.9.044017
[15]	Lv Z Y, Hu Q K, Xu Z X, et al. Organic memristor utilizing copper phthalocyanine nanowires with infrared response and cation regulating properties. Adv Electron Mater, 2019, 5, 1800793 doi: 10.1002/aelm.201800793
[16]	Lv Z Y, Wang Y, Chen Z H, et al. Phototunable biomemory based on light-mediated charge trap. Adv Sci, 2018, 5, 1800714 doi: 10.1002/advs.201800714
[17]	Xing B H, Gao R Z, Wu M, et al. Differentiation on crystallographic orientation dependence of hydrogen diffusion in α-Fe and γ-Fe: DFT calculation combined with SKPFM analysis. Appl Surf Sci, 2023, 615, 156395 doi: 10.1016/j.apsusc.2023.156395
[18]	Xie D D, Gao G, Tian B B, et al. Porous metal–organic framework/ReS₂ heterojunction phototransistor for polarization-sensitive visual adaptation emulation. Adv Mater, 2023, 35, 2212118 doi: 10.1002/adma.202212118
[19]	Xie D D, Li Y Z, He J, et al. 0D-carbon-quantum-dots/2D-MoS₂ mixed-dimensional heterojunction transistor for emulating pulsatile photoelectric therapy of visual amnesic behaviors. Sci China Mater, 2023, 66, 4814 doi: 10.1007/s40843-023-2638-y
[20]	Xie P S, Huang Y L, Wang W, et al. Ferroelectric P(VDF-TrFE) wrapped InGaAs nanowires for ultralow-power artificial synapses. Nano Energy, 2022, 91, 106654 doi: 10.1016/j.nanoen.2021.106654

24080018Supporting_Information.pdf

Search

GET CITATION

shu

Export: BibTex EndNote

Article Metrics

Article views: 817 Times PDF downloads: 100 Times Cited by: 0 Times

History

Received: 12 July 2024 Revised: 25 July 2024 Online: Accepted Manuscript: 11 September 2024Uncorrected proof: 11 September 2024Published: 15 February 2025

Article Navigation > Journal of Semiconductors > 2025 > 46(2): 022403

Xiangrong Pu, Fan Shu, Qifan Wang, Gang Liu, Zhang Zhang. Visual synapse based on reconfigurable organic photovoltaic cell[J]. Journal of Semiconductors, 2025, 46(2): 022403. doi: 10.1088/1674-4926/24080018 ****X R Pu, F Shu, Q F Wang, G Liu, and Z Zhang, Visual synapse based on reconfigurable organic photovoltaic cell[J]. J. Semicond., 2025, 46(2), 022403 doi: 10.1088/1674-4926/24080018

Citation:

Xiangrong Pu, Fan Shu, Qifan Wang, Gang Liu, Zhang Zhang. Visual synapse based on reconfigurable organic photovoltaic cell[J]. Journal of Semiconductors, 2025, 46(2): 022403. doi: 10.1088/1674-4926/24080018 ****

X R Pu, F Shu, Q F Wang, G Liu, and Z Zhang, Visual synapse based on reconfigurable organic photovoltaic cell[J]. J. Semicond., 2025, 46(2), 022403 doi: 10.1088/1674-4926/24080018

Citation:

X R Pu, F Shu, Q F Wang, G Liu, and Z Zhang, Visual synapse based on reconfigurable organic photovoltaic cell[J]. J. Semicond., 2025, 46(2), 022403 doi: 10.1088/1674-4926/24080018

PDF( 13180 KB)

Visual synapse based on reconfigurable organic photovoltaic cell

DOI: 10.1088/1674-4926/24080018

CSTR: 32376.14.1674-4926.24080018

Xiangrong Pu^{1, §},
Fan Shu^{2, §},
Qifan Wang¹,
Gang Liu^2,,
Zhang Zhang^1,

1.
School of Microelectronics, Hefei University of Technology, Hefei 230009, China
2.
National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai 200240, China

More Information

Xiangrong Pu is currently a Ph.D. student with the School of Microelectronics, Hefei University of Technology. His research interests include deep learning and mixed signal IC design
Gang Liu is currently a professor at Shanghai Jiao Tong University. After receiving his Ph.D. degree from the National University of Singapore in 2010, he worked at the Nanyang Technological University, the National University of Singapore and the Chinese Academy of Sciences between January 2010 and November 2018. In December 2018, he joined the School of Electronic Information and Electrical Engineering, SJTU. His research interests focus on memristor devices and neuromrphic computing
Zhang Zhang received the B.S. degree in electronic science and technology from the Hefei University of Technology, Hefei, China, in 2004, and the Ph.D. degree in microelectronics from Fudan University, Shanghai, China, in 2010. He was a Visiting Scholar with the Georgia Institute of Technology from 2016 to 2017. He is currently a Professor with the School of Microelectronics, Hefei University of Technology. His research interests include deep learning and mixed signal IC design
Corresponding author: gang.liu@sjtu.edu.cn; zhanzhang@hfut.edu.cn
Received Date: 2024-07-12
Revised Date: 2024-07-25

Available Online: 2024-09-11

Abstract

Abstract

The hierarchical and coordinated processing of visual information by the brain demonstrates its superior ability to minimize energy consumption and maximize signal transmission efficiency. Therefore, it is crucial to develop artificial visual synapses that integrate optical sensing and synaptic functions. This study fully leverages the excellent photoresponsivity properties of the PM6 : Y6 system to construct a vertical photo-tunable organic memristor and conducts in-depth research on its resistive switching performance, photodetection capability, and simulation of photo-synaptic behavior, showcasing its excellent performance in processing visual information and simulating neuromorphic behaviors. The device achieves stable and gradual resistance change, successfully simulating voltage-controlled long-term potentiation/depression (LTP/LTD), and exhibits various photo-electric synergistic regulation of synaptic plasticity. Moreover, the device has successfully simulated the image perception and recognition functions of the human visual nervous system. The non-volatile Au/PM6 : Y6/ITO memristor is used as an artificial synapse and neuron modeling, building a hierarchical coordinated processing SLP-CNN cascade neural network for visual image recognition training, its linear tunable photoconductivity characteristic serves as the weight update of the network, achieving a recognition accuracy of up to 93.4%. Compared with the single-layer visual target recognition model, this scheme has improved the recognition accuracy by 19.2%.
- organic memristor,
- visual synapse,
- neuromorphic computing,
- PM6 : Y6,
- image recognition

Supplementary materials to this article can be found online at https://doi.org/10.1088/1674-4926/24080018.
^§Xiangrong Pu and Fan Shu contributed equally to this work and should be considered as co-first authors.

FullText(HTML)

References(20)