SCIENCE CHINA Information Sciences, Volume 64 , Issue 6 : 162401(2021) https://doi.org/10.1007/s11432-020-3035-8

Silicon-based inorganic-organic hybrid optoelectronic synaptic devices simulating cross-modal learning

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  • ReceivedMar 14, 2020
  • AcceptedAug 17, 2020
  • PublishedMar 16, 2021



This work was mainly supported by National Key Research and Development Program of China (Grant Nos. 2017YFA0205704, 2018YFB2200101), Natural Science Foundation of China (Grant Nos. 91964107, 61774133), Fundamental Research Funds for the Central Universities (Grant No. 2018XZZX003-02), partial supported by Natural Science Foundation of China for Innovative Research Groups (Grant No. 61721005), and Zhejiang University Education Foundation Global Partnership Fund.


Figures S1–S3.


[1] Merolla P A, Arthur J V, Alvarez-Icaza R. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 2014, 345: 668-673 CrossRef ADS Google Scholar

[2] Davies M, Srinivasa N, Lin T H. Loihi: A Neuromorphic Manycore Processor with On-Chip Learning. IEEE Micro, 2018, 38: 82-99 CrossRef Google Scholar

[3] Tang J, Yuan F, Shen X. Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges. Adv Mater, 2019, 31: 1902761 CrossRef Google Scholar

[4] Zidan M A, Strachan J P, Lu W D. The future of electronics based on memristive systems. Nat Electron, 2018, 1: 22-29 CrossRef Google Scholar

[5] Kuzum D, Yu S, Wong H-S P. Synaptic electronics: materials, devices and applications. Nanotechnology, 2013, 24: 382001. Google Scholar

[6] Upadhyay N K, Joshi S, Yang J J. Synaptic electronics and neuromorphic computing. Sci China Inf Sci, 2016, 59: 061404 CrossRef Google Scholar

[7] Mao J Y, Zhou L, Zhu X. Photonic Memristor for Future Computing: A Perspective. Adv Opt Mater, 2019, 7: 1900766 CrossRef Google Scholar

[8] Yu F, Zhu L Q. Ionotronic Neuromorphic Devices for Bionic Neural Network Applications. Phys Status Solidi RRL, 2019, 13: pssr.201800674 CrossRef ADS Google Scholar

[9] Zhuge X, Wang J, Zhuge F. Photonic Synapses for Ultrahigh-Speed Neuromorphic Computing. Phys Status Solidi RRL, 2019, 13: 1900082 CrossRef ADS Google Scholar

[10] Dai S, Zhao Y, Wang Y. Recent Advances in Transistor?Based Artificial Synapses. Adv Funct Mater, 2019, 29: 1903700 CrossRef Google Scholar

[11] Atabaki A H, Moazeni S, Pavanello F. Integrating photonics with silicon nanoelectronics for the next generation of systems on a chip. Nature, 2018, 556: 349-354 CrossRef ADS Google Scholar

[12] Thomson D, Zilkie A, Bowers J E. Roadmap on silicon photonics. J Opt, 2016, 18: 073003 CrossRef ADS Google Scholar

[13] Rosenbluth D, Kravtsov K, Fok M P. A high performance photonic pulse processing device. Opt Express, 2009, 17: 22767-22772 CrossRef ADS Google Scholar

[14] Tan H, Ni Z, Peng W. Broadband optoelectronic synaptic devices based on silicon nanocrystals for neuromorphic computing. Nano Energy, 2018, 52: 422-430 CrossRef Google Scholar

[15] Ni Z, Wang Y, Liu L, et al. Hybrid structure of silicon nanocrystals and 2D WSe2 for broadband optoelectronic synaptic devices. In: 2018 IEEE International Electron Devices Meeting (IEDM), San Francisco, 2018. 1--4. Google Scholar

[16] Zhao S, Ni Z, Tan H. Electroluminescent synaptic devices with logic functions. Nano Energy, 2018, 54: 383-389 CrossRef Google Scholar

[17] Yin L, Han C, Zhang Q. Synaptic silicon-nanocrystal phototransistors for neuromorphic computing. Nano Energy, 2019, 63: 103859 CrossRef Google Scholar

[18] Li Y Y, Wang Y, Yang D R, et al. Recent progress on optoelectronic synaptic devices (in Chinese). Sci Sin Inform (in press). Google Scholar

[19] Chen B, Yu Z, Liu K. Grain Engineering for Perovskite/Silicon Monolithic Tandem Solar Cells with Efficiency of 25.4%. Joule, 2019, 3: 177-190 CrossRef Google Scholar

[20] Ni Z, Ma L, Du S. Plasmonic Silicon Quantum Dots Enabled High-Sensitivity Ultrabroadband Photodetection of Graphene-Based Hybrid Phototransistors. ACS Nano, 2017, 11: 9854-9862 CrossRef Google Scholar

[21] Chen S, Li W, Wu J. Electrically pumped continuous-wave III-V quantum dot lasers on silicon. Nat Photon, 2016, 10: 307-311 CrossRef ADS Google Scholar

[22] Qin S, Wang F, Liu Y. A light-stimulated synaptic device based on graphene hybrid phototransistor. 2D Mater, 2017, 4: 035022 CrossRef ADS arXiv Google Scholar

[23] Wang Y, Lv Z, Chen J. Photonic Synapses Based on Inorganic Perovskite Quantum Dots for Neuromorphic Computing. Adv Mater, 2018, 30: 1802883 CrossRef Google Scholar

[24] Wang S, Chen C, Yu Z. Adv Mater, 2019, 31: 1806227 CrossRef Google Scholar

[25] Wang K, Dai S, Zhao Y. Light?§timulated Synaptic Transistors Fabricated by a Facile Solution Process Based on Inorganic Perovskite Quantum Dots and Organic Semiconductors. Small, 2019, 15: 1900010 CrossRef Google Scholar

[26] Wang R, Wang Y, Wu C. Direct Observation of Conductive Polymer Induced Inversion Layer in n?§i and Correlation to Solar Cell Performance. Adv Funct Mater, 2020, 30: 1903440 CrossRef Google Scholar

[27] Mao J Y, Hu L, Zhang S-R, et al. Artificial synapses emulated through a light mediated organic-inorganic hybrid transistor. J Mater Chem C, 2019, 7: 48--59. Google Scholar

[28] Fang L, Dai S, Zhao Y. Light?§timulated Artificial Synapses Based on 2D Organic Field?Effect Transistors. Adv Electron Mater, 2020, 6: 1901217 CrossRef Google Scholar

[29] Wu X, Chu Y, Liu R. Pursuing Polymer Dielectric Interfacial Effect in Organic Transistors for Photosensing Performance Optimization. Adv Sci, 2017, 4: 1700442 CrossRef Google Scholar

[30] Dai S, Wu X, Liu D. Light-Stimulated Synaptic Devices Utilizing Interfacial Effect of Organic Field-Effect Transistors. ACS Appl Mater Interfaces, 2018, 10: 21472-21480 CrossRef Google Scholar

[31] Arias A C, MacKenzie J D, McCulloch I. Materials and Applications for Large Area Electronics: Solution-Based Approaches. Chem Rev, 2010, 110: 3-24 CrossRef Google Scholar

[32] Berger P R, Kim M. Polymer solar cells: P3HT:PCBM and beyond. J Renew Sustain Energy, 2018, 10: 013508 CrossRef Google Scholar

[33] Zhao S, Pi X, Mercier C. Silicon-nanocrystal-incorporated ternary hybrid solar cells. Nano Energy, 2016, 26: 305-312 CrossRef Google Scholar

[34] Shalu C, Yadav N, Bhargava K. All organic near ultraviolet photodetectors based on bulk hetero-junction of P3HT and DH6T. Semicond Sci Technol, 2018, 33: 095021 CrossRef ADS Google Scholar

[35] Zhao S, Wang Y, Huang W. Developing near-infrared quantum-dot light-emitting diodes to mimic synaptic plasticity. Sci China Mater, 2019, 62: 1470-1478 CrossRef Google Scholar

[36] Chu Y, Wu X, Lu J. Photosensitive and Flexible Organic Field-Effect Transistors Based on Interface Trapping Effect and Their Application in 2D Imaging Array. Adv Sci, 2016, 3: 1500435 CrossRef Google Scholar

[37] Yogev S, Matsubara R, Nakamura M. Fermi Level Pinning by Gap States in Organic Semiconductors. Phys Rev Lett, 2013, 110: 036803 CrossRef ADS Google Scholar

[38] Saidaminov M I, Adinolfi V, Comin R. Planar-integrated single-crystalline perovskite photodetectors. Nat Commun, 2015, 6: 8724 CrossRef ADS Google Scholar

[39] Yang Y, Lisberger S G. Purkinje-cell plasticity and cerebellar motor learning are graded by complex-spike duration. Nature, 2014, 510: 529-532 CrossRef ADS Google Scholar

[40] Alibart F, Pleutin S, Guérin D. An Organic Nanoparticle Transistor Behaving as a Biological Spiking Synapse. Adv Funct Mater, 2010, 20: 330-337 CrossRef Google Scholar

[41] Horii Y, Ikawa M, Sakaguchi K. Investigation of self-assembled monolayer treatment on SiO2 gate insulator of poly(3-hexylthiophene) thin-film transistors. Thin Solid Films, 2009, 518: 642-646 CrossRef ADS Google Scholar

[42] Zucker R S, Regehr W G. Short-Term Synaptic Plasticity. Annu Rev Physiol, 2002, 64: 355-405 CrossRef Google Scholar

[43] Jackman S L, Regehr W G. The Mechanisms and Functions of Synaptic Facilitation. Neuron, 2017, 94: 447-464 CrossRef Google Scholar

[44] Gkoupidenis P, Koutsouras D A, Malliaras G G. Neuromorphic device architectures with global connectivity through electrolyte gating. Nat Commun, 2017, 8: 15448 CrossRef ADS Google Scholar

[45] Lerch M M, Grinthal A, Aizenberg J. Viewpoint: Homeostasis as Inspiration-Toward Interactive Materials. Adv Mater, 2020, 32: 1905554 CrossRef Google Scholar

[46] Srinivasan M V, Zhang S W, Zhu H. Honeybees link sights to smells. Nature, 1998, 396: 637--638. Google Scholar

[47] Steward O. Functional neuroscience. Trends Neurosci, 2000, 23: 12. Google Scholar

[48] Guo J. Crossmodal Interactions Between Olfactory and Visual Learning in Drosophila. Science, 2005, 309: 307-310 CrossRef ADS Google Scholar

[49] Alquraishi W, Fu Y, Qiu W. Hybrid optoelectronic synaptic functionality realized with ion gel-modulated In2O3 phototransistors. Org Electron, 2019, 71: 72-78 CrossRef Google Scholar

[50] Jiang J, Hu W, Xie D. 2D electric-double-layer phototransistor for photoelectronic and spatiotemporal hybrid neuromorphic integration. Nanoscale, 2019, 11: 1360-1369 CrossRef Google Scholar

[51] Yin L, Huang W, Xiao R. Optically Stimulated Synaptic Devices Based on the Hybrid Structure of Silicon Nanomembrane and Perovskite. Nano Lett, 2020, 20: 3378-3387 CrossRef ADS Google Scholar

[52] Huang W, Hang P, Wang Y. Zero-power optoelectronic synaptic devices. Nano Energy, 2020, 73: 104790 CrossRef Google Scholar

  • Figure 1

    (Color online) Schematic illustrations of (a) a biological synapse and (b) an artificial Si/P3HT-based synaptic transistor. Inset: optical microscope image of the device. (c) UV-visible absorption spectrum of P3HT. (d) Atomic force microscope (AFM) images of a P3HT thin film. Inset: the thickness of the P3HT thin film measured via AFM line profiling. (e) Output curves of a typical synaptic transistor. (f) Transfer curves of the synaptic transistor obtained at $V_{\rm~D}$ = 5 V in darkness and under optical illumination at ($\lambda$) 532 nm and a power density of 3.5 mW/cm$^2$.

  • Figure 2

    (Color online) (a) EPSC of a synaptic transistor induced by a single optical spike ($\lambda$ = 532 nm) at $V_{\rm~D}$ = 5 V and $V_{\rm~G}$ = 0 V. The power density and duration of the optical pulse were 3.5 mW/cm$^2$ and 200 ms, respectively. (b) Dependence of the EPSC$_{\rm~max}$ on optical pulse duration. (c) EPSC induced by two consecutive optical pulses separated by a 300 ms pulse interval ($\Delta~t$). (d) Dependence of the PPF index on $\Delta~t$. (e) EPSC induced by 10 consecutive optical pulses. (f) Dependence of A$_{10}$/A$_1$ on $V_{\rm~G}$ at $V_{\rm~D}$ = 5 V.

  • Figure 3

    (Color online) (a) EPSC induced by a positive electrical spike (0.1 V) and IPSC induced by a negative electrical spike ($-0.1$ V) applied to the Si back gate of a synaptic transistor for 200 ms ($V_{\rm~D}$ = 5 V). (b) Dependence of the EPSC$_{\rm~max}$ and IPSC$_{\rm~max}$ on electrical pulse duration. (c) PPFs of a synaptic transistor stimulated with two consecutive positive (0.1 V) or negative ($-0.1$ V) electrical pulses ($\Delta~t$ = 200 ms). (d) Dependence of the PPF index on the $\Delta~t$ of electrical stimulation. (e) EPSC induced by consecutive positive (0.1 V) electrical pulses with durations of 500 ms, where pulse quantity $i$ = 5, 10, and 15. (f) EPSC induced by 10 positive (0.1 V) electrical pulses of 500 ms each at frequencies of 0.67, 1, and 1.67 Hz.

  • Figure 4

    (Color online) (a) Schematic illustration of spatiotemporal synaptic integration. The optical and electrical pulses correspond to visual presynaptic neuron and olfactory presynaptic neuron stimulation, respectively. The power density and duration of the optical pulse are 1.5 W/cm$^2$ and 200 ms, respectively. The electrical pulse has a voltage of 0.5 V and a duration of 200 ms. (b) EPSC with intervals ($\Delta~T$) of $-1$ s, $-0.2$ s, 0 s, and 1 s between visual and olfactory stimulation. (c) Dependence of perception (EPSC$_{\rm~max}$) on $\Delta~T$.