J. Semicond. > 2022, Volume 43 > Issue 5 > 051201, doi: 10.1088/1674-4926/43/5/051201
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REVIEWS

Multifunctional neurosynaptic devices for human perception systems

Wei Wen1, 2, Yunlong Guo1, 2, and Yunqi Liu1, 2,

+ Author Affiliations

 Corresponding author: Yunlong Guo, guoyunlong@iccas.ac.cn; Yunqi Liu, liuyq@iccas.ac.cn

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Abstract: The traditional Von Neumann architecture for processing information is difficult to meet the needs of the big data era, while low-power, small-sized neurosynaptic devices can operate and store information, so that they have received extensive attention. Due to the development of artificial intelligence and robotics, neurosynaptic devices have been given high expectations and requirements. The trend of functionalization, intelligence, and integration of computing and storage is obvious. In this review, the basic principles and types of neurosynaptic devices are summarized, the achievements of neurosynaptic devices for human perception systems are discussed and a prospect on the development trend is also given.

Key words: neurosynaptic devices, memristors, transistors, human perception system



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Fig. 1.  (Color online) The working mechanisms of two-terminal memristors. (a) Electrochemical metallization mechanism. (b) Valence change mechanism. (c) Phase change mechanism. (d) Ferroelectric mechanism.

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Fig. 2.  (Color online) The types of three-terminal synaptic transistors. (a) Floating-gate transistors. (b) Electrolyte-gate transistors. (c) Ferroelectric field-effect transistors. (d) Optoelectronic synaptic transistors.

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Fig. 3.  (Color online) (a) Schematic diagram of phototransistor structure. (b) Schematic diagram of the heterojunction band before and after light. (c) Sensor array chip, wires bonding on printed circuit board (scale bar: 5mm). (d) Optical micrograph of 32 × 32 sensor array (scale bar: 500 μm). (e) Measured training weight results of a number 8 pattern in the initial state and after training under 405 nm light with a lighting power density of 1 μW/cm2 (pulse width, 250 ms; pulse interval, 250 ms). (f) Measured training weight results of the sensor array after training with 10 pulses under a 405 nm light with various lighting power densities (pulse width, 250 ms; pulse interval, 250 ms). (g) Simulation results of a man’s face in the initial state and after training processes. Reproduced with permission[33]. Copyright 2021, Springer Nature.

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Fig. 4.  (Color online) (a) Schematic diagram of bioinspired visual memory unit integrated by image sensor and storage device. (b) Characteristic I–V curve of image sensor, memristor and bioinspired visual memory unit. (c) Imaging and memory behavior of flexible visual memory array. Reproduced with permission[32]. Copyright 2018, Wiley-VCH.

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Fig. 5.  (Color online) Comparison of the artificial afferent nervous system and biological afferent nervous system. (a) Biological afferent nerve stimulated by pressure. (b) An artificial afferent nerve made of pressure sensor, organic ring oscillator and synaptic transistor. (c) A photograph of an artificial afferent nerve system. Reproduced with permission[42]. Copyright 2021, American Association for the Advancement of Science.

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Fig. 6.  (Color online) (a) Schematic diagram of flexible ferroelectric organic field-effect transistor structure. (b) Schematic diagram of 2 × 2 sensor array of artificial tactile nerve. (c) Infer the order of touch in the 2 × 2 sensor array based on synaptic weight. Reproduced with permission. Copyright 2020[43]. Springer Nature.

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Fig. 7.  (Color online) (a) Schematic illustration of the human auditory pathway. (b) Basic structure scheme of the TENG acoustic receptor. (c) A schematic configuration of the acoustic synaptic transistor and the acoustic processing with neuromorphic function. Reproduced with permission[48]. Copyright 2020, Elsevier. (d) Schematics of biological synapse and structure of synaptic transistor. (e) A circuit diagram of an auditory nerve system. Reproduced with permission[49]. Copyright 2019, Elsevier.

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Fig. 8.  (Color online) (a) Schematic diagram of biological synapse and p–i–n JST. (b) Postsynaptic current of p–i–n JST under negative and positive pulses mimics the different functions of dopamine and acetylcholine in the nervous system: excitement response and memory formation. (c) Schematic diagram of high salt aversion and low salt attraction caused by the synergy of different gustatory receptor neurons. Reproduced with permission[52]. Copyright 2018, Wiley-VCH.

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Fig. 9.  (Color online) (a) Schematic diagram of an artificial olfactory inference system based on an RC system and a classifier for gas classification. (b) Temporal responses of the memristive devices to the spike trains of the response speeds. (c) Temporal responses of the memristive devices to the spike trains of the sensing responses. The complete output is segmented at 0.15 s interval, as presented in the inset of (b). (d) Classification accuracy for testing samples with two types of artificial synapses, that is, ideal and WO3-based ones. (e–g) Classification accuracy for testing samples with reduced (e) spatial, (f) temporal, and (g) spatial and temporal dimensions. Reproduced with permission[54]. Copyright 2021, Wiley-VCH.

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Table 1.   Multifunctional neurosynaptic devices shaping the human perception system.

ChannelElectrolyteElectrode(a)Mechanism(b)ApplicationRef.
P(IID-BT)P(VDF-TrFE)/ P(VP-EDMAEMAES)Au/Au/AlFeFETVisual system[56]
PEDOT:PSS/PEINafionITO/ITO/PEDOT:PSSEGTVisual system[57]
PIIDPMMA-MAAAu/Au/AlFGTVisual system[58]
α-MoO3LiClO4/PEO(Cr/Au)/(Cr/Au)/SiECTVisual system[59]
WSe2h-BN(Pt/Au)/(Pt/Au)/(Ti/Au)OSTVisual system[60]
IGZOSAOIZO/IZO/CrECTVisual system[61]
IGZOHfZrOxAl/Al/TiNFeFETVisual system[62]
PentaceneCDs/silk/SiO2Au/Au/SiFGTVisual system[63]
IGZOGO/P(VDF-HFP)/ (EMIM)(TFSI)Au/Au/AuFGTVisual system[64]
CNTSiO2/Au/SiO2Ti/Pd/PdFGTVisual system[65]
CH3NH3PbI3–Ag/ITOECMVisual system[66]
PDPP3TChitosanAu/Au/AlEDLTSomatosensory system[67]
PentaceneP(VDF-TrFE)/ BT NPsAu/Au/NiFeFETSomatosensory system[43]
Monolayer graphene[EMIM][TFSI]/ PEGDA/HOMPPAu/Au/AuEDLTSomatosensory system[68]
PDVT-10[LI] [TFSI]Au/Au/SiEDLTAuditory system[48]
MoS2SiO2(Cr/Au)/ (Cr/Au)/SiECTAuditory system[69]
PTIIG-NpPS-PMMA-PS /[EMIM][TFSI]Au/Au/SiEDLTAuditory system[49]
PEO/P3HTPMMA/TiO2Au/Au/SiEDLTGustatory system[52]
PCDTPTSiO2Au/Au/Si–Olfactory system[70]
WO3–W/(PEDOT:PSS/Pt)VCMOlfactory system[54]
SWCNTc-PVPAg/Ag/AgEDLTOlfactory system[55]
(a) Type of electrodes. Top electrode/bottom electrode for memristors; source/drain/gate for transistors. (b) Mechanism of devices. ECM: electrochemical metallization mechanism; VCM: valence change mechanism; FGT: floating gate transistor; EDLT: electric double layer transistor; ECT: electrochemical transistor; FeFET: ferroelectric field-effect transistor; OST: Optoelectronic Synaptic Transistor.
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[1]
Neumann J V. First draft of a report on the EDVAC. IEEE Ann Hist Comput, 1993, 15, 27 doi: 10.1109/85.238389
[2]
Drachman D A. Do we have brain to spare. Neurology, 2005, 64, 2004 doi: 10.1212/01.WNL.0000166914.38327.BB
[3]
Kuzum D, Yu S, Wong H S. Synaptic electronics: materials, devices and applications. Nanotechnology, 2013, 24, 382001 doi: 10.1088/0957-4484/24/38/382001
[4]
Wang Z, Wang L, Nagai M, et al. Nanoionics-enabled memristive devices: Strategies and materials for neuromorphic applications. Adv Electron Mater, 2017, 3, 1600510 doi: 10.1002/aelm.201600510
[5]
van de Burgt Y, Melianas A, Keene S T, et al. Organic electronics for neuromorphic computing. Nat Electron, 2018, 1, 386 doi: 10.1038/s41928-018-0103-3
[6]
Zhu J, Zhang T, Yang Y, et al. A comprehensive review on emerging artificial neuromorphic devices. Appl Phys Rev, 2020, 7, 011312 doi: 10.1063/1.5118217
[7]
Abbott L F, Regehr W G. Synaptic computation. Nature, 2004, 431, 796 doi: 10.1038/nature03010
[8]
Yang J J, Pickett M D, Li X, et al. Memristive switching mechanism for metal/oxide/metal nanodevices. Nat Nanotechnol, 2008, 3, 429 doi: 10.1038/nnano.2008.160
[9]
Sun W, Gao B, Chi M, et al. Understanding memristive switching via in situ characterization and device modeling. Nat Commun, 2019, 10, 3453 doi: 10.1038/s41467-019-11411-6
[10]
Yang R, Terabe K, Liu G, et al. On-demand nanodevice with electrical and neuromorphic multifunction realized by local ion migration. ACS Nano, 2012, 6, 9515 doi: 10.1021/nn302510e
[11]
Kim S J, Kim S B, Jang H W. Competing memristors for brain-inspired computing. iScience, 2021, 24, 101889 doi: 10.1016/j.isci.2020.101889
[12]
Scott J F, Paz de Araujo C A. Ferroelectric memories. Science, 1989, 246, 1400 doi: 10.1126/science.246.4936.1400
[13]
Dai S, Zhao Y, Wang Y, et al. Recent advances in transistor-based artificial synapses. Adv Funct Mater, 2019, 29, 1903700 doi: 10.1002/adfm.201903700
[14]
Van Tho L, Baeg K J, Noh Y Y. Organic nano-floating-gate transistor memory with metal nanoparticles. Nano Converg, 2016, 3, 10 doi: 10.1186/s40580-016-0069-7
[15]
Ren Y, Yang J Q, Zhou L, et al. Gate-tunable synaptic plasticity through controlled polarity of charge trapping in fullerene composites. Adv Funct Mater, 2018, 28, 1805599 doi: 10.1002/adfm.201805599
[16]
Liu Z C, Xue F L, Su Y, et al. Memory effect of a polymer thin-film transistor with self-assembled gold nanoparticles in the gate dielectric. IEEE Trans Nanotechnol, 2006, 5, 379 doi: 10.1109/TNANO.2006.876928
[17]
Baeg K J, Noh Y Y, Sirringhaus H, et al. Controllable shifts in threshold voltage of top-gate polymer field-effect transistors for applications in organic nano floating gate memory. Adv Funct Mater, 2010, 20, 224 doi: 10.1002/adfm.200901677
[18]
Talapin D V, Lee J S, Kovalenko M V, et al. Prospects of colloidal nanocrystals for electronic and optoelectronic applications. Chem Rev, 2010, 110, 389 doi: 10.1021/cr900137k
[19]
Kang M, Baeg K J, Khim D, et al. Printed, flexible, organic nano-floating-gate memory: effects of metal nanoparticles and blocking dielectrics on memory characteristics. Adv Funct Mater, 2013, 23, 3503 doi: 10.1002/adfm.201203417
[20]
Wang W, Shi J, Ma D. Organic thin-film transistor memory with nanoparticle floating gate. IEEE Trans Electron Devices, 2009, 56, 1036 doi: 10.1109/TED.2009.2016031
[21]
Joga R. Quantum dot floating gate transistor with multi-wall carbon nano tube channel for non-volatile memory devices. 2012 International Conference on Communication Systems and Network Technologies, 2012, 774 doi: 10.1109/CSNT.2012.169
[22]
Kim S H, Hong K, Xie W, et al. Electrolyte-gated transistors for organic and printed electronics. Adv Mater, 2013, 25, 1822 doi: 10.1002/adma.201202790
[23]
Xu W, Min S Y, Hwang H, et al. Organic core-sheath nanowire artificial synapses with femtojoule energy consumption. Sci Adv, 2016, 2, e1501326 doi: 10.1126/sciadv.1501326
[24]
Yu S. Neuro-inspired computing with emerging nonvolatile memorys. Proc IEEE, 2018, 106, 260 doi: 10.1109/JPROC.2018.2790840
[25]
Martins P, Lanceros-Méndez S. Polymer-based magnetoelectric materials. Adv Funct Mater, 2013, 23, 3371 doi: 10.1002/adfm.201202780
[26]
Benner A F, Ignatowski M, Kash J A, et al. Exploitation of optical interconnects in future server architectures. IBM J Res Dev, 2005, 49, 755 doi: 10.1147/rd.494.0755
[27]
Rosenbluth D, Kravtsov K, Fok M P, et al. A high performance photonic pulse processing device. Opt Express, 2009, 17, 22767 doi: 10.1364/OE.17.022767
[28]
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
[29]
Quiroga R Q, Reddy L, Kreiman G, et al. Invariant visual representation by single neurons in the human brain. Nature, 2005, 435, 1102 doi: 10.1038/nature03687
[30]
Sun F, Lu Q, Feng S, et al. Flexible artificial sensory systems based on neuromorphic devices. ACS Nano, 2021, 15, 3875 doi: 10.1021/acsnano.0c10049
[31]
Zhang J, Dai S, Zhao Y, et al. Recent progress in photonic synapses for neuromorphic systems. Adv Intell Syst, 2020, 2, 1900136 doi: 10.1002/aisy.201900136
[32]
Chen S, Lou Z, Chen D, et al. An artificial flexible visual memory system based on an UV-motivated memristor. Adv Mater, 2018, 30, 1705400 doi: 10.1002/adma.201705400
[33]
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    Received: 20 November 2021 Revised: 19 January 2022 Online: Accepted Manuscript: 08 April 2022Uncorrected proof: 13 April 2022Published: 01 May 2022

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      Article Navigation >  Journal of Semiconductors  > 2022  >  43(5): 051201
      Wei Wen, Yunlong Guo, Yunqi Liu. Multifunctional neurosynaptic devices for human perception systems[J]. Journal of Semiconductors, 2022, 43(5): 051201. doi: 10.1088/1674-4926/43/5/051201 W Wen, Y L Guo, Y Q Liu. Multifunctional neurosynaptic devices for human perception systems[J]. J. Semicond, 2022, 43(5): 051201. doi: 10.1088/1674-4926/43/5/051201Export: BibTex EndNote
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      Wei Wen, Yunlong Guo, Yunqi Liu. Multifunctional neurosynaptic devices for human perception systems[J]. Journal of Semiconductors, 2022, 43(5): 051201. doi: 10.1088/1674-4926/43/5/051201

      W Wen, Y L Guo, Y Q Liu. Multifunctional neurosynaptic devices for human perception systems[J]. J. Semicond, 2022, 43(5): 051201. doi: 10.1088/1674-4926/43/5/051201
      Export: BibTex EndNote
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      Multifunctional neurosynaptic devices for human perception systems

      doi: 10.1088/1674-4926/43/5/051201
      • 1.

        Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China

      • 2.

        University of Chinese Academy of Sciences, Beijing 100049, China

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      • Author Bio:

        Wei Wen got his BS from Zhengzhou University in 2020. Now he is a PhD student at the Institute of Chemistry, Chinese Academy of Sciences under the supervision of Prof. Yunqi Liu. His research focuses on neurosynaptic transistors

        Yunlong Guo received his PhD degree in physical chemistry from the Institute of Chemistry, Chinese Academy of Sciences (ICCAS), in 2010. From 04/2016, he became a project associate professor at the Department of Chemistry, University of Tokyo. Now, he is a professor at ICCAS. His research interest includes organic-inorganic hybrid perovskite electronics and functional organic field-effect transistors

        Yunqi Liu graduated from Nanjing University in 1975, received a doctorate from Tokyo Institute of Technology, Japan in 1991. Presently, he is a professor at the Institute of Chemistry, CAS, and Academician of CAS. His research interests include molecular materials and devices, the synthesis and applications of carbon nanomaterials, and organic electronics

      • Corresponding author: guoyunlong@iccas.ac.cn; liuyq@iccas.ac.cn
      • Received Date: 2021-11-20
      • Revised Date: 2022-01-19
        • Available Online: 2022-04-08

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