뉴로모픽 시스템용 시냅스 트랜지스터의 최근 연구 동향

  • 남재현 (충북대학교 신소재공학과) ;
  • 장혜연 (충북대학교 신소재공학과) ;
  • 김태현 (충북대학교 신소재공학과) ;
  • 조병진 (충북대학교 신소재공학과)
  • 발행 : 2018.06.30

초록

Lastly, neuromorphic computing chip has been extensively studied as the technology that directly mimics efficient calculation algorithm of human brain, enabling a next-generation intelligent hardware system with high speed and low power consumption. Three-terminal based synaptic transistor has relatively low integration density compared to the two-terminal type memristor, while its power consumption can be realized as being so low and its spike plasticity from synapse can be reliably implemented. Also, the strong electrical interaction between two or more synaptic spikes offers the advantage of more precise control of synaptic weights. In this review paper, the results of synaptic transistor mimicking synaptic behavior of the brain are classified according to the channel material, in order of silicon, organic semiconductor, oxide semiconductor, 1D CNT(carbon nanotube) and 2D van der Waals atomic layer present. At the same time, key technologies related to dielectrics and electrolytes introduced to express hysteresis and plasticity are discussed. In addition, we compared the essential electrical characteristics (EPSC, IPSC, PPF, STM, LTM, and STDP) required to implement synaptic transistors in common and the power consumption required for unit synapse operation. Generally, synaptic devices should be integrated with other peripheral circuits such as neurons. Demonstration of this neuromorphic system level needs the linearity of synapse resistance change, the symmetry between potentiation and depression, and multi-level resistance states. Finally, in order to be used as a practical neuromorphic applications, the long-term stability and reliability of the synapse device have to be essentially secured through the retention and the endurance cycling test related to the long-term memory characteristics.

키워드

참고문헌

  1. Gao, B. et al. Ultra-Low-Energy Three-Dimensional Oxide-Based Electronic Synapses for Implementation of Robust High-Accuracy Neuromorphic Computation Systems. ACS Nano 8, 6998-7004 (2014). https://doi.org/10.1021/nn501824r
  2. Kim, K., Chen, C. L., Truong, Q., Shen, A. M. & Chen, Y. A Carbon Nanotube Synapse with Dynamic Logic and Learning. Adv. Mater. 25, 1693-1698 (2013). https://doi.org/10.1002/adma.201203116
  3. Shen, A. M. et al. Analog Neuromorphic Module Based on Carbon Nanotube Synapses. ACS Nano 7, 6117-6122 (2013). https://doi.org/10.1021/nn401946s
  4. Cho, B. et al. Nonvolatile Analog Memory Transistor Based on Carbon Nanotubes and C60 Molecules. Small 9, 2283-2287 (2013). https://doi.org/10.1002/smll.201202593
  5. Strukov, D. B., Snider, G. S., Stewart, D. R. & Williams, R. S. The Missing Memristor Found. Nature 453, 80-83 (2008). https://doi.org/10.1038/nature06932
  6. Cheng, P., Sun, K. & Hu, Y. H. Memristive Behavior and Ideal Memristor of 1T Phase $MoS_2$ Nanosheets. Nano Lett. 16, 572-576 (2016). https://doi.org/10.1021/acs.nanolett.5b04260
  7. Chen, C. L. et al. A Spiking Neuron Circuit Based on a Carbon Nanotube Transistor. Nanotechnology 23, 275202-275207 (2012). https://doi.org/10.1088/0957-4484/23/27/275202
  8. Yu, F. et al. Chitosan-Based Polysaccharide Gated Flexible Indium-Tin-Oxide Synaptic Transistor with Learning Abilities. ACS Appl. Mater. Interfaces 10, 16881-16886 (2018). https://doi.org/10.1021/acsami.8b03274
  9. Yang, C. S. et al. A Synaptic Transistor Based on Quasi-2D Molybdenum Oxide. Adv. Mater. 29, 1700906 (2017). https://doi.org/10.1002/adma.201700906
  10. Zhou, J., Wan, C., Zhu, L., Shi, Y. & Wan, Q. Synaptic Behaviors Mimicked in Flexible Oxide-Based Transistors on Plastic Substrates. IEEE Electron Device Lett. 34, 1433-1435 (2013). https://doi.org/10.1109/LED.2013.2280663
  11. Wang, J., Li, Y., Yang, Y. & Ren, T. L. Top-Gate Electric-Double-Layer IZO-Based Synaptic Transistors for Neuron Networks. IEEE Electron Device Lett. 38, 588-591 (2017). https://doi.org/10.1109/LED.2017.2690278
  12. Park, J. et al. Compact Neuromorphic System with Four-Terminal Si-Based Synaptic Devices for Spiking Neural Networks. IEEE Trans. Electron Devices 64, 2438-2444 (2017). https://doi.org/10.1109/TED.2017.2685519
  13. Ziegler, M. & Kohlstedt, H. Mimic Synaptic Behavior with a Single Floating Gate Transistor: A MemFlash Synapse. J. Appl. Phys. 114, 194506 (2013). https://doi.org/10.1063/1.4832334
  14. Kim, H. et al. Silicon-Based Floating-Body Synaptic Transistor with Frequency-Dependent Short-and Long-Term Memories. IEEE Electron Device Lett. 37, 249-252 (2016). https://doi.org/10.1109/LED.2016.2521863
  15. Park, J., Kwon, M.-W., Kim, H. & Park, B.-G. Neuromorphic System Based on CMOS Inverters and Si-Based Synaptic Device. J. Nanosci. Nanotechnol. 16, 4709-4712 (2016). https://doi.org/10.1166/jnn.2016.12234
  16. Liu, M. et al. Artificial Neuron Synapse Transistor Based on Silicon Nanomembrane on Plastic Substrate. J. Semicond. 38, 064006 (2017). https://doi.org/10.1088/1674-4926/38/6/064006
  17. Gkoupidenis, P., Schaefer, N., Garlan, B. & Malliaras, G. G. Neuromorphic Functions in PEDOT:PSS Organic Electrochemical Transistors. Adv. Mater. 27, 7176-7180 (2015). https://doi.org/10.1002/adma.201503674
  18. Xu, W., Min, S.-Y., Hwang, H. & Lee, T.-W. Organic Core-Sheath Nanowire Artificial Synapses with Femtojoule Energy Consumption. Sci. Adv. 2, 1501326 (2016). https://doi.org/10.1126/sciadv.1501326
  19. Keene, S. T. et al. Optimized Pulse Write Schemes Improve Linearity and Write Speed for Low-Power Organic Neuromorphic Devices. J. Phys. D: Appl. Phys. 51, 224002 (2018). https://doi.org/10.1088/1361-6463/aabe70
  20. Gkoupidenis, P., Schaefer, N., Strakosas, X., Fairfield, J. A. & Malliaras, G. G. Synaptic Plasticity Functions in an Organic Electrochemical Transistor. Appl. Phys. Lett. 107, 263302 (2015). https://doi.org/10.1063/1.4938553
  21. Di Lauro, M. et al. Liquid-Gated Organic Electronic Devices Based on High-Performance Solution-Processed Molecular Semiconductor. Adv. Electron. Mater. 3, 1700159 (2017). https://doi.org/10.1002/aelm.201700159
  22. Kong, L.-an et al. Long-Term Synaptic Plasticity Simulated in Ionic Liquid/Polymer Hybrid Electrolyte Gated Organic Transistors. Org. Electron. 47, 126-132 (2017). https://doi.org/10.1016/j.orgel.2017.05.017
  23. Qian, C. et al. Artificial Synapses Based on in-Plane Gate Organic Electrochemical Transistors. ACS Appl. Mater. Interfaces 8, 26169-26175 (2016). https://doi.org/10.1021/acsami.6b08866
  24. Kim, C. H., Sung, S. & Yoon, M. H. Synaptic Organic Transistors with a Vacuum-Deposited Charge-Trapping Nanosheet. Sci. Rep. 6, 33355 (2016). https://doi.org/10.1038/srep33355
  25. Zang, Y., Shen, H., Huang, D., Di, C.-A. & Zhu, D. A Dual-Organic-Transistor-Based Tactile-Perception System with Signal-Processing Functionality. Adv. Mater. 29, 1606088 (2017). https://doi.org/10.1002/adma.201606088
  26. 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. 5, 3158 (2014). https://doi.org/10.1038/ncomms4158
  27. Kaneko, Y., Nishitani, Y. & Ueda, M. Ferroelectric Artificial Synapses for Recognition of a Multishaded Image. IEEE Trans. Electron Devices 61, 2827-2833 (2014). https://doi.org/10.1109/TED.2014.2331707
  28. Guo, L., Wan, Q., Wan, C., Zhu, L. & Shi, Y. Short-Term Memory to Long-Term Memory Transition Mimicked in IZO Homojunction Synaptic Transistors. IEEE Electron Device Lett. 34, 1581-1583 (2013). https://doi.org/10.1109/LED.2013.2286074
  29. Guo, Z., Guo, L., Zhu, L. & Zhu, Y. Short-Term Synaptic Plasticity Mimicked on Ionic/Electronic Hybrid Oxide Synaptic Transistor Gated by Nanogranular $SiO_2$ Films. J. Mater. Sci. Technol. 30, 1141-1144 (2014). https://doi.org/10.1016/j.jmst.2014.04.015
  30. Wu, G., Wan, C., Zhou, J., Zhu, L. & Wan, Q. Low-Voltage Protonic/Electronic Hybrid Indium Zinc Oxide Synaptic Transistors on Paper Substrates. Nanotechnology 25, 094001 (2014). https://doi.org/10.1088/0957-4484/25/9/094001
  31. Wu, G., Zhang, J., Wan, X., Yang, Y. & Jiang, S. Chitosan-Based Biopolysaccharide Proton Conductors for Synaptic Transistors on Paper Substrates. J. Mater. Chem. C 2, 6249-6255 (2014).
  32. Zhou, J., Liu, Y., Shi, Y. & Wan, Q. Solution-Processed Chitosan-Gated IZO-Based Transistors for Mimicking Synaptic Plasticity. IEEE Electron Device Lett. 35, 280-282 (2014). https://doi.org/10.1109/LED.2013.2295815
  33. Liu, R. et al. Biodegradable Oxide Synaptic Transistors Gated by a Biopolymer Electrolyte. J. Mater. Chem. C 4, 7744-7750 (2016). https://doi.org/10.1039/C6TC02693A
  34. Liu, Y. H., Zhu, L. Q., Feng, P., Shi, Y. & Wan, Q. Freestanding Artificial Synapses Based on Laterally Proton-Coupled Transistors on Chitosan Membranes. Adv. Mater. 27, 5599-5604 (2015). https://doi.org/10.1002/adma.201502719
  35. Lu, A., Sun, J., Jiang, J. & Wan, Q. One-Shadow-Mask Self-Assembled Ultralow-Voltage Coplanar Homojunction Thin-Film Transistors. IEEE Electron Device Lett. 31, 1137-1139 (2010). https://doi.org/10.1109/LED.2010.2061834
  36. Wan, C. J., Zhu, L. Q., Zhou, J. M., Shi, Y. & Wan, Q. Memory and Learning Behaviors Mimicked in Nanogranular $SiO_2$-Based Proton Conductor Gated Oxide-Based Synaptic Transistors. Nanoscale 5, 10194 (2013). https://doi.org/10.1039/c3nr02987e
  37. Zhu, L. Q. et al. Multi-Gate Synergic Modulation in Laterally Coupled Synaptic Transistors. Appl. Phys. Lett. 107, 143502 (2015). https://doi.org/10.1063/1.4932568
  38. Zhu, L. Q. et al. Flexible Proton-Gated Oxide Synaptic Transistors on Si Membrane. ACS Appl. Mater. Interfaces 8, 21770-21775 (2016). https://doi.org/10.1021/acsami.6b05167
  39. Kong, L.-an et al. Ion-Gel Gated Field-Effect Transistors with Solution-Processed Oxide Semiconductors for Bioinspired Artificial Synapses. Org. Electron. 39, 64-70 (2016). https://doi.org/10.1016/j.orgel.2016.09.029
  40. Guo, L., Wen, J., Cheng, G., Yuan, N. & Ding, J. Synaptic Behaviors Mimicked in Indium-Zinc-Oxide Transistors Gated by High-Proton-Conducting Graphene Oxide-Based Composite Solid Electrolytes. J. Mater. Chem. C 4, 9762-9770 (2016). https://doi.org/10.1039/C6TC02228F
  41. Wu, G. et al. Artificial Synaptic Devices Based on Natural Chicken Albumen Coupled Electric-Double-Layer Transistors. Sci. Rep. 6, 23578 (2016). https://doi.org/10.1038/srep23578
  42. Wan, C. J., Zhu, L. Q., Wan, X., Shi, Y. & Wan, Q. Organic/Inorganic Hybrid Synaptic Transistors Gated by Proton Conducting Methylcellulose Films. Appl. Phys. Lett. 108, 043508 (2016). https://doi.org/10.1063/1.4941080
  43. Wang, J. et al. Synaptic Computation Demonstrated in a Two-Synapse Network Based on Top-Gate Electric-Double-Layer Synaptic Transistors. IEEE Electron Device Lett. 38, 1496-1499 (2017). https://doi.org/10.1109/LED.2017.2745482
  44. Guo, L. Q., Zhu, L. Q., Ding, J. N. & Huang, Y. K. Paired-Pulse Facilitation Achieved in Protonic/Electronic Hybrid Indium Gallium Zinc Oxide Synaptic Transistors. AIP Adv. 5, 087112 (2015). https://doi.org/10.1063/1.4928386
  45. Zhou, J., Liu, N., Zhu, L., Shi, Y. & Wan, Q. Energy-Efficient Artificial Synapses Based on Flexible IGZO Electric-Double-Layer Transistors. IEEE Electron Device Lett. 36, 198-200 (2015). https://doi.org/10.1109/LED.2014.2381631
  46. Wan, X., Feng, P., Wu, G. D., Shi, Y. & Wan, Q. Simulation of Laterally Coupled InGaZnO4-Based Electric-Double-Layer Transistors for Synaptic Electronics. IEEE Electron Device Lett. 36, 204-206 (2015). https://doi.org/10.1109/LED.2015.2388952
  47. Shao, F., Yang, Y., Zhu, L. Q., Feng, P. & Wan, Q. Oxide-Based Synaptic Transistors Gated by Sol-Gel Silica Electrolytes. ACS Appl. Mater. Interfaces 8, 3050-3055 (2016). https://doi.org/10.1021/acsami.5b10195
  48. Kim, Y.-M., Kim, E.-J., Lee, W.-H., Oh, J.-Y. & Yoon, S.-M. Short-Term and Long-Term Memory Operations of Synapse Thin-Film Transistors Using an In-Ga-Zn-O Active Channel and a Poly(4-Vinylphenol)-Sodium ${\beta}$-Alumina Electrolytic Gate Insulator. RSC Adv. 6, 52913-52919 (2016). https://doi.org/10.1039/C6RA09503H
  49. Wan, C. J. et al. Short-Term Synaptic Plasticity Regulation in Solution-Gated Indium-Gallium-Zinc-Oxide Electric-Double-Layer Transistors. ACS Appl. Mater. Interfaces 8, 9762-9768 (2016). https://doi.org/10.1021/acsami.5b12726
  50. Li, H. K. et al. A Light-Stimulated Synaptic Transistor with Synaptic Plasticity and Memory Functions Based on $InGaZnO_x-Al_2O_3$ Thin Film Structure. J. Appl. Phys. 119, 244505 (2016). https://doi.org/10.1063/1.4955042
  51. Dai, M. et al. Realization of Tunable Artificial Synapse and Memory Based on Amorphous Oxide Semiconductor Transistor. Sci. Rep. 7, 10997 (2017). https://doi.org/10.1038/s41598-017-04641-5
  52. Yang, Y., He, Y., Nie, S., Shi, Y. & Wan, Q. Light Stimulated IGZO-Based Electric-Double-Layer Transistors for Photoelectric Neuromorphic Devices. IEEE Electron Device Lett. 39, 897-900 (2018). https://doi.org/10.1109/LED.2018.2824339
  53. Wang, J. et al. Long-Term Depression Mimicked in an IGZO-Based Synaptic Transistor. IEEE Electron Device Lett. 38, 191-194 (2017). https://doi.org/10.1109/LED.2016.2639539
  54. Yang, P. et al. Synaptic Transistor with a Reversible and Analog Conductance Modulation Using a Pt/HfOx/n-IGZO Memcapacitor. Nanotechnology 28, 225201 (2017). https://doi.org/10.1088/1361-6528/aa6dac
  55. Pillai, P. B. & De Souza, M. M. Nanoionics-Based Three-Terminal Synaptic Device Using Zinc Oxide. ACS Appl. Mater. Interfaces 9, 1609-1618 (2017). https://doi.org/10.1021/acsami.6b13746
  56. Wen, J. et al. Activity Dependent Synaptic Plasticity Mimicked on Indium-Tin-Oxide Electric-Double-Layer Transistor. ACS Appl. Mater. Interfaces 9, 37064-37069 (2017). https://doi.org/10.1021/acsami.7b13215
  57. Balakrishna Pillai, P., Kumar, A., Song, X. & De Souza, M. M. Diffusion-Controlled Faradaic Charge Storage in High-Performance Solid Electrolyte-Gated Zinc Oxide Thin-Film Transistors. ACS Appl. Mater. Interfaces 10, 9782-9791 (2018). https://doi.org/10.1021/acsami.7b14768
  58. John, R. A. et al. Flexible Ionic-Electronic Hybrid Oxide Synaptic TFTs with Programmable Dynamic Plasticity for Brain-Inspired Neuromorphic Computing. Small 13, 1701193 (2017). https://doi.org/10.1002/smll.201701193
  59. Fu, Y. M., Zhu, L. Q., Wen, J., Xiao, H. & Liu, R. Mixed Protonic and Electronic Conductors Hybrid Oxide Synaptic Transistors. J. Appl. Phys. 121, 205301 (2017). https://doi.org/10.1063/1.4983847
  60. Gou, G. et al. Artificial Synapses Based on Biopolymer Electrolyte-Coupled $SnO_2$ Nanowire Transistors. J. Mater. Chem. C 4, 11110-11117 (2016).
  61. Zou, C. et al. Polymer-Electrolyte-Gated Nanowire Synaptic Transistors for Neuromorphic Applications. Appl. Phys. A Mater. Sci. Process. 123, 597 (2017). https://doi.org/10.1007/s00339-017-1218-5
  62. Fuller, E. J. et al. Li-Ion Synaptic Transistor for Low Power Analog Computing. Adv. Mater. 29, 1604310 (2017). https://doi.org/10.1002/adma.201604310
  63. Guo, L. Q., Wen, J., Zhu, L. Q., Fu, Y. M. & Xiao, H. Humidity-Dependent Synaptic Plasticity for Proton Gated Oxide Synaptic Transistor. IEEE Electron Device Lett. 38, 1248-1251 (2017). https://doi.org/10.1109/LED.2017.2723917
  64. Wan, C. J. et al. Proton-Conducting Graphene Oxide-Coupled Neuron Transistors for Brain-Inspired Cognitive Systems. Adv. Mater. 28, 3557-3563 (2016). https://doi.org/10.1002/adma.201505898
  65. Feng, P., Du, P., Wan, C., Shi, Y. & Wan, Q. Proton Conducting Graphene Oxide/Chitosan Composite Electrolytes as Gate Dielectrics for New-Concept Devices. Sci. Rep. 6, 34065 (2016). https://doi.org/10.1038/srep34065
  66. Wan, C. et al. Indium-Zinc-Oxide Neuron Thin Film Transistors Laterally Coupled by Sodium Alginate Electrolytes. IEEE Trans. Electron Devices 63, 3958-3963 (2016). https://doi.org/10.1109/TED.2016.2601925
  67. Wan, C., Zhu, L., Liu, Y., Shi, Y. & Wan, Q. Laterally Coupled Synaptic Transistors Gated by Proton Conducting Sodium Alginate Films. IEEE Electron Device Lett. 35, 672-674 (2014). https://doi.org/10.1109/LED.2014.2316545
  68. Kim, S., Yoon, J., Kim, H.-D. & Choi, S.-J. Carbon Nanotube Synaptic Transistor Network for Pattern Recognition. ACS Appl. Mater. Interfaces 7, 25479-25486 (2015). https://doi.org/10.1021/acsami.5b08541
  69. Kim, S. et al. Pattern Recognition Using Carbon Nanotube Synaptic Transistors with an Adjustable Weight Update Protocol. ACS Nano 11, 2814-2822 (2017). https://doi.org/10.1021/acsnano.6b07894
  70. Qin, S. et al. A Light-Stimulated Synaptic Device Based on Graphene Hybrid Phototransistor. 2D Mater. 4, 035022 (2016).
  71. Shen, A. M., Kim, K., Tudor, A., Lee, D. & Chen, Y. Doping Modulated Carbon Nanotube Synapstors for a Spike Neuromorphic Module. Small 11, 1571-1579 (2015). https://doi.org/10.1002/smll.201402528
  72. Kim, Y. & Cho, B. Ultra-Low Powered CNT Synaptic Transistor Utilizing Double PI : PCBM Dielectric Layers. Krean J. Mater. Res. 27, 590-596 (2017). https://doi.org/10.3740/MRSK.2017.27.11.590
  73. Sangwan, V. K. et al. Multi-Terminal Memtransistors from Polycrystalline Monolayer Molybdenum Disulfide. Nature 554, 500-504 (2018). https://doi.org/10.1038/nature25747
  74. Arnold, A. J. et al. Mimicking Neurotransmitter Release in Chemical Synapses via Hysteresis Engineering in $MoS_2$ Transistors. ACS Nano 11, 3110-3118 (2017). https://doi.org/10.1021/acsnano.7b00113
  75. Jiang, J. et al. 2D $MoS_2$ Neuromorphic Devices for Brain-Like Computational Systems. Small 13, 1700933 (2017). https://doi.org/10.1002/smll.201700933
  76. Zhu, J. et al. Ion Gated Synaptic Transistors Based on 2D van der Waals Crystals with Tunable Diffusive Dynamics. Adv. Mater. 30, 1800195 (2018). https://doi.org/10.1002/adma.201800195
  77. Tian, H. et al. Anisotropic Black Phosphorus Synaptic Device for Neuromorphic Applications. Adv. Mater. 28, 4991-4997 (2016). https://doi.org/10.1002/adma.201600166
  78. Tian, H. et al. Emulating Bilingual Synaptic Response Using a Junction-Based Artificial Synaptic Device. ACS Nano 11, 7156-7163 (2017). https://doi.org/10.1021/acsnano.7b03033