Artificial Synapses

Shriram Ramanathan; Abhronil Sengupta

doi:10.1017/9781009564335.004

3 - Artificial Synapses

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Shriram Ramanathan and

Abhronil Sengupta

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Shriram Ramanathan: Affiliation:
Rutgers University, New Jersey
Abhronil Sengupta: Affiliation:
Pennsylvania State University

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Summary

This chapter offers an in-depth discussion of various nanoelectronic and nanoionic synapses along with the operational mechanisms, capabilities and limitations, and directions for further advancements in this field. We begin with overarching mechanisms to design artificial synapses and learning characteristics for neuromorphic computing. Silicon-based synapses using digital CMOS platforms are described followed by emerging device technologies. Filamentary synapses that utilize nanoscale conducting pathways for forming and breaking current shunting routes within two-terminal devices are then discussed. This is followed by ferroelectric devices wherein polarization states of a switchable ferroelectric layer are responsible for synaptic plasticity and memory. Insulator–metal transition-based synapses are described wherein a sharp change in conductance of a layer due to external stimulus offers a route for compact synapse design. Organic materials, 2D van der Waals, and layered semiconductors are discussed. Ionic liquids and solid gate dielectrics for multistate memory and learning are presented. Photonic and spintronic synapses are then discussed in detail.

Keywords

synapses synaptic memory ferroelectric synapse memristor insulator–metal transition correlated electron 2D synapse van der Waals synapse spintronic synapse photonic synapse plasticity phase change

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Type: Chapter
Information: Introduction to Neuromorphic Computing , pp. 27 - 76

DOI: https://doi.org/10.1017/9781009564335.004 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2026

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References

Xia, Q. and Yang, J. J., 2019. Memristive crossbar arrays for brain-inspired computing. Nature Materials, 18(4), pp. 309–323.Google Scholar PubMed

Li, Z., Liu, F., Yang, W., Peng, S. and Zhou, J., 2021. A survey of convolutional neural networks: Analysis, applications, and prospects. IEEE Transactions on Neural Networks and Learning Systems, 33(12), pp. 6999–7019.Google Scholar

LeCun, Y., Bengio, Y. and Hinton, G., 2015. Deep learning. Nature, 521(7553), pp. 436–444.10.1038/nature14539CrossRef Google Scholar PubMed

Sengupta, A., Banerjee, A. and Roy, K., 2016. Hybrid spintronic-CMOS spiking neural network with on-chip learning: Devices, circuits, and systems. Physical Review Applied, 6(6), p. 064003.10.1103/PhysRevApplied.6.064003CrossRef Google Scholar

Rajendran, B., Liu, Y., Seo, J. S., Gopalakrishnan, K., Chang, L., Friedman, D. J. and Ritter, M. B., 2012. Specifications of nanoscale devices and circuits for neuromorphic computational systems. IEEE Transactions on Electron Devices, 60(1), pp. 246–253.Google Scholar

Sengupta, A., Ankit, A. and Roy, K., 2017, May. Performance analysis and benchmarking of all-spin spiking neural networks (special session paper). In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 4557–4563). IEEE.CrossRef Google Scholar

Chua, L., 1971. Memristor: The missing circuit element. IEEE Transactions on Circuit Theory, 18(5), pp. 507–519.10.1109/TCT.1971.1083337CrossRef Google Scholar

Nandakumar, S. R., Le Gallo, M., Piveteau, C., Joshi, V., Mariani, G., Boybat, I., Karunaratne, G., Khaddam-Aljameh, R., Egger, U., Petropoulos, A. and Antonakopoulos, T., 2020. Mixed-precision deep learning based on computational memory. Frontiers in Neuroscience, 14, p. 406.10.3389/fnins.2020.00406CrossRef Google Scholar PubMed

Sun, X., Liu, R., Peng, X. and Yu, S., 2018, October. Computing-in-memory with SRAM and RRAM for binary neural networks. In 2018 14th IEEE International Conference on Solid-State and Integrated Circuit Technology (ICSICT) (pp. 1–4). IEEE.CrossRef Google Scholar

Jerry, M., Chen, P. Y., Zhang, J., Sharma, P., Ni, K., Yu, S. and Datta, S., 2017, December. Ferroelectric FET analog synapse for acceleration of deep neural network training. In 2017 IEEE International Electron Devices Meeting (IEDM) (pp. 6–2). IEEE.CrossRef Google Scholar

Saha, A., Islam, A. N. M., Zhao, Z., Deng, S., Ni, K. and Sengupta, A., 2021. Intrinsic synaptic plasticity of ferroelectric field effect transistors for online learning. Applied Physics Letters, 119(13), pp. 133701-1–133701-6.10.1063/5.0064860CrossRef Google Scholar

Agrawal, A., Chakraborty, I., Roy, D., Saxena, U., Sharmin, S., Koo, M., Shim, Y., Srinivasan, G., Liyanagedera, C., Sengupta, A. and Roy, K., 2020. Revisiting stochastic computing in the era of nanoscale nonvolatile technologies. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 28(12), pp. 2481–2494.CrossRef Google Scholar

Schmitt, S., Klähn, J., Bellec, G., Grübl, A., Guettler, M., Hartel, A., Hartmann, S., Husmann, D., Husmann, K., Jeltsch, S. and Karasenko, V., 2017, May. Neuromorphic hardware in the loop: Training a deep spiking network on the BrainScaleS wafer-scale system. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 2227–2234). IEEE.10.1109/IJCNN.2017.7966125CrossRef Google Scholar

Sengupta, A. and Roy, K., 2016. Short-term plasticity and long-term potentiation in magnetic tunnel junctions: Towards volatile synapses. Physical Review Applied, 5(2), p. 024012.10.1103/PhysRevApplied.5.024012CrossRef Google Scholar

Jeong, D. S., Kim, I., Ziegler, M. and Kohlstedt, H., 2013. Towards artificial neurons and synapses: A materials point of view. RSC Advances, 3(10), pp. 3169–3183.Google Scholar

Ohno, T., Hasegawa, T., Tsuruoka, T., Terabe, K., Gimzewski, J. K. and Aono, M., 2011. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nature Materials, 10(8), pp. 591–595.CrossRef Google Scholar PubMed

Du, C., Ma, W., Chang, T., Sheridan, P. and Lu, W. D., 2015. Biorealistic implementation of synaptic functions with oxide memristors through internal ionic dynamics. Advanced Functional Materials, 25(27), pp. 4290–4299.CrossRef Google Scholar

Kim, S. G., Han, J. S., Kim, H., Kim, S. Y. and Jang, H. W., 2018. Recent advances in memristive materials for artificial synapses. Advanced Materials Technologies, 3(12), p. 1800457.10.1002/admt.201800457CrossRef Google Scholar

Ielmini, D. and Waser, R. eds., 2015. Resistive switching: From fundamentals of nanoionic redox processes to memristive device applications. Weinheim: John Wiley & Sons.Google Scholar

Wang, Z., Joshi, S., Savel’ev, S. E., Jiang, H., Midya, R., Lin, P., Hu, M., Ge, N., Strachan, J. P., Li, Z. and Wu, Q., 2017. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nature Materials, 16(1), pp. 101–108.10.1038/nmat4756CrossRef Google Scholar PubMed

Chanthbouala, A., Garcia, V., Cherifi, R. O., Bouzehouane, K., Fusil, S., Moya, X., Xavier, S., Yamada, H., Deranlot, C., Mathur, N. D. and Bibes, M., 2012. A ferroelectric memristor. Nature Materials, 11(10), pp. 860–864.CrossRef Google Scholar PubMed

Böscke, T. S., Müller, J., Bräuhaus, D., Schröder, U. and Böttger, U. J. A. P. L., 2011. Ferroelectricity in hafnium oxide thin films. Applied Physics Letters, 99(10), pp. 102903-1–102903-3.10.1063/1.3634052CrossRef Google Scholar

Max, B., Hoffmann, M., Mulaosmanovic, H., Slesazeck, S. and Mikolajick, T., 2020. Hafnia-based double-layer ferroelectric tunnel junctions as artificial synapses for neuromorphic computing. ACS Applied Electronic Materials, 2(12), pp. 4023–4033.CrossRef Google Scholar

Wei, Y., Vats, G. and Noheda, B., 2022. Synaptic behaviour in ferroelectric epitaxial rhombohedral Hf0.5Zr0.5O2 thin films. Neuromorphic Computing and Engineering, 2(4), p. 044007.10.1088/2634-4386/ac970cCrossRef Google Scholar

Mulaosmanovic, H., Ocker, J., Müller, S., Noack, M., Müller, J., Polakowski, P., Mikolajick, T. and Slesazeck, S., 2017, June. Novel ferroelectric FET based synapse for neuromorphic systems. In 2017 Symposium on VLSI Technology (pp. T176–T177). IEEE.CrossRef Google Scholar

Majumdar, S., Tan, H., Qin, Q. H. and van Dijken, S., 2019. Energy‐efficient organic ferroelectric tunnel junction memristors for neuromorphic computing. Advanced Electronic Materials, 5(3), p. 1800795.10.1002/aelm.201800795CrossRef Google Scholar

Thakoor, S., Moopenn, A., Daud, T. and Thakoor, A. P., 1990. Solid‐state thin‐film memistor for electronic neural networks. Journal of Applied Physics, 67(6), pp. 3132–3135.10.1063/1.345390CrossRef Google Scholar

Yang, J. T., Ge, C., Du, J. Y., Huang, H. Y., He, M., Wang, C., Lu, H. B., Yang, G. Z. and Jin, K. J., 2018. Artificial synapses emulated by an electrolyte‐gated tungsten‐oxide transistor. Advanced Materials, 30(34), p. 1801548.CrossRef Google Scholar

Yang, Z., Zhou, Y. and Ramanathan, S., 2012. Studies on room-temperature electric-field effect in ionic-liquid gated VO₂ three-terminal devices. Journal of Applied Physics, 111(1), pp. 014506-1–014506-5.Google Scholar

Zhou, Y. and Ramanathan, S., 2012. Relaxation dynamics of ionic liquid – VO₂ interfaces and influence in electric double-layer transistors. Journal of Applied Physics, 111(8), pp. 084508-1–084508-7.CrossRef Google Scholar

Oh, C., Kim, I., Park, J., Park, Y., Choi, M. and Son, J., 2021. Deep proton insertion assisted by oxygen vacancies for long‐term memory in VO₂ synaptic transistor. Advanced Electronic Materials, 7(2), p. 2000802.CrossRef Google Scholar

Zhang, H. T., Park, T. J., Zaluzhnyy, I. A., Wang, Q., Wadekar, S. N., Manna, S., Andrawis, R., Sprau, P. O., Sun, Y., Zhang, Z. and Huang, C., 2020. Perovskite neural trees. Nature Communications, 11(1), p. 2245.Google Scholar PubMed

Shi, J., Ha, S. D., Zhou, Y., Schoofs, F. and Ramanathan, S., 2013. A correlated nickelate synaptic transistor. Nature Communications, 4(1), p. 2676.10.1038/ncomms3676CrossRef Google Scholar PubMed

Ha, S. D., Shi, J., Meroz, Y., Mahadevan, L. and Ramanathan, S., 2014. Neuromimetic circuits with synaptic devices based on strongly correlated electron systems. Physical Review Applied, 2(6), p. 064003.10.1103/PhysRevApplied.2.064003CrossRef Google Scholar

Du, H., Lin, X., Xu, Z. and Chu, D., 2015. Electric double-layer transistors: A review of recent progress. Journal of Materials Science, 50, pp. 5641–5673.CrossRef Google Scholar

Maass, W. and Zador, A., 1997. Dynamic stochastic synapses as computational units. Advances in Neural Information Processing Systems, 10, pp. 194–200.Google Scholar

Neftci, E. O., Pedroni, B. U., Joshi, S., Al-Shedivat, M. and Cauwenberghs, G., 2016. Stochastic synapses enable efficient brain-inspired learning machines. Frontiers in Neuroscience, 10, p. 241.10.3389/fnins.2016.00241CrossRef Google Scholar PubMed

Erokhin, V., 2022. Fundamentals of organic neuromorphic systems. Cham: Springer International Publishing.10.1007/978-3-030-79492-7CrossRef Google Scholar

Feofilova, E. P. and Mysyakina, I. S., 2016. Lignin: Chemical structure, biodegradation, and practical application (a review). Applied Biochemistry and Microbiology, 52, pp. 573–581.10.1134/S0003683816060053CrossRef Google Scholar

Park, Y. and Lee, J. S., 2017. Artificial synapses with short- and long-term memory for spiking neural networks based on renewable materials. ACS Nano, 11(9), pp. 8962–8969.10.1021/acsnano.7b03347CrossRef Google Scholar

Jang, B. C., Kim, S., Yang, S. Y., Park, J., Cha, J. H., Oh, J., Choi, J., Im, S. G., Dravid, V. P. and Choi, S. Y., 2019. Polymer analog memristive synapse with atomic-scale conductive filament for flexible neuromorphic computing system. Nano Letters, 19(2), pp. 839–849.10.1021/acs.nanolett.8b04023CrossRef Google Scholar PubMed

Ren, Y., Yang, J. Q., Zhou, L., Mao, J. Y., Zhang, S. R., Zhou, Y. and Han, S. T., 2018. Gate‐tunable synaptic plasticity through controlled polarity of charge trapping in fullerene composites. Advanced Functional Materials, 28(50), p. 1805599.Google Scholar

Gkoupidenis, P., Schaefer, N., Garlan, B. and Malliaras, G. G., 2015. Neuromorphic functions in PEDOT: PSS organic electrochemical transistors. Advanced Materials, 44(27), pp. 7176–7180.Google Scholar

van De Burgt, Y., Melianas, A., Keene, S. T., Malliaras, G. and Salleo, A., 2018. Organic electronics for neuromorphic computing. Nature Electronics, 1(7), pp. 386–397.10.1038/s41928-018-0103-3CrossRef Google Scholar

Lee, Y., Park, H. L., Kim, Y. and Lee, T. W., 2021. Organic electronic synapses with low energy consumption. Joule, 5(4), pp. 794–810.10.1016/j.joule.2021.01.005CrossRef Google Scholar

Pecqueur, S., Vuillaume, D. and Alibart, F., 2018. Perspective: Organic electronic materials and devices for neuromorphic engineering. Journal of Applied Physics, 124(15), pp. 151902-1–151902-11.CrossRef Google Scholar

Lee, G., Baek, J. H., Ren, F., Pearton, S. J., Lee, G. H. and Kim, J., 2021. Artificial neuron and synapse devices based on 2D materials. Small, 17(20), p. 2100640.Google Scholar PubMed

Shi, Y., Liang, X., Yuan, B., Chen, V., Li, H., Hui, F., Yu, Z., Yuan, F., Pop, E., Wong, H. S. P. and Lanza, M., 2018. Electronic synapses made of layered two-dimensional materials. Nature Electronics, 1(8), pp. 458–465.10.1038/s41928-018-0118-9CrossRef Google Scholar

Krishnaprasad, A., Dev, D., Han, S. S., Shen, Y., Chung, H. S., Bae, T. S., Yoo, C., Jung, Y., Lanza, M. and Roy, T., 2022. MoS₂ synapses with ultra-low variability and their implementation in Boolean logic. ACS Nano, 16(2), pp. 2866–2876.10.1021/acsnano.1c09904CrossRef Google Scholar PubMed

Chen, Y., Zhou, Y., Zhuge, F., Tian, B., Yan, M., Li, Y., He, Y. and Miao, X. S., 2019. Graphene–ferroelectric transistors as complementary synapses for supervised learning in spiking neural network. npj 2D Materials and Applications, 3(1), p. 31.CrossRef Google Scholar

Li, B., Li, S., Wang, H., Chen, L., Liu, L., Feng, X., Li, Y., Chen, J., Gong, X. and Ang, K. W., 2020. An electronic synapse based on 2D ferroelectric CuInP₂S₆. Advanced Electronic Materials, 6(12), p. 2000760.10.1002/aelm.202000760CrossRef Google Scholar

Zhu, X., Li, D., Liang, X. and Lu, W. D., 2019. Ionic modulation and ionic coupling effects in MoS₂ devices for neuromorphic computing. Nature Materials, 18(2), pp. 141–148.10.1038/s41563-018-0248-5CrossRef Google Scholar PubMed

Wang, Y., Yang, Y., He, Z., Zhu, H., Chen, L., Sun, Q. and Zhang, D. W., 2020. Laterally coupled 2D MoS₂ synaptic transistor with ion gating. IEEE Electron Device Letters, 41(9), pp. 1424–1427.10.1109/LED.2020.3008728CrossRef Google Scholar

Sun, L., Zhang, Y., Hwang, G., Jiang, J., Kim, D., Eshete, Y. A., Zhao, R. and Yang, H., 2018. Synaptic computation enabled by joule heating of single-layered semiconductors for sound localization. Nano Letters, 18(5), pp. 3229–3234.CrossRef Google Scholar PubMed

Cao, G., Meng, P., Chen, J., Liu, H., Bian, R., Zhu, C., Liu, F. and Liu, Z., 2021. 2D material based synaptic devices for neuromorphic computing. Advanced Functional Materials, 31(4), p. 2005443.Google Scholar

Huh, W., Lee, D. and Lee, C. H., 2020. Memristors based on 2D materials as an artificial synapse for neuromorphic electronics. Advanced Materials, 32(51), p. 2002092.10.1002/adma.202002092CrossRef Google Scholar

Sengupta, A. and Roy, K., 2018. Neuromorphic computing enabled by physics of electron spins: Prospects and perspectives. Applied Physics Express, 11(3), p. 030101.CrossRef Google Scholar

Fong, X., Kim, Y., Venkatesan, R., Choday, S. H., Raghunathan, A. and Roy, K., 2016. Spin-transfer torque memories: Devices, circuits, and systems. Proceedings of the IEEE, 104(7), pp. 1449–1488.10.1109/JPROC.2016.2521712CrossRef Google Scholar

Amiri, P. K. and Wang, K. L., 2012. Voltage-controlled magnetic anisotropy in spintronic devices. Spin, 2(3), p. 1240002.10.1142/S2010324712400024CrossRef Google Scholar

Hirsch, J. E., 1999. Spin hall effect. Physical Review Letters, 83(9), p. 1834.10.1103/PhysRevLett.83.1834CrossRef Google Scholar

Emori, S., Bauer, U., Ahn, S. M., Martinez, E. and Beach, G. S., 2013. Current-driven dynamics of chiral ferromagnetic domain walls. Nature Materials, 12(7), pp. 611–616.10.1038/nmat3675CrossRef Google Scholar PubMed

Fukami, S., Zhang, C., DuttaGupta, S., Kurenkov, A. and Ohno, H., 2016. Magnetization switching by spin–orbit torque in an antiferromagnet–ferromagnet bilayer system. Nature Materials, 15(5), pp. 535–541.CrossRef Google Scholar

Borders, W. A., Akima, H., Fukami, S., Moriya, S., Kurihara, S., Horio, Y., Sato, S. and Ohno, H., 2016. Analogue spin–orbit torque device for artificial-neural-network-based associative memory operation. Applied Physics Express, 10(1), p. 013007.Google Scholar

Song, K. M., Jeong, J. S., Pan, B., Zhang, X., Xia, J., Cha, S., Park, T. E., Kim, K., Finizio, S., Raabe, J. and Chang, J., 2020. Skyrmion-based artificial synapses for neuromorphic computing. Nature Electronics, 3(3), pp. 148–155.10.1038/s41928-020-0385-0CrossRef Google Scholar

Sengupta, A. and Roy, K., 2017. Encoding neural and synaptic functionalities in electron spin: A pathway to efficient neuromorphic computing. Applied Physics Reviews, 4(4), pp. 041105-1–041105-23.10.1063/1.5012763CrossRef Google Scholar

Kurenkov, A., Fukami, S. and Ohno, H., 2020. Neuromorphic computing with antiferromagnetic spintronics. Journal of Applied Physics, 128(1), pp. 010902-1–010902-12.10.1063/5.0009482CrossRef Google Scholar

Goodwill, J. M., Prasad, N., Hoskins, B. D., Daniels, M. W., Madhavan, A., Wan, L., Santos, T. S., Tran, M., Katine, J. A., Braganca, P. M. and Stiles, M. D., 2022. Implementation of a binary neural network on a passive array of magnetic tunnel junctions. Physical Review Applied, 18(1), p. 014039.10.1103/PhysRevApplied.18.014039CrossRef Google Scholar

Borders, W. A., Pervaiz, A. Z., Fukami, S., Camsari, K. Y., Ohno, H. and Datta, S., 2019. Integer factorization using stochastic magnetic tunnel junctions. Nature, 573(7774), pp. 390–393.CrossRef Google Scholar PubMed

Youngblood, N., Ríos Ocampo, C. A., Pernice, W. H. and Bhaskaran, H., 2023. Integrated optical memristors. Nature Photonics, 17(7), pp. 561–572.10.1038/s41566-023-01217-wCrossRef Google Scholar

Vandoorne, K., Mechet, P., Van Vaerenbergh, T., Fiers, M., Morthier, G., Verstraeten, D., Schrauwen, B., Dambre, J. and Bienstman, P., 2014. Experimental demonstration of reservoir computing on a silicon photonics chip. Nature Communications, 5(1), p. 3541.10.1038/ncomms4541CrossRef Google Scholar PubMed

Tait, A. N., Nahmias, M. A., Shastri, B. J. and Prucnal, P. R., 2014. Broadcast and weight: An integrated network for scalable photonic spike processing. Journal of Lightwave Technology, 32(21), pp. 3427–3439.CrossRef Google Scholar

Cheng, Z., Ríos, C., Pernice, W. H., Wright, C. D. and Bhaskaran, H., 2017. On-chip photonic synapse. Science Advances, 3(9), p. e1700160.10.1126/sciadv.1700160CrossRef Google Scholar PubMed

Ríos, C., Youngblood, N., Cheng, Z., Le Gallo, M., Pernice, W. H., Wright, C. D., Sebastian, A. and Bhaskaran, H., 2019. In-memory computing on a photonic platform. Science Advances, 5(2), p. eaau5759.10.1126/sciadv.aau5759CrossRef Google Scholar PubMed

Stegmaier, M., Ríos, C., Bhaskaran, H., Wright, C. D. and Pernice, W. H., 2017. Nonvolatile all‐optical 1 × 2 switch for chipscale photonic networks. Advanced Optical Materials, 5(1), p. 1600346.10.1002/adom.201600346CrossRef Google Scholar

Chakraborty, I., Saha, G. and Roy, K., 2019. Photonic in-memory computing primitive for spiking neural networks using phase-change materials. Physical Review Applied, 11(1), p. 014063.10.1103/PhysRevApplied.11.014063CrossRef Google Scholar

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