Tunable flexible artificial synapses: a new path toward a wearable electronic system
Source abstract
Abstract
Abstract The flexible electronics has been deemed to be a promising approach to the wearable electronic systems. However, the mismatching between the existing flexible deices and the conventional computing paradigm results an impasse in this field. In this work, a new way to access to this goal is proposed by combining flexible devices and the neuromorphic architecture together. To achieve that, a high-performance flexible artificial synapse is created based on a carefully designed and optimized memristive transistor. The device exhibits high-performance which has near-linear non-volatile resistance change under 10,000 identical pulse signals within the 515% dynamic range, and has the energy consumption as low as 45 fJ per pulse. It also displays multiple synaptic plasticity features, which demonstrates its potential for real-time online learning. Besides, the adaptability by virtue of its three-terminal structure specifically contributes its improved uniformity, repeatability, and reduced power consumption. This work offers a very viable solution for the future wearable computing.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Advanced Sensor and Energy Harvesting Materials · Transition Metal Oxide Nanomaterials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (40)
Show source-provided references
1. Trung, T. Q. & Lee, N. E. Flexible and stretchable physical sensor integrated platforms for wearable human-activity monitoringand personal healthcare. Adv. Mater. 28, 4338–4372 (2016).10.1002/adma.201504244 ↗
2. Zang, Y. et al. Flexible suspended gate organic thin-film transistors for ultra-sensitive pressure detection. Nat. Commun. 6, 6269 (2015).10.1038/ncomms7269 ↗
3. Someya, T. et al. A large-area, flexible pressure sensor matrix with organic field-effect transistors for artificial skin applications. Proc. Natl Acad. Sci. USA 101, 9966–9970 (2004).10.1073/pnas.0401918101 ↗
4. Zhao, Y. et al. All-solution-processed, high-performance n-channel organic transistors and circuits: toward low-cost ambient electronics. Adv. Mater. 23, 2448–2453 (2011).10.1002/adma.201004588 ↗
5. Kim, S. et al. Low-power flexible organic light-emitting diode display device. Adv. Mater. 23, 3511–3516 (2011).10.1002/adma.201101066 ↗
6. Sekitani, T. et al. Stretchable active-matrix organic light-emitting diode display using printable elastic conductors. Nat. Mater. 8, 494–499 (2009).10.1038/nmat2459 ↗
7. Tee, B. C.-K., Wang, C., Allen, R. & Bao, Z. An electrically and mechanically self-healing composite with pressure- and flexion-sensitive properties for electronic skin applications. Nat. Nano 7, 825–832 (2012).10.1038/nnano.2012.192 ↗
8. Chortos, A., Liu, J. & Bao, Z. Pursuing prosthetic electronic skin. Nat. Mater. 15, 937–950 (2016).10.1038/nmat4671 ↗
9. Sun, D. et al. Flexible high-performance carbon nanotube integrated circuits. Nat. Nanotechnol. 6, 156–161 (2011).10.1038/nnano.2011.1 ↗
10. Jeong, D. S., Kim, K. M., Kim, S., Choi, B. J. & Hwang, C. S. Memristors for energy-efficient new computing paradigms. Adv. Electron. Mater. 2, 1–27 (2016).
11. Lecun, Y., Bengio, Y. & Hinton, G. Deep Learning. https://doi.org/10.1038/nature14539 (2015).10.1038/nature14539 ↗
12. Prezioso, M. et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521, 61–64 (2015).10.1038/nature14441 ↗
13. Paul, M. et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345, 668–673 (2014).10.1126/science.1254642 ↗
14. Tees, R. C. The Organization of Behaviour: A Neuropsychological Theory. CanadianPsychology (MIT Press, 1961) https://doi.org/10.2307/1418888.10.2307/1418888 ↗
15. Wang, Z. et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat. Mater. 16, 101 (2016).10.1038/nmat4756 ↗
16. Xu, W. et al. Organometal halide perovskite artificial synapses. Adv. Mater. 28, 5916–5922 (2016).10.1002/adma.201506363 ↗
17. Wu, C. et al. Mimicking classical conditioning based on a single flexible memristor. Adv. Mater. 29, 1602890—n/a (2017).10.1002/adma.201602890 ↗
18. van de Burgt, Y. et al. A nonvolatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat. Mater. 16, 414–418 (2017).10.1038/nmat4856 ↗
19. Gergel-Hackett, N. et al. A flexible solution-processed memristor. IEEE Electron Device Lett. 30, 706–708 (2009).10.1109/LED.2009.2021418 ↗
20. Waser, R. & Aono, M. Nanoionics-based resistive switching memories. Nat. Mater. 6, 833–840 (2007).10.1038/nmat2023 ↗
21. Yang, J. J., Strukov, D. B. & Stewart, D. R. Memristive devices for computing. Nat. Nanotechnol. 8, 13–24 (2013).10.1038/nnano.2012.240 ↗
22. Egginger, M., Bauer, S., Schwödiauer, R., Neugebauer, H. & Sariciftci, N. S. Current versus gate voltage hysteresis in organic field-effect transistors. Mon. Chem. 140, 735–750 (2009).10.1007/s00706-009-0149-z ↗
23. Late, D. J., Liu, B., Matte, H. S. S. R., Dravid, V. P. & Rao, C. N. R. Hysteresis in single-layer MoS2 field-effect transistors. ACS Nano 6, 5635–5641 (2012).10.1021/nn301572c ↗
24. Aguirre, C. M. et al. The role of the oxygen/water redox couple in suppressing electron conduction in field-effect transistors. Adv. Mater. 21, 3087–3091 (2009).10.1002/adma.200900550 ↗
25. Qu, M., Li, H., Liu, R., Zhang, S.-L. & Qiu, Z.-J. Interaction of bipolaron with the H2O/O2 redox couple causes current hysteresis in organic thin-film transistors. Nat. Commun. 5, 3185 (2014).10.1038/ncomms4185 ↗
26. Alibart, F. et al. A memristive nanoparticle/organic hybrid synapstor for neuroinspired computing. Adv. Funct. Mater. 22, 609–616 (2012).10.1002/adfm.201101935 ↗
27. Wang, L. et al. Controllable multiple depression in a graphene oxide artificial synapse. Adv. Electron. Mater. 3, 1600244 (2017).10.1002/aelm.201600244 ↗
28. Charlier, J. C., Blase, X. & Roche, S. Electronic and transport properties of nanotubes. Rev. Mod. Phys. 79, 677–732 (2007).10.1103/RevModPhys.79.677 ↗
29. Diamond, B. et al. Charge transfer equilibria between diamond and an aqueous oxygen electrochemical redox couple. Science 318, 1424–1431 (2007).10.1126/science.1151148 ↗
30. Strukov, D. B., Snider, G. S., Stewart, D. R. & Williams, R. S. The missing memristor found. Nature 453, 80–83 (2008).10.1038/nature06932 ↗
31. Wang, C. et al. Memristive devices with highly repeatable analog states boosted by graphene quantum dots. Small 13, 1–8 (2017).
32. Hebb, O. D. The Organisation of Behaviour: A Neuropsychological Theory. (Wiley, New York, 1961).
33. Natschläger, T., Maass, W. & Zador, A. Efficient temporal processing with biologically realistic dynamic synapses. Network 12, 75–87 (2001).10.1080/net.12.1.75.87 ↗
34. Daoudal, G. & Debanne, D. Long-term plasticity of intrinsic excitability: learning rules and mechanisms. Learn. Mem. 10, 456–465 (2003).10.1101/lm.64103 ↗
35. Gerrow, K. & Triller, A. Synaptic stability and plasticity in a floating world. Curr. Opin. Neurobiol. 20, 631–639 (2010).10.1016/j.conb.2010.06.010 ↗
36. Bi, G. Q. & Poo, M. M. Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 18, 10464–10472 (1998).10.1523/JNEUROSCI.18-24-10464.1998 ↗
37. Bichler, O., Querlioz, D., Thorpe, S. J., Bourgoin, J. P. & Gamrat, C. Extraction of temporally correlated features from dynamic vision sensors with spike-timing-dependent plasticity. Neural Netw. 32, 339–348 (2012).10.1016/j.neunet.2012.02.022 ↗
38. O’Connor, P., Neil, D., Liu, S. C., Delbruck, T. & Pfeiffer, M. Real-time classification and sensor fusion with a spiking deep belief network. Front. Neurosci. 7, 1–13 (2013).
39. Xiao, Z. & Huang, J. Energy-efficient hybrid perovskite memristors and synaptic devices. Adv. Electron. Mater. 2, 1–8 (2016).10.1002/aelm.201600100 ↗
40. Schiffmann, W., Joost, M. & Werner, R. Optimization of the Backpropagation Algorithm for Training Multilayer Perceptrons. Physics (College Park, Md). 1–36 (1994).