Thermodynamic Equilibrium-Nonequilibrium Competition in Nanofilaments for Achieving Complementary Electronic Synapses
- Authors
- Guo, Jiawei; Li, Wenhao; Liao, Yitao; Shen, Yiwei; Wang, Kun; Suk, Chan Hee; Zhou, Xiongtu; Zhang, Yongai; Wu, Chaoxing; Guo, Tailiang; Kim, Tae Whan
- Issue Date
- Dec-2022
- Publisher
- WILEY-V C H VERLAG GMBH
- Keywords
- chemical functional groups; complementary e-synapses; electrochemical metallization mechanisms; electronic synapses; metal-ion nanochannels
- Citation
- ADVANCED FUNCTIONAL MATERIALS, v.32, no.51, pp.1 - 10
- Indexed
- SCIE
SCOPUS
- Journal Title
- ADVANCED FUNCTIONAL MATERIALS
- Volume
- 32
- Number
- 51
- Start Page
- 1
- End Page
- 10
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/185224
- DOI
- 10.1002/adfm.202207885
- ISSN
- 1616-301X
- Abstract
- In the human brain, the natural complementary behaviors of synapse connections, which are enhanced and weakened through the participation of astrocytes and microglias, play an important role in the process of training and memory. Even though memristors based on the electrochemical metallization mechanism (ECM) have great potential in realizing electronic synapses (e-synapses), the lack of complementary ECM e-synapses has, to a certain extent, hindered the construction of artificial neural networks. In this study, the thermodynamic equilibrium-nonequilibrium competition (TC) in nanofilaments is meticulously designed by modifying the metal-ion nanochannels to achieve complementary ECM e-synapses. The evolution in the morphology of a TC-induced nanofilament includes a thinning behavior of the nanofilaments caused by electrical stimulation and a coarsening behavior caused by metal-ion chelation. The TC effect leads to a decrease in the conductance under positive pulse stimulation and to an increase in the conductance while resting, these behaviors being exactly complementary to those of a conventional ECM e-synapse. Finally, the feasibility of fabricating logic gate circuits with learning capability based on complementary ECM e-synapses is verified using behavioral-level modeling. These ECM e-synapses with complementary functions are expected to be significant to researchers in the field of large neural network systems.
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