Ferroelectric polymer-based artificial synapse for neuromorphic computing
- Authors
- Kim, S.[Kim, S.]; Heo, K.[Heo, K.]; Lee, S.[Lee, S.]; HWAN, S. S.[HWAN, SEO SEUNG]; JUN, K. H.[JUN, KIM HYEONG]; ICK, C. J.[ICK, CHO JEONG]; Lee, H.[Lee, H.]; Lee, K.-B.[Lee, K.-B.]; Park, J.-H.[Park, J.-H.]
- Issue Date
- Feb-2021
- Publisher
- Royal Society of Chemistry
- Citation
- Nanoscale Horizons, v.6, no.2, pp.139 - 147
- Indexed
- SCIE
SCOPUS
- Journal Title
- Nanoscale Horizons
- Volume
- 6
- Number
- 2
- Start Page
- 139
- End Page
- 147
- URI
- https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/89892
- DOI
- 10.1039/d0nh00559b
- ISSN
- 2055-6756
- Abstract
- Recently, various efforts have been made to implement synaptic characteristics with a ferroelectric field-effect transistor (FeFET), but in-depth physical analyses have not been reported thus far. Here, we investigated the effects by (i) the formation temperature of the ferroelectric material, poly(vinylidene fluoride-trifluoroethylene) P(VDF-TrFE) and (ii) the nature of the contact metals (Ti, Cr, Pd) of the FeFET on the operating performance of a FeFET-based artificial synapse in terms of various synaptic performance indices. Excellent ferroelectric properties were induced by maximizing the size and coverage ratio of the β-phase domains by annealing the P(VDF-TrFE) film at 140 °C. A metal that forms a relatively high barrier improved the dynamic range and nonlinearity by suppressing the contribution of the tunneling current to the post-synaptic current. Subsequently, we studied the influence of the synaptic characteristics on the training and recognition tasks by using two MNIST datasets (fashion and handwritten digits) and the multi-layer perceptron concept of neural networks. © The Royal Society of Chemistry.
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Collections - Information and Communication Engineering > School of Electronic and Electrical Engineering > 1. Journal Articles
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