Electrolyte-Gated Vertical Synapse Array based on Van Der Waals Heterostructure for Parallel Computingopen access
- Authors
- Oh Seyong; Lee Ju-Hee; Seo Seunghwan; Choo Hyongsuk; Lee Dongyoung; Cho Jeong-Ick; Park Jin-Hong
- Issue Date
- Feb-2022
- Publisher
- Wiley-VCH Verlag
- Keywords
- hardware artificial neural networks; ion gel; parallel computing; synapse arrays; van der Waals heterostructures; vertical synaptic devices
- Citation
- Advanced Science, v.9, no.6, pp 1 - 10
- Pages
- 10
- Indexed
- SCIE
SCOPUS
- Journal Title
- Advanced Science
- Volume
- 9
- Number
- 6
- Start Page
- 1
- End Page
- 10
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/112833
- DOI
- 10.1002/advs.202103808
- ISSN
- 2198-3844
- Abstract
- Recently, three-terminal synaptic devices, which separate read and write terminals, have attracted significant attention because they enable nondestructive read-out and parallel-access for updating synaptic weights. However, owing to their structural features, it is difficult to address the relatively high device density compared with two-terminal synaptic devices. In this study, a vertical synaptic device featuring remotely controllable weight updates via e-field-dependent movement of mobile ions in the ion-gel layer is developed. This synaptic device successfully demonstrates all essential synaptic characteristics, such as excitatory/inhibitory postsynaptic current (E/IPSC), paired-pulse facilitation (PPF), and long-term potentiation/depression (LTP/D) by electrical measurements, and exhibits competitive LTP/D characteristics with a dynamic range (G(max)/G(min)) of 31.3, and asymmetry (AS) of 8.56. The stability of the LTP/D characteristics is also verified through repeated measurements over 50 cycles; the relative standard deviations (RSDs) of G(max)/G(min) and AS are calculated as 1.65% and 0.25%, respectively. These excellent synaptic properties enable a recognition rate of approximate to 99% in the training and inference tasks for acoustic and emotional information patterns. This study is expected to be an important foundation for the realization of future parallel computing networks for energy-efficient and high-speed data processing.
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