Bidirectionally Modulated Synaptic Plasticity with Optically Tunable Ionic Electrolyte Transistors Bidirectionally Modulated Synaptic Plasticity with Optically Tunable Ionic Electrolyte Transistors AITranslate
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Recently, hardware implementation of neuromorphic device in the optical domain is considered as one of the most promising routes to realize energy-efficient neuromorphic computing systems. Especially, a complete plasticity modulation by all-photonic stimulation has been one of the most important challenges for implementation of an optoelectronic neuromorphic device. Here, we demonstrate a fully optically driven bidirectional synaptic device using ionic electrolyte transistors. The photovoltaic divider enables wavelength-selective light-to-voltage conversion and subsequently induces ionic migration in the electrolyte, resulting in the synaptic potentiation or depression. Based on the synaptic characteristics, pattern recognition with an accuracy up to 90.1% is obtained in the Modified National Institute of Standards and Technology simulation.
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DOI:https://doi.org/10.1021/acsaelm.2c00289
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Recently, hardware implementation of neuromorphic device in the optical domain is considered as one of the most promising routes to realize energy-efficient neuromorphic computing systems. Especially, a complete plasticity modulation by all-photonic stimulation has been one of the most important challenges for implementation of an optoelectronic neuromorphic device. Here, we demonstrate a fully optically driven bidirectional synaptic device using ionic electrolyte transistors. The photovoltaic divider enables wavelength-selective light-to-voltage conversion and subsequently induces ionic migration in the electrolyte, resulting in the synaptic potentiation or depression. Based on the synaptic characteristics, pattern recognition with an accuracy up to 90.1% is obtained in the Modified National Institute of Standards and Technology simulation.
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| GB/T 7714-2015 | [1] Sung Min Kwon, SeungHan Kang, Sung Soo Cho, et al. ACS Applied Electronic Materials, 2022(4). DOI:10.1021/acsaelm.2c00289. |
| MLA | [1] Sung Min Kwon, et al., ACS Applied Electronic Materials, no. 4, 2022, https://doi.org/10.1021/acsaelm.2c00289. |
| APA | [1] Sung Min Kwon, SeungHan Kang, Sung Soo Cho, YoungWoo Jang, SeungJi Nam, Jaehyun Kim, UnChul Moon, JongWook Shin, YongHoon Kim, & Sung Kyu Park. (2022). ACS Applied Electronic Materials(4). https://doi.org/10.1021/acsaelm.2c00289 |
| IEEE | [1] Sung Min Kwon, SeungHan Kang, Sung Soo Cho, YoungWoo Jang, SeungJi Nam, Jaehyun Kim, UnChul Moon, JongWook Shin, YongHoon Kim, and Sung Kyu Park, ACS Applied Electronic Materials, no. 4, 2022, doi: 10.1021/acsaelm.2c00289. keywords: {neuromorphic computing optically driven synaptic device metal chalcogenide pattern recognition ionic electrolyte transistor} |
