Lateral, Optically Controlled, Artificial Synapses Realized in Room-Temperature RF-Sputtered SnO2 for Neuromorphic Computing and Visual Recognition Lateral, Optically Controlled, Artificial Synapses Realized in Room-Temperature RF-Sputtered SnO2 for Neuromorphic Computing and Visual Recognition AITranslate
Abstract AITranslate
Volatile lateral memristors fabricated from amorphous SnO2 have exhibited synaptic properties including conductance modulation that depended on the number, width, and amplitude of voltage pulses within a sequence. Similar systematic conductance modulation was achieved using incident pulsed (UVC) light. By combining light and voltage pulses, OR/AND logic functions were implemented using single devices. Measurements performed in either N2 or air suggested that the device conductance, response, and decay times were affected by surface water chemisorption. Importantly, the aforementioned device functions were reliably demonstrated irrespective of this interaction with the ambient. Finally, simulations were performed to show that when placed in a suitable array, the measured device characteristics and responses to optical signals could enable hand-written digit recognition with accuracy exceeding 90%.
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DOI:https://doi.org/10.1021/acsaelm.3c00375
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Volatile lateral memristors fabricated from amorphous SnO2 have exhibited synaptic properties including conductance modulation that depended on the number, width, and amplitude of voltage pulses within a sequence. Similar systematic conductance modulation was achieved using incident pulsed (UVC) light. By combining light and voltage pulses, OR/AND logic functions were implemented using single devices. Measurements performed in either N2 or air suggested that the device conductance, response, and decay times were affected by surface water chemisorption. Importantly, the aforementioned device functions were reliably demonstrated irrespective of this interaction with the ambient. Finally, simulations were performed to show that when placed in a suitable array, the measured device characteristics and responses to optical signals could enable hand-written digit recognition with accuracy exceeding 90%.
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| GB/T 7714-2015 | [1] Phuong Y. Le, Hilal Nagib, Luke A. Sylvander, et al. ACS Applied Electronic Materials, 2023(5). DOI:10.1021/acsaelm.3c00375. |
| MLA | [1] Phuong Y. Le, et al., ACS Applied Electronic Materials, no. 5, 2023, https://doi.org/10.1021/acsaelm.3c00375. |
| APA | [1] Phuong Y. Le, Hilal Nagib, Luke A. Sylvander, Martin W. Allen, Dougal G. McCulloch, James G. Partridge, & Hiep N. Tran. (2023). ACS Applied Electronic Materials(5). https://doi.org/10.1021/acsaelm.3c00375 |
| IEEE | [1] Phuong Y. Le, Hilal Nagib, Luke A. Sylvander, Martin W. Allen, Dougal G. McCulloch, James G. Partridge, and Hiep N. Tran, ACS Applied Electronic Materials, no. 5, 2023, doi: 10.1021/acsaelm.3c00375. keywords: {memristors tin oxide neuromorphic computing artificial synapses pattern recognition} |
