Nonvolatile Bioresistive Random Access Memory Based on Glycine max and Graphene Oxide Nonvolatile Bioresistive Random Access Memory Based on Glycine max and Graphene Oxide AITranslate
Abstract AITranslate
Devices that realize memory functions and simulate synaptic characteristics are an important part of realizing artificial neural networks. In this article, a bioresistive random access memory based on Glycine max and graphene oxide (GO) is proposed. The devices can simulate the potentiation and depression of synapses under pulsed voltage stimulation. The device has different low resistance values under different compliance currents, which can realize multilevel data storage (4 levels, 2 bits) on the same cell. With the change in GO concentration, the ON/OFF current ratio of the devices can be adjusted in the range of 10–104. Since the minimum operating voltage is ±1 V, the power consumption of the devices is low. Multilevel tunable nonvolatile resistive random access memory has great potential in data storage and artificial synapses in the future.
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DOI:https://doi.org/10.1021/acsaelm.3c00558
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Devices that realize memory functions and simulate synaptic characteristics are an important part of realizing artificial neural networks. In this article, a bioresistive random access memory based on Glycine max and graphene oxide (GO) is proposed. The devices can simulate the potentiation and depression of synapses under pulsed voltage stimulation. The device has different low resistance values under different compliance currents, which can realize multilevel data storage (4 levels, 2 bits) on the same cell. With the change in GO concentration, the ON/OFF current ratio of the devices can be adjusted in the range of 10–104. Since the minimum operating voltage is ±1 V, the power consumption of the devices is low. Multilevel tunable nonvolatile resistive random access memory has great potential in data storage and artificial synapses in the future.
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| GB/T 7714-2015 | [1] Lu Wang, Jiazhuang Li, Wantao Su, et al. ACS Applied Electronic Materials, 2023(5). DOI:10.1021/acsaelm.3c00558. |
| MLA | [1] Lu Wang, et al., ACS Applied Electronic Materials, no. 5, 2023, https://doi.org/10.1021/acsaelm.3c00558. |
| APA | [1] Lu Wang, Jiazhuang Li, Wantao Su, & Dianzhong Wen. (2023). ACS Applied Electronic Materials(5). https://doi.org/10.1021/acsaelm.3c00558 |
| IEEE | [1] Lu Wang, Jiazhuang Li, Wantao Su, and Dianzhong Wen, ACS Applied Electronic Materials, no. 5, 2023, doi: 10.1021/acsaelm.3c00558. keywords: {graphene oxide Glycine max bioresistive random access memory multilevel storage artificial synapse} |
