Amylum Resistive Random Access Memory Based on the Quantization Effect of Gold Nanoparticles Amylum Resistive Random Access Memory Based on the Quantization Effect of Gold Nanoparticles AITranslate
Abstract AITranslate
Resistive random access memory (RRAM) is integrated computing and storage, but most devices have problems, such as high power consumption and poor consistency, so it is critical to optimize the performance of the device. In this paper, amylum RRAM is prepared by using the quantization effect of gold nanoparticles (Au NPs), which not only improves the consistency of the threshold voltage distribution of the device but also reduces the power consumption of the device and significantly increases the ON/OFF current ratio of the device. This work not only realized multilevel data storage (4 levels, 2 bits) on the same unit, improving the storage density of starch RRAM, but also simulated the synaptic potentiation and depression behavior, providing a way of thinking for the next generation of artificial intelligence and brain-like neural computing.
KeyWords AITranslate
Basic Information:
DOI:https://doi.org/10.1021/acsaelm.2c01432
Chinese Library Classification Number:
Citation Information:
Resistive random access memory (RRAM) is integrated computing and storage, but most devices have problems, such as high power consumption and poor consistency, so it is critical to optimize the performance of the device. In this paper, amylum RRAM is prepared by using the quantization effect of gold nanoparticles (Au NPs), which not only improves the consistency of the threshold voltage distribution of the device but also reduces the power consumption of the device and significantly increases the ON/OFF current ratio of the device. This work not only realized multilevel data storage (4 levels, 2 bits) on the same unit, improving the storage density of starch RRAM, but also simulated the synaptic potentiation and depression behavior, providing a way of thinking for the next generation of artificial intelligence and brain-like neural computing.
quote
| GB/T 7714-2015 | [1] Lu Wang, Jiazhuang Li, Dianzhong Wen. ACS Applied Electronic Materials, 2023(5). DOI:10.1021/acsaelm.2c01432. |
| MLA | [1] Lu Wang, et al., ACS Applied Electronic Materials, no. 5, 2023, https://doi.org/10.1021/acsaelm.2c01432. |
| APA | [1] Lu Wang, Jiazhuang Li, & Dianzhong Wen. (2023). ACS Applied Electronic Materials(5). https://doi.org/10.1021/acsaelm.2c01432 |
| IEEE | [1] Lu Wang, Jiazhuang Li, and Dianzhong Wen, ACS Applied Electronic Materials, no. 5, 2023, doi: 10.1021/acsaelm.2c01432. keywords: {amylum gold nanoparticles quantization effect simulate synapses multilevel storage} |
