R. K. Raj, Ravi Shankar Verma, Shailendra Yadav, B. Kaushik
{"title":"基于SAF skyrion的泄漏集成火神经元装置","authors":"R. K. Raj, Ravi Shankar Verma, Shailendra Yadav, B. Kaushik","doi":"10.1117/12.2681617","DOIUrl":null,"url":null,"abstract":"The magnetic skyrmion has distinct features like nanoscale size, particle-like behavior, low driving current, and topologically stable which makes it a suitable candidate for neuromorphic computing. Synthetic antiferromagnetic (SAF) skyrmions consist of a pair of coupled ferromagnetic (FM) skyrmions, each in its respective sub-layers that are favourable over the FM skyrmions as they follow the straight trajectories and prevent its annihilation at the nanotrack edge. In this work, a leaky integrate and fire neuronal device model is proposed based on SAF skyrmions with voltage control magnetic anisotropy (VCMA) as a leaky effect for the tunability of the device. The anisotropy is directly correlated with the size of the skyrmion meaning that in the region with larger anisotropy, the skyrmion size is smaller and hence, more energy. However, the skyrmions have the tendency to move toward the minimum energy state means it will move towards the lower anisotropy. This behavior of SAF skyrmion on a nanotrack with anisotropy gradient corresponds to the leaky-integrate-fire (LIF) functionality of the neuron device. Moreover, device performance is also realized at room temperature for practical implementation. Hence, the proposed device possesses an energy-efficient artificial neuron opens up the path for the development of next-generation skyrmionic devices for neuromorphic computing.","PeriodicalId":13820,"journal":{"name":"International Conference on Nanoscience, Engineering and Technology (ICONSET 2011)","volume":"25 1","pages":"126560Y - 126560Y-7"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SAF skyrmion-based leaky-integrate fire neuron device\",\"authors\":\"R. K. Raj, Ravi Shankar Verma, Shailendra Yadav, B. Kaushik\",\"doi\":\"10.1117/12.2681617\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The magnetic skyrmion has distinct features like nanoscale size, particle-like behavior, low driving current, and topologically stable which makes it a suitable candidate for neuromorphic computing. Synthetic antiferromagnetic (SAF) skyrmions consist of a pair of coupled ferromagnetic (FM) skyrmions, each in its respective sub-layers that are favourable over the FM skyrmions as they follow the straight trajectories and prevent its annihilation at the nanotrack edge. In this work, a leaky integrate and fire neuronal device model is proposed based on SAF skyrmions with voltage control magnetic anisotropy (VCMA) as a leaky effect for the tunability of the device. The anisotropy is directly correlated with the size of the skyrmion meaning that in the region with larger anisotropy, the skyrmion size is smaller and hence, more energy. However, the skyrmions have the tendency to move toward the minimum energy state means it will move towards the lower anisotropy. This behavior of SAF skyrmion on a nanotrack with anisotropy gradient corresponds to the leaky-integrate-fire (LIF) functionality of the neuron device. Moreover, device performance is also realized at room temperature for practical implementation. Hence, the proposed device possesses an energy-efficient artificial neuron opens up the path for the development of next-generation skyrmionic devices for neuromorphic computing.\",\"PeriodicalId\":13820,\"journal\":{\"name\":\"International Conference on Nanoscience, Engineering and Technology (ICONSET 2011)\",\"volume\":\"25 1\",\"pages\":\"126560Y - 126560Y-7\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-09-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Nanoscience, Engineering and Technology (ICONSET 2011)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2681617\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Nanoscience, Engineering and Technology (ICONSET 2011)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2681617","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
SAF skyrmion-based leaky-integrate fire neuron device
The magnetic skyrmion has distinct features like nanoscale size, particle-like behavior, low driving current, and topologically stable which makes it a suitable candidate for neuromorphic computing. Synthetic antiferromagnetic (SAF) skyrmions consist of a pair of coupled ferromagnetic (FM) skyrmions, each in its respective sub-layers that are favourable over the FM skyrmions as they follow the straight trajectories and prevent its annihilation at the nanotrack edge. In this work, a leaky integrate and fire neuronal device model is proposed based on SAF skyrmions with voltage control magnetic anisotropy (VCMA) as a leaky effect for the tunability of the device. The anisotropy is directly correlated with the size of the skyrmion meaning that in the region with larger anisotropy, the skyrmion size is smaller and hence, more energy. However, the skyrmions have the tendency to move toward the minimum energy state means it will move towards the lower anisotropy. This behavior of SAF skyrmion on a nanotrack with anisotropy gradient corresponds to the leaky-integrate-fire (LIF) functionality of the neuron device. Moreover, device performance is also realized at room temperature for practical implementation. Hence, the proposed device possesses an energy-efficient artificial neuron opens up the path for the development of next-generation skyrmionic devices for neuromorphic computing.