{"title":"倾斜单脉冲时钟驱动多铁神经元器件的性能仿真","authors":"Shuqing Dou;Xiaokuo Yang;Jiahui Yuan;Yongshun Xia;Xin Bai;Huanqing Cui;Bo Wei","doi":"10.1109/LMAG.2023.3287396","DOIUrl":null,"url":null,"abstract":"Multiferroic nanomagnet neuron devices have the advantages of ultralow power consumption and high integration, which give them promising applications in neuromorphic computing. In this letter, a multiferroic nanomagnet neuron device driven by an inclined monopulse clock is modeled. The strain field direction of the device is at an angle to the nanomagnet's long axis, and the nanomagnet's magnetic moment can be driven to switch randomly 0°/180° by applying a pulse voltage of 0.1 ns pulse width only, thus realizing artificial neuron functions. The numerical model of the neuron device is established based on the Landau–Lifshitz–Gilbert equation. The numerical simulation results indicate that the neuron device can complete high-speed neuromorphic computation with tiny energy use (∼2.65 aJ). Additionally, a three-layer artificial neural network based on neuron devices is built. The simulation results demonstrate that the network can recognize handwritten digits in the Modified National Institute of Standards and Technology (MNIST) dataset at a rate of more than 98% and has a high tolerance for process error. The device has significant advantages over conventional spin neuron devices, including a simple structure, ultralow energy consumption, fast computation capabilities, and a wide fabrication process error tolerance range. The study results in this letter offer crucial theoretical recommendations for applying strain magneto-electronic devices in neuromorphic computing.","PeriodicalId":13040,"journal":{"name":"IEEE Magnetics Letters","volume":"14 ","pages":"1-5"},"PeriodicalIF":1.1000,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Performance Simulation of Multiferroic Neuron Device Driven by an Inclined Monopulse Clock\",\"authors\":\"Shuqing Dou;Xiaokuo Yang;Jiahui Yuan;Yongshun Xia;Xin Bai;Huanqing Cui;Bo Wei\",\"doi\":\"10.1109/LMAG.2023.3287396\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Multiferroic nanomagnet neuron devices have the advantages of ultralow power consumption and high integration, which give them promising applications in neuromorphic computing. In this letter, a multiferroic nanomagnet neuron device driven by an inclined monopulse clock is modeled. The strain field direction of the device is at an angle to the nanomagnet's long axis, and the nanomagnet's magnetic moment can be driven to switch randomly 0°/180° by applying a pulse voltage of 0.1 ns pulse width only, thus realizing artificial neuron functions. The numerical model of the neuron device is established based on the Landau–Lifshitz–Gilbert equation. The numerical simulation results indicate that the neuron device can complete high-speed neuromorphic computation with tiny energy use (∼2.65 aJ). Additionally, a three-layer artificial neural network based on neuron devices is built. The simulation results demonstrate that the network can recognize handwritten digits in the Modified National Institute of Standards and Technology (MNIST) dataset at a rate of more than 98% and has a high tolerance for process error. The device has significant advantages over conventional spin neuron devices, including a simple structure, ultralow energy consumption, fast computation capabilities, and a wide fabrication process error tolerance range. The study results in this letter offer crucial theoretical recommendations for applying strain magneto-electronic devices in neuromorphic computing.\",\"PeriodicalId\":13040,\"journal\":{\"name\":\"IEEE Magnetics Letters\",\"volume\":\"14 \",\"pages\":\"1-5\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2023-06-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Magnetics Letters\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10155227/\",\"RegionNum\":4,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Magnetics Letters","FirstCategoryId":"101","ListUrlMain":"https://ieeexplore.ieee.org/document/10155227/","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Performance Simulation of Multiferroic Neuron Device Driven by an Inclined Monopulse Clock
Multiferroic nanomagnet neuron devices have the advantages of ultralow power consumption and high integration, which give them promising applications in neuromorphic computing. In this letter, a multiferroic nanomagnet neuron device driven by an inclined monopulse clock is modeled. The strain field direction of the device is at an angle to the nanomagnet's long axis, and the nanomagnet's magnetic moment can be driven to switch randomly 0°/180° by applying a pulse voltage of 0.1 ns pulse width only, thus realizing artificial neuron functions. The numerical model of the neuron device is established based on the Landau–Lifshitz–Gilbert equation. The numerical simulation results indicate that the neuron device can complete high-speed neuromorphic computation with tiny energy use (∼2.65 aJ). Additionally, a three-layer artificial neural network based on neuron devices is built. The simulation results demonstrate that the network can recognize handwritten digits in the Modified National Institute of Standards and Technology (MNIST) dataset at a rate of more than 98% and has a high tolerance for process error. The device has significant advantages over conventional spin neuron devices, including a simple structure, ultralow energy consumption, fast computation capabilities, and a wide fabrication process error tolerance range. The study results in this letter offer crucial theoretical recommendations for applying strain magneto-electronic devices in neuromorphic computing.
期刊介绍:
IEEE Magnetics Letters is a peer-reviewed, archival journal covering the physics and engineering of magnetism, magnetic materials, applied magnetics, design and application of magnetic devices, bio-magnetics, magneto-electronics, and spin electronics. IEEE Magnetics Letters publishes short, scholarly articles of substantial current interest.
IEEE Magnetics Letters is a hybrid Open Access (OA) journal. For a fee, authors have the option making their articles freely available to all, including non-subscribers. OA articles are identified as Open Access.