Pub Date : 2022-08-01DOI: 10.1109/tmrc56419.2022.9918539
S. Greaves, Y. Kanai
A potential technology for use in future hard disk drives is microwave-assisted magnetic recording (MAMR). In a MAMR drive a spin-torque oscillator (STO) is integrated into the write head and applies a high frequency (HF) magnetic field to the recording medium along with the field from the write head. The HF field reduces the switching field of the medium, allowing media with higher uniaxial anisotropy to be used. As the write head moves over the recording medium the HF field seen by the medium grains changes from elliptical to circular to linear and back again. In this work the influence of circular, elliptical and linear HF fields on recording media was examined by calculating hysteresis loops for single and multiple grains.
{"title":"Hysteresis loops of recording media grains under the influence of high frequency fields","authors":"S. Greaves, Y. Kanai","doi":"10.1109/tmrc56419.2022.9918539","DOIUrl":"https://doi.org/10.1109/tmrc56419.2022.9918539","url":null,"abstract":"A potential technology for use in future hard disk drives is microwave-assisted magnetic recording (MAMR). In a MAMR drive a spin-torque oscillator (STO) is integrated into the write head and applies a high frequency (HF) magnetic field to the recording medium along with the field from the write head. The HF field reduces the switching field of the medium, allowing media with higher uniaxial anisotropy to be used. As the write head moves over the recording medium the HF field seen by the medium grains changes from elliptical to circular to linear and back again. In this work the influence of circular, elliptical and linear HF fields on recording media was examined by calculating hysteresis loops for single and multiple grains.","PeriodicalId":432413,"journal":{"name":"2022 IEEE 33rd Magnetic Recording Conference (TMRC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127940065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-06DOI: 10.1109/TMRC56419.2022.9918163
E. Sadeghian
The advent of multiple readers in magnetic recording opens the possibility of replacing the current industry's single-track detection with the more promising multitrack detection architectures. We have proposed a first solution, a generalized partial-response maximum-likelihood (GPRML) architecture, that extends the conventional PRML paradigm to jointly detect multiple asynchronous tracks. In this paper, we propose to replace the conventional communication-theoretic multiple-input multiple-output equalizer in the GPRML architecture with a neural network equalizer for better adaption to the nonlinearity of the underlying channel. We evaluate the proposed equalization strategy on a realistic two-dimensional magnetic-recording channel, and find that the proposed equalizer outperforms the conventional linear equalizer, by a 37% reduction in the bit-error rate and a 33% gain in the areal density.
{"title":"Neural Network Equalization for Asynchronous Multitrack Detection in TDMR","authors":"E. Sadeghian","doi":"10.1109/TMRC56419.2022.9918163","DOIUrl":"https://doi.org/10.1109/TMRC56419.2022.9918163","url":null,"abstract":"The advent of multiple readers in magnetic recording opens the possibility of replacing the current industry's single-track detection with the more promising multitrack detection architectures. We have proposed a first solution, a generalized partial-response maximum-likelihood (GPRML) architecture, that extends the conventional PRML paradigm to jointly detect multiple asynchronous tracks. In this paper, we propose to replace the conventional communication-theoretic multiple-input multiple-output equalizer in the GPRML architecture with a neural network equalizer for better adaption to the nonlinearity of the underlying channel. We evaluate the proposed equalization strategy on a realistic two-dimensional magnetic-recording channel, and find that the proposed equalizer outperforms the conventional linear equalizer, by a 37% reduction in the bit-error rate and a 33% gain in the areal density.","PeriodicalId":432413,"journal":{"name":"2022 IEEE 33rd Magnetic Recording Conference (TMRC)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117262441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-16DOI: 10.1109/TMRC56419.2022.9918590
Jonathan M. Goodwill, N. Prasad, B. Hoskins, M. Daniels, A. Madhavan, L. Wan, T. Santos, M. Tran, J. Katine, P. Braganca, M. Stiles, J. McClelland
Magnetic tunnel junctions (MTJs) provide an attractive platform for implementing neural networks because of their simplicity, non-volatility, and scalability. However, in hardware realizations, device variations, write errors, and parasitic resistance degrade performance. To quantify such effects, we perform inference experiments on a 2-layer perceptron constructed from a 15 x 15 passive array of MTJs, examining classification accuracy and write fidelity. Despite imperfections, we achieve median accuracy of 95.3% with proper tuning of network parameters. The success of this tuning process shows that new metrics are needed to characterize and optimize networks reproduced in mixed signal hardware.
磁隧道结(MTJs)因其简单、无波动性和可扩展性而为实现神经网络提供了一个有吸引力的平台。然而，在硬件实现中，器件变化、写错误和寄生电阻会降低性能。为了量化这种影响，我们在一个由15 x 15被动mtj阵列构建的2层感知器上进行了推理实验，检查了分类精度和写入保真度。尽管存在缺陷，但通过适当调整网络参数，我们实现了95.3%的中位数精度。这种调整过程的成功表明，需要新的指标来表征和优化在混合信号硬件中再现的网络。
{"title":"Implementation of a Binary Neural Network on a Passive Array of Magnetic Tunnel Junctions","authors":"Jonathan M. Goodwill, N. Prasad, B. Hoskins, M. Daniels, A. Madhavan, L. Wan, T. Santos, M. Tran, J. Katine, P. Braganca, M. Stiles, J. McClelland","doi":"10.1109/TMRC56419.2022.9918590","DOIUrl":"https://doi.org/10.1109/TMRC56419.2022.9918590","url":null,"abstract":"Magnetic tunnel junctions (MTJs) provide an attractive platform for implementing neural networks because of their simplicity, non-volatility, and scalability. However, in hardware realizations, device variations, write errors, and parasitic resistance degrade performance. To quantify such effects, we perform inference experiments on a 2-layer perceptron constructed from a 15 x 15 passive array of MTJs, examining classification accuracy and write fidelity. Despite imperfections, we achieve median accuracy of 95.3% with proper tuning of network parameters. The success of this tuning process shows that new metrics are needed to characterize and optimize networks reproduced in mixed signal hardware.","PeriodicalId":432413,"journal":{"name":"2022 IEEE 33rd Magnetic Recording Conference (TMRC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133299248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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