CIF:Small:Machine Learning Based Turbo Detection for Two and Three Dimensional Magnetic Recording

该项目研究用于下一代硬盘驱动器的二维和三维磁记录(TDMR和3DMR)。在TDMR中，位被写在磁存储盘的二维块上，而在3DMR中，位被写在多个盘层上。TDMR是一种新兴技术，它承诺每单位磁盘面积的信息位增加高达一个数量级，而不需要对磁盘进行彻底的重新设计。3DMR是一种比TDMR更新的技术，它有望显著提高区域信息密度。TDMR和3DMR中的一个关键问题是，在高密度下，一些位不会写入磁盘上的任何磁性颗粒。此外，还必须与信号色散作斗争：沿磁道、跨磁道和层间。本项目研究机器学习，用于在每个编码比特1到4个磁粒的高密度下对TDMR和3DMR通道进行Turbo检测。所考虑的机器学习主题包括研究人员最近介绍的局部区域影响概率模型检测器的设计，以及用于TDMR和3DMR的深度神经网络检测器的设计。通过已建立的合作，研究人员将用真实的波形验证开发的技术，并将促进技术转让。该项目还包括教育和外联部分。该项目的具体技术目标是：(I)开发深度神经网络的信息论设计技术，(Ii)为TDMR涡轮探测器设计基于机器学习的介质噪声预测器，(Iii)设计同时处理二维符号间干扰和介质噪声的深层神经网络探测器，(Iv)推广基于机器学习的3DMR探测器设计，以及(V)评估使用TDMR和3DMR波形开发的设计，这些设计来自国际合作者的真实微磁模拟。这项工作预计将在实现行业每平方英寸10太比特的信息密度目标方面取得重大进展。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project investigates two and three dimensional magnetic recording (TDMR and 3DMR) for next generation hard disk drives. In TDMR, bits are written on two-dimensional patches of a magnetic storage disk, whereas in 3DMR bits are written on multiple disk layers. TDMR is an emerging technology that promises up to an order of magnitude increase in information bits per unit of disk area, without requiring radical redesign of the disk. 3DMR is an even newer technology that has the promise of significant areal information density increases over TDMR. A key problem in TDMR and 3DMR is that, at high densities, some bits are not written to any of the magnetic grains on the disk. Moreover, one must contend with signal dispersion: along-track, across-tracks, and between layers. This project investigates machine learning for turbo detection of TDMR and 3DMR channels at high densities of between 1 and 4 magnetic grains per coded bit. The considered machine learning topics include design of local area influence probabilistic model detectors, recently introduced by the investigators, and design of deep neural network detectors for TDMR and 3DMR. Through established collaborations, the investigators will validate the developed techniques with realistic waveforms and will facilitate technology transfer. The project also includes educational and outreach components. The investigators will work with the Voiland College of Engineering Diversity Programs office to identify potential undergraduate researchers from underrepresented groups to participate in the project.The specific technical objectives of this project are: (i) developing information-theoretic design techniques for deep neural networks, (ii) designing machine learning based media noise predictors for TDMR turbo-detectors, (iii) designing deep neural network detectors that handle both two-dimensional intersymbol interference and media noise, (iv) generalizing the machine learning based detector designs for 3DMR, and (v) evaluating the developed designs with TDMR and 3DMR waveforms derived from realistic micromagnetic simulations, obtained from international collaborators. This work is expected to provide significant progress toward the industry's information density target of 10 Terabits per square inch.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.