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

本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，比特被写入磁盘的二维补丁上，而在3DMR中，比特被写入多个磁盘层。TDMR是一种新兴技术，它承诺在不需要对磁盘进行彻底重新设计的情况下，将每单位磁盘面积的信息位数提高一个数量级。3DMR是一种更新的技术，与TDMR相比，它有望显著提高面信息密度。TDMR和3DMR的一个关键问题是，在高密度下，一些比特没有写入磁盘上的任何磁性颗粒。此外，人们还必须应对信号的分散：沿轨道、跨轨道和层与层之间。该项目研究了TDMR和3DMR通道在每编码位1到4个磁粒密度下的涡轮检测机器学习。考虑的机器学习主题包括研究人员最近介绍的局部影响概率模型检测器的设计，以及TDMR和3DMR的深度神经网络检测器的设计。通过已建立的合作，研究人员将用实际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。研究人员将与沃伊兰工程学院多样性项目办公室合作，从代表性不足的群体中确定潜在的本科生研究人员参与该项目。该项目的具体技术目标是：(i)开发深度神经网络的信息理论设计技术，（ii）为TDMR涡轮探测器设计基于机器学习的媒体噪声预测器，（iii）设计处理二维符号间干扰和媒体噪声的深度神经网络探测器，（iv）推广基于机器学习的3DMR探测器设计，(v)使用来自现实微磁模拟的TDMR和3DMR波形评估开发的设计。从国际合作者处获得。这项工作预计将为业界每平方英寸10太比特的信息密度目标提供重大进展。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.