This project concerns two-dimensional magnetic recording (TDMR), a novel recording technology for hard disk drives that allows for a drastic increase in data density, up to 10 terabits per square inch. Gains from TDMR come from two directions, namely (i) the shingled writing mechanism whereby adjacent data tracks are written with partial overlap, like roof shingles, in order to squeeze many more tracks on the disk and increase data density, and (ii) powerful signal processing algorithms that enable efficient data recovery from noisy readback signals in the presence of high levels of interference both within and across data tracks. Techniques from machine learning (ML) will be used in developing such data recovery algorithms in the presence of two-dimensional interference, and data-dependent and colored media noise. The proposed work aims to achieve significant improvements in TDMR, eventually allowing exponentially increasing volumes of data to be stored on fewer disk drives with higher capacities. This award partially supports a PhD student to be trained in TDMR read channel design, ultimately creating career opportunities for the student in the data storage industry.

The research objective is the development of efficient ML based equalization algorithms that outperform conventional communication-theoretic equalization for high density TDMR. The TDMR channel being highly nonlinear, ML approaches are expected to better learn its characteristics, potentially leading to higher bit-error rates when compared to conventional linear communication-theoretic schemes. The desired neural network equalization schemes seek to (i) incorporate the prediction and cancellation of the media noise, and (ii) be compatible with a novel read channel architecture, developed by the investigator, that extends the partial-response paradigm to the case of multitrack detection of asynchronous tracks. To realize this read channel, the developed equalizers will be followed by the rotating-target (ROTAR) algorithm, a multitrack detector of asynchronous tracks, also developed by the investigator. The resulting read channel is expected to yield gains in areal density and throughput over the communication-theoretic and single-track detection schemes currently used in the industry. The performance of the developed algorithms will be compared against that of conventional algorithms using realistic waveforms provided by international collaborators.

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.

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Stevens Institute of Technology
United States
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