PROJECT TITLE :

Skew-Dependent Performance Evaluation of Array-Reader-Based Magnetic Recording With Dual-Reader

ABSTRACT:

Array-reader-based mostly magnetic recording (ARMR) shows potential to attain areal density capability (ADC) beyond 1 Tb/in $^mathrm 2$ by jointly processing multiple readback streams. Dual-reader ARMR with two scan sensors and associated scan channel signal processing algorithms are currently being actively investigated. In this paper, twin-reader ARMR performance is evaluated that specialize in skew-induced variation in cross-track separation (CTS) between the 2 browse sensors. Spin-stand captured waveforms based analysis is presented for the cases where a twin-reader with bound CTS and skew is emulated using captures from one-reader at different cross-track locations plus for the case of actual twin-reader-based mostly captures, where the latter also accounts for head rotation. Based on bit error rate scan along cross-track under numerous squeezed recording and skew conditions, squeeze-to-death margin-primarily based ADC gain of ARMR is predicted. Twin-reader ARMR shows five%–10% ADC gain over single-reader for CTS but 0.vi track pitch, whereas showing limited gains for larger CTS. Additionally presented is the performance evaluation of dual-reader ARMR on spin-stand employing a hardware accelerated ARMR performance analysis platform, known as Stingray, which uses four Avago scan channel silicon chips and a customized field programmable gate array to enable high-speed joint equalization and detection using twin-reader readback streams.