Dynamic magnetic information storage or retrieval – Monitoring or testing the progress of recording
Reexamination Certificate
2002-03-21
2004-09-07
Faber, Alan T. (Department: 2651)
Dynamic magnetic information storage or retrieval
Monitoring or testing the progress of recording
C360S045000, C360S053000, C360S051000
Reexamination Certificate
active
06788481
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates to measuring the nonlinear transition shift (NLTS) in a magnetic data storage system and more particularly to determining NLTS in a high-density data store having a readback channel employing a giant magnetoresistive (GMR) sensor.
2. Description of the Related Art
Introduction
In a typical recording channel, nonlinearities can occur for several different reasons during both writing and reading. Firstly, it is well-known that when closely-spaced data bits are written on a magnetic storage medium, such as a magnetic disk, the magnetic transition positions are shifted by the magnetostatic interactions between adjacent transitions. This shift, herein denominated nonlinear transition shift (NLTS), contributes to the total recording channel nonlinearity. The art is replete with descriptions of NLTS; for example, reference is made to Zhang et al., “A Theoretical Study of Nonlinear Transition Shift,”
IEEE Trans. Magn
., Vol.34, pp. 1955-1957, July 1998. For example, in
FIG. 1
, a dibit pair of flux transitions
30
and
32
are illustrated schematically as adjacent flux reversals separated by the bit cell length B in a recording medium
34
. The NLTS is shown conceptually as a shift of d in flux transition
32
to a new position by virtue of the magnetostatic interactions between the transitions.
Secondly, a closely-adjacent recorded transition may also cause a transition broadening effect (TBE) during the writing of the next transition because of the consequential reduction in recording field gradient induced magnetostatically by the adjacent transition. Reduction of the recording field gradient broadens the width of the resulting transition in the recording medium. Moreover, as the transition spacing is reduced, causing two adjacent transitions to approach one another, the opposing magnetostatic potentials may locally annihilate one another in an unpredictable fashion; a condition herein denominated partial erasure (PE). As a result of PE, a single transition is broken into many transition segments or islands distributed over the locale intended for the single transition. PE arises gradually as transition spacing is reduced and is exacerbated in closely-spaced transitions because of TBE. The art is also replete with descriptions of TBE and PE; for example, reference is made to Che, “Nonlinearity Measurements and Write Precompensation Studies for a PRML Recording Channel,”
IEEE Trans. Magn
., Vol.31, pp. 3021-3026, November 1995. Although the two nonlinear phenomena, PE and TBE, are different microscopically and arise from different causes, the resulting playback waveform distortions are similar and may together be reasonably assumed to be a single recording channel nonlinearity herein denominated as TBE/PE or simply PE.
Finally, the giant magnetoresistive (GMR) sensor is known to exhibit severe nonlinearity because of its nonlinear response to magnetic fields. During reading, this nonlinearity is manifested as a nonlinear transfer curve (NTC) of the GMR sensor. GMR heads are widely preferred in magnetic recording because of their high signal output levels compared to earlier MR heads and inductive heads, but their nonlinear signal response characterized by NTC is also well-known. The art is replete with descriptions of the transfer characteristics of magnetoresistive (MR) and GMR sensors; for example, reference is made to Cai, “Magnetoresistive Read Nonlinearity Correction by a Frequency-Domain Approach,”
IEEE Trans. Magn
., Vol.35, pp. 4532-4534, November 1999.
NLTS can largely degrade the performance of the partial-response maximum-likelihood (PRML) channel that is widely preferred in disk-drive read channels. A PRML detector expects playback waveforms to be composed of a linear superposition of isolated pulses (transition detections) and its performance is significantly degraded by nonlinear distortion of the playback pulses, whether from NLTS, TBE/PE or the NTC of a GMR readback sensor.
Fortunately, in the recording channel, the effects of NLTS can be reduced significantly by properly controlling the recording timing of each transition. In particular, it is possible to compensate for the NLTS effect by pre-shifting the transition positions during the write operation, but this requires some means for accurately measuring the NLTS for the particular write head, transition spacing and recording medium employed. There are several different methods known in the art for measuring NLTS. Currently, the most reliable and popular NLTS test method used in manufacturer testing lines at recording head companies is the fifth-harmonic elimination (5HE) method. However, the accuracy of 5HE NLTS measurements is significantly affected by the other two kinds of nonlinearity described above, TBE/PE and NTC. Because each of these three kinds of nonlinearity, NLTS, TBE/PE and NTC, arises from a different component, such as writer, reader or data storage medium, in a different manner during the recording process, it is very important for head designers to distinguish among them and to effectively measure each of them accurately and separately. The NLTS must be distinguished from the TBE/PE nonlinearities, which also can be minimized by imposing a lower limit on transition spacing. It is also possible to compensate for the NTC of a GMR sensor in the readback channel, provided that the NTC can be measured and distinguished from the other nonlinearities. Accordingly, the accurate testing and quantitative determination of each of these non-linearities in a playback waveform is important for optimizing performance of a PRML detector. A well-known problem in the art is how to accurately separate the contributions of each nonlinearity (NLTS, TBE/PE and NTC) from the overall nonlinear distortion that can be measured in the recording channel and the art is replete with proposals for measuring and compensating for these recording channel nonlinearities. Several useful testing methods have been developed in the art for characterizing playback waveform nonlinearities, which may be loosely classified as time-domain methods and frequency-domain methods.
The dominant time-domain technique for measuring NLTS, herein denominated the Pseudo-Random Sequence (PRS) Method, was first proposed by Palmer et al. (Palmer et al., “Identification of Nonlinear Write Effects Using a Pseudorandom Sequence,”
IEEE Trans. Magn
., Vol. 23, pp. 2377-2379, September 1987). By recording and reading a pseudorandom sequence (PRS) of transitions and then processing it with Fourier transform methods, the nonlinearities are identified from small perturbations or echoes, usually well-separated from the main linear part of the response of the system. The original pseudorandom sequence is deconvolved from the playback waveform to yield the linear dipulse response and any echoes arising from nonlinear effects. By measuring the amplitude of these echoes relative to the main dipulse response, the nonlinear distortion may be quantitatively assessed as a percentage of the primary dipulse signal level. This PRS method can be used to systematically analyze all nonlinear mechanisms and to characterize the entire recording channel. However, this method requires a sophisticated measurement procedure that includes complicated waveform triggering, data acquisition and manipulations. The exact original data sequence must be known to process the data. Any noise and the DC offset in the readback waveform may give rise to a considerable error with this method. In most cases only one nonlinear mechanism dominates and using this complex and difficult method is inefficient.
Other practitioners have proposed various improvements and simplifications to the original PRS method, often by suggesting useful assumptions about the various nonlinearity phenomena and simplifying the procedure to capitalize on the new assumptions. For example, Che et al. (Che et al., “A Time-Correlation Method of Calculating Nonlinearities Utilizing Pseudorandom Sequences,”
IEEE Trans. Magn
., Vol.30, pp. 4239
Fang Peter Chen-I
Feng Xiangjun
Lam Terence Tin-Lok
Lin Zhong-heng
Faber Alan T.
International Business Machines - Corporation
Johnston Ervin F.
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