Western Digital Technologies, Inc.

Pradhan Bellam

Richard Leo Galbraith

Niranjay Ravindran

Iouri Oboukhov

Weldon Mark Hanson

Jonas Andrew Goode

Henry Yip

John Thomas Contreras

Daniel Bedau

Derrick E Burton

Michael J Ross

Example channel circuits, data storage devices, and methods for asynchronous sampling from an oversampled analog-to-digital converter are described. The channel circuit may include an analog-to-digital converter configured to generate an oversampled digital signal from an analog data signal using a sample rate that is an integer multiple of the baud rate of the channel circuit. A digital sample interpolator may then interpolate interpolated digital signal values from multiple signal values of the oversampled digital signal and select values at baud rate to generate a baud rate digital signal. The baud rate digital signal may be used by an iterative detector in a timing loop and, once a target timing is achieved, for the iterative detector to detect data bits from the interpolated digital signal.

Channel circuit with asynchronous sampling from an oversampled analog-to-digital converter

Example channel circuits, data storage devices, and methods for using zero force adaptation to equalize a read data signal based on known data are described. A known user data signal may be determined during prior read operations and used with a residue term from the equalized read data signal to adapt the tap weights for an equalizer filter using a zero force adaptation algorithm. For example, the known user data signal may be determined by a soft output detector (e.g., SOVA detector) or full or partial decoding by an iterative decoder (e.g., LDPC decoder) and fed back for adapting the equalizer.

Channel circuit with zero force equalizer

Example systems, read channel circuits, data storage devices, and methods to use a global variance parameter based on mutual information to modify operation of a soft output detector in a read channel are described. The read channel circuit includes a soft output detector, such as a soft output Viterbi algorithm (SOVA) detector that includes variance terms. The variance terms are modified by a global variance parameter based on mutual information values. The soft output detector processes an input signal using the modified branch variance terms to determine data bits and corresponding soft information for decoding data in the read channel.

Global variance parameter based on mutual information in a data channel

Example systems, read channels, and methods provide bit value detection from an encoded data signal using a neural network soft information detector. The neural network detector determines a set of probabilities for possible states of a data symbol from the encoded data signal. A soft output detector uses the set of probabilities for possible states of the data symbol to determine a set of bit probabilities that are iteratively exchanged as extrinsic information with an iterative decoder for making decoding decisions. The iterative decoder outputs decoded bit values for a data unit that includes the data symbol.

Neural network soft information detector in a read channel

Example systems, read channel circuits, data storage devices, and methods to provide signal correction based on soft information in a read channel are described. The read channel circuit includes a soft output detector, such as a soft output Viterbi algorithm (SOVA) detector, and a signal correction circuit. The soft output detector passes detected data bits and corresponding soft information to the signal correction circuit. The signal correction circuit uses the soft information to determine a signal correction value, which is combined with input signal to return a corrected signal to the soft output detector for a next iteration. In some configurations, the signal correction value may compensate for DC offset, AC coupling poles, and/or signal asymmetries to reduce baseline wander in the read channel.

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Rochester, MN, United States of America

Pradhan Bellam