Abstract: The disclosure relates in some aspects to optimizing writes levels used for programming a non-volatile memory device. In some aspects, the disclosure relates to an algorithmic approach for adjusting write levels for a NAND flash device. For example, write level gradients may be iteratively generated based on memory cell bin distribution statistics relating to the number and direction of errors across bin boundaries.

Abstract: A data storage device is disclosed comprising a head actuated over a disk comprising a data track having at least a first data segment and a second data segment. A first plurality of codewords are generated, and a first parity sector is generated over the first plurality of codewords. The first plurality of codewords and the first parity sector are written to the first data segment. A second plurality of codewords are generated, and a second parity sector is generated over the second plurality of codewords. The second plurality of codewords and the second parity sector are written to the second data segment. During a read operation the data segments of the data track are processed sequentially to decode the codewords using a low density parity check (LDPC) decoder, wherein the reliability metrics of un-converged codewords are stored in a codeword buffer and updated using the respective parity sector.

Abstract: A data storage device is disclosed comprising a non-volatile storage medium (NVSM), wherein a plurality of codewords and corresponding parity sector are written to the NVSM and then read from the NVSM. Each codeword read from the NVSM is processed using a Viterbi-type detector, thereby generating codeword reliability metrics. The codeword reliability metrics for at least some of the codewords are processed using a low density parity check (LDPC) type decoder, thereby generating a LDPC reliability metric for each symbol of at least one codeword. The LDPC reliability metrics for at least one of an un-converged codeword are processed using the parity sector, thereby updating the un-converged codeword reliability metrics. Processing the codeword reliability metrics with the LDPC decoder and updating the reliability metrics with the parity sector is repeated at least once before updating the LDPC reliability metrics of at least the un-converged codeword using the Viterbi-type detector.

Abstract: A data storage device is disclosed wherein a first plurality of codewords are generated each comprising a plurality of symbols, and a first parity sector is generated over the first plurality of codewords. A second plurality of codewords are generated each comprising a plurality of symbols, and a second parity sector is generated over the second plurality of codewords. A third parity sector is generated over a first subset of the first plurality of codewords and a first subset of the second plurality of codewords, and a fourth parity sector is generated over a second subset of the first plurality of codewords and a second subset of the second plurality of codewords. When processing of a first codeword fails, the first codeword and the first parity sector are processed using a LDPC type decoder, and the first codeword and the third parity sector are processed using the LDPC type decoder.

Abstract: A data storage device is disclosed comprising a non-volatile storage medium (NVSM). A reliability metric for each symbol of each of a plurality of codewords read from the NVSM is generated, and a number of erasures for a first codeword are generated, wherein the number of erasures exceeds the correction power of the first codeword. A reliability metric of the first codeword is modified corresponding to one of the erasures. The reliability metrics for each codeword including the modified reliability metrics of the first codeword are first iteratively processed using a low density parity check (LDPC) type decoder, thereby first updating the reliability metric for each symbol of each codeword. The reliability metrics for the first codeword are second updated using the parity sector, and the second updated reliability metrics for the first codeword are second iteratively processed using the LDPC-type decoder.

Abstract: A method and data storage system receives a confidence vector for a non-binary symbol value read from a memory cell of a non-volatile memory device, where the confidence vector includes a first plurality of confidence values and transforms the first plurality of confidence values into a first plurality of likelihood values using a forward tensor-product transform. A respective binary message passing decoding operation is performed with each of the first plurality of likelihood values to generate a second plurality of likelihood values, and the second plurality of likelihood values are transformed into a second plurality of confidence values of the confidence vector using a reverse tensor-product transform.

Abstract: A data storage device is disclosed comprising a non-volatile storage medium (NVSM). A reliability metric for each symbol of each of a plurality of codewords read from the NVSM is generated, and a number of erasures for a first codeword are generated, wherein the number of erasures exceeds the correction power of the first codeword. A reliability metric of the first codeword is modified corresponding to one of the erasures. The reliability metrics for each codeword including the modified reliability metrics of the first codeword are first iteratively processed using a low density parity check (LDPC) type decoder, thereby first updating the reliability metric for each symbol of each codeword. The reliability metrics for the first codeword are second updated using the parity sector, and the second updated reliability metrics for the first codeword are second iteratively processed using the LDPC-type decoder.

Abstract: Systems and methods for read level tracking and optimization are described. Pages from a wordline of a flash memory device read and the raw page data read from the wordline may be buffered in a first set of buffers. The raw page data for each of the pages may be provided to a decoder for decoding and the decoded page data for each of the pages buffered in a second set of buffers. First bin identifiers may be identified for memory cells of the wordline based on the raw page data and second bin identifiers may be identified for the memory cells of the wordline based on the decoded page data. Cell-level statistics may be accumulated based on the first bin identifiers and the second bin identifiers, and a gradient may be determined for respective read levels based on decoding results for each of the pages and the cell-level statistics. Settings for the read levels may be configured in the flash memory device based on the determined gradients.

Abstract: Systems and methods for read level tracking and optimization are described. Pages from a wordline of a flash memory device read and the raw page data read from the wordline may be buffered in a first set of buffers. The raw page data for each of the pages may be provided to a decoder for decoding and the decoded page data for each of the pages buffered in a second set of buffers. First bin identifiers may be identified for memory cells of the wordline based on the raw page data and second bin identifiers may be identified for the memory cells of the wordline based on the decoded page data. Cell-level statistics may be accumulated based on the first bin identifiers and the second bin identifiers, and a gradient may be determined for respective read levels based on decoding results for each of the pages and the cell-level statistics. Settings for the read levels may be configured in the flash memory device based on the determined gradients.

Abstract: Systems and methods for read level tracking and optimization are described. Pages from a wordline of a flash memory device read and the raw page data read from the wordline may be buffered in a first set of buffers. The raw page data for each of the pages may be provided to a decoder for decoding and the decoded page data for each of the pages buffered in a second set of buffers. First bin identifiers may be identified for memory cells of the wordline based on the raw page data and second bin identifiers may be identified for the memory cells of the wordline based on the decoded page data. Cell-level statistics may be accumulated based on the first bin identifiers and the second bin identifiers, and a gradient may be determined for respective read levels based on decoding results for each of the pages and the cell-level statistics. Settings for the read levels may be configured in the flash memory device based on the determined gradients.

Abstract: A data storage system and method are provided for storing data in non-volatile memory devices. Binary data is received for storage in a non-volatile memory device. The binary data is converted into non-binary data comprising base-X values, where X is an integer greater than two. The non-binary data is encoded to generate a codeword and the codeword is written to a wordline of the non-volatile memory device.

Abstract: A method and data storage system receives a confidence vector for a non-binary symbol value read from a memory cell of a non-volatile memory device, where the confidence vector includes a first plurality of confidence values and transforms the first plurality of confidence values into a first plurality of likelihood values using a forward tensor-product transform. A respective binary message passing decoding operation is performed with each of the first plurality of likelihood values to generate a second plurality of likelihood values, and the second plurality of likelihood values are transformed into a second plurality of confidence values of the confidence vector using a reverse tensor-product transform.