Methods, systems, and devices for programmable metadata and related operations are described. A method may include receiving signaling that indicates a set of rules for transitions of states of metadata at a memory device storing the metadata. The memory device may receive a command from a host device associated with a set of data after receiving the set of rules. The memory device may transition metadata associated with the set of data stored at the memory device from a first state to a second state based in part on the set of rules and the command. The memory device may execute the command received from the host device.

A method for product decoding within a data storage system includes receiving data to be decoded within a first decoder; performing a plurality of decoding iterations to decode the data utilizing a first decoder and a second decoder; and outputting fully decoded data based on the performance of the plurality of decoding iterations. Each of the plurality of decoding iterations includes (i) decoding the data with the first decoder operating at a first decoder operational mode to generate once decoded data; (ii) sending the once decoded data from the first decoder to the second decoder; (iii) receiving error information from the first decoder with an artificial intelligence system; (iv) selecting a second decoder operational mode based at least in part on the error information that is received by the artificial intelligence system; and (v) decoding the once decoded data with the second decoder operating at the second decoder operational mode to generate twice decoded data; and outputting fully decoded data based on the performance of the plurality of decoding iterations.

Methods, apparatuses and systems related to managing repair assets are described. An apparatus stores a repair segment locator and a repair address for each defect repair. The apparatus may be configured to selectively apply a repair asset to one of multiple sections according to the repair segment locator.

A mobile electronic device may include a memory device and a memory controller including an error correction code (ECC) encoder to encode data, a constrained channel encoder configured to encode an output of the ECC encoder based on one or more constraints, a reinforcement learning pulse programming (RLPP) component configured to identify a programming algorithm for programming the data to the memory device, an expectation maximization (EM) signal processing component configured to receive a noisy multi-wordline voltage vector from the memory device and classify each bit of the vector with a log likelihood ration (LLR) value, a constrained channel decoder configured to receive a constrained vector from the EM signal processing component and produce an unconstrained vector, and an ECC decoder configured to decode the unconstrained vector. A machine learning interference cancellation component may operate based on or independent of input from the EM signal processing component.

In general, according to an embodiment, a memory system includes a memory device including a memory cell; and a controller. The controller is configured to: receive first data from the memory cell in a first data reading; receive second data from the memory cell in a second data reading that is different from the first data reading; convert a first value that is based on the first data and the second data, to a second value in accordance with a first relationship; and convert the first value to a third value in accordance with a second relationship that is different from the first relationship.

A zero padding apparatus and method for fixed length signaling information are disclosed. A zero padding apparatus according to an embodiment of the present invention includes a processor configured to generate a LDPC information bit string by deciding a number of groups whose all bits are to be filled with 0 using a difference between a length of the LDPC information bit string and a length of a BCH-encoded bit string, selecting the groups using a shortening pattern order to fill all the bits of the groups with 0, and filling at least a part of remaining groups, which are not filled with 0, with the BCH-encoded bit string; and memory configured to provide the LDPC information bit string to an LDPC encoder.

A data storage device includes a memory device and a controller coupled to the memory device. The controller is configured to create a dual parity matrix. The dual parity matrix includes a full parity form that includes a payload, a first parity portion, and a second parity portion and a reduced parity form that includes the payload and the first parity portion. The second parity portion is 0. The controller is further configured to create an incremental parity construction matrix. The incremental parity construction matrix includes two arrays. A first array includes a first payload portion, a first, first parity portion, and a first, second parity portion and a second array includes a second payload portion, a second, first parity portion, and a second, second parity portion. The incremental parity construction matrix is arranged in either a block triangular construction or a block diagonal construction.

A bit interleaver, a bit-interleaved coded modulation (BICM) device and a bit interleaving method are disclosed herein. The bit interleaver includes a first memory, a processor, and a second memory. The first memory stores a low-density parity check (LDPC) codeword having a length of 16200 and a code rate of 4/15. The processor generates an interleaved codeword by interleaving the LDPC codeword on a bit group basis. The size of the bit group corresponds to a parallel factor of the LDPC codeword. The second memory provides the interleaved codeword to a modulator for 64-symbol mapping.

Examples described herein utilize multi-layer neural networks, such as multi-layer recurrent neural networks to estimate message probability compute data based on encoded data (e.g., data encoded using one or more encoding techniques). The neural networks and/or recurrent neural networks may have nonlinear mapping and distributed processing capabilities which may be advantageous in many systems employing a neural network or recurrent neural network to estimate message probability compute data for a message probability compute (MPC) decoder. In this manner, neural networks or recurrent neural networks described herein may be used to implement aspects of error correction coding (ECC) decoders, e.g., an MPC decoder that iteratively decodes encoded data.

An apparatus is described. The apparatus includes a memory chip. The memory chip has an error correction code (ECC) encoder logic circuit and an ECC decoder logic circuit. The ECC decoder logic circuit is to place an additional one or more errors that result from incorrect error correction applied to a read code word into a same block of multiple blocks of the read code word's raw data bit portion where original errors in the read code word existed before the read code word was decoded by the ECC decoder logic circuit.