Abstract: A storage device includes a memory including a plurality of word lines, a plurality of bit lines and a plurality of memory cells, and a controller configured to control the memory and perform a read retry operation for the memory using a read retry table. The memory includes a special block that stores a read retry table in which a plurality of read retry values are set for each of a plurality of first conditions and each of a plurality of second conditions corresponding to each of the plurality of first conditions.

Abstract: Provided herein may be an electronic device using an artificial neural network. The electronic device may include a training data generator configured to determine an input vector corresponding to a trapping set, detected during error correction decoding corresponding to a codeword, and a target vector corresponding to the input vector, and a training component configured to train an artificial neural network based on supervised learning by inputting the input vector to an input layer of the artificial neural network and by inputting the target vector to an output layer of the artificial neural network.

Abstract: A storage device includes a memory device and a controller. The memory device includes a memory region which includes a first sub-region and a second sub-region. The controller reads assist data from a plurality of memory cells according to an assist read voltage during a read voltage adjusting operation on the first sub-region as a target sub-region, and re-utilizes the read assist data during the read voltage adjusting operation on the second sub-region as the target sub-region.

Abstract: A storage device includes: a memory device including a plurality of memory cells, the memory device performing a read operation of reading data stored in selected memory cells among the plurality of memory cells; and a memory controller for receiving a read request from a host, and controlling the memory device to perform the read operation corresponding to the read request. The memory controller includes a read voltage inferrer for, when the read operation is completed, receiving read information on the read operation from the memory device, performing a read quality evaluation operation of evaluating the read operation based on the read information, and performing a read voltage inference operation of inferring a secondary read level corresponding to the read information according to a result of the performing the read quality evaluation operation.

Abstract: Disclosed is a LiDAR window integrated optical filter that includes a window of a polymer material for absorbing a visible light band and transmitting a near-infrared band; and an upper reflective layer and a lower reflective layer formed on the upper surface and the lower surface of the window. The upper reflective layer and the lower reflective layer may be formed in a thin film including titanium dioxide (TiO2) and silicon dioxide (SiO2).

Abstract: A memory device includes: one or more planes each including a plurality of memory blocks; and a control circuit for selectively performing a dummy read operation before a valid read operation on the first memory block, according to whether a read command on the first memory block is firstly received from a host after a program operation is performed on a plane including the first memory block.

Abstract: Devices for using a neural network to choose an optimal error correction algorithm are disclosed. An example device includes a decoding controller inputting at least one of the number of primary unsatisfied check nodes (UCNs), the number of UCNs respectively corresponding to at least one iteration, and the number of correction bits respectively corresponding to the at least one iteration to a trained artificial neural network, and selecting any one of a first error correction decoding algorithm and a second error correction decoding algorithm based on an output of the trained artificial neural network corresponding to the input, and an error correction decoder performing error correction decoding on a read vector using the selected error correction decoding algorithm. The output of the trained artificial neural network may include a first predicted value indicating a possibility that a first error correction decoding using the first error correction decoding algorithm is successful.

Abstract: A controller for controlling a memory device comprising a plurality of multi-level cell memory blocks, the controller includes: a processor suitable for controlling the memory device to perform a read operation on a target logical page using some of the plurality of read voltages in a selected read voltage set; and an error correction code (ECC) component suitable for determining whether the read operation is successful, by performing error detection and correction on data generated in the read operation and output from the memory device, wherein, when the read operation is determined to be successful, the processor updates the selected read voltage set with the read voltages used in the read operation when it is successful and estimated values of unused read voltages of the selected read voltage set, the estimated values being determined based on the used read voltages.

Abstract: Provided herein may be an electronic device using an artificial neural network. The electronic device may include a training data generator configured to determine an input vector corresponding to a trapping set, detected during error correction decoding corresponding to a codeword, and a target vector corresponding to the input vector, and a training component configured to train an artificial neural network based on supervised learning by inputting the input vector to an input layer of the artificial neural network and by inputting the target vector to an output layer of the artificial neural network.

Abstract: The present technology includes an electronic device and a method of operating the same using an artificial neural network. The electronic device according to the present technology includes a decoding controller inputting a primary syndrome vector generated based on a read vector and a parity check matrix to a trained artificial neural, and selecting any one of a first error correction decoding algorithm and a second error correction decoding algorithm based on an output of the trained artificial neural network corresponding to the input, and an error correction decoder performing error correction decoding on a read vector using the selected error correction decoding algorithm. The output of the trained artificial neural network includes a first predicted value indicating a probability that a first error correction decoding using the first error correction decoding algorithm is successful.

Abstract: Provided herein may be an electronic device using an artificial neural network. The electronic device may include a training data generator configured to determine an input vector corresponding to a trapping set, detected during error correction decoding corresponding to a codeword, and a target vector corresponding to the input vector, and a training component configured to train an artificial neural network based on supervised learning by inputting the input vector to an input layer of the artificial neural network and by inputting the target vector to an output layer of the artificial neural network.

Abstract: Disclosed are devices, systems and methods for error correction encoding and decoding. A memory controller includes an error correction encoder for generating a codeword by performing error correction encoding, using a parity check matrix including a plurality of sub-matrices; and an error correction decoder for performing error correction decoding on a read vector corresponding to the codeword on a column layer basis while sequentially selecting column layers of the parity check matrix used for the error correction encoding, in the error correction decoding, the column layer including a set of columns of the parity check matrix. Rows included in the parity check matrix are grouped into a plurality of row groups, and at most one cyclic permutation matrix (CPM) is included for each column layer in each of the row groups.

Abstract: Disclosed are devices, systems and methods for error correction decoding using an iterative decoding scheme. An error correction circuit includes a node processor to perform a plurality of iterations for updating values of one or more variable nodes and one or more check nodes using initial values assigned to the one or more variable nodes, respectively, a trapping set detector to detect a trapping set in at least one of the plurality of iterations by applying a predetermined trapping set determination policy, and a post processor to reduce at least one of the initial values or invert at least one of values of the variable nodes corresponding to an iteration in which the trapping set is detected, upon detection of the trapping set.

Abstract: A controller for controlling a memory device comprising a plurality of multi-level cell memory blocks, the controller includes: a processor suitable for controlling the memory device to perform a read operation on a target logical page using some of the plurality of read voltages in a selected read voltage set; and an error correction code (ECC) component suitable for determining whether the read operation is successful, by performing error detection and correction on data generated in the read operation and output from the memory device, wherein, when the read operation is determined to be successful, the processor updates the selected read voltage set with the read voltages used in the read operation when it is successful and estimated values of unused read voltages of the selected read voltage set, the estimated values being determined based on the used read voltages.

Abstract: An H matrix generating circuit for generating an H matrix of a QC-LDPC code may include: a conversion value calculation unit calculating conversion values corresponding to column sections of an original H matrix including a plurality of circulant matrices; and a shift unit generating an advanced H matrix by circularly shifting circulant matrices positioned in column sections of the original H matrix by amounts of the conversion values, respectively.

Abstract: A memory device includes: one or more planes each including a plurality of memory blocks; and a control circuit for selectively performing a dummy read operation before a valid read operation on the first memory block, according to whether a read command on the first memory block is firstly received from a host after a program operation is performed on a plane including the first memory block.

Abstract: Provided herein may be an electronic device using an artificial neural network. The electronic device may include a training data generator configured to determine an input vector corresponding to a trapping set, detected during error correction decoding corresponding to a codeword, and a target vector corresponding to the input vector, and a training component configured to train an artificial neural network based on supervised learning by inputting the input vector to an input layer of the artificial neural network and by inputting the target vector to an output layer of the artificial neural network.

Abstract: The present technology includes an electronic device and a method of operating the same using an artificial neural network. The electronic device according to the present technology includes a decoding controller inputting a primary syndrome vector generated based on a read vector and a parity check matrix to a trained artificial neural network to a trained artificial neural network, and selecting any one of a first error correction decoding algorithm and a second error correction decoding algorithm based on an output of the trained artificial neural network corresponding to the input, and an error correction decoder performing error correction decoding on a read vector using the selected error correction decoding algorithm. The output of the trained artificial neural network includes a first predicted value indicating a probability that a first error correction decoding using the first error correction decoding algorithm is successful.

Abstract: Devices for using a neural network to choose an optimal error correction algorithm are disclosed. An example device includes a decoding controller inputting at least one of the number of primary unsatisfied check nodes (UCNs), the number of UCNs respectively corresponding to at least one iteration, and the number of correction bits respectively corresponding to the at least one iteration to a trained artificial neural network, and selecting any one of a first error correction decoding algorithm and a second error correction decoding algorithm based on an output of the trained artificial neural network corresponding to the input, and an error correction decoder performing error correction decoding on a read vector using the selected error correction decoding algorithm. The output of the trained artificial neural network may include a first predicted value indicating a possibility that a first error correction decoding using the first error correction decoding algorithm is successful.

Abstract: An error correction circuit includes a control unit suitable for receiving a data chunk including a plurality of data blocks, each of the data blocks being included in a corresponding codeword of a first direction and a corresponding codeword of a second direction; and a decoder suitable for performing a decoding operation on a codeword, which is selected by the control unit, in the data chunk, wherein the control unit calculates a first reference value by applying a correction capability value of the first direction to a flag of the first direction, calculates a second reference value by applying a correction capability value of the second direction to a flag of the second direction, selects a priority direction from the first direction and the second direction based on the first reference value and the second reference value, and preferentially selects codewords of the priority direction for decoding operations.