Storart Technology (shenzhen) Co., Ltd.

Macronix International Co., Ltd.

Storart Technology Co., Ltd.

Sheng-Han Wu

Hsiang-En Peng

Yu-Ming Huang

Systems, methods, and apparatus including computer-readable mediums for managing error corrections for memory systems are provided. In one aspect, a memory system includes a memory and a memory controller coupled to the memory. The memory controller is configured to: read data from a data page of the memory, perform a first phase Error-Correcting Code (ECC) test on the read data based on first ECC data associated with the data, and in response to determining that the read data fails to pass the first phase ECC test, perform a second phase ECC test on a portion of the read data based on second ECC data. The first ECC data is stored together with the data in the data page. The second ECC data is associated with a portion of the data corresponding to the portion of the read data, and stored in a redundancy page different from the data page.

Managing error corrections for memory systems

The present disclosure provides a method of training artificial intelligence to estimate a lifetime of a storage device, which includes steps of: determining whether an operating parameter of the storage device executing a processing program on bit value values is smaller than an operational threshold parameter or not, if yes, decoding the bit value values stored in the storage device by a decoder; determining whether the bit value values stored in the storage device are successfully decoded by the decoder or not, if yes, classifying the memory unit of the storage device into a strong correct region, a weak correct region, a strong error region or a weak error region; determining whether the number of the memory units falls within an allowable number range or not, if not, initiating an artificial intelligence neural network system to use machine learning to estimate the lifetime of the storage device.

Method of training artificial intelligence to estimate lifetime of storage device

A method of training artificial intelligence to estimate sensing voltages for a storage device is provided, which includes steps of: supplying initial sensing voltages to memory units; defining various storing states; comparing threshold voltages of the memory units with the initial sensing voltages to classify the memory units; calculating a ratio of the number of the memory units in a strong correct region to the number of in the strong correct region and a weak correct region; calculating a ratio of the number of the memory units in a strong error region to the number of in the strong error region and a weak error region; calculating the number of the memory units in the weak correct and error regions to obtain a histogram parameter; inputting the ratios and parameter to an artificial intelligence neural network; and using machine learning to analyze practical sensing voltages.

Method of training artificial intelligence to estimate sensing voltages for storage device

A method for detecting storing states of a solid state storage device is provided, including steps of: applying sensing voltages to memory units; comparing threshold voltages of the memory units with the sensing voltages and accordingly to define the storing states including a strong correct region, a weak correct region, a strong error region and a weak error region, in which the memory units are classified; calculating the number of the memory units in the storing states; calculating a strong correct ratio of the number of the memory units in the strong correct region to the number of the memory units in the strong and weak correct regions; calculating a strong error ratio of the number of the memory units in the strong error region to the number of the memory units in the strong and weak error regions; and generating a log-likelihood ratio based on said ratios.

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Hsinchu, Taiwan

Sheng-Han Wu