Abstract: 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.

Abstract: A method for transferring a command from a host to a device controller and a system using the method are disclosed. The method includes the steps of: A. determining a segment size; B. dividing a command into a number of sections each having a size the same as the segment size; C. sequentially distributing the sections to n groups; D. changing distributing order to a reverse order or keeping the same distributing order in step C if a cycle of distribution is finished while there are sections left for distributing; E. restructuring the section(s) in each group as a sub-command after all sections are distributed; and F. providing the sub-commands to a device controller synchronously.

Abstract: 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.

Abstract: A method for detecting problem cells of a SATA SSD and a SATA SSD having self-detecting function looking for problem cells are disclosed. The method includes the steps of: providing a detecting program used to detect aged and died cells in a SATA SSD; writing the detecting program to a MCU (Micro Control Unit) in the SATA SSD; pulling high electric potential of a communicating pin of a SATA connector of the SATA SSD to initiate the detecting program; collecting location data of aged and died cells in the SATA SSD by the detecting program; and storing the location data in a storage area in the SSD. The present invention utilizes the DAS/DSS pin as a channel to initiate detecting program. It has advantages of using current interface of SSD, no effort on taking apart hardware and automatically running the detecting program without human control.

Abstract: A partial concatenated coding system using an algebraic code and LDPC code is disclosed. The partial concatenated coding system includes an ECC encoder, a received codeword monitoring module and an ECC decoder. The ECC encoder has a LDPC code encoding module and an algebraic code encoding module. The ECC decoder has a LDPC code decoding module and an algebraic code decoding module. Comparing with conventional concatenating coding systems, the present invention has advantages of less spare spaces, better error-correcting performance, lower hardware complexity, better decoding throughput and fixable code length.