TWI690860B - Method of training artificial intelligence to estimate lifetime of storage device - Google Patents
Method of training artificial intelligence to estimate lifetime of storage device Download PDFInfo
- Publication number
- TWI690860B TWI690860B TW108100343A TW108100343A TWI690860B TW I690860 B TWI690860 B TW I690860B TW 108100343 A TW108100343 A TW 108100343A TW 108100343 A TW108100343 A TW 108100343A TW I690860 B TWI690860 B TW I690860B
- Authority
- TW
- Taiwan
- Prior art keywords
- storage device
- strong
- artificial intelligence
- correct
- estimate
- Prior art date
Links
Images
Landscapes
- Techniques For Improving Reliability Of Storages (AREA)
Abstract
Description
本發明涉及一種儲存裝置,特別是涉及一種訓練人工智慧估測儲存裝置的使用壽命的方法。The invention relates to a storage device, in particular to a method for training artificial intelligence to estimate the service life of a storage device.
現在記憶體應用越來越普及化,在使用過程中會隨著抹除與寫入次數一些因素造成記憶體內部損傷,進而造成錯誤率上升,使得非揮發性記憶體(non-volatile memory)可靠度急遽下降,因此可以透過可靠性設計技術,特別是更正錯誤技術來提高非揮發性記憶體的可靠性,可以讓產品更為長壽與穩定。Nowadays, memory applications are becoming more and more popular. In the course of use, some factors will cause internal damage to the memory due to factors such as the number of erases and writes, which will cause the error rate to rise, making non-volatile memory (non-volatile memory) reliable The degree of rapid decline has been reduced, so the reliability of non-volatile memory can be improved through reliability design technology, especially error correction technology, which can make the product more long-lived and stable.
為了確保延長非揮發性記憶體的使用壽命,控制電路中會設計錯誤更正模塊,對從非揮發性記憶體讀出來的數據進行錯誤糾正,消除非揮發性記憶體因為外在因素造成的錯誤。傳統上,主流的錯誤更正編碼都是採用BCH (Bose-Chaudhuri-Hochquenghem) Code,這種編碼的計算速度相當快,糾正能力隨著冗餘位元越多,更正能力會越強。但是隨著非揮發性記憶體製造技術越來越高,BCH編碼技術已經無法提供足夠的更正能力,所以開始轉向使用在通訊領域廣泛應用的低密度奇偶檢查碼(Low Density Parity Code, LDPC)糾錯技術,藉由強大的更正能力開始成為儲存領域中的新趨勢。In order to ensure the extension of the service life of the non-volatile memory, an error correction module is designed in the control circuit to correct the data read from the non-volatile memory to eliminate the errors caused by external factors in the non-volatile memory. Traditionally, the mainstream error correction coding is BCH (Bose-Chaudhuri-Hochquenghem) Code. The calculation speed of this coding is quite fast, and the correction capability will be stronger with more redundant bits. However, as the manufacturing technology of non-volatile memory becomes higher and higher, BCH encoding technology has been unable to provide sufficient correction capabilities, so it began to use the low density parity check code (LDPC) correction widely used in the field of communication Wrong technology, with powerful correction capabilities, has become a new trend in the storage field.
本發明所要解決的技術問題在於,針對現有技術的不足提供一種訓練人工智慧估測儲存裝置的使用壽命的方法一種訓練人工智慧估測儲存裝置的使用壽命的方法,適用於儲存裝置,儲存裝置包含多個記憶單元。方法包含以下步驟:(a)利用儲存裝置對一或多個位元值執行數據位元處理程序;(b)計算儲存裝置對多個位元值執行數據位元處理程序的運作參數;(c)判斷運作參數是否小於第一運作臨界參數值,若是,執行步驟(a),若否,執行下一步驟(d);(d)利用解碼器解碼儲存裝置所儲存的各位元值;(e)判斷解碼器是否成功解碼儲存裝置所儲存的多個位元值,若是,執行步驟(a),若否,執行下一步驟(f);(f)定義多個儲存狀態區域,多個儲存狀態區域包含強正確區、弱正確區、強錯誤區以及弱錯誤區;(g)依據各記憶單元的儲存狀態,分類各記憶單元屬於強正確區、弱正確區、強錯誤區或弱錯誤區;(h)計算分類在儲存狀態區域的多個記憶單元的記憶單元數量;(i)判斷記憶單元數量是否介於第一數量允許範圍內,若是,執行步驟(a),若否,執行下一步驟(j);(j)啟動人工智慧類神經網路系統,使用機器學習分析運作參數是否小於第二運作臨界參數值、解碼器是否成功解碼儲存裝置所儲存的多個位元值並且記憶單元數量是否介於第二數量允許範圍內,若否,估測儲存裝置的使用壽命時間,若是,執行步驟(a)。The technical problem to be solved by the present invention is to provide a method for training artificial intelligence to estimate the service life of a storage device in view of the deficiencies of the prior art, and a method for training artificial intelligence to estimate the service life of a storage device, which is suitable for storage devices. The storage device includes Multiple memory units. The method includes the following steps: (a) use the storage device to execute the data bit processing procedure on one or more bit values; (b) calculate the operating parameters of the storage device to execute the data bit processing procedure on the multiple bit values; (c ) Determine whether the operation parameter is less than the first operation critical parameter value, if yes, go to step (a), if not, go to the next step (d); (d) use the decoder to decode each bit value stored in the storage device; (e ) Determine whether the decoder successfully decodes the multiple bit values stored in the storage device, if so, perform step (a), if not, perform the next step (f); (f) define multiple storage status areas, multiple storage The status area includes a strong correct area, a weak correct area, a strong error area, and a weak error area; (g) According to the storage status of each memory unit, each memory unit is classified as a strong correct area, a weak correct area, a strong error area, or a weak error area ; (H) Calculate the number of memory cells classified into multiple memory cells in the storage status area; (i) Determine whether the number of memory cells is within the allowable range of the first number. If so, perform step (a). If not, execute the following One step (j); (j) Start the artificial intelligence neural network system, use machine learning to analyze whether the operating parameter is less than the second operating critical parameter value, and whether the decoder successfully decodes the multiple bit values stored in the storage device and remembers Whether the number of units is within the allowable range of the second quantity, if not, the service life of the storage device is estimated, and if so, step (a) is performed.
如上所述,本發明所提供的訓練人工智慧估測儲存裝置的使用壽命的方法,其可依據儲存裝置的讀寫次數、抹除次數以及忙碌時間等運作參數值、儲存裝置存取數據位元值的錯誤率(即分類在強錯誤區或其他儲存狀態的記憶單元數量),以及解碼器對儲存裝置所儲存的數據位元值的更正能力,以有效估測儲存裝置的使用壽命。As described above, the method for training artificial intelligence provided by the present invention to estimate the service life of a storage device can be based on the operating parameter values of the storage device such as the number of reads and writes, the number of erasures and the busy time, and the storage device access data bits The error rate of the value (that is, the number of memory cells classified in a strong error area or other storage state), and the decoder's ability to correct the data bit value stored in the storage device, so as to effectively estimate the service life of the storage device.
為使能更進一步瞭解本發明的特徵及技術內容,請參閱以下有關本發明的詳細說明與圖式,然而所提供的圖式僅用於提供參考與說明,並非用來對本發明加以限制。In order to further understand the features and technical contents of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are for reference and explanation only, and are not intended to limit the present invention.
以下是通過特定的具體實施例來說明本發明的實施方式,本領域技術人員可由本說明書所提供的內容瞭解本發明的優點與效果。本發明可通過其他不同的具體實施例加以施行或應用,本說明書中的各項細節也可基於不同觀點與應用,在不悖離本發明的構思下進行各種修改與變更。另外,本發明的附圖僅為簡單示意說明,並非依實際尺寸的描繪,事先聲明。以下的實施方式將進一步詳細說明本發明的相關技術內容,但所提供的內容並非用以限制本發明的保護範圍。The following is a specific specific example to illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content provided in this specification. The present invention can be implemented or applied through other different specific embodiments. Various details in this specification can also be based on different viewpoints and applications, and various modifications and changes can be made without departing from the concept of the present invention. In addition, the drawings of the present invention are merely schematic illustrations, and are not drawn according to actual sizes, and are declared in advance. The following embodiments will further describe the related technical content of the present invention in detail, but the content provided is not intended to limit the protection scope of the present invention.
應當可以理解的是,雖然本文中可能會使用到 “第一”、“第二”、“第三”等術語來描述各種元件或者訊號,但這些元件或者訊號不應受這些術語的限制。這些術語主要是用以區分一元件與另一元件,或者一訊號與另一訊號。另外,本文中所使用的術語“或”,應視實際情況可能包含相關聯的列出項目中的任一個或者多個的組合。It should be understood that although terms such as “first”, “second”, and “third” may be used herein to describe various elements or signals, these elements or signals should not be limited by these terms. These terms are mainly used to distinguish one component from another component, or one signal from another signal. In addition, the term "or" as used herein may include any one or a combination of more than one of the associated listed items, depending on the actual situation.
[第一實施例][First embodiment]
請參閱圖1,其為本發明第一實施例的訓練人工智慧估測儲存裝置的使用壽命的方法的步驟流程圖。如圖1所示,本實施例訓練人工智慧估測儲存裝置的使用壽命的方法,包含以下步驟S101~S119,適用於儲存裝置例如固態硬碟(Solid state drive)等,儲存裝置包含多個記憶單元例如記憶晶胞(memory cell)。Please refer to FIG. 1, which is a flowchart of steps of a method for training artificial intelligence to estimate the service life of a storage device according to a first embodiment of the present invention. As shown in FIG. 1, the method for training artificial intelligence to estimate the service life of a storage device in this embodiment includes the following steps S101 to S119, which are suitable for storage devices such as solid state drives (Solid state drive), etc. The storage device includes multiple memories The unit is, for example, a memory cell.
在步驟S101,利用儲存裝置對一或多個位元值執行數據位元處理程序。舉例來說,儲存裝置更包含儲存控制單元,配置以將一或多個數據位元值或由多個位元值組成的位元串流寫入記憶單元,即控制記憶單元存取一或多個數據位元值。進一步,儲存裝置的儲存控制單元可讀取記憶單元所儲存的數據位元值以及控制記憶單元抹除所儲存的數據位元值。In step S101, the storage device executes a data bit processing procedure on one or more bit values. For example, the storage device further includes a storage control unit configured to write one or more data bit values or a bit stream composed of multiple bit values to the memory unit, that is, to control the memory unit to access one or more Data bit values. Further, the storage control unit of the storage device can read the data bit value stored in the memory unit and control the memory unit to erase the stored data bit value.
在步驟S103,計算儲存裝置對一或多個位元值執行數據位元處理程序的運作參數,包含執行數據位元處理程序的次數、每次執行數據位元處理程序的運作時間或其組合。In step S103, the operation parameters of the storage device executing the data bit processing procedure on one or more bit values include the number of times the data bit processing procedure is executed, the operation time of each execution of the data bit processing procedure, or a combination thereof.
在步驟S105,判斷儲存裝置的運作參數是否小於第一運作臨界參數值。舉例來說,判斷儲存裝置執行數據位元處理程序的次數是否小於第一運作次數臨界值。另外或替換地,判斷儲存裝置每次執行數據位元處理程序的運作時間是否小於第一運作時間臨界值。In step S105, it is determined whether the operation parameter of the storage device is less than the first operation threshold parameter value. For example, it is determined whether the number of times the storage device executes the data bit processing procedure is less than the threshold of the first number of operations. Additionally or alternatively, it is determined whether the operation time of the storage device each time the data bit processing program is executed is less than the first operation time threshold.
若判斷儲存裝置的運作參數小於第一運作臨界參數值時,例如儲存裝置的運作次數小於第一運作次數臨界值以及每次執行程序所耗費的時間小於第一運作時間臨界值時,判斷儲存裝置仍具有良好性能。因此,可重覆執行步驟S101,儲存裝置可再次執行數據位元處理程序,例如記憶單元可存取新數據位元值,或可抹除記憶單元中所儲存的其他數據位元值。If it is determined that the operation parameter of the storage device is less than the first operation threshold parameter value, for example, when the operation times of the storage device is less than the first operation threshold value and the time taken for each execution of the procedure is less than the first operation time threshold, the storage device is determined It still has good performance. Therefore, step S101 can be repeatedly executed, and the storage device can execute the data bit processing procedure again, for example, the memory unit can access the new data bit value, or can erase other data bit values stored in the memory unit.
相反地,若判斷儲存裝置的運作參數等於或大於第一運作臨界參數值時,例如儲存裝置的運作次數等於或大於第一運作次數臨界值以及每次執行程序所耗費的時間等於或大於第一運作時間臨界值時,初步推測儲存裝置可能因使用次數或其他環境因素過多導致儲存裝置運作的性能變差,進而導致運作時間延長,藉以可初步估測儲存裝置的使用壽命縮短,甚至可初步推測儲存裝置即將損壞或已損壞而不堪使用。為了更精準地估測儲存裝置的使用壽命,可進一步執行下一步驟S107。Conversely, if it is determined that the operation parameter of the storage device is equal to or greater than the first operation threshold parameter value, for example, the operation count of the storage device is equal to or greater than the first operation threshold value and the time taken for each execution of the procedure is equal to or greater than the first When the operating time is critical, it is preliminarily estimated that the storage device may deteriorate due to the number of uses or other environmental factors, which will lead to the deterioration of the operating performance of the storage device, which will lead to the extension of the operating time, so that the service life of the storage device can be preliminarily estimated or even preliminarily estimated. The storage device is about to be damaged or damaged and cannot be used. In order to estimate the service life of the storage device more accurately, the next step S107 may be further executed.
在步驟S107,利用解碼器解碼儲存裝置記憶單元所儲存的一或多個位元值。具體地,解碼器可同時或依序解碼同一或不同記憶單元所儲存的多個單個位元值、同一位元串流的多個位元值。舉例來說,解碼器可依據儲存裝置儲存多個位元值的順序,以依序解碼多個位元值。替換地,若多個記憶單元在儲存裝置中以陣列排列,則可依據多個記憶單元排列在陣列中的行/列位置以決定依序解碼多個位元值的順序。In step S107, the decoder is used to decode one or more bit values stored in the memory unit of the storage device. Specifically, the decoder can simultaneously or sequentially decode multiple single bit values stored in the same or different memory units, and multiple bit values in the same bit stream. For example, the decoder may decode the multiple bit values sequentially according to the order in which the storage device stores the multiple bit values. Alternatively, if multiple memory cells are arranged in an array in the storage device, the order of decoding multiple bit values sequentially may be determined according to the row/column positions of the multiple memory cells arranged in the array.
在步驟S109,判斷解碼器是否成功解碼位元值。在本實施例中,假設解碼器不具翻轉儲存裝置的記憶單元所儲存的位元值的能力,即解碼器不將記憶單元所儲存的位元值邏輯1翻轉成0或將位元值邏輯0翻轉成邏輯1。在此條件下,若解碼器解碼儲存裝置所儲存的位元值失敗時,則判斷儲存裝置可能在存取數據時誤判位元值,例如將位元值邏輯0誤判為邏輯1或將邏輯1誤判為邏輯0,而儲存錯誤位元值。若發生此情況,可重覆執行步驟S101,利用儲存裝置重新存取數據位元值。In step S109, it is determined whether the decoder successfully decoded the bit value. In this embodiment, it is assumed that the decoder does not have the ability to invert the bit value stored in the memory unit of the storage device, that is, the decoder does not invert the bit value logic 1 stored in the memory unit to 0 or the bit value logic 0. Flip into logic 1. Under this condition, if the decoder fails to decode the bit value stored in the storage device, it is determined that the storage device may misjudge the bit value when accessing the data, for example, the bit value logic 0 is misjudged as logic 1 or logic 1 Misjudgment is logic 0, and the wrong bit value is stored. If this happens, step S101 can be repeated to use the storage device to re-access the data bit value.
相反地,若解碼器成功解碼儲存裝置所儲存的位元值,判斷儲存裝置在存取數據位元值時,可正確判讀位元值以及儲存正確的位元值,進而初步推斷儲存裝置仍具有良好的性能。因此,接著執行步驟S111。Conversely, if the decoder successfully decodes the bit value stored in the storage device, it is determined that the storage device can correctly interpret the bit value and store the correct bit value when accessing the data bit value, and then preliminarily infer that the storage device still has Good performance. Therefore, step S111 is next performed.
在步驟S111,定義多個儲存狀態區域。舉例來說,多個儲存狀態區域可包含強正確區、弱正確區、強錯誤區以及弱錯誤區等更多區域。In step S111, a plurality of storage state areas are defined. For example, the plurality of storage state areas may include more areas such as strong correct areas, weak correct areas, strong error areas, and weak error areas.
在步驟S113,依據記憶單元的儲存狀態,分類記憶單元屬於強正確區、弱正確區、強錯誤區或弱錯誤區。舉例來說,當判斷記憶單元判讀位元值的正確率高於一正確率門檻值時,將此記憶單元分類在強正確區。相反地,當判斷記憶單元判讀位元值的正確率低於正確率門檻值時,將此記憶單元弱正確區。另外,當判斷記憶單元判讀位元值的錯誤率高於錯誤率門檻值時,將此記憶單元分類在強錯誤區。相反地,當判斷記憶單元判讀位元值的錯誤率低於錯誤率門檻值時,將此記憶單元分類在弱錯誤區。In step S113, according to the storage state of the memory unit, the classified memory unit belongs to a strong correct area, a weak correct area, a strong error area, or a weak error area. For example, when the correct rate of the judgment bit value of the memory unit is higher than a correct rate threshold, the memory unit is classified in the strong correct area. Conversely, when it is judged that the correct rate of the memory cell's interpretation bit value is lower than the correct rate threshold, the memory cell is weakly corrected. In addition, when it is judged that the error rate of the read bit value of the memory unit is higher than the error rate threshold, the memory unit is classified in the strong error area. Conversely, when it is judged that the error rate of the read bit value of the memory unit is lower than the error rate threshold, the memory unit is classified in the weak error area.
在步驟S115,計算分類在儲存狀態區域例如強正確區、弱正確區、強錯誤區以及弱錯誤區中的任一者或每一個的記憶單元的數量。In step S115, the number of memory cells classified in any or each of the storage state areas such as the strong correct area, the weak correct area, the strong error area, and the weak error area is calculated.
在步驟S117,判斷分類在儲存狀態區域例如強正確區、弱正確區、強錯誤區以及弱錯誤區中的任一者或每一個的記憶單元數量是否介於第一數量允許範圍內。若依據記憶單元的儲存狀態分類在任一或每一儲存狀態區域中的記憶單元的數量在預設的第一數量允許範圍內時,判斷儲存裝置仍可繼續使用。相反地,判斷分類在儲存狀態區域的記憶單元的數量超出預設的第一數量允許範圍內時,則初步估測儲存裝置可能不堪使用。In step S117, it is determined whether the number of memory cells classified in any one or each of the storage state areas such as the strong correct area, the weak correct area, the strong error area, and the weak error area is within the first number allowable range. If the number of memory units classified in any or each storage state area according to the storage state of the memory unit is within the preset first number allowable range, it is determined that the storage device can continue to be used. Conversely, when it is determined that the number of memory cells classified in the storage state area exceeds the preset first number allowable range, it is preliminarily estimated that the storage device may be unusable.
在步驟S119,啟動人工智慧類神經網路系統,利用機器學習利用上述儲存裝置的相關數值,更精確地分析儲存裝置的使用壽命。關於人工智慧類神經網路系統的操作如下第二實施例更詳細的描述。In step S119, an artificial intelligence neural network system is started, and the relevant values of the storage device are utilized by machine learning to more accurately analyze the service life of the storage device. The operation of the artificial intelligence neural network system is described in more detail in the second embodiment below.
[第二實施例][Second Embodiment]
請參閱圖2,其為本發明第二實施例的訓練人工智慧估測儲存裝置的使用壽命的方法的步驟流程圖。如圖2所示,本實施例訓練人工智慧估測儲存裝置的使用壽命的方法包含以下步驟S201~S215,適用於儲存裝置,儲存裝置包含多個記憶單元。應理解,本文所述多個實施例的步驟可依實際需求組合實施。Please refer to FIG. 2, which is a flowchart of steps of a method for training artificial intelligence to estimate the service life of a storage device according to a second embodiment of the present invention. As shown in FIG. 2, the method for training artificial intelligence to estimate the service life of a storage device in this embodiment includes the following steps S201 to S215, which are applicable to a storage device, and the storage device includes multiple memory units. It should be understood that the steps of the multiple embodiments described herein can be implemented in combination according to actual needs.
在執行步驟S119或步驟S203啟動人工智慧類神經網路系統後,可接著執行以下步驟S205~S215。也就是說,在步驟S103~S117中對儲存裝置進行第一次檢測,而在步驟S205~S215中對儲存裝置進行第二次檢測藉由兩次對儲存裝置的檢測,可精確地估測儲存裝置的使用壽命。After performing step S119 or step S203 to activate the artificial intelligence neural network system, the following steps S205 to S215 can be performed. That is to say, the storage device is tested for the first time in steps S103-S117, and the storage device is tested for the second time in steps S205-S215. By detecting the storage device twice, the storage can be accurately estimated The service life of the device.
替換地,如圖2所示,儲存裝置在步驟S101或S201中對位元值執行數據位元處理程序之後,可省略步驟S103~S117,直接啟動人工智慧類神經網路系統執行步驟S205~S215估測對儲存裝置的使用壽命。應理解,本文所述的步驟例如步驟S207、S209、S213可依實際需求交換執行順序或同時執行。Alternatively, as shown in FIG. 2, after performing the data bit processing procedure on the bit value in step S101 or S201, the storage device may omit steps S103~S117 and directly start the artificial intelligence neural network system to execute steps S205~S215 Estimate the service life of the storage device. It should be understood that the steps described in this document, such as steps S207, S209, and S213, may be executed in a swapped order or performed simultaneously according to actual needs.
在步驟S201,利用儲存裝置對位元值執行數據位元處理程序,例如將位元值寫入記憶單元、讀取記憶單元所儲存的位元值以及抹除記憶單元所儲存的位元值等作業。In step S201, a data bit processing procedure is performed on the bit value using the storage device, such as writing the bit value into the memory unit, reading the bit value stored in the memory unit, and erasing the bit value stored in the memory unit, etc. operation.
在步驟S203,啟動人工智慧類神經網路系統。In step S203, an artificial intelligence neural network system is activated.
在步驟S205,利用人工智慧類神經網路系統使用機器學習針對不同型態且具有不同的使用壽命的儲存裝置分析第二運作臨界參數值。In step S205, the artificial intelligence neural network system is used to analyze the second operation critical parameter value for different types of storage devices with different service lives using machine learning.
在步驟S207,使用機器學習判斷儲存裝置的運作參數是否小於第二運作臨界參數值。在本實施例中,此第二運作臨界參數值小於上述第一運作臨界參數值,但本發明不以此為限。In step S207, machine learning is used to determine whether the operating parameter of the storage device is less than the second operating critical parameter value. In this embodiment, the second operation critical parameter value is less than the first operation critical parameter value, but the invention is not limited to this.
舉例來說,運作臨界參數值包含存取數據位元值的運作時間,例如若儲存裝置欲存取位元量在一特定位元量範圍內的數據,第一運作臨界參數值設定為存取時間0.5ms,而第二運作臨界參數值設定為存取時間0.2ms。首先,在步驟S105中,利用人工智慧類神經網路系統使用機器學習分析儲存裝置存取數據位元值的時間例如0.3ms小於第一運作臨界參數值例如0.5ms。接著,在此步驟S203,利用人工智慧類神經網路系統使用機器學習分析儲存裝置存取此數據位元值的時間例如0.3ms小於第二運作臨界參數值例如0.2ms。For example, the operation critical parameter value includes the operation time for accessing the data bit value. For example, if the storage device wants to access data with a bit amount within a specific bit amount range, the first operation critical parameter value is set to access The time is 0.5ms, and the second operation critical parameter value is set as the access time 0.2ms. First, in step S105, the time when the artificial intelligence neural network system uses machine learning to analyze the storage device to access the data bit value, for example, 0.3ms is less than the first operation critical parameter value, for example, 0.5ms. Next, in this step S203, the time when the artificial intelligence neural network system uses the machine learning analysis storage device to access the data bit value, for example, 0.3ms is less than the second operation critical parameter value, for example, 0.2ms.
實務上,可依據不同類型儲存裝置設定不同的運作臨界參數值。另外或替換地,可依據儲存裝置的記憶單元可存取的上限位元量例如單階記憶晶胞(Single-Level Cell, SLC)可存取1個位元值、多階記憶晶胞(Multi-Level Cell, MLC)可存取2個位元值、四階記憶晶胞Quad-Level Cell, QLC)可存取4個位元值以及目前實際存取的數據位元量,以設定對應的運作臨界參數值。In practice, different operation critical parameter values can be set according to different types of storage devices. In addition or alternatively, the maximum number of bits that can be accessed by the memory unit of the storage device, such as single-level cell (Single-Level Cell, SLC), can access 1 bit value, multi-level memory cell (Multi -Level Cell, MLC) can access 2 bit values, the fourth-order memory cell Quad-Level Cell (QLC) can access 4 bit values and the amount of data bits actually accessed to set the corresponding Operational critical parameter value.
若利用人工智慧類神經網路系統使用機器學習分析儲存裝置的運作參數小於第二運作臨界參數值時,例如儲存裝置的運作次數小於第二運作次數臨界值以及每次執行程序所耗費的時間小於第二運作時間臨界值時,判斷儲存裝置仍具有良好性能而可持續使用。因此,可再次執行步驟S201。If the artificial intelligence neural network system is used to analyze the operation parameters of the storage device using machine learning to be less than the second operation critical parameter value, for example, the operation times of the storage device are less than the second operation threshold value and the time taken for each execution of the procedure is less than At the second operating time threshold, it is determined that the storage device still has good performance and can be used continuously. Therefore, step S201 can be executed again.
相反地,若利用人工智慧類神經網路系統使用機器學習分析儲存裝置的運作參數大於或等於第二運作臨界參數值時,例如儲存裝置的運作次數大於或等於第二運作次數臨界值以及每次執行程序所耗費的時間大於或等於第二運作時間臨界值時,則初步推斷儲存裝置可能無法再持續使用或可再運作的次數剩下不多。進一步地,為更精準地估測儲存裝置的使用壽命,接著執行步驟S209。Conversely, if the artificial intelligence neural network system is used to analyze the operating parameters of the storage device by using machine learning to be greater than or equal to the second operating critical parameter value, for example, the operating times of the storage device is greater than or equal to the second operating critical value and each time When the time taken to execute the procedure is greater than or equal to the second operating time threshold, it is preliminarily inferred that the storage device may no longer be used continuously or the number of re-operable times is not much. Further, to more accurately estimate the service life of the storage device, step S209 is then executed.
在步驟S209,利用人工智慧類神經網路系統使用機器學習判斷解碼器是否成功解碼儲存裝置的記憶單元所儲存的一或多個位元值。In step S209, an artificial intelligence neural network system is used to determine whether the decoder successfully decodes one or more bit values stored in the memory unit of the storage device using machine learning.
若利用人工智慧類神經網路系統使用機器學習判斷解碼器解碼記憶單元所儲存的位元值失敗時,重覆執行步驟S201。相反地,若利用人工智慧類神經網路系統使用機器學習判斷解碼器可透過一次或多次解碼程序成功解碼記憶單元所儲存的位元值,執行步驟S211。If the artificial intelligence neural network system is used to determine whether the decoder fails to decode the bit value stored in the memory unit using machine learning, step S201 is repeatedly executed. On the contrary, if the artificial intelligence neural network system is used to determine that the decoder can successfully decode the bit value stored in the memory unit through one or more decoding procedures, step S211 is executed.
在步驟S211,使用機器學習分析針對不同型態且具有不同的使用壽命的儲存裝置第二數量允許範圍。此第二數量允許範圍小於上述第一數量允許範圍,但本發明不以此為限。In step S211, machine learning is used to analyze the second allowable range of storage devices for different types and having different service lives. The second quantity allowable range is smaller than the first quantity allowable range, but the invention is not limited thereto.
在步驟S213,使用機器學習判斷分類在儲存狀態區域例如強正確區、弱正確區、強錯誤區以及弱錯誤區中的任一者或每一個的記憶單元數量是否介於第二數量允許範圍內。In step S213, use machine learning to determine whether the number of memory cells classified in any one or each of the storage status areas such as strong correct area, weak correct area, strong error area, and weak error area is within the second number allowable range .
在步驟S215,啟動人工智慧類神經網路系統,利用機器學習依據步驟S201~S213的判斷結果,以判斷儲存裝置目前是否可堪用,推斷是否已因老舊或毀損而不堪使用,是否需維修或直接更換新儲存裝置。甚至,啟動人工智慧類神經網路系統,利用機器學習再次執行據步驟S201~S213,以更精確地估測儲存裝置的使用壽命時間是否即將到期,例如使用壽命時間是否小於使用壽命臨界時間,以大致地估測使用裝置是否還可使用多久時間例如至少半年以上。In step S215, the artificial intelligence neural network system is started, and the machine learning is used to determine whether the storage device is currently usable according to the judgment results of steps S201~S213, and it is inferred whether it is out of use due to old or damaged, and whether it needs repair Or directly replace the new storage device. Moreover, the artificial intelligence neural network system is activated, and machine learning is used to execute steps S201-S213 again to more accurately estimate whether the storage device's service life is about to expire, for example, whether the service life time is less than the service life critical time, In order to roughly estimate whether the use device can still be used for at least half a year or more.
[第三實施例][Third Embodiment]
請參閱圖3,其為本發明第三實施例的訓練人工智慧估測儲存裝置的使用壽命的方法的步驟流程圖。如圖3所示,本實施例訓練人工智慧估測儲存裝置的使用壽命的方法包含以下步驟S301~S317,適用於儲存裝置,儲存裝置包含多個記憶單元。本實施例的步驟S301~S317是針對第一實施例的步驟S103、S105以及第二實施例的步驟S205、S207更進一步的舉例。應理解,本文所述多個實施例的步驟可依實際需求組合實施。Please refer to FIG. 3, which is a flowchart of steps of a method for training artificial intelligence to estimate the service life of a storage device according to a third embodiment of the present invention. As shown in FIG. 3, the method for training artificial intelligence to estimate the service life of a storage device in this embodiment includes the following steps S301 to S317, which are applicable to a storage device. The storage device includes multiple memory units. Steps S301 to S317 of this embodiment are further examples of steps S103 and S105 of the first embodiment and steps S205 and S207 of the second embodiment. It should be understood that the steps of the multiple embodiments described herein can be implemented in combination according to actual needs.
在步驟S301,利用儲存裝置的儲存控制模組寫入數據位元值至記憶單元。例如,儲存裝置的同一記憶單元可僅寫入單一數據位元值,或依序讀寫多個數據位元值,或寫入由多個數據位元值組成的數據位元串流。數據位元值例如邏輯0或邏輯1。應理解,本發明不受限於記憶單元讀取的位元值數量。In step S301, the storage control module of the storage device is used to write the data bit value to the memory unit. For example, the same memory unit of the storage device can write only a single data bit value, or sequentially read and write multiple data bit values, or write a data bit stream composed of multiple data bit values. The data bit value is, for example, logic 0 or logic 1. It should be understood that the present invention is not limited to the number of bit values read by the memory unit.
在步驟S303,計數儲存裝置的讀寫次數。In step S303, the number of reading and writing of the storage device is counted.
在步驟S305,比對儲存裝置目前累積的讀寫次數是否小於預設的讀寫次數臨界值。若比對儲存裝置目前累積的讀寫次數小於預設的讀寫次數臨界值時,判斷儲存裝置可再次使用於執行步驟S301的作業。相反地,若比對儲存裝置目前累積的讀寫次數大於或等於預設的讀寫次數臨界值時,則再進一步執行後續步驟,以藉由儲存裝置的各種運作參數更準確地估測儲存裝置是否尚可使用。In step S305, it is compared whether the currently accumulated reading and writing times of the storage device is less than a preset threshold of reading and writing times. If the current accumulated reading and writing times of the comparison storage device is less than the preset threshold of reading and writing times, it is determined that the storage device can be used again to perform the operation of step S301. Conversely, if the current accumulated number of reads and writes of the compared storage device is greater than or equal to the preset threshold of read and write times, then further steps are performed to estimate the storage device more accurately through various operating parameters of the storage device Whether it is still usable.
在步驟S307,利用儲存裝置的儲存控制模組抹除一或多個記憶單元所儲存的一或多個數據位元值。In step S307, the storage control module of the storage device is used to erase one or more data bit values stored in the one or more memory units.
在步驟S309,計數儲存裝置的儲存控制模組抹除記憶單元所儲存的數據位元值的抹除次數。In step S309, the number of erasures of the data bit value stored in the storage control module of the storage device to erase the memory unit is counted.
在步驟S311,比對儲存裝置目前累積的抹除次數是否小於預設的抹除次數臨界值。若比對儲存裝置目前累積的抹除次數小於預設的抹除次數臨界值時,判斷儲存裝置可再次使用於執行步驟S301的作業。相反地,若比對儲存裝置目前累積的抹除次數不小於預設的抹除次數臨界值時,則須再執行後續步驟S313進一步判斷儲存裝置的狀態。In step S311, it is compared whether the current accumulated erasure times of the storage device is less than a preset erasure count threshold. If the current accumulated erasure count of the comparison storage device is less than the preset erasure count threshold, it is determined that the storage device can be used again to perform the operation of step S301. Conversely, if the current accumulated erasing count of the storage device is not less than the preset erasure count threshold, the subsequent step S313 must be executed to further determine the state of the storage device.
在步驟S313,計數儲存裝置的忙碌時間。舉例來說,計數儲存裝置對數據位元值執行數據位元處理程序的時間,包含儲存控制模組接收和傳輸數據位元值至記憶單元的時間、儲存控制模組控制記憶單元存取數據位元值的時間以及抹除記憶單元所儲存的位元值的時間。In step S313, the busy time of the storage device is counted. For example, counting the time that the storage device executes the data bit processing procedure on the data bit value includes the time for the storage control module to receive and transmit the data bit value to the memory unit, and the storage control module controls the memory unit to access the data bit The time of the meta value and the time of erasing the bit value stored in the memory unit.
在步驟S315,比對儲存裝置的忙碌時間是否小於忙碌時間臨界值。若比對儲存裝置的忙碌時間小於忙碌時間臨界值時,判斷儲存裝置的累積運作時間不長,尚應具有良好的運作性能而可重覆執行步驟S301。相反地,若比對儲存裝置的忙碌時間不小於忙碌時間臨界值時,判定儲存裝置仍可持續使用以執行步驟S317。In step S315, compare whether the busy time of the storage device is less than the busy time threshold. If the busy time of the comparison storage device is less than the busy time threshold, it is judged that the accumulated operating time of the storage device is not long, and it should still have good operating performance and step S301 can be repeated. Conversely, if the busy time of the comparison storage device is not less than the busy time threshold, it is determined that the storage device can still be used continuously to perform step S317.
在步驟S317,估測儲存裝置的使用壽命時間是否將到期。詳言之,若在步驟S305中比對儲存裝置目前累積的讀寫次數不小於預設的讀寫次數臨界值、在步驟S311中比對儲存裝置目前累積的抹除次數不小於預設的抹除次數臨界值,以及在步驟S315比對儲存裝置最新的忙碌時間不小於忙碌時間臨界值時,則可初步估測儲存裝置的可使用時間可能剩下不長,即使用壽命快到期。In step S317, it is estimated whether the service life of the storage device will expire. In detail, if the current cumulative number of reads and writes in the comparison storage device is not less than the preset threshold of read and write times in step S305, and the current cumulative number of erasures in the comparison storage device in step S311 is not less than the preset erase Except for the critical number of times, and when the latest busy time of the storage device compared in step S315 is not less than the busy time critical value, it can be preliminarily estimated that the usable time of the storage device may not be long, that is, the useful life is about to expire.
[第四實施例][Fourth embodiment]
請參閱圖4,其為本發明第四實施例的訓練人工智慧估測儲存裝置的使用壽命的方法的步驟流程圖。如圖4所示,本實施例訓練人工智慧估測儲存裝置的使用壽命的方法包含以下步驟S401~S409,適用於儲存裝置,儲存裝置包含多個記憶單元。本實施例步驟S401~S403是針對第一實施例的步驟S115~S117以及步驟S407是針對第一實施例的步驟S213更進一步的舉例。應理解,本文所述多個實施例的步驟可依實際需求組合實施。Please refer to FIG. 4, which is a flowchart of steps of a method for training artificial intelligence to estimate the service life of a storage device according to a fourth embodiment of the present invention. As shown in FIG. 4, the method for training artificial intelligence to estimate the service life of the storage device in this embodiment includes the following steps S401 to S409, which are applicable to the storage device, and the storage device includes multiple memory units. Steps S401 to S403 of this embodiment are directed to steps S115 to S117 and step S407 of the first embodiment are further examples of step S213 of the first embodiment. It should be understood that the steps of the multiple embodiments described herein can be implemented in combination according to actual needs.
在步驟S401,依據儲存裝置的多個記憶單元的數據位元值的儲存狀態例如依據記憶單元判讀位元值的正確率和錯誤率,分類儲存裝置的多個記憶單元屬於相同或不同的儲存狀態區域,例如強正確區、弱正確區、強錯誤區以及弱錯誤區等更多區域。接著,計算分類在強錯誤區的記憶單元的數量,即計算誤判位元值的錯誤率高於錯誤率門檻值的記憶單元的數量。In step S401, according to the storage state of the data bit values of the plurality of memory units of the storage device, for example, according to the accuracy and error rate of the memory unit to judge the bit value, the plurality of memory units of the classification storage device belong to the same or different storage states Areas, such as strong correct areas, weak correct areas, strong error areas, weak error areas, and more. Next, the number of memory cells classified in the strong error area is calculated, that is, the number of memory cells whose error rate of the miscalculated bit value is higher than the error rate threshold is calculated.
在步驟S403,判斷分類在強錯誤區的記憶單元的數量是否介於第一強錯誤數量允許範圍內,即判斷是否小於第一強錯誤數量門檻值。若分類在強錯誤區的記憶單元的數量介於第一強錯誤數量允許範圍內,即小於第一強錯誤數量門檻值時,判斷儲存裝置能具有一定的正確判讀位元值的能力,而可繼續使用儲存裝置。In step S403, it is determined whether the number of memory cells classified in the strong error area is within the allowable range of the first strong error number, that is, whether it is less than the threshold value of the first strong error number. If the number of memory cells classified in the strong error area is within the allowable range of the first strong error number, that is, less than the threshold value of the first strong error number, it is determined that the storage device has a certain ability to correctly interpret the bit value, and Continue to use the storage device.
在繼續使用儲存裝置後,可能會因儲存裝置使用次數增多、使用時間增長、環境因素等等,影響儲存裝置的運作性能導致記憶單元的儲存狀態例如判讀能力下降,而多個記憶單元屬被重新分類到不同的儲存狀態區域。舉例來說,原本被分類到弱錯誤區的記憶單元,在使用一段時間後重新被分類到強錯誤區。因此,若判斷分類在強錯誤區的記憶單元的數量介於第一強錯誤數量允許範圍內,可再次執行步驟S401。而若判斷分類在強錯誤區的記憶單元的數量超出第一強錯誤數量允許範圍內,即判斷大於第一強錯誤數量門檻值時,始執行下一步驟S405。After the storage device continues to be used, the storage device's storage status, such as the ability to read, may be reduced due to the increased number of storage device usage, increased use time, environmental factors, etc. Classified into different storage status areas. For example, a memory unit that was originally classified into a weak error zone is reclassified into a strong error zone after a period of use. Therefore, if it is determined that the number of memory cells classified in the strong error area is within the allowable range of the first strong error number, step S401 may be executed again. If it is determined that the number of memory cells classified in the strong error area exceeds the allowable range of the first strong error number, that is, if the determination is greater than the threshold value of the first strong error number, the next step S405 is executed.
在步驟S405,啟動人工智慧類神經網路系統。In step S405, the artificial intelligence neural network system is activated.
在步驟S407,利用人工智慧類神經網路系統使用機器學習分析分類在強錯誤區域的記憶單元的數量是否超出第二強錯誤數量允許範圍,即判斷是否小於第二強錯誤數量門檻值。此第二強錯誤數量允許範圍可小於第一強錯誤數量允許範圍,即第二強錯誤數量門檻值可小於第一強錯誤數量門檻值。In step S407, the artificial intelligence neural network system is used to analyze whether the number of memory cells classified in the strong error region exceeds the allowable range of the second strong error number, that is, whether it is smaller than the threshold value of the second strong error number. The allowable range of the second strongest error quantity may be smaller than the allowable range of the first strongest error quantity, that is, the second strongest error quantity threshold may be smaller than the first strongest error quantity threshold.
若利用人工智慧類神經網路系統使用機器學習分析分類在強錯誤區域的記憶單元的數量介於第二強錯誤數量允許範圍,即判斷小於第二強錯誤數量門檻值時,在經過一段時間後可再次執行步驟S401。相反地,分析分類在強錯誤區域的記憶單元的數量超出第二強錯誤數量允許範圍,即判斷大於第二強錯誤數量門檻值時,則執行步驟S409。If the artificial intelligence neural network system is used to analyze the number of memory units classified in the strong error area using machine learning analysis is within the allowable range of the second strong error number, that is, when the judgment is less than the threshold value of the second strong error number, after a period of time Step S401 can be executed again. Conversely, if the number of memory cells classified in the strong error area exceeds the allowable range of the second strong error number, that is, it is judged to be greater than the threshold value of the second strong error number, step S409 is executed.
在步驟S409, 利用人工智慧類神經網路系統使用機器學習依據在強錯誤區域的記憶單元的數量多寡和與第二強錯誤數量門檻值的差量,估測儲存裝置的使用壽命時間。In step S409, the artificial intelligence neural network system is used to estimate the service life of the storage device based on the number of memory cells in the strong error area and the difference from the threshold of the second strong error number using machine learning.
[第五實施例][Fifth Embodiment]
請參閱圖5,其為本發明第五實施例的訓練人工智慧估測儲存裝置的使用壽命的方法的步驟流程圖。如圖5所示,本實施例訓練人工智慧估測儲存裝置的使用壽命的方法包含以下步驟S501~S513,適用於儲存裝置,儲存裝置包含多個記憶單元。應理解,本文所述多個實施例的步驟可依實際需求組合實施。Please refer to FIG. 5, which is a flowchart of steps of a method for training artificial intelligence to estimate the service life of a storage device according to a fifth embodiment of the present invention. As shown in FIG. 5, the method for training artificial intelligence to estimate the service life of a storage device in this embodiment includes the following steps S501 to S513, which are applicable to a storage device. The storage device includes multiple memory units. It should be understood that the steps of the multiple embodiments described herein can be implemented in combination according to actual needs.
在啟動人工智慧類神經網路系統之前,使用其他系統例如儲存裝置的儲存控制模組內設的系統初步執行本實施例步驟S501~S511。接著,在啟動人工智慧類神經網路系統之後,再次執行本實施例步驟S501~S511,其中可設定不同的第二強錯誤數量允許範圍以及第二強錯誤臨界比例已分別替換第一強錯誤數量允許範圍以及第一強錯誤臨界比例。另一實施方式,在啟動人工智慧類神經網路系統之後,始執行本實施例步驟S501~S511。Before starting the artificial intelligence neural network system, using other systems such as the system built in the storage control module of the storage device to initially perform steps S501 to S511 of this embodiment. Then, after the artificial intelligence neural network system is activated, steps S501 to S511 of this embodiment are executed again, wherein different allowable ranges of the second strongest error quantity and the second strongest error critical ratio have been replaced with the first strongest error quantity respectively Allowable range and critical ratio of the first strongest error. In another embodiment, after the artificial intelligence neural network system is started, steps S501 to S511 of this embodiment are executed.
在步驟S501,利用儲存裝置的多個記憶單元對多個位元值執行數據位元處理程序。In step S501, a plurality of memory cells of the storage device are used to perform a data bit processing procedure on a plurality of bit values.
在步驟S503,計算分類在強錯誤區的記憶單元的強錯誤數量。In step S503, the number of strong errors of the memory cells classified in the strong error area is calculated.
在步驟S505,判斷強錯誤數量是否介於第一強錯誤數量允許範圍內。若判斷強錯誤數量介於第一強錯誤數量允許範圍內時,可再次執行步驟S501。相反地,若判斷強錯誤數量超出第一強錯誤數量允許範圍內時,執行步驟S507。In step S505, it is determined whether the number of strong errors is within the allowable range of the number of first strong errors. If it is determined that the number of strong errors is within the allowable range of the first strong errors, step S501 may be executed again. Conversely, if it is determined that the number of strong errors exceeds the allowable range of the first strong errors, step S507 is executed.
在步驟S507,計算分類在弱錯誤區的記憶單元的弱錯誤記憶單元數量。In step S507, the number of weak error memory cells classified into the memory cells in the weak error area is calculated.
在步驟S509,計算分類在強錯誤區的多個記憶單元數量占分類在強錯誤區與弱錯誤區總和的多個記憶單元數量的強錯誤比例。強錯誤比例(Strong Error Ratio, SER)以下列計算式表示為: SER= , 其中,SER代表強錯誤比例, SE表示強錯誤區的記憶單元數量,WE表示弱錯誤區的記憶單元數量。 In step S509, the ratio of strong errors in the number of multiple memory units classified in the strong error area to the number of multiple memory units classified in the strong error area and the weak error area is calculated. The Strong Error Ratio (SER) is expressed as the following formula: SER= , Where SER represents the proportion of strong errors, SE represents the number of memory units in the strong error area, and WE represents the number of memory units in the weak error area.
在步驟S511,判斷強錯誤比例是否小於第一強錯誤臨界比例。若判斷強錯誤比例小於第一強錯誤臨界比例時,可再次執行步驟S501。相反地,若強錯誤比例等於或大於第一強錯誤臨界比例時,執行步驟S513。In step S511, it is determined whether the strong error ratio is smaller than the first strong error critical ratio. If it is determined that the strong error ratio is less than the first strong error critical ratio, step S501 may be executed again. Conversely, if the strong error rate is equal to or greater than the first strong error critical rate, step S513 is executed.
在步驟S513,啟動人工智慧類神經網路系統估測儲存裝置的使用壽命,更精確地估測儲存裝置的每個記憶單元的使用壽命。In step S513, an artificial intelligence neural network system is activated to estimate the service life of the storage device and more accurately estimate the service life of each memory unit of the storage device.
應理解,實務上,可省略步驟S505執行分類在強錯誤區的記憶單元的數量與第一強錯誤數量允許範圍的比對作業,而僅執行步驟S511比對強錯誤比例與小於第一強錯誤臨界比例的作業。It should be understood that, in practice, step S505 may be omitted to perform a comparison operation between the number of memory cells classified in the strong error area and the allowable range of the number of first strong errors, while only performing step S511 to compare the ratio of strong errors and less than the first strong errors Critical proportion of operations.
[第六實施例][Sixth Embodiment]
請參閱圖6,其為本發明第六實施例的訓練人工智慧估測儲存裝置的使用壽命的方法的步驟流程圖。如圖6所示,本實施例訓練人工智慧估測儲存裝置的使用壽命的方法包含以下步驟S601~S609,適用於儲存裝置,儲存裝置包含多個記憶單元。應理解,本文所述多個實施例的步驟可依實際需求組合實施。Please refer to FIG. 6, which is a flowchart of steps of a method for training artificial intelligence to estimate the service life of a storage device according to a sixth embodiment of the present invention. As shown in FIG. 6, the method for training artificial intelligence to estimate the service life of a storage device in this embodiment includes the following steps S601 to S609, which are applicable to a storage device, and the storage device includes a plurality of memory units. It should be understood that the steps of the multiple embodiments described herein can be implemented in combination according to actual needs.
在步驟S601,依據儲存裝置的多個記憶單元的數據位元值的儲存狀態例如依據記憶單元判讀位元值的正確率和錯誤率,分類儲存裝置的多個記憶單元屬於相同或不同的儲存狀態區域,例如包含強正確區、弱正確區、強錯誤區以及弱錯誤區等更多區域。接著,計算分類在強正確區的記憶單元的強正確數量。In step S601, according to the storage state of the data bit values of the plurality of memory units of the storage device, for example, according to the accuracy and error rate of the memory unit to determine the bit value, the plurality of memory units of the classification storage device belong to the same or different storage states Areas include, for example, strong correct areas, weak correct areas, strong error areas, weak error areas, and more. Next, the number of strongly correct memory cells classified in the strongly correct area is calculated.
在步驟S603,判斷分類在強正確區的記憶單元的強正確數量是否大於第一強正確數量門檻值例如8個記憶單元。若判斷分類在強正確區的記憶單元的強正確數量例如10個記憶單元大於第一強正確數量門檻值例如8個記憶單元時,判斷儲存裝置可持續運作,並在儲存裝置後續運作時可再次執行步驟S601。相反地,若判斷分類在強正確區的記憶單元例如6個記憶單元的強正確數量小於或等於第一強正確數量門檻值例如8個記憶單元時,則執行步驟S605。In step S603, it is determined whether the strong correct number of the memory cells classified in the strong correct area is greater than the first strong correct number threshold, for example, 8 memory cells. If it is judged that the strong correct number of memory units classified in the strong correct area, such as 10 memory units, is greater than the first strong correct number threshold, such as 8 memory units, it is judged that the storage device can continue to operate, and the storage device can be operated again when the storage device is subsequently operated Go to step S601. Conversely, if it is determined that the number of memory units classified in the strong correct area, such as 6 memory units, is less than or equal to the first strong correct number threshold, such as 8 memory units, step S605 is executed.
在步驟S605,啟動人工智慧類神經網路系統。In step S605, the artificial intelligence neural network system is activated.
在步驟S607,使用機器學習分析分類在強正確區的記憶單元的強正確數量是否大於第二強正確數量門檻值。舉例來說,此第二強正確數量門檻值例如5個記憶單元小於第一強正確數量門檻值例如8個記憶單元。In step S607, use machine learning to analyze whether the number of strong correct numbers of the memory cells classified in the strong correct area is greater than the threshold value of the second strong correct number. For example, the second strongest correct quantity threshold value, for example, 5 memory units is less than the first strongest correct quantity threshold value, for example, 8 memory units.
若判斷分類在強正確區的記憶單元的強正確數量例如6個記憶單元大於第二強正確數量門檻值例如5個記憶單元時,判斷儲存裝置可持續運作,並在儲存裝置後續運作時可再次執行步驟S601。相反地,若判斷分類在強正確區的記憶單元的強正確數量例如4個記憶單元小於或等於第二強正確數量門檻值例如5個記憶單元時,則執行步驟S609。If it is judged that the strong correct number of memory units classified in the strong correct area, for example, 6 memory units is greater than the second strong correct number threshold, for example, 5 memory units, the storage device is judged to be able to operate continuously, and can be re-executed when the storage device is subsequently operated Go to step S601. Conversely, if it is determined that the number of strong correct units of memory units classified in the strong correct area, for example, 4 memory units is less than or equal to the second strong correct number threshold, such as 5 memory units, step S609 is executed.
在步驟S609, 估測儲存裝置的使用壽命時間即將到期。In step S609, it is estimated that the service life of the storage device is about to expire.
[第七實施例][Seventh Embodiment]
請參閱圖7,其為本發明第七實施例的訓練人工智慧估測儲存裝置的使用壽命的方法的步驟流程圖。如圖7所示,本實施例訓練人工智慧估測儲存裝置的使用壽命的方法包含以下步驟S701~S713,適用於儲存裝置,儲存裝置包含多個記憶單元。應理解,本文所述多個實施例的步驟可依實際需求組合實施。Please refer to FIG. 7, which is a flowchart of steps of a method for training artificial intelligence to estimate the service life of a storage device according to a seventh embodiment of the present invention. As shown in FIG. 7, the method for training artificial intelligence to estimate the service life of a storage device in this embodiment includes the following steps S701 to S713, which are applicable to a storage device, and the storage device includes multiple memory units. It should be understood that the steps of the multiple embodiments described herein can be implemented in combination according to actual needs.
在步驟S701,利用儲存裝置的多個記憶單元對多個位元值執行數據位元處理程序。In step S701, a plurality of memory cells of the storage device are used to perform a data bit processing procedure on a plurality of bit values.
在步驟S703,計算分類在強正確區的記憶單元的強正確數量。In step S703, the number of strongly correct memory cells classified in the strongly correct area is calculated.
在步驟S705,判斷分類在強正確區的記憶單元的強正確數量是否大於第一強正確數量門檻值。若判斷分類在強正確區的記憶單元的強正確數量大於第一強正確數量門檻值,可重覆執行步驟S701。相反地,若判斷分類在強正確區的記憶單元的強正確數量小於或等於第一強正確數量門檻值,執行步驟S707。In step S705, it is determined whether the number of strong correct numbers of the memory cells classified in the strong correct area is greater than the threshold value of the first strong correct number. If it is determined that the number of strong correct numbers of the memory cells classified in the strong correct area is greater than the threshold value of the first strong correct number, step S701 may be repeatedly executed. On the contrary, if it is determined that the number of strong correct numbers of the memory cells classified in the strong correct area is less than or equal to the threshold value of the first strong correct number, step S707 is executed.
在步驟S707,計算分類在弱正確區的記憶單元的弱正確記憶單元數量。In step S707, the number of weakly correct memory cells classified in the weakly correct area is calculated.
在步驟S709,計算分類在強正確區的記憶單元數量占分類在強正確區與弱正確區總和的記憶單元數量的強正確比例。強正確比例(Strong correct ratio, SCR)以下列計算式表示為: SCR= 其中,SCR代表強正確比例,SC表示強正確區的記憶單元數量,WC表示弱正確區的記憶單元數量。 In step S709, the ratio of the number of memory units classified in the strong correct area to the number of memory units classified in the sum of the strong correct area and the weak correct area is calculated. Strong correct ratio (SCR) is expressed by the following calculation formula: SCR= Among them, SCR stands for the strong correct ratio, SC stands for the number of memory units in the strong correct area, and WC stands for the number of memory units in the weak correct area.
在步驟S711,判斷強正確比例是否大於第一強正確臨界比例。若判斷強正確比例大於第一強正確臨界比例,可重覆執行步驟S701。相反地,若判斷強正確比例是否不大於第一強正確臨界比例,執行步驟S713。In step S711, it is determined whether the strong correct ratio is greater than the first strong correct critical ratio. If it is determined that the strong correct ratio is greater than the first strong correct critical ratio, step S701 may be repeatedly executed. On the contrary, if it is determined whether the strong correct ratio is not greater than the first strong correct critical ratio, step S713 is executed.
在步驟S713,啟動人工智慧類神經網路系統依據上述的判斷結果估測儲存裝置的使用壽命。甚至,可啟動人工智慧類神經網路系統再次執行上述步驟S701~S711,第一強正確數量門檻值與第一強正確臨界比例可替換為具不同數值的第二強正確數量門檻值與第二強正確臨界比例。In step S713, the artificial intelligence neural network system is activated to estimate the service life of the storage device based on the above determination result. Even, the artificial intelligence neural network system can be activated to perform the above steps S701 to S711 again, the threshold value of the first strong correct quantity and the threshold of the first strong correct quantity can be replaced with the second strong correct quantity threshold and the second Strong correct critical ratio.
誠如上述,第六實施例計算強錯誤比例,而第七實施例計算強正確比例。替換地,又另一實施方式,計算分類在強錯誤區的記憶單元數量與分類在強正確區的記憶單元數量的強正確錯誤比例。接著,判斷強正確錯誤比例是否小於強正確錯誤臨界比例,或判斷分類在強錯誤區的記憶單元數量是否小於分類在強正確區的記憶單元數量。若判斷強正確錯誤比例小於強正確錯誤臨界比例,或判斷分類在強錯誤區的記憶單元數量小於分類在強正確區的記憶單元數量時,可重覆執行步驟S701。相反地,則執行步驟S713。As described above, the sixth embodiment calculates the strong error ratio, and the seventh embodiment calculates the strong correct ratio. Alternatively, in yet another embodiment, the ratio of strong correct errors of the number of memory units classified in the strong error area to the number of memory units classified in the strong correct area is calculated. Next, it is determined whether the proportion of strong correct errors is less than the critical ratio of strong correct errors, or whether the number of memory units classified in the strong error area is smaller than the number of memory units classified in the strong correct area. If it is judged that the proportion of strong correct errors is smaller than the critical ratio of strong correct errors, or if the number of memory cells classified in the strong error area is smaller than the number of memory cells classified in the strong correct area, step S701 may be repeatedly executed. On the contrary, step S713 is executed.
[第八實施例][Eighth Embodiment]
請參閱圖8,其為本發明第八實施例的訓練人工智慧估測儲存裝置的使用壽命的方法的步驟流程圖。如圖8所示,本實施例訓練人工智慧估測儲存裝置的使用壽命的方法包含以下步驟S801~S817,適用於儲存裝置,儲存裝置包含多個記憶單元。應理解,本文所述多個實施例的步驟可依實際需求組合實施。Please refer to FIG. 8, which is a flowchart of steps of a method for training artificial intelligence to estimate the service life of a storage device according to an eighth embodiment of the present invention. As shown in FIG. 8, the method for training artificial intelligence to estimate the service life of a storage device in this embodiment includes the following steps S801 to S817, which are applicable to a storage device, and the storage device includes multiple memory units. It should be understood that the steps of the multiple embodiments described herein can be implemented in combination according to actual needs.
在步驟S801,利用解碼器解碼儲存裝置的記憶單元所儲存的位元值。In step S801, the decoder decodes the bit value stored in the memory unit of the storage device.
在步驟S803,判斷解碼器是否成功解碼儲存裝置的記憶單元所儲存的位元值。為了精確地估測儲存裝置的使用壽命,解碼器可解碼儲存裝置的多個記憶單元所儲存的所有或大部分的位元值,並記錄每次解碼所耗費的時間,但本發明不以此為限。實務上,為節省估測時間,亦可僅解碼記憶單元所儲存的部分位元值。In step S803, it is determined whether the decoder successfully decodes the bit value stored in the memory unit of the storage device. In order to accurately estimate the service life of the storage device, the decoder can decode all or most of the bit values stored in multiple memory units of the storage device and record the time spent in each decoding, but the present invention does not use this Limited. In practice, in order to save the estimation time, only part of the bit values stored in the memory unit can be decoded.
若解碼器解碼儲存裝置的記憶單元失敗時,執行步驟S805:記錄解碼器解碼位元值的解碼時間。接著,重覆執行步驟S803,解碼器可以一定機率翻轉儲存裝置儲存錯誤的位元值,或解碼器可解碼儲存裝置重新存取的位元值。相反地,若解碼器成功解碼儲存裝置的記憶單元,則執行步驟S807。If the decoder fails to decode the memory unit of the storage device, step S805 is executed: record the decoding time of the decoder bit value. Then, by repeating step S803, the decoder can flip the storage device to store the wrong bit value with a certain probability, or the decoder can decode the bit value re-accessed by the storage device. On the contrary, if the decoder successfully decodes the memory unit of the storage device, step S807 is executed.
在步驟S807,判斷解碼器目前解碼位元值的解碼時間是否小於預設的第一解碼臨界時間。In step S807, it is determined whether the decoding time of the decoder's current decoding bit value is less than the preset first decoding critical time.
在步驟S809,啟動人工智慧類神經網路系統。In step S809, the artificial intelligence neural network system is activated.
在步驟S811,利用人工智慧類神經網路系統使用機器學習分析判斷解碼時間是否小於預設的第二解碼臨界時間。此第二解碼臨界時間可小於第一解碼臨界時間。In step S811, an artificial intelligence neural network system is used to determine whether the decoding time is less than a preset second decoding critical time using machine learning analysis. This second decoding critical time may be less than the first decoding critical time.
在步驟S813,利用人工智慧類神經網路系統使用機器學習依據上述判斷結果估測解碼器對儲存裝置的更正能力。In step S813, the artificial intelligence neural network system is used to estimate the correction ability of the decoder to the storage device based on the above judgment result using machine learning.
詳言之,在理想情況下,解碼器在解碼儲存裝置的記憶單元所儲存的位元值時,可執行一位元值更正程序,以一特定機率翻轉記憶單元所儲存的錯誤位元值為正確位元值,例如儲存裝置欲儲存邏輯0的位元值在存取時誤判和誤存為邏輯1,解碼器執行位元值更正程序將邏輯1翻轉回邏輯0的位元值,以實現位元值的更正,並將正確的位元值儲存至儲存裝置的記憶單元。In detail, under ideal circumstances, the decoder can perform a one-bit value correction procedure when decoding the bit value stored in the memory unit of the storage device, and flip the error bit value stored in the memory unit with a specific probability The correct bit value, for example, the storage device wants to store the bit value of logic 0 is misjudged and stored as a logic 1 during access, the decoder executes the bit value correction process to flip the logic 1 back to the bit value of logic 0 to achieve Correct the bit value and store the correct bit value in the memory unit of the storage device.
然而,若儲存裝置的性能太差導致儲存裝置誤判大量數據位元值或儲存無效數據即誤判位元值的錯誤率高於錯誤率門檻值,或解碼器性能變得太差,導致超過解碼器更正能力的負荷程度或解碼器不具良好的更正能力,導致解碼器無法有效更正儲存裝置儲存的錯誤數據時,則可估測解碼器或儲存裝置的使用壽命將到期,而需更換新的可用解碼器、儲存裝置或兩者。However, if the performance of the storage device is too poor, the storage device misjudges a large number of data bit values or stores invalid data, that is, the error rate of the miscalculated bit value is higher than the error rate threshold, or the decoder performance becomes too poor, resulting in exceeding the decoder The load level of the correction capability or the decoder does not have a good correction capability, resulting in the decoder cannot effectively correct the erroneous data stored in the storage device, it can be estimated that the service life of the decoder or storage device will expire, and a new one needs to be replaced. Decoder, storage, or both.
在步驟S815,利用人工智慧類神經網路系統使用機器學習定義持續使用群組以及更正極限群組。In step S815, an artificial intelligence neural network system is used to define a continuous use group and a correction limit group using machine learning.
在步驟S817,利用人工智慧類神經網路系統使用機器學習依據上述估測結果分析儲存裝置可繼續使用時,分類歸類儲存裝置至持續使用群組。相反地,若判斷儲存裝置無法繼續使用時,分類歸類儲存裝置至更正極限群組。In step S817, when the artificial intelligence neural network system is used to analyze the storage device according to the above estimation result using machine learning and the storage device can be used continuously, the storage device is classified into the continuous use group. On the contrary, if it is judged that the storage device can no longer be used, the storage device is classified into the correction limit group.
[實施例的有益效果][Beneficial effect of embodiment]
綜上所述,本發明所提供的訓練人工智慧估測儲存裝置的使用壽命的方法,其可依據儲存裝置的讀寫次數、抹除次數以及忙碌時間等運作參數值、儲存裝置存取數據位元值的錯誤率(即分類在強錯誤區或其他儲存狀態的記憶單元數量),以及解碼器對儲存裝置所儲存的數據位元值的更正能力,以有效估測儲存裝置的使用壽命。In summary, the method for training artificial intelligence provided by the present invention to estimate the service life of a storage device can be based on the operating parameter values of the storage device such as the number of reads and writes, the number of erasures and the busy time, and the storage device access data bits The error rate of the meta value (that is, the number of memory cells classified in a strong error area or other storage state), and the decoder's ability to correct the data bit value stored in the storage device, to effectively estimate the service life of the storage device.
以上所提供的內容僅為本發明的優選可行實施例,並非因此侷限本發明的申請專利範圍,所以凡是運用本發明說明書及圖式內容所做的等效技術變化,均包含於本發明的申請專利範圍內。The content provided above is only a preferred and feasible embodiment of the present invention, and therefore does not limit the scope of the patent application of the present invention, so all equivalent technical changes made by using the description and drawings of the present invention are included in the application of the present invention. Within the scope of the patent.
S101~S119、S201~S215、S301~S317、S401~S409、S501~S513、S601~S609、S701~S713、S801~S817:步驟S101~S119, S201~S215, S301~S317, S401~S409, S501~S513, S601~S609, S701~S713, S801~S817: Steps
圖1為本發明第一實施例的訓練人工智慧估測儲存裝置的使用壽命的方法的步驟流程圖。FIG. 1 is a flowchart of steps of a method for training artificial intelligence to estimate the service life of a storage device according to a first embodiment of the invention.
圖2為本發明第二實施例的訓練人工智慧估測儲存裝置的使用壽命的方法的步驟流程圖。2 is a flowchart of steps of a method for training artificial intelligence to estimate the service life of a storage device according to a second embodiment of the invention.
圖3為本發明第三實施例的訓練人工智慧估測儲存裝置的使用壽命的方法的步驟流程圖。3 is a flowchart of steps of a method for training artificial intelligence to estimate the service life of a storage device according to a third embodiment of the present invention.
圖4為本發明第四實施例的訓練人工智慧估測儲存裝置的使用壽命的方法的步驟流程圖。4 is a flowchart of steps of a method for training artificial intelligence to estimate the service life of a storage device according to a fourth embodiment of the present invention.
圖5為本發明第五實施例的訓練人工智慧估測儲存裝置的使用壽命的方法的步驟流程圖。5 is a flowchart of steps of a method for training artificial intelligence to estimate the service life of a storage device according to a fifth embodiment of the present invention.
圖6為本發明第六實施例的訓練人工智慧估測儲存裝置的使用壽命的方法的步驟流程圖。6 is a flowchart of steps in a method for training artificial intelligence to estimate the service life of a storage device according to a sixth embodiment of the invention.
圖7為本發明第七實施例的訓練人工智慧估測儲存裝置的使用壽命的方法的步驟流程圖。7 is a flowchart of steps of a method for training artificial intelligence to estimate the service life of a storage device according to a seventh embodiment of the present invention.
圖8為本發明第八實施例的訓練人工智慧估測儲存裝置的使用壽命的方法的步驟流程圖。8 is a flowchart of steps of a method for training artificial intelligence to estimate the service life of a storage device according to an eighth embodiment of the present invention.
S201~S215:步驟 S201~S215: Steps
Claims (8)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
TW108100343A TWI690860B (en) | 2019-01-04 | 2019-01-04 | Method of training artificial intelligence to estimate lifetime of storage device |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
TW108100343A TWI690860B (en) | 2019-01-04 | 2019-01-04 | Method of training artificial intelligence to estimate lifetime of storage device |
Publications (2)
Publication Number | Publication Date |
---|---|
TWI690860B true TWI690860B (en) | 2020-04-11 |
TW202026956A TW202026956A (en) | 2020-07-16 |
Family
ID=71134481
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
TW108100343A TWI690860B (en) | 2019-01-04 | 2019-01-04 | Method of training artificial intelligence to estimate lifetime of storage device |
Country Status (1)
Country | Link |
---|---|
TW (1) | TWI690860B (en) |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
TW200413904A (en) * | 2003-01-29 | 2004-08-01 | Inventec Appliances Corp | Consumption balancing method of non-volatile memory |
TWI257044B (en) * | 2004-11-26 | 2006-06-21 | Hon Hai Prec Ind Co Ltd | Apparatus and method for prolonging useful life of non-volatile memory |
US20090161466A1 (en) * | 2007-12-20 | 2009-06-25 | Spansion Llc | Extending flash memory data retension via rewrite refresh |
CN101752008A (en) * | 2008-12-05 | 2010-06-23 | 财团法人工业技术研究院 | Method for testing reliability of solid-state storage media |
TWM520713U (en) * | 2015-08-13 | 2016-04-21 | Apacer Technology Inc | Hard disk monitoring system |
CN108734649A (en) * | 2017-04-24 | 2018-11-02 | 英特尔公司 | Neural metwork training mechanism |
-
2019
- 2019-01-04 TW TW108100343A patent/TWI690860B/en active
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
TW200413904A (en) * | 2003-01-29 | 2004-08-01 | Inventec Appliances Corp | Consumption balancing method of non-volatile memory |
TWI257044B (en) * | 2004-11-26 | 2006-06-21 | Hon Hai Prec Ind Co Ltd | Apparatus and method for prolonging useful life of non-volatile memory |
US20090161466A1 (en) * | 2007-12-20 | 2009-06-25 | Spansion Llc | Extending flash memory data retension via rewrite refresh |
CN101752008A (en) * | 2008-12-05 | 2010-06-23 | 财团法人工业技术研究院 | Method for testing reliability of solid-state storage media |
TWM520713U (en) * | 2015-08-13 | 2016-04-21 | Apacer Technology Inc | Hard disk monitoring system |
CN108734649A (en) * | 2017-04-24 | 2018-11-02 | 英特尔公司 | Neural metwork training mechanism |
Also Published As
Publication number | Publication date |
---|---|
TW202026956A (en) | 2020-07-16 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
EP2530595B1 (en) | System and method of tracking error data within a storage device | |
KR101951988B1 (en) | Word line defect detection and handling for a data storage device | |
US8539311B2 (en) | System and method for data recovery in multi-level cell memories | |
US10936391B2 (en) | Memory management method and storage controller | |
US9244766B2 (en) | Variable data recovery scheme hierarchy | |
US9734919B2 (en) | Recovery of interfacial defects in memory cells | |
US9342399B2 (en) | Threshold voltage calibration using reference pattern detection | |
US8996936B2 (en) | Enhanced error correction in memory devices | |
US20140136884A1 (en) | Read disturb handling for non-volatile solid state media | |
US20220068422A1 (en) | Managing block retirement for temporary operational conditions | |
US8730724B2 (en) | Common line current for program level determination in flash memory | |
US20200135279A1 (en) | Implementing sticky read using error control success rate associated with a memory sub-system | |
US11688467B2 (en) | Defect detection in memories with time-varying bit error rate | |
US11348643B2 (en) | Identifying failure type in NVM programmed in SLC mode using a single programming pulse with no verification | |
US10665309B2 (en) | Memory endurance measures based on an extrapolated function fit to metric points for a memory sub-system | |
US10324648B1 (en) | Wear-based access optimization | |
KR20090036146A (en) | Allowable bit errors per sector in memory devices | |
US11955194B2 (en) | Tracking and refreshing state metrics in memory sub-systems | |
TWI690860B (en) | Method of training artificial intelligence to estimate lifetime of storage device | |
TWI442406B (en) | Method for enhancing verification efficiency regarding error handling mechanism of a controller of a flash memory, and associated memory device and controller thereof | |
CN111427713B (en) | Method for training artificial intelligence to estimate service life of storage device | |
TWI684106B (en) | Method of training artificial intelligence to correct log-likelihood ratio for storage device | |
CN113380309A (en) | Method for searching optimal reading voltage and flash memory system | |
CN113555052A (en) | Semiconductor memory device with a plurality of memory cells | |
CN114023372A (en) | Memory management method, memory storage device and memory controller |