CN110580932A - Memory cell quality measurement method applied to wear leveling - Google Patents
Memory cell quality measurement method applied to wear leveling Download PDFInfo
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- CN110580932A CN110580932A CN201910807106.5A CN201910807106A CN110580932A CN 110580932 A CN110580932 A CN 110580932A CN 201910807106 A CN201910807106 A CN 201910807106A CN 110580932 A CN110580932 A CN 110580932A
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- G—PHYSICS
- G11—INFORMATION STORAGE
- G11C—STATIC STORES
- G11C16/00—Erasable programmable read-only memories
- G11C16/02—Erasable programmable read-only memories electrically programmable
- G11C16/06—Auxiliary circuits, e.g. for writing into memory
- G11C16/34—Determination of programming status, e.g. threshold voltage, overprogramming or underprogramming, retention
- G11C16/349—Arrangements for evaluating degradation, retention or wearout, e.g. by counting erase cycles
- G11C16/3495—Circuits or methods to detect or delay wearout of nonvolatile EPROM or EEPROM memory devices, e.g. by counting numbers of erase or reprogram cycles, by using multiple memory areas serially or cyclically
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Abstract
The invention discloses a memory cell quality measurement method applied to wear leveling, which comprises the following steps: s1, selecting a plurality of characteristic values from the characteristic set of the storage unit as the quality measurement characteristics of the storage unit according to the measurement capability of the actual storage system; and S2, calculating to obtain a memory cell quality measurement result according to the memory cell quality measurement characteristics and the pre-trained memory cell quality measurement model. The invention considers the loss degree difference of the storage units in different periods, adopts a plurality of measurement characteristics such as operation time, threshold voltage distribution and the like which can more accurately reflect the current reliability state of the storage units, establishes an accurate storage unit quality measurement model applied to loss balance, and can greatly improve the accuracy of the quality measurement result, thereby improving the loss balance execution efficiency of the storage system and prolonging the service life of the storage system.
Description
Technical Field
The invention belongs to the field of quality measurement of storage units, and particularly relates to a quality measurement method of a storage unit applied to wear leveling.
Background
With the development of information technology, memories have become more and more important as carriers for storing data in electronic devices in the fields of communication, consumption, computers, industrial control, military and the like, but low reliability has been one of the main problems of memories.
Wear leveling (also referred to as wear leveling) is a technique to extend the useful life of erasable computer storage media such as solid state drives SSDs, USB flash drives, phase change memories, and the like. Without wear leveling, the underlying flash controller must permanently assign the logical address of the operating system to the physical address of the flash. This means that each write to a previously written block must first read, erase, modify, and rewrite to the same location. This method is time consuming and often written locations wear out quickly, and once a frequently used memory block reaches the end of life, the memory system will not work properly. The service life of the storage system without using wear leveling is greatly reduced. The quality measurement of the memory unit is a core step of wear leveling operation of the memory system, and has high research value.
Currently, the quality of memory cells is typically measured in erase counts in wear leveling operations. The measuring method does not consider the loss degree difference of the storage unit in different periods, the actual reliability degree of the storage unit is difficult to accurately reflect, and the reliability is low.
Therefore, it is an urgent need to provide a method for measuring the quality of a memory cell with high accuracy.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a memory cell quality measuring method applied to wear leveling, and aims to solve the problem that the memory cell quality measuring method in the prior art is low in accuracy because the loss degree difference of memory cells in different periods is not considered.
In order to achieve the above object, the present invention provides a method for measuring the quality of a memory cell applied to wear leveling, comprising the following steps:
S1, selecting a plurality of characteristic values from the storage unit characteristic set as storage unit quality measurement characteristics according to the measurement capability of the actual storage system;
And S2, calculating to obtain a memory cell quality measurement result according to the memory cell quality measurement characteristics and the pre-trained memory cell quality measurement model.
Further preferably, the characteristic set of memory cells includes an operation time of the memory cells, a current on the memory cells, a power consumption of the memory cells, a threshold voltage distribution, a memory block cell location flag, a number of program/erase cycles currently experienced by the memory cells, a number of conditionally erroneous memory cells, a number of error bits and an error rate of any subset of memory cells, and a number of error bits and an error rate of any subset of memory cells under a special data pattern.
Further preferably, the special data pattern includes: data patterns for enhancing wear of memory cells, and data patterns for enhancing interference between memory cells.
more preferably, the memory cell quality metric feature is a subset of a memory cell feature set, and the number of features included in the subset is 2 or more.
further preferably, the memory cell quality metric characteristic comprises an operating time of the memory cell.
Further preferably, the method for measuring the operation time of the memory cell includes: and detecting the R/B signal state of the chip to which the storage unit belongs, if the R/B signal state is low, recording the operation time and the operation type of the storage unit, and when the programming time, the reading time and the erasing time of the storage unit are not 0, obtaining the current programming time, the reading time and the erasing time as the operation time of the storage unit.
Further preferably, the quality metric characteristics of the memory cells corresponding to the values of the remaining available program/erase cycles of the memory cells in the closed interval range from the maximum value to the minimum value are sequentially measured, and the normalized remaining available program/erase cycles and the quality metric characteristics of the memory cells corresponding to the remaining available program/erase cycles are used for training to obtain the quality metric model of the memory cells.
Further preferably, in the process of measuring the memory cell by using the memory cell quality metric model, when the remaining available program/erase cycles of the memory cell reach the minimum value, the corresponding memory cell quality metric feature is calculated, the normalized remaining available program/erase cycles and the corresponding memory cell quality metric feature are used as a group of samples and recorded, and when the number of recorded samples reaches a preset sample threshold, the recorded samples are used to train and update the memory cell quality metric model.
Further preferably, the memory cell quality measurement method provided by the invention is applied to the quality measurement of wear-leveling memory cells.
Further preferably, the detected quality measurement result of the storage unit is stored in a quality metric table of the storage system, and when the storage system performs wear leveling operation, the storage system performs wear leveling address mapping change and data migration operation according to the quality metric value of the storage unit stored in the quality metric table, so as to prolong the service life of the storage system.
Through the technical scheme, compared with the prior art, the invention can obtain the following beneficial effects:
1. The invention provides a memory cell quality measuring method applied to wear leveling, which considers the wear degree difference of memory cells in different periods, adopts various measuring characteristics such as operation time, threshold voltage distribution and the like, and can more accurately reflect the current reliability state of the memory cells, calculates the quality measurement value through a mathematical model, solves the problem of low accuracy of the memory cell quality measuring method caused by not considering the wear degree difference of the memory cells in different periods in the prior art, and has higher measuring accuracy.
2. The invention provides a method for measuring the operation time of a storage unit, which measures the operation time through a mark signal of the storage unit, does not need to occupy more computing resources of a storage system, can realize the measurement of the operation time under the condition of not influencing the operation efficiency of the storage system, and has higher measurement efficiency.
3. the storage unit quality measurement method applied to wear leveling provided by the invention can select specific storage unit quality measurement characteristics through the measurement capability of an actual storage system, so that the storage unit quality measurement characteristics are measured under the condition of not sacrificing larger equipment resources, and the measurement cost is lower.
4. the memory unit quality measurement model obtained by training in the memory unit quality measurement method applied to wear leveling provided by the invention can be optimized and updated in real time in the operation process of the memory system, and is more intelligent compared with the method for measuring units in different reliability stages by adopting the same judgment standard in the prior art.
5. The storage unit quality measurement method applied to the loss balance can update the storage unit quality measurement model under the condition that the loss balance algorithm of the storage system is not changed, so that the quality measurement accuracy is improved, and the loss balance algorithm is assisted to further prolong the service life of the storage system.
drawings
FIG. 1 is a flow chart of a method for measuring the quality of a memory cell for wear leveling according to the present invention;
FIG. 2 is a flowchart of a method for calculating an operating time of a memory cell according to an embodiment of the present invention;
FIG. 3 is a flow chart of a method for training a memory cell quality metric model according to an embodiment of the present invention;
FIG. 4 is a block diagram of an artificial neural network employed by embodiments of the present invention.
Detailed Description
in order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In order to achieve the above object, the present invention provides a method for measuring quality of a memory cell applied to wear leveling, as shown in fig. 1, including the following steps:
S1, selecting a plurality of characteristic values from the storage unit characteristic set as storage unit quality measurement characteristics according to the measurement capability of the actual storage system;
Specifically, the memory cell feature set includes an operation time of the memory cell, a current on the memory cell, a power consumption of the memory cell, a threshold voltage distribution, a memory block cell location flag, a number of program/erase cycles currently experienced by the memory cell, a number of conditionally erroneous memory cells, a number of error bits and an error rate of any subset of memory cells, and a number of error bits and an error rate of any subset of memory cells under a special data pattern. Wherein the special data pattern includes: data patterns for enhancing wear of memory cells, and data patterns for enhancing interference between memory cells.
specifically, the memory cell quality metric features are subsets of a memory cell feature set, and the number of features included in the subsets is 2 or more. Further, the memory cell quality metric characteristic includes an operating time of the memory cell. Specifically, the method for measuring the operating time of the memory cell includes: and detecting the R/B signal state of the chip to which the storage unit belongs, if the R/B signal state is low, recording the operation time and the operation type of the storage unit, and when the programming time, the reading time and the erasing time of the storage unit are not 0, obtaining the current programming time, the reading time and the erasing time as the operation time of the storage unit.
And S2, calculating to obtain a memory cell quality measurement result according to the memory cell quality measurement characteristics and the pre-trained memory cell quality measurement model.
Specifically, the quality measurement characteristics of the memory cells corresponding to the values of the remaining available program/erase cycles of the memory cells in the closed interval range from the maximum value to the minimum value are sequentially measured, and the normalized remaining available program/erase cycles and the quality measurement characteristics of the memory cells corresponding to the remaining available program/erase cycles are adopted for training to obtain the quality measurement model of the memory cells. Further, in the process of measuring the memory cell by using the memory cell quality measurement model, when the remaining available program/erase cycles of the memory cell reach the minimum value, the corresponding memory cell quality measurement feature is calculated, the normalized remaining available program/erase cycles and the corresponding memory cell quality measurement feature are used as a group of samples and recorded, and when the recorded samples reach a preset sample threshold, the recorded samples are used for training and updating the memory cell quality measurement model.
To further illustrate the method for measuring the quality of a memory cell applied to wear leveling proposed by the present invention, the following detailed description is made with reference to the following embodiments:
Examples of the following,
the quality measurement method includes the following steps that memory cells, which are memory blocks in a flash memory (NAND flash) under a certain manufacturing process, are taken as a measurement object and a quality measurement object of the embodiment:
S1, selecting a plurality of characteristic values from the storage unit characteristic set as storage unit quality measurement characteristics according to the measurement capability of the actual storage system;
Specifically, the operating time of a memory unit, the number of program/erase cycles currently experienced by the memory unit, and the number of error bits can be measured by a memory system in which a memory block in a flash memory (NAND flash) is located without sacrificing large device resources, so that the operating time of the memory unit, the number of program/erase cycles currently experienced by the memory unit, and the number of error bits are selected as the quality measurement characteristics of the memory unit for performing quality measurement; the operation time of the memory cell includes programming time, reading time and erasing time of the memory cell.
Specifically, the operation time of the memory cell, the number of program/erase cycles currently experienced by the memory cell, and the number of error bits are calculated respectively.
Specifically, the method for calculating the operation time of the memory cell, as shown in fig. 2, includes:
S11, detecting the R/B signal state of the chip to which the memory cell belongs, if the R/B signal state is low, recording the operation time and the operation type of the memory cell, when the operation time corresponding to each operation type is not 0, namely the programming time, the reading time and the erasing time of the memory cell are not 0, the currently obtained programming time, reading time and erasing time are the operation time of the memory cell, finishing the algorithm, and repeating the step S11 except the other conditions.
the method can obtain the operation time value required by the quality measurement under the condition of not reducing the reading and writing speed of the storage system, and has less resource occupation and higher efficiency.
Specifically, when the number of program/erase cycles that the memory cell has been currently subjected to is calculated, the number of program/erase cycles that the memory cell has been currently subjected to is obtained by counting the number of times that the memory completes the program and erase operations.
Specifically, when calculating the number of error bits of the memory cell, the original data is written into the memory cell, and then the data read from the memory cell is compared with the original data to record the number of error bits.
and S2, calculating to obtain a memory cell quality measurement result according to the memory cell quality measurement characteristics and the pre-trained memory cell quality measurement model.
Specifically, the quality metric characteristics of the memory cells corresponding to the values of the remaining available program/erase cycles of the memory cells in the closed interval range from the maximum value to the minimum value are sequentially measured, and taking a flash memory chip produced by using magnesium light with the model number of NW587 as an example, the maximum value of the remaining available program/erase cycles of the memory cells is 20000, the minimum value is 0, and the unit is the program/erase cycles. And then forming a data set by using the normalized remaining available programming/erasing period number and the corresponding quality measurement characteristics of the storage unit, dividing the data set into a training set, a verification set and a test set, and training an artificial neural network by using the training set to obtain a quality measurement model of the storage unit. Wherein, the calculation expression of the normalized remaining available programming/erasing period number is as follows:
In this embodiment, the training set accounts for 60% of the entire data set, the validation set accounts for 20% of the entire data set, and the test set accounts for 20% of the entire data set.
Specifically, as shown in fig. 3, the method for training the memory cell quality metric model includes the following steps:
S21, initializing the artificial neural network, setting the iteration number Epoch as 1, and defining the input and output number of the artificial neural network; specifically, in this embodiment, the artificial neural network inputs the quality metric characteristics corresponding to the memory cells, including the operation time of the memory cells, the number of program/erase cycles currently experienced by the memory cells, and the number of error bits, where the operation time of the memory cells includes the program time, the read time, and the erase time of the memory cells, so the number of inputs is 5, and the input number corresponds to the program time PT of the memory cells, the read time RT of the memory cells, the erase time ET of the memory cells, the number of program/erase cycles CN currently experienced by the memory cells, and the number of error bits ER, respectively. The output corresponds to the number of remaining available program/erase cycles RP after normalization, which is 1. In this embodiment, the structure of the artificial neural network is shown in fig. 4, in which the number of hidden layers (hidden layers) of the artificial neural network is 60, and the activation function used is a sigmod function.
S22, inputting the training set into an artificial neural network to train the training set, and calculating an error value of the training set; specifically, in this embodiment, a Mean Square Error (MSE) is used as an error value, and its expression is:wherein n is the total number of training samples; y isobs,iIs an actual value; y ismodel,iIs an artificial neural output value.
S23, adjusting the connection weight of the artificial neural network and the value of a threshold value by adopting a Levenberg-Marquardt algorithm;
S24, inputting the verification set into an artificial neural network to adjust parameters of the verification set, and calculating an error value of the verification set;
S25, judging the sizes of the error value of the verification set and the error value of the training set, if the error value of the verification set is smaller than the error value of the training set, finishing the algorithm, and obtaining the artificial neural network which is the storage unit quality measurement model at present; otherwise, repeating the steps S22-S24 to iterate until the error value of the verification set is less than or equal to the error value of the training set;
S26, respectively inputting the test set and the training set into an artificial neural network to obtain an error value of the test set and an error value of the training set;
s27, judging the error value of the test set and the error value of the training set, if the error value of the test set is smaller than the error value of the training set, finishing the algorithm, and obtaining the artificial neural network which is the storage unit quality measurement model at present; otherwise, judging whether the iteration number Epoch is greater than 1000, if the iteration number Epoch is greater than 1000, ending the algorithm, and obtaining the artificial neural network which is the storage unit quality measurement model at present; otherwise, setting the iteration number Epoch as Epoch +1, and going to step S22.
Further, in the process of measuring the memory cell by using the memory cell quality measurement model, when the remaining available program/erase cycles of the memory cell reach the minimum value, the corresponding memory cell quality measurement feature is calculated, the normalized remaining available program/erase cycles and the corresponding memory cell quality measurement feature are used as a group of samples and recorded, and when the recorded samples reach a preset sample threshold, the recorded samples are used for training and updating the memory cell quality measurement model. In this embodiment, the preset sample threshold value is 100. The measurement accuracy of the storage unit quality measurement model can be greatly improved by continuously training and updating the storage unit quality measurement model.
And calculating the quality measurement characteristics of the memory cells to be measured, inputting the quality measurement characteristics of the memory cells into a trained memory cell quality measurement model, and calculating to obtain the residual available programming/erasing period number RP so as to obtain the quality measurement result of the memory cells. Specifically, the higher the value of the remaining available program/erase cycles RP of the memory cell, the higher the quality of the memory cell.
In this embodiment, after the storage system completes the quality measurement operation of the storage unit, the quality measurement result is stored in the system in the form of a table, the quality measurement table is called when the system performs wear leveling, and the system performs wear leveling address mapping change and data migration operation according to the quality measurement value of the storage unit stored in the table, thereby prolonging the service life of the storage system.
the invention aims to provide a more accurate and reliable method for measuring the quality of a storage unit for wear leveling in a storage system. Compared with the prior art, the method and the device take the reliability characteristic quantities of various storage units as the quality measurement basis, and can greatly improve the accuracy of the quality measurement result, thereby improving the wear leveling execution efficiency of the storage system and prolonging the service life of the system. Meanwhile, the invention also provides a method for measuring the operation time of the storage unit, which measures the operation time through the self-marking signal of the storage unit, does not need to occupy more computing resources of the storage system, and can realize the operation time measurement under the condition of not influencing the operation efficiency of the storage system. The model adopted by the quality measurement method can be optimized and updated in real time in the operation process of the storage system, and is more intelligent compared with the method for measuring units in different reliability stages by adopting the same judgment standard in the prior art.
it will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method for memory cell quality measurement for wear leveling, comprising the steps of:
S1, selecting a plurality of characteristic values from the characteristic set of the storage unit as the quality measurement characteristics of the storage unit according to the measurement capability of the actual storage system;
And S2, calculating to obtain a memory cell quality measurement result according to the memory cell quality measurement characteristics and the pre-trained memory cell quality measurement model.
2. the method of claim 1, wherein the set of memory cell characteristics comprises an operating time of a memory cell, a current across a memory cell, a power consumption of a memory cell, a threshold voltage distribution, a memory block cell location flag, a number of program/erase cycles a memory cell has currently undergone, a number of conditionally erroneous memory cells, a number of error bits and an error rate for any subset of memory cells in a particular data pattern.
3. The method of claim 2, wherein the special data patterns comprise data patterns for enhancing wear of memory cells and data patterns for enhancing interference between memory cells.
4. the method of claim 1, wherein the storage unit quality metric features are a subset of the set of storage unit features, and wherein the number of features included in the subset is 2 or more.
5. the memory cell quality metric method of claim 4, characterized in that the memory cell quality metric characteristic comprises an operating time of a memory cell.
6. the method of claim 5, wherein the measuring the operating time of the memory cell comprises: and detecting the R/B signal state of the chip to which the storage unit belongs, if the R/B signal state is low, recording the operation time and the operation type of the storage unit, and when the programming time, the reading time and the erasing time of the storage unit are not 0, obtaining the current programming time, the reading time and the erasing time as the operation time of the storage unit.
7. The method of claim 1, wherein the memory cell quality metric features corresponding to the values of the remaining available program/erase cycles of the memory cells within the closed interval from the maximum value to the minimum value are sequentially measured, and the normalized remaining available program/erase cycles and the corresponding memory cell quality metric features are used to train and obtain the memory cell quality metric model.
8. the method of claim 7, wherein in the process of measuring the memory cell by using the memory cell quality metric model, when the number of remaining available program/erase cycles of the memory cell reaches a minimum value, the corresponding memory cell quality metric feature is calculated, the normalized number of remaining available program/erase cycles and the corresponding memory cell quality metric feature are recorded as a set of samples, and when the number of recorded samples reaches a preset sample threshold, the memory cell quality metric model is trained and updated by using the recorded samples.
9. The memory cell quality metric method of claim 1, wherein the memory cell quality metric method is applied to a quality metric of wear-leveled memory cells.
10. The memory cell quality measurement method according to claim 1, wherein the detected memory cell quality measurement result is stored in a quality metric table of the memory system, and when the memory system performs a wear leveling operation, the memory system performs wear leveling address mapping change and data migration operation according to the memory cell quality metric value stored in the quality metric table.
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