CN111223787B - Groove structure measuring method, device, equipment and medium of three-dimensional memory - Google Patents
Groove structure measuring method, device, equipment and medium of three-dimensional memory Download PDFInfo
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Abstract
The invention provides a method, a device, equipment and a medium for measuring a groove structure of a three-dimensional memory, wherein the method comprises the following steps: acquiring a microscopic image of the three-dimensional memory; determining a pore channel boundary characteristic curve according to the microscopic image; determining the bottom position coordinates of the groove structure in the pore channel according to the pore channel boundary characteristic curve; extracting the position coordinates of the boundary line between the storage layer and the channel layer in the microscopic image; determining the depth of the groove structure according to the bottom position coordinates and the boundary position coordinates; and outputting the depth of the groove structure. The depth of the groove structure of the three-dimensional memory is determined by carrying out image processing and automatic identification on the microscopic image of the three-dimensional memory, and the bottom position and the boundary position of the groove structure can be more accurately and quickly positioned by utilizing the microscopic image for carrying out automatic identification, so that the automatic measurement of the depth of the groove structure of the three-dimensional memory is realized, and the measurement efficiency and the accuracy are improved.
Description
Technical Field
The present invention relates to the field of semiconductor memories, and in particular, to a method, an apparatus, a device, and a medium for measuring a trench structure of a three-dimensional memory.
Background
With the development of memory technology, NAND flash memories with planar structures are gradually approaching the limit of their expansion, and in order to realize a larger-capacity storage function, a new flash memory technology, 3D NAND (three-dimensional memory technology), is proposed, which solves the limitation caused by planar NAND flash memories by vertically stacking multiple layers of data storage units, and increases the capacity of storage devices by several times.
In the prior art, all memory cells of a three-dimensional memory are formed in a pore canal with a high aspect ratio, the bottom of the pore canal is provided with a trench structure, the depth of the trench structure is the vertical distance from the deepest part of the pore canal to the junction of a memory layer and a channel layer, and the depth of the trench structure can directly determine the performance of the memory device. By sampling, slicing and observing the three-dimensional memory sample, the depth of the groove structure of the three-dimensional memory sample can be obtained.
However, at present, the measurement method for the trench structure of the three-dimensional memory still adopts manual measurement, so that the measurement efficiency is low and the accuracy is poor.
Disclosure of Invention
The invention provides a method, a device, equipment and a medium for measuring a groove structure of a three-dimensional memory, which are used for solving the problems of low measurement efficiency and poor accuracy in the measurement process of the groove structure of the three-dimensional memory.
According to a first aspect of the embodiments of the present disclosure, the present disclosure provides a trench structure measurement method of a three-dimensional memory, the method including:
acquiring a microscopic image of the three-dimensional memory;
determining a pore channel boundary characteristic curve according to the microscopic image;
determining the bottom position coordinates of the groove structure in the pore channel according to the pore channel boundary characteristic curve;
extracting the position coordinates of the boundary line between the storage layer and the channel layer in the microscopic image;
determining the depth of the groove structure according to the bottom position coordinates and the boundary position coordinates;
and outputting the depth of the groove structure.
Optionally, the determining a characteristic curve of the boundary of the pore channel according to the microscopic image includes:
acquiring pixel information of each pixel point in the microscopic image; the pixel information comprises pixel coordinates and pixel values corresponding to the pixel coordinates;
and determining a pore channel boundary characteristic curve according to a preset first image processing strategy and the pixel information.
Optionally, the determining the characteristic curve of the pore channel boundary according to a preset first image processing strategy and the pixel information includes:
acquiring pixel values of all pixel points corresponding to the same pixel abscissa;
calculating the accumulated value of the pixel values of all the corresponding pixel points;
determining the pore channel boundary characteristic curve according to the accumulated value of each pixel abscissa; and the abscissa of the pore channel boundary characteristic curve takes a pixel abscissa sequence corresponding to the pixel abscissa, and the ordinate takes an accumulated value sequence corresponding to the accumulated value.
Optionally, the bottom position coordinates comprise a bottom position abscissa and a bottom position ordinate; determining the bottom position coordinates of the trench structure in the pore according to the pore boundary characteristic curve, wherein the determining comprises the following steps:
according to the pixel abscissa sequence, obtaining a peak abscissa corresponding to the peak position of the pore boundary characteristic curve;
acquiring a pore channel interval according to the interval of the horizontal coordinates of the wave crests;
calculating the abscissa of the pore channel boundary characteristic curve corresponding to the minimum value of the pore channel boundary characteristic curve in the pore channel interval;
determining the abscissa of the pore channel boundary characteristic curve corresponding to the minimum value as the bottom position abscissa;
and determining the bottom position ordinate according to the bottom position abscissa and the microscopic image.
Optionally, the determining the bottom position ordinate according to the bottom position abscissa and the microscopic image includes:
acquiring a bottom pixel abscissa corresponding to the bottom position abscissa in the microscopic image;
and calculating a bottom position ordinate corresponding to the bottom pixel abscissa according to a preset second image processing strategy.
Optionally, the calculating, according to a preset second image processing policy, a bottom ordinate corresponding to the bottom pixel abscissa includes:
acquiring pixel values of all pixel points corresponding to the same bottom pixel abscissa;
calculating the range of the pixel values of all the corresponding pixel points;
acquiring a pixel point with the maximum range;
and determining the vertical coordinate of the pixel corresponding to the pixel with the maximum range as the vertical coordinate of the bottom position.
Optionally, the extracting coordinates of the boundary between the oxide and the polysilicon in the microscopic image includes:
acquiring pixel information in the microscopic image; the pixel information comprises pixel coordinates and pixel values corresponding to the pixel coordinates;
and extracting the position coordinates of the boundary line according to a preset third image processing strategy and pixel information.
Optionally, the pixel coordinates include a pixel abscissa and a pixel ordinate, and the extracting the boundary line position coordinates according to a preset third image processing policy and pixel information includes:
acquiring pixel vertical coordinates of all pixel points corresponding to the bottom pixel horizontal coordinate
Acquiring pixel values of all pixel points corresponding to the same pixel vertical coordinate;
calculating the accumulated value of the pixel values of all the corresponding pixel points;
calculating the range of the accumulated value in the direction of the vertical coordinate of the pixel;
and determining the position coordinates of the boundary lines according to the pixel vertical coordinates of the pixel points corresponding to the accumulated value with the maximum range.
Optionally, before the acquiring pixel information in the microscopic image, the method further includes:
and performing directional filtering and contrast enhancement on the microscopic image.
Optionally, the determining the depth of the trench structure according to the bottom position coordinate and the boundary position coordinate includes:
and determining the difference value of the bottom position coordinate and the boundary position coordinate as the depth of the groove structure.
According to a second aspect of the embodiments of the present disclosure, the present disclosure provides a trench structure measurement apparatus of a three-dimensional memory, including:
the microscopic image acquisition module is used for acquiring a microscopic image of the three-dimensional memory;
the characteristic curve determining module is used for determining a pore channel boundary characteristic curve according to the microscopic image;
the bottom position determining module is used for determining the bottom position coordinates of the groove structure in the pore channel according to the pore channel boundary characteristic curve;
the boundary line position extraction module is used for extracting the coordinates of the boundary line position of the storage layer and the channel layer in the microscopic image;
the groove depth determining module is used for determining the structural depth of the groove according to the bottom position coordinates and the boundary position coordinates;
and the output module is used for outputting the depth of the groove structure.
Optionally, the characteristic curve determining module is specifically configured to:
acquiring pixel information of each pixel point in the microscopic image; the pixel information comprises pixel coordinates and pixel values corresponding to the pixel coordinates;
and determining a pore channel boundary characteristic curve according to a preset first image processing strategy and the pixel information.
Optionally, the pixel coordinates include a pixel abscissa and a pixel ordinate, and the characteristic curve determining module is specifically configured to, when determining the pore channel boundary characteristic curve according to a preset first image processing policy and the pixel information:
acquiring pixel values of all pixel points corresponding to the same pixel abscissa;
calculating the accumulated value of the pixel values of all the corresponding pixel points;
determining the pore channel boundary characteristic curve according to the accumulated value of each pixel abscissa; and the abscissa of the pore channel boundary characteristic curve takes a pixel abscissa sequence corresponding to the pixel abscissa, and the ordinate takes an accumulated value sequence corresponding to the accumulated value.
Optionally, the bottom position coordinates comprise a bottom position abscissa and a bottom position ordinate; the bottom position determining module is specifically configured to:
acquiring a peak abscissa corresponding to the peak position of the pore channel boundary characteristic curve according to the pixel abscissa sequence;
acquiring a pore channel interval according to the interval of the horizontal coordinates of the wave crests;
calculating the abscissa of the pore channel boundary characteristic curve corresponding to the minimum value of the pore channel boundary characteristic curve in the pore channel interval;
determining the abscissa of the pore channel boundary characteristic curve corresponding to the minimum value as the bottom position abscissa;
and determining the bottom position ordinate according to the bottom position abscissa and the microscopic image.
Optionally, when the bottom position determining module determines the bottom position ordinate according to the bottom position abscissa and the microscopic image, the bottom position determining module is specifically configured to:
acquiring a bottom pixel abscissa corresponding to the bottom position abscissa in the microscopic image;
and calculating a bottom position ordinate corresponding to the bottom pixel abscissa according to a preset second image processing strategy.
Optionally, when the bottom position determining module calculates the bottom ordinate corresponding to the bottom pixel abscissa according to a preset second image processing policy, the bottom position determining module is specifically configured to:
acquiring pixel values of all pixel points corresponding to the same bottom pixel abscissa;
calculating the range of the pixel values of all the corresponding pixel points;
acquiring a pixel point with the maximum range;
and determining the vertical coordinate of the pixel corresponding to the pixel with the maximum range as the vertical coordinate of the bottom position.
Optionally, the boundary line position extracting module is specifically configured to: acquiring pixel information in the microscopic image; the pixel information comprises pixel coordinates and pixel values corresponding to the pixel coordinates;
and extracting the position coordinates of the boundary line according to a preset third image processing strategy and pixel information.
Optionally, the pixel coordinates include a pixel abscissa and a pixel ordinate, and the boundary line position extracting module is specifically configured to, when extracting the boundary line position coordinate according to a preset third image processing policy and pixel information:
acquiring pixel values of all pixel points corresponding to the same pixel vertical coordinate;
calculating the accumulated value of the pixel values of all the corresponding pixel points;
calculating the range of the accumulated value in the direction of the vertical coordinate of the pixel;
and determining the position coordinates of the boundary lines according to the pixel vertical coordinates of the pixel points corresponding to the accumulated value with the maximum range.
Optionally, the boundary line position extracting module is further configured to:
performing directional filtering and contrast enhancement on the microscopic image before the acquiring of the pixel information in the microscopic image.
Optionally, the trench depth determining module is specifically configured to:
and determining the difference value of the bottom position coordinate and the boundary position coordinate as the depth of the groove structure.
According to a third aspect of the embodiments of the present disclosure, the present invention provides an electronic apparatus including: a memory, a processor, and a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to perform the method of traffic monitoring according to any one of the first aspect of the embodiments of the present disclosure.
According to a fourth aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium having stored therein computer-executable instructions, which when executed by a processor, are configured to implement the trench structure measurement method of the three-dimensional memory according to any one of the first aspect of the embodiments of the present disclosure.
The invention provides a method, a device, equipment and a medium for measuring a groove structure of a three-dimensional memory, wherein the method comprises the following steps: acquiring a microscopic image of the three-dimensional memory; determining a pore channel boundary characteristic curve according to the microscopic image; determining the bottom position coordinates of the groove structure in the pore channel according to the pore channel boundary characteristic curve; extracting the position coordinates of the boundary line between the storage layer and the channel layer in the microscopic image; determining the depth of the groove structure according to the bottom position coordinates and the boundary position coordinates; the groove structure depth is output, the groove structure depth of the three-dimensional memory is determined by carrying out image processing and automatic identification on the microscopic image of the three-dimensional memory, and the bottom position and the boundary position of the groove structure can be more accurately and quickly positioned by utilizing the microscopic image for carrying out automatic identification, so that more accurate groove structure depth data can be obtained.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1A is a schematic structural diagram of a three-dimensional memory according to an embodiment of the present invention;
FIG. 1B is a schematic diagram of a microscopic image of a three-dimensional memory according to an embodiment of the present invention;
FIG. 2 is a flowchart of a trench structure measurement method of a three-dimensional memory according to an embodiment of the present invention;
FIG. 3 is a flowchart of a trench structure measurement method of a three-dimensional memory according to another embodiment of the present invention;
FIG. 4 is a flowchart of step S303 in the embodiment shown in FIG. 3;
FIG. 5 is a flowchart of step S304 in the embodiment shown in FIG. 3;
FIG. 6 is a flowchart of step S3045 in the embodiment of FIG. 5;
FIG. 7 is a flowchart of step S30452 in the embodiment of FIG. 6;
FIG. 8 is a flowchart illustrating a method for measuring a trench structure of a three-dimensional memory according to another embodiment of the present invention;
FIG. 9 is a flowchart of step S407 in the embodiment of FIG. 8;
FIG. 10 is a schematic structural diagram of a trench structure measurement apparatus of a three-dimensional memory according to an embodiment of the present invention;
fig. 11 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
With the foregoing drawings in mind, certain embodiments of the disclosure have been shown and described in more detail below. These drawings and written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the concepts of the disclosure to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The terms to which the present invention relates will be explained first:
a three-dimensional memory: a three-dimensional memory refers to a memory device having a three-dimensional (3D) structure. Among them, the 3D NAND memory is a new flash memory technology developed on the basis of a NAND memory of a planar structure (2D NAND). The 3D NAND is formed by vertically stacking a plurality of layers of memory cells in such a manner that the memory cells are three-dimensionally formed. Compared with 2D NAND and 3D NAND, the memory has the advantages of large capacity, low energy consumption and low cost, but the processing technology of the 3D NAND memory is relatively complex.
Fig. 1A is a schematic structural diagram of a three-dimensional memory according to an embodiment of the present invention, as shown in fig. 1A, at present, memory cells of the three-dimensional memory are all formed in a high aspect ratio via 11, a trench structure 11A is formed at the bottom of the via, a depth (Si gouging) of the trench structure is a parameter characterizing the via, and directly determines performance of the memory device, and a bottom trench depth is a vertical distance from a deepest portion of the via to an interface 11b between a memory layer and a channel layer.
Fig. 1B is a schematic diagram of a microscopic image of a three-dimensional memory according to an embodiment of the present invention, as shown in fig. 1B, in the prior art, a method for measuring a bottom trench depth is to obtain a microscopic image after slicing and sampling the three-dimensional memory through a scanning electron microscope, a pore boundary 12 includes a main body boundary 12a and a curved boundary 12B, and since a luminance value of the pore boundary 12 is high, that is, a pixel value is large, the pore boundary 12 can be located according to the microscopic image, and then the bottom trench depth is determined by observing and measuring a distance from a lowest end position of the pore boundary 12 to an intersection line 11B. The measurement process is inefficient and the accuracy is poor because the channel boundaries 12 and the bottom trench 11a are located by manual selection.
The invention provides a method and a device for measuring a groove structure of a three-dimensional memory, electronic equipment and a storage medium, and aims to solve the technical problems in the prior art.
The following describes the technical solutions of the present invention and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.
Fig. 2 is a flowchart of a method for measuring a trench structure of a three-dimensional memory according to an embodiment of the present invention, and as shown in fig. 2, the method for measuring a trench structure of a three-dimensional memory according to the embodiment includes the following steps:
step S201, a microscopic image of the three-dimensional memory is acquired.
Specifically, the microscopic image includes image information of an internal structure of the three-dimensional memory, and is used for displaying an internal microstructure of the three-dimensional memory, such as a pore channel, a trench structure, a memory layer, a channel layer, and the like in the three-dimensional memory. Optionally, the microscopic image is obtained by scanning, shooting, etc. the slice of the three-dimensional memory through an electron microscope, and it is understood that the microscopic image may also be obtained by the apparatus or manner thereof, and is not limited specifically herein. The display precision of the microscopic image is determined by the equipment and/or the method for generating the microscopic image, and the display precision of the microscopic image can be set according to specific requirements, but the display precision of the microscopic image can be ensured to clearly and accurately display the microstructure in the three-dimensional memory. Optionally, in order to reduce the measurement error caused by the processing precision, after the microscopic image is acquired, the microscopic image may be subjected to gaussian blurring, so as to reduce the pixel fluctuation in the image and improve the measurement precision.
And step S202, determining a pore channel boundary characteristic curve according to the microscopic image.
The microscopic image comprises image information for describing the microstructure in the three-dimensional memory later, wherein the microstructure in the three-dimensional memory comprises a pore channel, and the image characteristics of the pore channel boundary, such as the coordinates of the pore channel boundary in the microscopic image, can be determined according to the image information, and a curve formed by the pore channel boundary along with the change of the characteristic coordinates is a pore channel boundary characteristic curve.
Step S203, determining the bottom position coordinates of the groove structure in the pore channel according to the pore channel boundary characteristic curve.
The trench structure is located at the bottom of the trench, and therefore, the boundary of the trench has a specific mapping relationship with the position of the boundary of the feature and the trench structure, for example, the boundary point at the deepest part of the trench coincides with the convenient point at the deepest part of the trench structure. According to the pore boundary characteristic curve, the position coordinate of the groove structure at the bottom of the pore can be correspondingly determined, and according to the position coordinate, the position coordinate of the bottom of the groove structure can be determined.
Due to the fact that the processing technology of the pore channel structure and the groove structure is complex, the processing difficulty is high, and the fact that the processing sizes of a plurality of pore channels and groove structures in the three-dimensional memory are completely consistent cannot be guaranteed, so that the bottom position coordinates of the groove structures corresponding to different pore channels are inconsistent, and deviation occurs in a measurement result.
Step S204, extracting the position coordinates of the boundary line between the storage layer and the channel layer in the microscopic image.
The vertical distance from the boundary between the memory layer and the channel layer to the deepest part of the pore channel is the depth of the trench structure, and therefore, the coordinate of the position of the boundary between the memory layer and the channel layer needs to be confirmed as a parameter for calculating the depth of the trench structure. Because the memory layer and the channel layer are made of different materials and are not of an integrally formed structure, the position of a boundary line of the memory layer and the channel layer can be positioned according to the microscopic image, and the coordinate of the position of the boundary line on the microscopic image, namely the coordinate of the position of the boundary line, is calculated according to the position of the boundary line.
Step S205, determining the depth of the trench structure according to the bottom position coordinates and the boundary position coordinates.
The distance between the bottom position coordinate and the boundary position coordinate, i.e. the trench structure depth.
In step S206, the trench structure depth is output.
The groove structure depth can evaluate the performance of the three-dimensional memory, and the output groove structure depth is counted and displayed, so that a user can better know the performance condition of the three-dimensional memory.
In the embodiment, a microscopic image of a three-dimensional memory is obtained; determining a pore channel boundary characteristic curve according to the microscopic image; determining the bottom position coordinates of the groove structure in the pore channel according to the pore channel boundary characteristic curve; extracting the position coordinates of the boundary line between the storage layer and the channel layer in the microscopic image; determining the depth of the groove structure according to the bottom position coordinates and the boundary position coordinates; the depth of the groove structure is output, and the scheme of recognizing the characteristics of the microscopic image of the memory is utilized, so that the automatic measurement of the groove structure of the memory is realized, and the measurement efficiency and the measurement accuracy are improved.
Fig. 3 is a flowchart of a trench structure measuring method of a three-dimensional memory according to another embodiment of the present invention, and as shown in fig. 3, the trench structure measuring method of a three-dimensional memory according to the present embodiment further refines steps S202 to S205 on the basis of the trench structure measuring method of a three-dimensional memory according to the embodiment shown in fig. 2, and then the trench structure measuring method of a three-dimensional memory according to the present embodiment includes the following steps:
step S301, a microscopic image of the three-dimensional memory is acquired.
Step S302, acquiring pixel information of each pixel point in the microscopic image; the pixel information includes pixel coordinates and pixel values corresponding to the pixel coordinates.
And the pixel points in the microscopic image correspond to different pixel information, wherein the pixel coordinates are used for determining the positions of the pixel points in the microscopic image. For example, a coordinate system is constructed by taking the top left corner vertex of the microscopic image as an origin, and the positions of the pixel points can be uniquely determined through the pixel coordinates. The pixel values are corresponding values of the pixel points, and the pixel values of the pixel points corresponding to different positions of the three-dimensional memory in the microscopic image are different, for example, the pixel values at the boundary of the channel in the three-dimensional memory are higher. Optionally, the microscopic image is represented in a form of a gray scale image, and a pixel value corresponding to each pixel point is a gray scale value of the pixel point. Of course, it is understood that the pixel values may be represented in other ways, such as RGB values, and the specific form of the pixel values is not limited herein.
Step S303, determining a pore channel boundary characteristic curve according to a preset first image processing strategy and pixel information.
The first image processing strategy comprises a mapping relation between pixel information and a pore path boundary characteristic curve, and the pore path boundary characteristic curve corresponding to the pixel information, namely the pore path boundary characteristic curve of the three-dimensional memory, is determined through a preset first image processing strategy.
Optionally, the pixel coordinates include a pixel abscissa and a pixel ordinate, and as shown in fig. 4, step S303 includes three specific implementation steps S3031, S3032, and S3033:
step S3031, obtaining pixel values of all pixel points corresponding to the same pixel abscissa.
Specifically, the microscopic image is a two-dimensional picture, and therefore, the pixel coordinates in the microscopic image can be expressed in a two-dimensional coordinate system. The pixel coordinates comprise a pixel abscissa and a pixel ordinate, wherein the pixel abscissa is a horizontal axis coordinate of a two-dimensional coordinate system; the pixel ordinate is the ordinate of the two-dimensional coordinate system. The method for constructing the two-dimensional coordinate system is related to the direction of the three-dimensional memory in the microscopic image, and optionally, the extending direction of the pore canal of the three-dimensional memory in the microscopic image is taken as a longitudinal axis, and the vertical direction of the extending direction of the pore canal is taken as a transverse axis.
Step S3032, calculating the accumulated values of the pixel values of all the corresponding pixel points.
As shown in fig. 1B, according to the structural features of the cell in the three-dimensional memory, the cell boundary 1 includes a main body boundary 11 and a curved boundary 12, the main body boundary 11 of the cell is parallel to the longitudinal axis of the two-dimensional coordinate system, and since the luminance value of the cell boundary 1 is high, that is, the pixel value is large, the main body boundary 11 of the cell boundary 1 can be located by the accumulated value of the pixel values.
S3033, determining a pore channel boundary characteristic curve according to the accumulated value under each pixel abscissa; the abscissa of the pore channel boundary characteristic curve takes the pixel abscissa sequence corresponding to the pixel abscissa, and the ordinate takes the accumulated value sequence corresponding to the accumulated value.
Specifically, the pixel values of all the pixel points corresponding to each pixel abscissa are accumulated one by one to obtain a change curve of the accumulated value of the pixel values with respect to the pixel abscissa, and the change curve is normalized to obtain the pore channel boundary characteristic curve.
Optionally, the pixel abscissa and the pixel abscissa sequence have a corresponding relationship, the pixel abscissa sequence may be increased or decreased relative to the pixel abscissa, and the accuracy of the tunnel boundary characteristic curve is improved by removing abnormal data points or adding data points.
In the step, the pore channel boundary characteristic curve corresponding to each pixel abscissa is obtained by obtaining the pixel values of all the pixel points corresponding to the same pixel abscissa and calculating the accumulated value of the pixel values, and the characteristic of the pore channel boundary can be highlighted in an accumulation mode due to the fact that the pixel values at the pore channel boundary are large, so that the interference of image noise is reduced, the change precision of the pore channel boundary characteristic in the pixel abscissa direction is improved, and the positioning precision of the pore channel boundary is further improved.
And step S304, determining the bottom position coordinates of the groove structure in the pore channel according to the pore channel boundary characteristic curve.
Optionally, the bottom position coordinates include a bottom position abscissa and a bottom position ordinate, and as shown in fig. 5, step S304 includes five specific implementation steps S3041-S3045:
step S3041, obtaining a peak abscissa corresponding to a peak position of the pore boundary characteristic curve according to the pixel abscissa sequence.
Specifically, the boundary characteristic curve has a peak representing a high accumulated value of pixel values, and a peak abscissa corresponding to the peak position, that is, a pixel abscissa corresponding to the main body boundary of the pore channel boundary.
Step S3042, a tunnel interval is obtained according to the interval of the crest abscissa.
The spacing of the abscissa of the peak, i.e., the spacing of the main body boundary of the tunnel boundary. Optionally, in order to obtain better positioning effect, the peaks may be screened, for example, the relative height of the peaks, and/or the absolute height is larger than a preset threshold.
Step S3043, calculating an abscissa of the pore boundary characteristic curve corresponding to a minimum value of the pore boundary characteristic curve within the pore interval.
The pore channel interval corresponds to a pore channel in the three-dimensional memory, and the bottommost end of the bent boundary of the pore channel boundary is the bottom position of the groove structure. Therefore, according to the variation relation of the accumulated value of the pixel values in the pore channel boundary characteristic curve with respect to the abscissa of the pixel, the minimum value of the pore channel boundary characteristic curve in the pore channel interval corresponds to the bottom position of the trench structure, and according to the minimum value, the abscissa of the pore channel boundary characteristic curve corresponding to the minimum value can be calculated.
The minimum value of a plurality of same pore channel boundary characteristic curves may exist in the pore channel boundary under the influence of the processing precision of the pore channel, and optionally, the pore channel boundary characteristic curves in the pore channel boundary can be fitted in a least square fitting mode and the like so as to eliminate errors and obtain the minimum value, thereby achieving the purposes of reducing errors caused by fluctuation of pixel values and improving the precision of the pore channel boundary characteristic curves.
Step S3044, determining the abscissa of the pore boundary characteristic curve corresponding to the minimum value as the abscissa of the bottom position.
Alternatively, if there are multiple abscissa of the boundary characteristic curve of the pore channel corresponding to the minimum value, the average value of the multiple abscissa can be used as the abscissa of the bottom position, so as to reduce the influence of the measurement error.
Step S3045, determining a bottom position ordinate according to the bottom position abscissa and the microscopic image.
And substituting the horizontal coordinate of the bottom position into the pixel information of the original microscopic image, and determining the vertical coordinate of the bottom position according to the mapping relation between the horizontal coordinate of the bottom position and the pixel information.
Further, optionally, as shown in fig. 6, step S3045 includes two specific implementation steps of S30451 and S30452:
step S30451, a bottom pixel abscissa corresponding to the bottom position abscissa in the microscope image is acquired.
Optionally, a coordinate system of the bottom position abscissa is consistent with a coordinate system of the pixel coordinate of the pixel information in the microscope image, and therefore, according to the bottom position abscissa, the corresponding bottom pixel abscissa can be obtained. Of course, it can be understood that, in order to obtain a better positioning effect, in the process of obtaining the abscissa of the bottom position, the data points are increased or decreased, so that the abscissa of the bottom position is not consistent with the abscissa of the bottom pixel, at this time, the abscissa of the bottom position and the abscissa of the bottom pixel still have a corresponding relationship, and corresponding correction is performed according to a method for performing data processing on the abscissa of the bottom position.
And S30452, calculating a bottom position ordinate corresponding to the bottom pixel abscissa according to a preset second image processing strategy.
Optionally, as shown in fig. 7, the step S30452 includes four specific steps S30452a to S30452 d:
and S30452a, acquiring pixel values of all pixel points corresponding to the abscissa of the same bottom pixel.
In the microscopic image, a bottom pixel abscissa represents a pixel abscissa, which corresponds to N pixel points, the number of N being related to the resolution in the microscopic image. For example, the microscopic image is a picture with resolution of 1024 × 960 pixels, and the microscopic image has 1024 pixel abscissas, wherein the 100 th pixel abscissa is the bottom pixel abscissa, which corresponds to 960 pixels, and each pixel has a unique pixel value.
Step S30452b, calculating the range of the pixel values of all the corresponding pixel points.
The pixel value of the pore channel boundary is high, but the pixel value of the non-pore channel boundary is low, so that the pixel point at the pore channel boundary has a large range difference value, the groove structure is positioned at the lower part of the pore channel, and the bottom of the groove structure belongs to a part of the pore channel boundary, so that the groove structure also has the characteristics. The pixel point with the largest pixel value change can be positioned by calculating the range difference, so that the bottom of the groove structure can be positioned, the difference value between each point in the range of the range difference value and a target point needs to be considered in the calculation process of the range difference value, the effect of average fluctuation is achieved, the stability is higher, the positioning precision of the bottom of the groove structure is improved,
and S30452c, obtaining the pixel point with the largest range difference.
Optionally, if there are multiple maximum range values, multiple pixel points may be obtained.
Step S30452d, determining the vertical coordinate of the pixel corresponding to the pixel with the maximum range as the vertical coordinate of the bottom position.
And obtaining a pixel point with the maximum range of pixel values in the pixel points corresponding to the horizontal coordinates of the bottom pixels, namely the pixel point corresponding to the bottom position of the groove structure. And taking the vertical coordinate of the pixel point as the vertical coordinate of the bottom position.
Optionally, if there are a plurality of pixels with the largest range difference, the corresponding bottom position vertical coordinates are respectively obtained, the vertical coordinates of all the bottom positions are averaged, and the result is used as the final bottom position vertical coordinate. By taking the average value, better result stability can be obtained, and the positioning accuracy is improved.
Step S305, extracting the position coordinates of the boundary line between the storage layer and the channel layer in the microscopic image.
Step S306, determining the depth of the groove structure according to the bottom position coordinates and the boundary line position coordinates.
In step S307, the trench structure depth is output.
In this embodiment, the implementation manners of steps S301 and S305 to S307 are the same as the implementation manners of steps S201 and S204 to S206 in the embodiment shown in fig. 2 of the present invention, and are not described again.
Fig. 8 is a flowchart of a trench structure measuring method of a three-dimensional memory according to still another embodiment of the present invention, and as shown in fig. 8, the trench structure measuring method of a three-dimensional memory according to the present embodiment further refines step S305 on the basis of the trench structure measuring method of a three-dimensional memory according to the embodiment shown in fig. 3, and then the trench structure measuring method of a three-dimensional memory according to the present embodiment includes the following steps:
step S401, a microscopic image of the three-dimensional memory is acquired.
Step S402, acquiring pixel information of each pixel point in the microscopic image; the pixel information includes pixel coordinates and pixel values corresponding to the pixel coordinates.
Step S403, determining a tunnel boundary characteristic curve according to a preset first image processing policy and pixel information.
Step S404, determining the bottom position coordinates of the groove structure in the pore channel according to the pore channel boundary characteristic curve.
Step S406, acquiring pixel information in the microscopic image; the pixel information includes pixel coordinates and pixel values corresponding to the pixel coordinates.
Optionally, the pixel information obtained from the microscopic image is pixel information of a part of the pixel points, that is, only necessary pixel points for extracting the boundary line position are obtained, and unnecessary pixel points which may interfere with the extraction result are removed, so that a better boundary line position extraction effect is achieved.
Step 407, extracting boundary line position coordinates according to a preset third image processing strategy and pixel information.
Optionally, the pixel coordinates include a pixel abscissa and a pixel ordinate, and as shown in fig. 9, step S407 includes four specific implementation steps of S4071, S4072, S4073, and S4074:
step S4071: and acquiring pixel values of all pixel points corresponding to the same pixel ordinate.
In the microscopic image, a pixel ordinate corresponds to N pixel points, and the number of N is related to the resolution in the microscopic image. For example, the microscopic image is a picture with resolution of 1024 × 960 pixels, and the microscopic image has 960 pixel vertical coordinates, each pixel vertical coordinate corresponds to 1024 pixels, and each pixel has a unique pixel value.
Step S4072: and calculating the accumulated value of the pixel values of all the corresponding pixel points.
Because the memory layer and the channel layer are made of different materials and are not of an integrally formed structure, a corresponding boundary line can be formed, so that the pixel value of the boundary line between the memory layer and the channel layer is higher according to the microscopic image, and meanwhile, the boundary line is kept horizontal in the microscopic image, namely, the high-pixel-value pixel points forming the boundary line are under the same or adjacent several pixel vertical coordinates. The boundary line can be positioned by calculating the accumulated value of the pixel values of the pixel points under the same pixel vertical coordinate one by one along the vertical coordinate direction of the microscopic image.
Step S4073: and calculating the range of the accumulated value in the direction of the vertical coordinate of the pixel.
Step S4074: and determining the position coordinates of the boundary lines according to the pixel vertical coordinates of the pixel points corresponding to the accumulated value with the maximum range.
The range of the accumulated value corresponding to the pixel ordinate represents the fluctuation of the pixel value between the pixel point corresponding to the pixel ordinate and the pixel point corresponding to the adjacent pixel ordinate, the pixel point at the boundary is larger, and the difference with the pixel point adjacent to the ordinate is obvious, namely, the range is larger. Therefore, the pixel vertical coordinate of the pixel point corresponding to the accumulated value with the maximum range is used for determining the position coordinate of the boundary line.
Alternatively, if there are a plurality of pixel vertical coordinates having the largest polar difference, the pixel vertical coordinates having the largest polar difference are averaged, and the result is used as the final boundary line position coordinate. By averaging, better result stability can be obtained and the accuracy of the boundary line position location can be improved.
Optionally, before step S406, the method further includes:
step S405, directional filtering and contrast enhancement are carried out on the microscopic image.
In the microscopic image, compared with the pore channel boundary, the image of the boundary is not clear, the contrast is poor, the coordinates of the position of the boundary are directly extracted from the microscopic image, and the accuracy is achieved. Optionally, firstly, extracting partial pixel points from the microscopic image to remove unnecessary pixel points in the microscopic image, for example, intercepting a lower half part of the microscopic image, which contains a groove structure and a boundary line; then, performing directional filtering on the microscopic image through a specific algorithm, for example, performing directional filtering on the extension directions of the pore channel and the boundary line through a wavelet reconstruction algorithm, and filtering out the influence of a curved boundary in the pore channel boundary on the position of a positioning boundary line; and then, the contrast of the boundary is increased through a contrast enhancement algorithm, and the accuracy of positioning the boundary is improved.
Because the microscopic image is obtained by shooting through a scanning electron microscope, the microscopic image is easily influenced by various factors to cause poor image definition in the microscopic image, the microscopic image is directly identified and measured to cause inaccurate measuring results, in the step, before the pixel information in the microscopic image is obtained, the microscopic image is subjected to directional filtering and contrast enhancement, so that the contrast of the boundary is increased, the identification precision of the boundary is improved, and the measurement accuracy of the depth of the trench structure is further improved.
In step S408, the difference between the bottom position coordinate and the boundary position coordinate is determined as the trench structure depth.
The bottom position coordinate and the boundary line position coordinate are both located in a two-dimensional coordinate system in the microscopic image, and the depth of the groove structure can be directly determined through the difference value of the bottom position coordinate and the boundary line position. Optionally, a scaling factor exists between the microscopic image and the three-dimensional memory, and the product of the depth of the trench structure and the scaling factor is used as the depth of the trench structure corresponding to the final three-dimensional memory.
Step S409, outputting the trench structure depth.
In this embodiment, the implementation manners of steps S401 to S404 and step S409 are the same as the implementation manners of steps S301 to S304 and step S307 in the embodiment shown in fig. 3 of the present invention, and are not described in detail herein.
Fig. 10 is a schematic structural diagram of a trench structure measuring device of a three-dimensional memory according to an embodiment of the present invention, and as shown in fig. 10, the trench structure measuring device 5 of the three-dimensional memory according to the embodiment includes:
and a microscopic image acquisition module 51 for acquiring a microscopic image of the three-dimensional memory.
And a characteristic curve determining module 52, configured to determine a characteristic curve of the pore channel boundary according to the microscopic image.
And a bottom position determining module 53, configured to determine a bottom position coordinate of the trench structure in the pore channel according to the pore channel boundary characteristic curve.
And a boundary line position extracting module 54, configured to extract coordinates of a boundary line between the storage layer and the channel layer in the microscopic image.
The trench depth determination module 55 determines the trench structure depth based on the bottom position coordinates and the boundary position coordinates.
And an output module 56 for outputting the depth of the trench structure.
The microscopic image obtaining module 51, the characteristic curve determining module 52, the bottom position determining module 53, the boundary line position extracting module 54, the groove depth determining module 55 and the output module 56 are connected in sequence. The trench structure measurement apparatus 5 of the three-dimensional memory provided in this embodiment may implement the technical solution of the method embodiment shown in fig. 2, and the implementation principle and the technical effect are similar, and are not described herein again.
Optionally, the characteristic curve determining module is specifically configured to:
acquiring pixel information of each pixel point in the microscopic image; the pixel information includes pixel coordinates and pixel values corresponding to the pixel coordinates;
and determining a pore channel boundary characteristic curve according to a preset first image processing strategy and pixel information.
Optionally, the pixel coordinates include a pixel abscissa and a pixel ordinate, and the characteristic curve determining module is specifically configured to, when determining the pore channel boundary characteristic curve according to a preset first image processing policy and pixel information:
acquiring pixel values of all pixel points corresponding to the same pixel abscissa;
calculating the accumulated value of the pixel values of all the corresponding pixel points;
determining a pore channel boundary characteristic curve according to the accumulated value under each pixel abscissa; the abscissa of the pore channel boundary characteristic curve takes the pixel abscissa sequence corresponding to the pixel abscissa, and the ordinate takes the accumulated value sequence corresponding to the accumulated value.
Optionally, the bottom position coordinates comprise a bottom position abscissa and a bottom position ordinate; the bottom position determining module is specifically configured to:
acquiring a peak abscissa corresponding to the peak position of the pore channel boundary characteristic curve according to the pixel abscissa sequence;
acquiring a pore channel interval according to the interval of the horizontal coordinates of the wave crests;
calculating the abscissa of the pore channel boundary characteristic curve corresponding to the minimum value of the pore channel boundary characteristic curve in the pore channel interval;
determining the abscissa of the pore channel boundary characteristic curve corresponding to the minimum value as the abscissa of the bottom position;
and determining the bottom position ordinate according to the bottom position abscissa and the microscopic image.
Optionally, the bottom position determining module, when determining the bottom position ordinate according to the bottom position abscissa and the microscope image, is specifically configured to:
acquiring a bottom pixel abscissa corresponding to the bottom position abscissa in the microscopic image;
and calculating a bottom position ordinate corresponding to the bottom pixel abscissa according to a preset second image processing strategy.
Optionally, the bottom position determining module is specifically configured to, when calculating a bottom ordinate corresponding to the bottom pixel abscissa according to a preset second image processing policy:
acquiring pixel values of all pixel points corresponding to the same bottom pixel abscissa;
calculating the range of the pixel values of all the corresponding pixel points;
acquiring a pixel point with the maximum range;
and determining the vertical coordinate of the pixel corresponding to the pixel with the maximum range as the vertical coordinate of the bottom position.
Optionally, the boundary line position extracting module is specifically configured to: acquiring pixel information in the microscopic image; the pixel information includes pixel coordinates and pixel values corresponding to the pixel coordinates;
and extracting the position coordinates of the boundary line according to a preset third image processing strategy and the pixel information.
Optionally, the pixel coordinates include a pixel abscissa and a pixel ordinate, and the boundary line position extracting module is specifically configured to, when extracting the boundary line position coordinates according to a preset third image processing policy and pixel information:
acquiring pixel values of all pixel points corresponding to the same pixel vertical coordinate;
calculating the accumulated value of the pixel values of all the corresponding pixel points;
calculating the range of the accumulated value in the direction of the vertical coordinate of the pixel;
and determining the position coordinates of the boundary lines according to the pixel vertical coordinates of the pixel points corresponding to the accumulated value with the maximum range.
Optionally, the boundary line position extracting module is further configured to:
the microscopic image is subjected to directional filtering and contrast enhancement prior to acquiring pixel information in the microscopic image.
Optionally, the trench depth determining module is specifically configured to:
and determining the difference value of the bottom position coordinate and the boundary line position coordinate as the depth of the groove structure.
The trench structure measurement apparatus 5 of the three-dimensional memory provided in this embodiment may further perform the technical solutions of the method embodiments shown in fig. 3 to fig. 9, which have similar implementation principles and technical effects, and are not described herein again.
Fig. 11 is a schematic view of an electronic device according to an embodiment of the present invention, and as shown in fig. 11, a cloud server according to the embodiment includes: memory 601, processor 602, and computer programs.
The computer program is stored in the memory 601 and configured to be executed by the processor 602 to implement the trench structure measurement method of the three-dimensional memory according to any embodiment of the invention corresponding to fig. 2 to 9.
The memory 601 and the processor 602 are connected by a bus 603.
The relevant description may be understood by referring to the relevant description and effect corresponding to the steps in fig. 2 to fig. 9, and redundant description is not repeated here.
One embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the trench structure measurement method of a three-dimensional memory according to any one of the embodiments of fig. 2 to 9.
The computer readable storage medium may be, among others, ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of modules is merely a division of logical functions, and an actual implementation may have another division, for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed coupling or direct coupling or communication connection between each other may be through some interfaces, indirect coupling or communication connection between devices or modules, and may be in an electrical, mechanical or other form.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (11)
1. A method for measuring a trench structure of a three-dimensional memory, the method comprising:
acquiring a microscopic image of the three-dimensional memory;
determining a pore channel boundary characteristic curve according to the microscopic image;
determining the bottom position coordinates of the groove structure in the pore channel according to the pore channel boundary characteristic curve;
extracting the position coordinates of the boundary line between the storage layer and the channel layer in the microscopic image;
determining the depth of the groove structure according to the bottom position coordinates and the boundary position coordinates;
outputting the depth of the groove structure;
determining a pore boundary characteristic curve according to the microscopic image, wherein the step of determining the pore boundary characteristic curve comprises the following steps:
acquiring pixel information of each pixel point in the microscopic image; the pixel information comprises pixel coordinates and pixel values corresponding to the pixel coordinates;
determining a pore channel boundary characteristic curve according to a preset first image processing strategy and the pixel information;
the pixel coordinates comprise a pixel abscissa and a pixel ordinate, and the determining of the pore channel boundary characteristic curve according to a preset first image processing strategy and the pixel information comprises the following steps:
acquiring pixel values of all pixel points corresponding to the same pixel abscissa;
calculating the accumulated value of the pixel values of all the corresponding pixel points;
determining the pore channel boundary characteristic curve according to the accumulated value of each pixel abscissa; and the abscissa of the pore channel boundary characteristic curve takes a pixel abscissa sequence corresponding to the pixel abscissa, and the ordinate takes an accumulated value sequence corresponding to the accumulated value.
2. The method of claim 1, wherein the bottom position coordinates comprise a bottom position abscissa and a bottom position ordinate; determining the bottom position coordinates of the trench structure in the pore according to the pore boundary characteristic curve, wherein the determining comprises the following steps:
acquiring a peak abscissa corresponding to the peak position of the pore channel boundary characteristic curve according to the pixel abscissa sequence;
acquiring a pore channel interval according to the interval of the horizontal coordinates of the wave crests;
calculating the abscissa of the pore channel boundary characteristic curve corresponding to the minimum value of the pore channel boundary characteristic curve in the pore channel interval;
determining the abscissa of the pore channel boundary characteristic curve corresponding to the minimum value as the bottom position abscissa;
and determining the bottom position ordinate according to the bottom position abscissa and the microscopic image.
3. The method of claim 2, wherein said determining the bottom position ordinate from the bottom position abscissa and the microscope image comprises:
acquiring a bottom pixel abscissa corresponding to the bottom position abscissa in the microscopic image;
and calculating a bottom position ordinate corresponding to the bottom pixel abscissa according to a preset second image processing strategy.
4. The method according to claim 3, wherein said calculating a bottom ordinate corresponding to the bottom pixel abscissa according to a preset second image processing policy comprises:
acquiring pixel values of all pixel points corresponding to the same bottom pixel abscissa;
calculating the range of the pixel values of all the corresponding pixel points;
acquiring a pixel point with the maximum range;
and determining the vertical coordinate of the pixel corresponding to the pixel with the maximum range as the vertical coordinate of the bottom position.
5. The method of claim 1, wherein said extracting coordinates of boundary line positions of a storage layer and a channel layer in said microscopic image comprises:
acquiring pixel information in the microscopic image; the pixel information comprises pixel coordinates and pixel values corresponding to the pixel coordinates;
and extracting the position coordinates of the boundary line according to a preset third image processing strategy and pixel information.
6. The method according to claim 5, wherein the pixel coordinates comprise a pixel abscissa and a pixel ordinate, and the extracting the boundary line position coordinates according to a preset third image processing strategy and pixel information comprises:
acquiring pixel values of all pixel points corresponding to the same pixel vertical coordinate;
calculating the accumulated value of the pixel values of all the corresponding pixel points;
calculating the range of the accumulated value in the direction of the vertical coordinate of the pixel;
and determining the position coordinates of the boundary lines according to the pixel vertical coordinates of the pixel points corresponding to the accumulated value with the maximum range.
7. The method of claim 5, further comprising, prior to said acquiring pixel information in said microscopic image:
and performing directional filtering and contrast enhancement on the microscopic image.
8. The method of any of claims 1-7, wherein determining the trench structure depth based on the bottom position coordinates and the boundary position coordinates comprises:
and determining the difference value of the bottom position coordinate and the boundary line position coordinate as the depth of the groove structure.
9. A trench structure measurement device of a three-dimensional memory, comprising:
the microscopic image acquisition module is used for acquiring a microscopic image of the three-dimensional memory;
the characteristic curve determining module is used for determining a pore channel boundary characteristic curve according to the microscopic image;
the bottom position determining module is used for determining the bottom position coordinates of the groove structure in the pore channel according to the pore channel boundary characteristic curve;
the boundary line position extraction module is used for extracting the coordinates of the boundary line position of the storage layer and the channel layer in the microscopic image;
the groove depth determining module is used for determining the structural depth of the groove according to the bottom position coordinates and the boundary position coordinates;
the output module is used for outputting the depth of the groove structure;
the characteristic curve determining module is specifically configured to: acquiring pixel information of each pixel point in the microscopic image; the pixel information comprises pixel coordinates and pixel values corresponding to the pixel coordinates; determining a pore channel boundary characteristic curve according to a preset first image processing strategy and the pixel information;
the pixel coordinates include a pixel abscissa and a pixel ordinate, and the characteristic curve determining module is specifically configured to, when determining the pore channel boundary characteristic curve according to a preset first image processing policy and the pixel information: acquiring pixel values of all pixel points corresponding to the same pixel abscissa; calculating the accumulated value of the pixel values of all the corresponding pixel points; determining the pore channel boundary characteristic curve according to the accumulated value of each pixel abscissa; and the abscissa of the pore channel boundary characteristic curve takes a pixel abscissa sequence corresponding to the pixel abscissa, and the ordinate takes an accumulated value sequence corresponding to the accumulated value.
10. An electronic device, comprising: a memory, a processor, and a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the trench structure measurement method of the three-dimensional memory according to any one of claims 1-8.
11. A computer-readable storage medium having computer-executable instructions stored therein, which when executed by a processor, are configured to implement the trench structure measurement method of the three-dimensional memory according to any one of claims 1 to 8.
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