CN116777841A - Method and device for determining wafer yield loss - Google Patents
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Abstract
The application provides a method and a device for determining wafer yield loss, and relates to the technical field of semiconductors, wherein the method comprises the following steps: obtaining defect distribution data and electrical test yield data corresponding to a wafer; processing the defect distribution data to obtain a first gray level image; processing the electrical test yield data to obtain a second gray level image; determining the similarity between the first gray scale image and the second gray scale image; and determining the yield loss corresponding to the wafer based on the similarity. Therefore, the similarity between the first gray level image corresponding to the defect distribution data and the second gray level image corresponding to the electrical test yield data can be determined, and then the yield loss of the wafer is determined based on the similarity.
Description
Technical Field
The present application relates to the field of semiconductor technologies, and in particular, to a method and an apparatus for determining wafer yield loss.
Background
In the integrated circuit production process, a plurality of processes are involved, and each process may pollute a wafer, damage the surface of the wafer, and the like, so that the wafer yield may be poor.
In the related art, the type and the corresponding number of defects can be found by manually screening according to the number of defects provided by each detection station, then the number of defects is multiplied by a mortality rate (KR) according to the type of defects to obtain the loss rate corresponding to the defects, and then the loss rate of each defect is added and converted into a final yield loss. Therefore, how to improve the accuracy of wafer yield loss determination is of great importance.
Disclosure of Invention
The application provides a method and a device for determining wafer yield loss, which are used for solving the technical problem of low accuracy of wafer yield loss determined in the prior art.
According to a first aspect of the present application, there is provided a method for determining wafer yield loss, the method comprising: obtaining defect distribution data and electrical test yield data corresponding to a wafer; processing the defect distribution data to obtain a first gray level image; processing the electrical test yield data to obtain a second gray level image; determining the similarity between the first gray scale image and the second gray scale image; and determining the yield loss corresponding to the wafer based on the similarity.
In some embodiments, the processing the defect distribution data to obtain a first grayscale image includes: clustering the defect distribution data according to coordinates to obtain a first clustering result corresponding to the defect distribution data; determining a second aggregation result corresponding to the defect distribution data based on the defect coordinates of the defect distribution data, the range of the defect in the wafer and the background image of the defect; combining the first clustering result and the second clustering result to obtain a combined defect distribution diagram; and carrying out gray processing on the combined defect distribution map to obtain a first gray image.
In some embodiments, the determining the second classification result corresponding to the defect distribution data based on the defect coordinates of the defect distribution data, the range of the defect in the wafer, and the background image of the defect includes: performing coding processing on the defect coordinates of the defect distribution data, the range of the defects in the wafer and the background image of the defects according to a mapping channel to form a color image; gray processing is carried out on the color image so as to obtain a third gray image; performing binary processing on the third gray level image to obtain a binary image; and determining a second aggregation result corresponding to the defect distribution data based on the binary image.
In some embodiments, the processing the electrical test yield data to obtain a second gray scale image includes: and processing an electrical test result corresponding to each crystal grain in the electrical test yield data based on a preset mapping relation table to obtain a second gray level image.
In some embodiments, the determining the similarity between the first grayscale image and the second grayscale image includes: inputting the first gray level image into a first classification model to obtain a first classification result; inputting the second gray level graph into a second classification model to obtain a second classification result; and under the condition that the first classification result is the same as the second classification result, determining the similarity between the first gray level image and the second gray level image.
In some embodiments, the determining the similarity between the first grayscale image and the second grayscale image if the first classification result is the same as the second classification result includes: determining a similarity parameter between the first gray scale image and the second gray scale image, wherein the similarity parameter comprises at least one of the following: signal-to-noise ratio, structural similarity, mean square error; and if the similar parameters comprise a plurality of items, carrying out fusion processing on the similar parameters to determine the similarity between the first gray level image and the second gray level image.
In some embodiments, the determining, based on the similarity, a yield loss corresponding to the wafer includes: determining that the wafer passes inspection if the similarity is less than a threshold; determining a fatal defect in the wafer from a defect distribution map corresponding to the defect distribution data based on an electrical test yield map corresponding to the electrical test yield data under the condition that the similarity is greater than or equal to a threshold value; and determining the yield loss corresponding to the wafer based on the fatal defect in the wafer.
In some embodiments, after the inputting the second gray level map into the second classification model to obtain a second classification result, the method further includes: and under the condition that the first classification result is different from the second classification result, determining the yield loss corresponding to the wafer based on the defect distribution data and the electrical test yield data corresponding to each crystal grain in the wafer.
According to a second aspect of the present application, there is provided a wafer yield loss determination apparatus, comprising: the acquisition module is used for acquiring defect distribution data and electrical test yield data corresponding to the wafer; the first processing module is used for processing the defect distribution data to obtain a first gray level image; the second processing module is used for processing the electrical test yield data to obtain a second gray level image; the first determining module is used for determining the similarity between the first gray level image and the second gray level image; and the second determining module is used for determining the yield loss corresponding to the wafer based on the similarity.
In some embodiments, the first processing module comprises: the first processing unit is used for clustering the defect distribution data according to coordinates to obtain a first clustering result corresponding to the defect distribution data; a first determining unit, configured to determine a second classification result corresponding to the defect distribution data based on defect coordinates of the defect distribution data, a range of the defect in the wafer, and a background image of the defect; the merging unit is used for merging the first clustering result and the second clustering result to obtain a merged defect distribution diagram; and the second processing unit is used for carrying out gray processing on the combined defect distribution diagram so as to obtain a first gray image.
In some embodiments, the first determining unit is specifically configured to: performing coding processing on the defect coordinates of the defect distribution data, the range of the defects in the wafer and the background image of the defects according to a mapping channel to form a color image; gray processing is carried out on the color image so as to obtain a third gray image; performing binary processing on the third gray level image to obtain a binary image; and determining a second aggregation result corresponding to the defect distribution data based on the binary image.
In some embodiments, the second processing module is specifically configured to: and processing an electrical test result corresponding to each crystal grain in the electrical test yield data based on a preset mapping relation table to obtain a second gray level image.
In some embodiments, the first determining module includes: the first input unit is used for inputting the first gray level image into a first classification model to obtain a first classification result; the second input unit is used for inputting the second gray level graph into a second classification model to obtain a second classification result; and a second determining unit configured to determine a similarity between the first grayscale image and the second grayscale image when the first classification result is the same as the second classification result.
In some embodiments, the second determining unit is specifically configured to: determining a similarity parameter between the first gray scale image and the second gray scale image, wherein the similarity parameter comprises at least one of the following: signal-to-noise ratio, structural similarity, mean square error; and if the similar parameters comprise a plurality of items, carrying out fusion processing on the similar parameters to determine the similarity between the first gray level image and the second gray level image.
In some embodiments, the second determining module is specifically configured to: determining that the wafer passes inspection if the similarity is less than a threshold; determining a fatal defect in the wafer from a defect distribution map corresponding to the defect distribution data based on an electrical test yield map corresponding to the electrical test yield data under the condition that the similarity is greater than or equal to a threshold value; and determining the yield loss corresponding to the wafer based on the fatal defect in the wafer.
In some embodiments, the second determining unit is further configured to determine, when the first classification result is different from the second classification result, a yield loss corresponding to the wafer based on defect distribution data and electrical test yield data corresponding to each die in the wafer.
According to a third aspect of the present application, there is provided an electronic device comprising: a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, implements any of the above methods for determining wafer yield loss.
According to a fourth aspect of the present application, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement any of the above-described methods for determining wafer yield loss.
In summary, the method and the device for determining wafer yield loss provided by the application have at least the following beneficial effects: the defect distribution data and the electrical test yield data corresponding to the wafer can be acquired first, then the defect distribution data can be processed to obtain a first gray level image, the electrical test yield data can be processed to obtain a second gray level image, then the similarity between the first gray level image and the second gray level image is determined, and the yield loss corresponding to the wafer is determined based on the similarity. Therefore, the similarity between the first gray level image corresponding to the defect distribution data and the second gray level image corresponding to the electrical test yield data can be determined, and then the yield loss of the wafer is determined based on the similarity.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are some embodiments of the application and that other drawings can be obtained from them without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for determining wafer yield loss according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a second gray scale image according to an embodiment of the present application;
FIG. 3 is a flowchart of a method for determining wafer yield loss according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a third gray scale image according to an embodiment of the present application;
FIG. 5 is a block diagram of a wafer yield loss determination apparatus according to an embodiment of the present application;
fig. 6 is a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
To further clarify the above and other features and advantages of the present application, a further description of the application will be rendered by reference to the appended drawings. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not limiting, as to those skilled in the art.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. It will be apparent, however, to one skilled in the art that the specific details need not be employed to practice the present application. In other instances, well-known steps or operations have not been described in detail in order to avoid obscuring the application.
The method for determining the wafer yield loss provided by the embodiment of the application can be executed by the device for determining the wafer yield loss provided by the embodiment of the application, and the device can be configured in electronic equipment.
Referring to fig. 1, the application provides a method for determining wafer yield loss, which comprises the following steps:
step 101, obtaining defect distribution data and electrical test yield data corresponding to a wafer.
The defect distribution data may be used to characterize possible defect conditions on the wafer, and may be obtained by a defect scanner, or may be obtained by any desirable means, and the application is not limited thereto.
The electrical test yield data may be used to characterize an electrical condition of each die in the wafer, for example, electrical test is performed on each die in the wafer, and an electrical test result of each die is obtained as electrical test yield data of the wafer, where any desirable manner may be used to determine electrical test yield data corresponding to the wafer, and the application is not limited thereto.
And 102, processing the defect distribution data to obtain a first gray level image.
The defect distribution data may be converted into a color image, and then the color image is subjected to gray processing to obtain a first gray image.
For example, the defect distribution data may be encoded to obtain a color image, and then the color image is processed to obtain a first gray scale image. For example, the defect distribution data may be encoded according to coordinates, shapes, contours, and the like in the defect distribution data to obtain a color image, and then subjected to graying processing to obtain a first gray-scale image, and the like. Alternatively, the coordinates of the defect distribution data, the range of the defect distribution data in the wafer, and the background image of the defect may be mapped to a red (red, R), a green (green, G), and a blue (blue, B) channel, respectively, that is, a color image is formed by encoding, and then subjected to graying processing to obtain a first gray image, which is not limited in the present application.
Or, the defect distribution data may be clustered in different manners to obtain a corresponding clustering result, and then the clustering result is processed to obtain the first gray level image.
Alternatively, the defect distribution data may be clustered according to coordinates to obtain a first clustering result corresponding to the defect distribution data. And then determining a second clustering result corresponding to the defect distribution data based on the defect coordinates of the defect distribution data, the range of the defects in the wafer and the background image of the defects, merging the first clustering result and the second clustering result to obtain a merged defect distribution map, and performing gray processing on the merged defect distribution map to obtain a first gray image.
For example, a distance threshold may be set in advance, and any two defects with a distance smaller than the distance threshold are determined as one type, so that all defect data in the defect distribution data are clustered according to coordinates, so as to obtain a corresponding first clustering result. Or after clustering any two defects with a distance smaller than the distance threshold, if the distance between another defect and the clustered defect is still smaller than the distance threshold, further clustering may be performed until all defects in the defect distribution data are traversed, which is not limited in the present application.
Then, the coordinates of the defect distribution data, the range of the defects in the wafer and the background image of the defects can be mapped to a red channel, a green channel and a blue channel respectively, namely, a color image is formed in a coding mode, gray level and binarization processing are carried out on the color image to obtain a second clustering result, and then the first clustering result and the second clustering result are combined and processed to obtain a first gray level image and the like.
And 103, processing the electrical test yield data to obtain a second gray level image.
The second gray level image may be obtained based on an electrical test result corresponding to each die in the electrical test yield data.
Optionally, the electrical test result corresponding to each die in the electrical test yield data may be processed based on a preset mapping table, so as to obtain a second gray level image.
The preset mapping relation table can be used for representing the mapping relation between the electrical test result of the crystal grain and the gray value. For example, the mapping table may be: the electrical test results [0 ] correspond to 0, [ 1 ] correspond to [ 1, 15 ] of the gray values, [ 2 ] correspond to [ 16, 30 ] of the gray values, [ 3 ] correspond to [ 30, 45 ], … …, [ 15 ] correspond to [ 210, 225 ] of the gray values. Or the mapping relation table may be: the electrical test result passes the corresponding gray value [0 ], the electrical test result does not pass the corresponding gray value [ 1 ], and the like, which are not limited by the present application.
For example, if there are 1-15 dies, each die is electrically tested, and the yield of the electrical test is as follows: 1. 2, 3, 5, 0,6, 7, 9, 10, 11, 12, 14, 15, the preset mapping relation table may represent a mapping relation between the electrical test result [0, 15 ] and the gray value [0,255], for example, the preset mapping relation table may be: the electrical test results (0) correspond to any one of the gray values (0), (1) correspond to the gray values (1, 15), and (2) correspond to any one of the gray values (16, 30), and (3) correspond to any one of the gray values (30, 45), and (… …) and (15) correspond to any one of the gray values (210, 225). And then, according to the electrical test result of each crystal grain and the mapping relation, obtaining a gray value corresponding to each crystal grain, and further obtaining a second gray image. For example, fig. 2 is a second gray scale image converted from electrical test yield data.
The above examples are only illustrative, and are not intended to limit the manner in which the second gray level image is obtained in the embodiment of the present application.
It should be noted that step 102 may be performed first, and then step 103 may be performed; or step 103 may be performed first and then step 102 may be performed first; alternatively, steps 102 and 103 may be performed in parallel, which is not limited by the present application.
Step 104, determining the similarity between the first gray scale image and the second gray scale image.
There are various ways to determine the similarity between the first gray scale image and the second gray scale image. For example, the first gray scale image and the second gray scale image may be first subjected to a difference to obtain a fourth gray scale image, and then the signal-to-noise ratio, the structural similarity, and the like of the fourth gray scale image may be calculated to determine the similarity between the first gray scale image and the second gray scale image, which is not limited in the present application.
It can be understood that, because the defect distribution data and the electrical test yield data are obtained based on different dimensions, in the embodiment of the application, the defect distribution data are processed to obtain the first gray level image, the electrical test yield data are processed to obtain the second gray level image, and then the similarity between the first gray level image and the second gray level image can be determined based on the first gray level image and the second gray level image.
Step 105, determining the yield loss corresponding to the wafer based on the similarity.
It can be appreciated that the lower the yield loss, the higher the wafer yield; the higher the yield loss, the lower the wafer yield. The similarity between the first gray level image and the second gray level image may be positively correlated with the yield loss of the wafer, that is, the higher the similarity is, the higher the yield loss corresponding to the wafer is, and the application is not limited thereto.
Alternatively, it may be determined that the wafer passes the inspection if the similarity is less than the threshold, and that the wafer fails the inspection if the similarity is greater than or equal to the threshold, with a yield loss.
The threshold may be a value set in advance, for example, may be 60%, 95%, etc., which is not limited in the present application.
For example, if the similarity between the current first gray scale image and the second gray scale image is 80% with a threshold of 60%, it may be determined that the wafer passes the inspection, a "pass" prompt may be issued, and so on. Or, if the similarity between the current first gray scale image and the second gray scale image is 96% when the threshold is 95%, it may be determined that the wafer fails the inspection, and a "fail", "fail" prompt may be sent, which is not limited in the present application.
Optionally, a yield loss tracking prompt can be given according to the yield loss of the wafer, so that a user can display a die position and the like with larger yield loss or easy yield loss to the user, so that the user can trace and trace the source based on the tracking prompt, thereby greatly reducing the workload of manually searching for deadly defects, saving the labor cost, further improving the product quality of wafer manufacturing, improving the yield of the wafer, and saving the efficiency.
Therefore, in the embodiment of the application, the defect distribution data can be processed to obtain the first gray level image, the electrical test yield data is processed to obtain the second gray level image, then the similarity between the first gray level image and the second gray level image is determined from the image angle based on the first gray level image and the second gray level image, and then the yield loss corresponding to the wafer is determined based on the similarity. Because the similarity is determined based on the image angle, the method is more comprehensive and complete, and the yield loss of the determined wafer is more accurate and reliable based on the comprehensive and complete similarity, so that tracking and tracing can be easily performed based on the yield loss of the wafer, and further, conditions are provided for subsequently improving the quality of products manufactured by the wafer.
According to the embodiment of the application, the defect distribution data and the electrical test yield data corresponding to the wafer can be acquired first, then the defect distribution data can be processed to obtain the first gray level image, the electrical test yield data is processed to obtain the second gray level image, then the similarity between the first gray level image and the second gray level image is determined, and the yield loss corresponding to the wafer is determined based on the similarity. Therefore, the similarity between the first gray level image corresponding to the defect distribution data and the second gray level image corresponding to the electrical test yield data can be determined, and then the yield loss of the wafer is determined based on the similarity.
As shown in fig. 3, the method for determining the wafer yield loss may include the following steps:
Step 301, obtaining defect distribution data and electrical test yield data corresponding to the wafer.
Step 302, clustering the defect distribution data according to coordinates to obtain a first clustering result corresponding to the defect distribution data.
In this case, the coordinates of all defects in the wafer may be converted into uniform absolute coordinates according to the coordinates of the die, and the coordinates of the center of the wafer, which may be simply referred to as "defect coordinates" for convenience of description, and the application is not limited thereto.
It will be appreciated that after unifying the coordinates of all defects in the wafer, the defect coordinates may be stored in an ordered container, the distance between each defect and the following defects may be calculated, and then the defects that meet the distance threshold may be grouped into a class. If the distance between a certain defect and the clustered defect is smaller than the distance threshold, the certain defect and the clustered defect can be continuously combined, so that a first clustering result corresponding to the defect distribution data can be obtained through the clustering processing, and the method is not limited to the first clustering result.
Step 303, determining a second classification result corresponding to the defect distribution data based on the defect coordinates in the defect distribution data, the range of the defects in the wafer and the background image of the defects.
It is understood that, in addition to the processing of the defects in the wafer by the distance between the two defects, the clustering process may be performed on the defects in the wafer from the viewpoint of the image by referring to information considering the shape, position or contour of the defects, and the like, which is not limited in the present application.
Optionally, the defect coordinates of the defect distribution data, the range of the defect in the wafer and the background image of the defect may be encoded according to the mapping channel to form a color image, then the color image may be subjected to gray processing to obtain a third gray image, and then the third gray image is subjected to binary processing to obtain a binary image, and then a second classification result corresponding to the defect distribution data is determined based on the binary image.
Wherein the mapping channels may be R channel, G channel, and B channel, then the defect coordinates in the defect distribution data may be mapped to R channel in the image, the range of the defect in the wafer may be mapped to G channel in the image, the background image of the defect may be mapped to B channel in the image, and based on RGB channel in the image, a color image may be formed in an encoded form, and then the color image may be subjected to gray processing to obtain a third gray image.
For example, in the schematic diagram shown in fig. 4, part (a) of fig. 4 is an image formed by mapping the defect coordinates to the R channel, part (B) of fig. 4 is an image formed by mapping the defect coordinates to the G channel, part (c) of fig. 4 is an image formed by mapping the defect coordinates to the B channel, and part (d) of fig. 4 is a third grayscale image obtained by clustering the images shown in part (a), part (B) and part (c) of fig. 4 to obtain a color image and converting the color image. There are various ways of gray scale processing, and the present application is not limited thereto.
Alternatively, after the color image is subjected to gray processing to obtain a third gray image, the third gray image may be downsampled, and the downsampled third gray image may be subjected to morphological opening and closing operations, so as to combine the discrete defects after downsampling. And then, setting a gray threshold value, performing binary processing on the processed third gray image to form a binary image, and obtaining a contour image with a certain size based on a contour in the binary image through downsampling, namely, obtaining a second clustering result and the like.
And step 304, merging the first clustering result and the second clustering result to obtain a merged defect distribution diagram.
When the first clustering result and the second clustering result are combined, there are multiple combining modes, and the application is not limited to this, and the combining modes can be according to the positions close, the moment approximation of the outline, the slope and intercept of the outline and the like.
For example, if the contour of a defect 1 in the first clustering result is identical to the contour shape and position of a defect 2 in the second clustering result, the two may be considered to be the same defect, and any one of the two may be reserved. Or if the positions of the defects in the first clustering result and the defects in the second clustering result are similar, the defects in the first clustering result and the defects in the second clustering result may be overlapped according to the positions, so as to obtain a combined defect distribution diagram, and the application is not limited to this.
Therefore, in the embodiment of the application, not only the first clustering result can be obtained based on the defect coordinates, but also the defects can be processed by combining the defect coordinates of the defect distribution data, the range of the defects in the wafer, the background images of the defects and the like to obtain the second clustering result, so that the clustering result is more accurate and reliable by comprehensively considering the detailed information of the defects and the macroscopic information of the shapes, positions and the like of the defects in the image unit, and conditions are provided for subsequently improving the accuracy of wafer yield loss.
In step 305, gray scale processing is performed on the combined defect distribution map to obtain a first gray scale image.
Step 306, processing the electrical test yield data to obtain a second gray level image.
Step 307, inputting the first gray scale map into the first classification model to obtain a first classification result.
The defect type may be various, for example, center type (center), ring type (donut), edge position type (edge-loc), edge line type (edge-ring), position type (loc), near full type (near full), random type (random), scratch type (scratch), etc. Accordingly, the first classification result may be one of the above defect types, or may be multiple types, which is not limited in the present application.
The first classification model may be used to process the first gray level image input therein to obtain a corresponding first classification result, that is, may obtain a defect type corresponding to the first gray level image, and the application is not limited thereto.
Alternatively, the initial classification model may be trained first to obtain a corresponding first classification model. The initial classification model can be any model capable of realizing classification, and gray images corresponding to the defect distribution data can be used as a training data set to train the initial classification model continuously so as to obtain a first classification model after training. Then, the first classification model can be used to process the first gray level image input therein, so as to obtain a first classification result corresponding to the first gray level image, and the application is not limited to this.
Step 308, inputting the second gray level map into the second classification model to obtain a second classification result.
The second classification model may be used to process the second gray level image input therein to obtain a corresponding second classification result, that is, may obtain a defect type corresponding to the second gray level image, and the application is not limited thereto.
Alternatively, the initial classification model may be trained first to obtain a corresponding second classification model. The initial classification model can be any model capable of realizing classification, and gray images corresponding to the electrical test yield data can be used as a training data set to continuously train the initial classification model so as to obtain a trained second classification model. And then the second classification model can be used for processing the second gray level image input into the second classification model, so as to obtain a second classification result corresponding to the second gray level image, and the like.
In step 309, in the case that the first classification result is the same as the second classification result, the similarity between the first gray scale image and the second gray scale image is determined.
Wherein the first classification result may be the same as the second classification result or may be different. For example, if the first classification result includes only one defect type, and the second classification result includes only one defect type, then the first classification result may be considered to be the same as the second classification result if the two types are identical. Alternatively, in the case that the first classification result includes a plurality of defect types and the second classification result includes a plurality of defect types, if there are partially different defect types, the first classification result and the second classification result may be considered as the first classification result and the second classification result, and the application is not limited thereto. In addition, the similarity between the first gray scale image and the second gray scale image may be determined in any desirable manner, which is not limited in the present application.
Alternatively, the similarity parameter between the first gray scale image and the second gray scale image may be determined first, and then the similarity between the first gray scale image and the second gray scale image may be determined based on the similarity parameter.
Wherein the similarity parameter may include at least one of: signal-to-noise ratio (SNR), structural similarity (structual similarity, SSIM), mean square error (mean square error, MSE), etc., as the application is not limited in this regard.
In addition, in the case where the similarity parameter includes only one of the above items, the similarity is the numerical value of the similarity parameter. In the case where the similarity parameter includes a plurality of items, the similarity parameter may be subjected to fusion processing to determine the similarity between the first gray scale image and the second gray scale image.
For example, if the first gray-scale image and the second gray-scale image are subjected to difference according to each similar point, a fourth gray-scale image is obtained, and then a similar parameter corresponding to the fourth gray-scale image can be further calculated, for example, a corresponding SNR, SSIM, MSE is determined. The above-mentioned similarity parameters may then be fused based on the corresponding weights of SNR, SSIM, MSE to obtain the corresponding similarities.
The weights corresponding to the similar parameters may be set in advance, or may be adjusted according to actual conditions, which is not limited in the present application.
The above examples are only illustrative, and are not intended to limit the manner in which the similarity between the first gray scale image and the second gray scale image is determined in the embodiment of the present application.
In step 310, a yield loss corresponding to the wafer is determined based on the similarity.
Alternatively, it may be determined that the wafer passes inspection in the case where the similarity is smaller than the threshold. And under the condition that the similarity is greater than or equal to the threshold value, determining the deadly defect in the wafer from the defect distribution diagram corresponding to the defect distribution data based on the electrical test yield map corresponding to the electrical test yield data, and determining the yield loss corresponding to the wafer based on the deadly defect in the wafer.
For example, if the threshold is 93%, if the similarity between the current first gray scale image and the second gray scale image is 96%, it may be determined that the wafer fails inspection. And then, determining a first position of yield loss in the wafer based on an electrical test yield map corresponding to the electrical test yield data, determining a second position corresponding to the first position from a defect distribution map corresponding to the defect distribution data, determining a fatal defect in the wafer according to whether the defect and the defect type exist at the second position, and then determining yield loss corresponding to the wafer based on the fatal defect in the wafer.
Alternatively, any desirable means may be employed to determine whether a critical defect exists in the wafer.
For example, if a defect exists in a review image, it may be further determined whether a picture element (pattern) exists in the image containing the defect, and if so, it may be determined that the defect is a fatal defect. Alternatively, it is also possible to determine whether the defect is a fatal defect based on the size of the defect and the position of the defect in the corresponding design (GDS) file. For example, if the defect is determined to be located at the center based on the GDS file, it can be determined that the defect is a fatal defect. Alternatively, if a defect does not have a corresponding review image, it can be determined whether it is a fatal defect according to whether the defect forms a cluster (cluster). For example, in the process of obtaining the first clustering result, if a certain defect is clustered and forms clusters, the defect may be regarded as a fatal defect.
The above examples are illustrative only, and are not intended to limit the manner in which fatal defects are identified in the embodiments of the present application.
It can be understood that the more the fatal defects, the higher the yield loss corresponding to the wafer; the fewer the fatal defects, the lower the yield loss corresponding to the wafer. Therefore, the yield loss and the like corresponding to the wafer can be determined according to the number of the fatal defects, and the application is not limited to the above.
Optionally, after determining the yield loss corresponding to the wafer, a yield loss tracking prompt may also be given. The tracking cues may include yield loss of the wafer, type of deadly defect, location of deadly defect, shape of deadly defect, and the like. Therefore, a user can better trace and trace the fatal defects according to the tracing prompt, so that the workload of manually searching the fatal defects can be greatly reduced, the labor cost is saved, the product quality of wafer manufacturing is further improved, the yield of the wafer is improved, and meanwhile, the efficiency is also saved.
Step 311, determining a yield loss corresponding to the wafer based on the defect distribution data and the electrical test yield data corresponding to each die in the wafer when the first classification result is different from the second classification result.
It may be appreciated that, in the case that the first classification result is different from the second classification result, the first yield loss may be determined based on the defect distribution data corresponding to each die in the wafer, then the second yield loss corresponding to the wafer may be determined based on the electrical yield data corresponding to each die in the wafer, and then the first yield loss and the second yield loss may be fused to obtain the yield loss corresponding to the wafer.
Alternatively, the defect distribution map corresponding to the defect distribution data and the electrical test yield map corresponding to the electrical test yield data may be superimposed, if the positions of the defects in the defect distribution map and the failed dies in the electrical test yield map are the same in the same dies, the defect distribution map and the failed dies are recorded as one repetition, then the repetition rate may be determined according to the ratio of the number of the defective dies in the defect distribution map in the failed dies in the electrical test yield map, and then the yield loss corresponding to the wafer is determined according to the repetition rate.
The above examples are merely illustrative, and are not intended to limit the manner in which the yield loss corresponding to the wafer is determined in the embodiment of the present application.
According to the embodiment of the application, the defect distribution data and the electrical test yield data corresponding to the wafer can be obtained firstly, then the defect distribution data can be clustered according to coordinates to obtain a first clustering result corresponding to the defect distribution data, then a second clustering result corresponding to the defect distribution data is determined based on the defect coordinates in the defect distribution data, the range of defects in the wafer and the background image of the defects, then the first clustering result and the second clustering result can be combined to obtain a combined defect distribution map, gray processing is carried out on the combined defect distribution map to obtain a first gray image, the electrical test yield data is processed to obtain a second gray image, then the first gray image can be input into a first classification model to obtain a first classification result, the second gray image is input into a second classification model to obtain a second classification result, the similarity between the first gray image and the second gray image is determined based on the similarity, the corresponding yield loss of the wafer is determined based on the similarity, and the wafer is determined based on the wafer corresponding to the failure loss of the first classification result and the electrical test yield loss data. From the first gray level image corresponding to the defect distribution data and the second gray level image corresponding to the electrical property test yield data, the similarity between the first gray level image and the second gray level image is determined, and then the yield loss of the wafer is determined based on the similarity.
According to the present application, as shown in fig. 5, there is provided a wafer yield loss determining apparatus, which includes an obtaining module 510, a first processing module 520, a second processing module 530, a first determining module 540, and a second determining module 550.
The acquiring module 510 is configured to acquire defect distribution data and electrical test yield data corresponding to a wafer; the first processing module 520 is configured to process the defect distribution data to obtain a first gray scale image; the second processing module 530 is configured to process the electrical test yield data to obtain a second gray level image; the first determining module 540 is configured to determine a similarity between the first gray scale image and the second gray scale image; the second determining module 550 is configured to determine a yield loss corresponding to the wafer based on the similarity.
In some embodiments, the first processing module 510 includes: the first processing unit is used for carrying out clustering processing on the defect distribution data according to coordinates so as to obtain a first clustering result corresponding to the defect distribution data; the first determining unit is used for determining a second classification result corresponding to the defect distribution data based on the defect coordinates of the defect distribution data, the range of the defects in the wafer and the background image of the defects; the merging unit is used for merging the first clustering result and the second clustering result to obtain a merged defect distribution diagram; and the second processing unit is used for carrying out gray processing on the combined defect distribution graphs so as to obtain a first gray image.
In some embodiments, the first determining unit is specifically configured to: performing coding processing on the defect coordinates of the defect distribution data, the range of the defects in the wafer and the background image of the defects according to a mapping channel to form a color image; gray processing is carried out on the color image so as to obtain a third gray image; performing binary processing on the third gray level image to obtain a binary image; and determining a second aggregation result corresponding to the defect distribution data based on the binary image.
In some embodiments, the second processing module 530 is specifically configured to: and processing an electrical test result corresponding to each crystal grain in the electrical test yield data based on a preset mapping relation table to obtain a second gray level image.
In some embodiments, the first determining module 540 includes: the first input unit is used for inputting the first gray level image into a first classification model so as to obtain a first classification result; the second input unit is used for inputting the second gray level graph into a second classification model to obtain a second classification result; the second determining unit is used for determining the similarity between the first gray level image and the second gray level image under the condition that the first classification result is the same as the second classification result.
In some embodiments, the second determining unit is specifically configured to: determining a similarity parameter between the first gray scale image and the second gray scale image, wherein the similarity parameter comprises at least one of the following: signal-to-noise ratio, structural similarity, mean square error; and if the similar parameters comprise a plurality of items, carrying out fusion processing on the similar parameters to determine the similarity between the first gray level image and the second gray level image.
In some embodiments, the second determining module 550 is specifically configured to: determining that the wafer passes inspection if the similarity is less than a threshold; determining a fatal defect in the wafer from a defect distribution map corresponding to the defect distribution data based on an electrical test yield map corresponding to the electrical test yield data under the condition that the similarity is greater than or equal to a threshold value; and determining the yield loss corresponding to the wafer based on the fatal defect in the wafer.
In some embodiments, the second determining unit is further configured to determine, when the first classification result is different from the second classification result, a yield loss corresponding to the wafer based on defect distribution data and electrical test yield data corresponding to each die in the wafer.
The device for determining the wafer yield loss can acquire the defect distribution data and the electrical test yield data corresponding to the wafer, process the defect distribution data to obtain a first gray level image, process the electrical test yield data to obtain a second gray level image, determine the similarity between the first gray level image and the second gray level image, and determine the yield loss corresponding to the wafer based on the similarity. Therefore, the similarity between the first gray level image corresponding to the defect distribution data and the second gray level image corresponding to the electrical test yield data can be determined, and then the yield loss of the wafer is determined based on the similarity.
It is to be understood that the specific features, operations and details described herein before with respect to the method of the application may also be similarly applied to the apparatus and system of the application, or vice versa. In addition, each step of the method of the present application described above may be performed by a corresponding component or unit of the apparatus or system of the present application.
It is to be understood that the various modules/units of the apparatus of the application may be implemented in whole or in part by software, hardware, firmware, or a combination thereof. Each module/unit may be embedded in a processor of the electronic device in hardware or firmware or may be independent of the processor, or may be stored in a memory of the electronic device in software for the processor to call to perform the operations of each module/unit. Each module/unit may be implemented as a separate component or module, or two or more modules/units may be implemented as a single component or module.
As shown in fig. 6, the present application provides an electronic device 600 comprising a processor 601 and a memory 602 storing computer program instructions. The steps of the method for determining wafer yield loss described above are implemented by the processor 601 when executing the computer program instructions. The electronic device 600 may be broadly a server, a terminal, or any other electronic device having the necessary computing and/or processing capabilities.
In one embodiment, the electronic device 600 may include a processor, memory, network interface, communication interface, etc. connected by a system bus. The processor of the electronic device 600 may be used to provide the necessary computing, processing, and/or control capabilities. The memory of the electronic device 600 may include non-volatile storage media and internal memory. The non-volatile storage medium may store an operating system, computer programs, and the like. The internal memory may provide an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface and communication interface of the electronic device 600 may be used to connect and communicate with external devices via a network. Which when executed by a processor performs the steps of the method of the application.
The application provides a computer readable storage medium, wherein computer program instructions are stored on the computer readable storage medium, and the method for determining wafer yield loss is realized when the computer program instructions are executed by a processor.
Those skilled in the art will appreciate that the method steps of the present application may be implemented by a computer program, which may be stored on a non-transitory computer readable storage medium, to instruct related hardware such as the electronic device 600 or the processor, which when executed causes the steps of the present application to be performed. Any reference herein to memory, storage, or other medium may include non-volatile or volatile memory, as the case may be. Examples of nonvolatile memory include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage, hard disk, solid state disk, and the like. Examples of volatile memory include Random Access Memory (RAM), external cache memory, and the like.
The technical features described above may be arbitrarily combined. Although not all possible combinations of features are described, any combination of features should be considered to be covered by the description provided that such combinations are not inconsistent.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application.
Claims (10)
1. The method for determining the wafer yield loss is characterized by comprising the following steps of:
obtaining defect distribution data and electrical test yield data corresponding to a wafer;
processing the defect distribution data to obtain a first gray level image;
processing the electrical test yield data to obtain a second gray level image;
determining the similarity between the first gray scale image and the second gray scale image;
and determining the yield loss corresponding to the wafer based on the similarity.
2. The method of claim 1, wherein processing the defect distribution data to obtain a first gray scale image comprises:
Clustering the defect distribution data according to coordinates to obtain a first clustering result corresponding to the defect distribution data;
determining a second aggregation result corresponding to the defect distribution data based on the defect coordinates of the defect distribution data, the range of the defect in the wafer and the background image of the defect;
combining the first clustering result and the second clustering result to obtain a combined defect distribution diagram;
and carrying out gray processing on the combined defect distribution map to obtain a first gray image.
3. The method for determining wafer yield loss according to claim 2, wherein determining the second classification result corresponding to the defect distribution data based on defect coordinates of the defect distribution data, a range of the defect in the wafer, and a background image of the defect comprises:
performing coding processing on the defect coordinates of the defect distribution data, the range of the defects in the wafer and the background image of the defects according to a mapping channel to form a color image;
gray processing is carried out on the color image so as to obtain a third gray image;
performing binary processing on the third gray level image to obtain a binary image;
And determining a second aggregation result corresponding to the defect distribution data based on the binary image.
4. The method of claim 1, wherein processing the electrical test yield data to obtain a second gray scale image comprises:
and processing an electrical test result corresponding to each crystal grain in the electrical test yield data based on a preset mapping relation table to obtain a second gray level image.
5. The method of claim 1, wherein determining a similarity between the first gray scale image and the second gray scale image comprises:
inputting the first gray level image into a first classification model to obtain a first classification result;
inputting the second gray level graph into a second classification model to obtain a second classification result;
and under the condition that the first classification result is the same as the second classification result, determining the similarity between the first gray level image and the second gray level image.
6. The method of claim 5, wherein determining a similarity between the first gray scale image and the second gray scale image if the first classification result is the same as the second classification result comprises:
Determining a similarity parameter between the first gray scale image and the second gray scale image, wherein the similarity parameter comprises at least one of the following: signal-to-noise ratio, structural similarity, mean square error;
and if the similar parameters comprise a plurality of items, carrying out fusion processing on the similar parameters to determine the similarity between the first gray level image and the second gray level image.
7. The method for determining wafer yield loss according to claim 5, wherein determining the wafer corresponding yield loss based on the similarity comprises:
determining that the wafer passes inspection if the similarity is less than a threshold;
determining a fatal defect in the wafer from a defect distribution map corresponding to the defect distribution data based on an electrical test yield map corresponding to the electrical test yield data under the condition that the similarity is greater than or equal to a threshold value;
and determining the yield loss corresponding to the wafer based on the fatal defect in the wafer.
8. The method of claim 5, further comprising, after said inputting the second gray scale map into a second classification model to obtain a second classification result:
And under the condition that the first classification result is different from the second classification result, determining the yield loss corresponding to the wafer based on the defect distribution data and the electrical test yield data corresponding to each crystal grain in the wafer.
9. A wafer yield loss determination apparatus, the apparatus comprising:
the acquisition module is used for acquiring defect distribution data and electrical test yield data corresponding to the wafer;
the first processing module is used for processing the defect distribution data to obtain a first gray level image;
the second processing module is used for processing the electrical test yield data to obtain a second gray level image;
the first determining module is used for determining the similarity between the first gray level image and the second gray level image;
and the second determining module is used for determining the yield loss corresponding to the wafer based on the similarity.
10. An electronic device, wherein the electrons comprise: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements a method for determining wafer yield loss as claimed in any one of claims 1-8.
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