Semiconductor scratch automatic analysis system

Abstract

The invention provides a semiconductor scratch automatic analysis system, which is characterized by comprising a wafer defect map acquisition module used for acquiring a defectmap detected by a detection machine; the new coordinate system definition module is used for redefining the coordinates of the defectmap; the cluster analysis module is used for analyzing whether a cluster exists in the wafer defect graph based on a DBSCAN clustering algorithm, determining that the wafer defect graph is scratched if the cluster exists, and determining that the wafer defect graph is not scratched if the cluster does not exist; the scratch defect analysis module is used for analyzing the defect graph of the wafer with scratches; and the scratch result display module is used for displaying a scratch defect analysis result on the wafer defect graph. According to the automatic analysis system for the semiconductor scratch, the scratch defect of the semiconductor defect map can be automatically analyzed, the requirement of manual measurement is reduced, and errors and subjectivity caused by human factors are reduced.

Description

Technical Field

[0001] The invention relates to the technical field of semiconductor quality detection, and in particular to an automatic analysis system for semiconductor scratches. Background Art

[0002] In the semiconductor manufacturing process, defect analysis on the wafer surface is a key step in ensuring product quality. The current common practice is to manually measure scratches on the wafer surface. The current practice of using manual measurement to analyze scratches on the wafer surface has the following disadvantages: (1) Subjectivity: Manual measurement is easily affected by personal experience and judgment, resulting in inconsistency and errors in measurement results. (2) Inefficiency: The manual measurement process is time-consuming, especially when a large number of wafers need to be tested, which greatly reduces production efficiency. (3) Poor repeatability: There may be differences in the measurements made by different engineers, resulting in poor repeatability and difficulty in ensuring data consistency. (4) Difficulty in data integration: Since the defect coordinate systems generated under different equipment and process conditions may be different, it is very difficult to manually integrate these data into a unified database. (4) Poor degree of automation: Due to reliance on manual operation, it is difficult to achieve automation and intelligence of the entire production process. (5) Imperfect feedback mechanism: There may be delays in the feedback of manual measurement and analysis results to the production process, affecting the timeliness of problem solving. Summary of the invention

[0003] In order to solve the above problems, the present invention aims to provide a semiconductor scratch automatic analysis system.

[0004] To achieve the above object, the present invention adopts the following technical solution:

[0005] The present invention provides a semiconductor scratch automatic analysis system, which is characterized by comprising: a wafer defect map acquisition module, which is used to acquire a defectmap detected by a detection machine; a new coordinate system definition module, which redefines the coordinates of the defectmap; a cluster analysis module, which analyzes whether there are clusters in the wafer defect map based on the DBSCAN clustering algorithm, and if there are clusters, it is determined that there are scratches, and if there are no clusters, it is determined that no scratches are found; a scratch defect analysis module, which analyzes the defect map of the wafer with scratches;

[0006] The scratch result display module displays the scratch defect analysis results in the wafer defect map.

[0007] Furthermore, the semiconductor scratch automatic analysis system provided by the present invention may also have the following features: in the coordinate system redefined by the new coordinate system definition module, the origin is determined by the transverse tangent and the longitudinal tangent of the wafer, and the origin is located on the longitudinal tangent below the intersection of the transverse tangent and the longitudinal tangent.

[0008] Furthermore, the semiconductor scratch automatic analysis system provided by the present invention may also have the following features: wherein the analysis process of the cluster analysis module is as follows:

[0009] Step 2-1, define two parameters eps and min_samples in the DBSCAN clustering algorithm:

[0010] eps is the neighborhood radius, which is used here to define the distance threshold between data points;

[0011] min_samples is used to define the minimum number of data points in the neighborhood of the core point;

[0012] Step 2-2, traverse all data points, if a data point's eps neighborhood contains at least min_samples data points, then the point is marked as a core point;

[0013] Step 2-3, starting from a core point, assign the core point and all points in its eps neighborhood to the same cluster; recursively, if a point in a cluster is a core point, all points in its eps neighborhood are also added to the cluster; repeat the above steps until all core points are assigned to a cluster;

[0014] Step 2-4, mark boundary points and noise points: points that are assigned to a cluster but are not core points are marked as boundary points, and points that do not belong to any cluster are marked as noise points.

[0015] Furthermore, the semiconductor scratch automatic analysis system provided by the present invention may also have the following features: wherein the scratch defect analysis module includes calculating the distance from a point to a straight line and the angle of the scratch.

[0016] Furthermore, the semiconductor scratch automatic analysis system provided by the present invention may also have the following features: wherein the calculation process of the point-to-straight-line distance is as follows:

[0017] Step 3-1, construct the formula for calculating the distance from a point to a straight line:

[0018] Assuming that the straight line passes through points (x1, y1) and (x2, y2), the distance d from point (x0, y0) to the straight line is expressed by the following formula:

[0019]

[0020] Step 3-2, construct the vector:

[0021] Constructing the direction vector is the vector from point (x1, y1) to point (x0, y0):

[0022]

[0023] Constructing point line vector is the vector from point (x1,y1) to point (x2,y2):

[0024]

[0025] Step 3-3, calculate the projection vector:

[0026] Point Line Vector In the direction vector The projection vector on It is expressed by the formula as follows:

[0027]

[0028] in, It is the dot product of the point vector and the direction vector, and the calculation formula is as follows:

[0029]

[0030] is the square magnitude of the direction vector, calculated as follows:

[0031] (x2-x1) 2 +(y2-y1) 2

[0032] Then, the projection vector The calculation formula is as follows:

[0033]

[0034] Step 3-4, calculate the distance from the point to the straight line:

[0035] The distance d from a point to a line is also called the point-line vector and the projection vector The difference in modulus length:

[0036]

[0037] After calculation, the distance d from the point to the straight line is expressed as:

[0038]

[0039] Where P x and P y They are the projection vectors The x and y components of .

[0040] Furthermore, the semiconductor scratch automatic analysis system provided by the present invention may also have the following features: wherein the calculation process of the scratch angle is as follows:

[0041] Suppose all the points of the scratch are (x i ,y i ), where i = 1, 2, ..., n, where n is the total number of scratch points, and the scratch direction angle θ is expressed by the formula as follows:

[0042]

[0043] Where Δy=max(y i )-min(y i ) is the span of the scratch in the y direction,

[0044] Δx=max(x i )-min(x i ) is the span of the scratch in the x direction,

[0045] The above direction angle θ is expressed in radians. To convert it into degrees, the formula is as follows:

[0046]

[0047] The scratch angle output by the scratch defect analysis module is θ degree .

[0048] Furthermore, in the semiconductor scratch automatic analysis system provided by the present invention, it is characterized in that it also includes: a machine information database module, which stores machine information, and the machine information includes the machine Arm width; a machine information matching module, after the scratch result display module displays the analysis result, searches for the corresponding machine information from the machine information database module; a matching result output module, which is used to output the matching result of the machine information matching module.

[0049] Furthermore, in the semiconductor scratch automatic analysis system provided by the present invention, it is characterized by further comprising: a machine information supplement module, which is used for the user to input and supplement the machine information in the machine information database module.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] 1) Unified coordinate system: The semiconductor scratch automatic analysis system of the present invention solves the computational complexity and inconsistency problems caused by different coordinate systems in the prior art by introducing a new coordinate system with the lower left corner of the wafer tangent line as the origin.

[0052] 2) Automated measurement: The method for automatically calculating the scratch width of the semiconductor scratch automatic analysis system of the present invention reduces the need for manual measurement, thereby reducing errors and subjectivity caused by human factors.

[0053] 3) Improving analysis speed: The semiconductor scratch automatic analysis system of the present invention improves the speed of defect analysis through automated analysis, thus meeting the needs of large-scale production in the semiconductor industry.

[0054] 4) Improved accuracy: The semiconductor scratch automatic analysis system of the present invention uses mathematical models and automation methods to reduce measurement errors and improve the accuracy of analysis results.

[0055] 5) Reducing the influence of human factors: The semiconductor scratch automatic analysis system of the present invention reduces the interference of human subjective factors in the analysis process and improves the reliability of the results.

[0056] 6) Improved data comparison: The semiconductor scratch automatic analysis system of the present invention is also provided with a machine information matching module to compare with the data in the database, quickly match possible problem equipment, and improve the efficiency of problem diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is a block diagram of a semiconductor scratch automatic analysis system in an embodiment of the present invention;

[0058] Figure 2 is a flow chart of a semiconductor scratch automatic analysis system in an embodiment of the present invention;

[0059] Figure 3 is a schematic diagram of a new coordinate system definition in an embodiment of the present invention;

[0060] Figure 4 It is a schematic diagram showing the scratch defect analysis results in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the following embodiments are combined with the accompanying drawings to specifically illustrate the technical solutions of the present invention.

[0062] <Example>

[0063] See also Figure 1 The present embodiment provides a semiconductor scratch automatic analysis system, which includes the following logical function modules run by computer programs: a wafer defect map acquisition module 101, a new coordinate system definition module 102, a cluster analysis module 103, a scratch defect analysis module 104, a scratch result display module 105, a machine information database module 106, a machine information matching module 107, a machine information output module 108, and a machine information supplement module 109.

[0064] The wafer defect map acquisition module 101 is used to acquire the defect map obtained by the detection machine. The defect map is a wafer defect map, which is output by the self-detection of the monitoring machine of the production line, and the file format is the klarf format. The monitoring machine is connected to the semiconductor scratch automatic analysis system of the embodiment in communication, and the wafer defect map acquisition module 101 automatically acquires the defect map uploaded by the monitoring machine.

[0065] The new coordinate system definition module 102 redefines the coordinates of the defect map.

[0066] The cluster analysis module 103 analyzes whether there are clusters in the wafer defect map based on the DBSCAN clustering algorithm. If there are clusters, it is determined that there are scratches; if there are no clusters, it is determined that no scratches are found.

[0067] The scratch defect analysis module 104 analyzes the defect map of the scratched wafer.

[0068] The scratch result display module 105 displays the scratch defect analysis result in the wafer defect map.

[0069] The machine information database module 106 stores machine information including machine Arm width. Such information forms a ProcessArm database.

[0070] The machine information matching module 107 is used to search for matching corresponding machine information from the database.

[0071] The machine information output module 108 is used to output the matching result of the machine information matching module 107 .

[0072] The machine information supplement module 109 is used for users to input and supplement the machine information in the machine information database module 106 .

[0073] See also Figure 2 The working process of the semiconductor scratch automatic analysis system of this embodiment is as follows:

[0074] Step 1: redefine new coordinates for the defectmap through the new coordinate system definition module 102.

[0075] See also Figure 3 In the redefined coordinate system, the origin is determined by the lateral and longitudinal tangents of the wafer. Figure 3 Die(0,0) is located on the longitudinal tangent below the intersection of the transverse tangent and the longitudinal tangent. The position of the origin is preset by the user.

[0076] Step 2: The cluster analysis module 103 analyzes whether there is a cluster in the wafer defect map based on the DBSCAN clustering algorithm. If there is a cluster, it is determined that there is a scratch; if there is no cluster, it is determined that no scratch is found.

[0077] The analysis process of the cluster analysis module is as follows:

[0078] Step 2-1, define two parameters eps and min_samples in the DBSCAN clustering algorithm:

[0079] eps is the neighborhood radius, which is used here to define the distance threshold between data points;

[0080] min_samples is used to define the minimum number of data points in the neighborhood of the core point;

[0081] Step 2-2, traverse all data points, if a data point's eps neighborhood contains at least min_samples data points, then the point is marked as a core point;

[0082] Step 2-3, starting from a core point, assign the core point and all points in its eps neighborhood to the same cluster;

[0083] Recursively, if a point in a cluster is a core point, all points in its eps neighborhood are also added to the cluster;

[0084] Repeat the above steps until all core points are assigned to a cluster;

[0085] Step 2-4, mark boundary points and noise points: points that are assigned to a cluster but are not core points are marked as boundary points, and points that do not belong to any cluster are marked as noise points.

[0086] Step 3: Analyze the scratched wafer defect image through the scratch defect analysis module 104.

[0087] The scratch defect analysis module calculates the distance from point to line and the angle of the scratch. The distance from point to line, where the point refers to the origin of the new coordinate system, that is, Figure 3 In Die(0,0), the straight line refers to the scratch, that is, the straight line formed by cluster fitting.

[0088] (1) The calculation process of the distance from a point to a straight line is as follows:

[0089] Step 3-1, construct the formula for calculating the distance from a point to a straight line:

[0090] Assume that the straight line passes through the points (x1, y1) and (x2, y2), and its slope m is:

[0091]

[0092] The equation of the line is:

[0093] y-y1=(x-x1)

[0094] Convert it to standard form: the equation of Ax+By+C=0:

[0095] (y2-y1)x-(x2-x1)y+[(x2-x1)y1-(y2-y1)x1]=0

[0096] Then we have:

[0097] A=y2-y1, B=-(x2-x1), C=(x2-x1)y1-(y2-y1)x1

[0098] Thus, the distance from the origin (x0, y0) to the straight line Ax+By+C=0 is expressed as:

[0099]

[0100] Substituting the above A, B, and C into formula (5), we get the formula for the final distance d:

[0101]

[0102] Step 3-2, construct the vector:

[0103] Constructing the direction vector is the vector from point (x1, y1) to point (x0, y0):

[0104]

[0105] Constructing point line vector is the vector from point (x1,y1) to point (x2,y2):

[0106]

[0107] Step 3-3, calculate the projection vector:

[0108] Projection vector Represents the point-line vector from point (x0, y0) to the line The projection in the direction of the straight line can be understood as its direction vector The component on , that is, the projection of the point (x0, y0) in the direction of the line, represents the "component" of the point to the line in that direction.

[0109] Point Line Vector In the direction vector The projection vector on It is expressed by the formula as follows:

[0110]

[0111] in, It is the dot product of the point vector and the direction vector, and the calculation formula is as follows:

[0112]

[0113] is the squared magnitude of the direction vector, since the magnitude of the direction vector is:

[0114]

[0115] so, The calculation formula is as follows:

[0116] (x2-x1) 2 +(y2-y1) 2

[0117] Then, the projection vector The calculation formula is as follows:

[0118]

[0119] Step 3-4, calculate the distance from the point to the straight line:

[0120] The distance d from a point to a line is also called the point-line vector and the projection vector The difference in modulus length:

[0121]

[0122] Substitute the above known parameters into the formula and calculate:

[0123]

[0124] Thus, the distance d from the point to the line is obtained:

[0125]

[0126] Where P x and P y They are the projection vectors The x and y components of .

[0127] (2) The calculation process of the scratch angle is as follows:

[0128] Step 3-5, let all the points of the scratch be (x i ,y i ), where i = 1, 2, ..., n, where n is the total number of scratch points, and the scratch direction angle θ is expressed by the formula as follows:

[0129]

[0130] Where Δy=max(y i )-min(y i ) is the span of the scratch in the y direction,

[0131] Δx=max(x i )-min(x i ) is the span of the scratch in the x direction,

[0132] The above direction angle θ is expressed in radians. To convert it into degrees, the formula is as follows:

[0133]

[0134] The scratch angle output by the scratch defect analysis module is θ degree .

[0135] The scratch result display module 105 displays the scratch defect analysis results in the wafer defect map. Figure 4 ,The figure shows the scratch result of a case, scratch is the distance from a point to a straight line, and angle is the angle of the scratch.

[0136] Step 4: After the system gives the scratch result, the machine information matching module 107 searches the database for the corresponding machine information, and the machine information output module 108 is used to output the matching result. This step quickly matches the possible problem equipment and improves the efficiency of problem diagnosis.

[0137] Step 5: When there is no matching result, the user can input and supplement the machine information in the machine information database module 106 through the machine information supplement module.

[0138] The above embodiments are only preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the scope of protection of the present invention.

Claims

1. A semiconductor scratch automatic analysis system, characterized in that: include: Wafer defect map acquisition module, used to obtain the defect map obtained by the inspection machine; New coordinate system definition module, redefines the coordinates of defectmap; Cluster analysis module, which uses DBSCAN clustering algorithm to analyze whether there are clusters in the wafer defect map. If there are clusters, it is considered that there are scratches. If there are no clusters, it is considered that no scratches are found. Scratch defect analysis module, which analyzes the defect image of scratched wafers; The scratch result display module displays the scratch defect analysis results in the wafer defect map.

2. The semiconductor scratch automatic analysis system according to claim 1, characterized in that: in, In the coordinate system redefined by the new coordinate system definition module, the origin is determined by the transverse tangent and the longitudinal tangent of the wafer, and the origin is located on the longitudinal tangent below the intersection of the transverse tangent and the longitudinal tangent.

3. The semiconductor scratch automatic analysis system according to claim 1, characterized in that: in, The analysis process of the cluster analysis module is as follows: Step 2-1, define two parameters eps and min_samples in the DBSCAN clustering algorithm: eps is the neighborhood radius, which is used here to define the distance threshold between data points; min_samples is used to define the minimum number of data points in the neighborhood of the core point; Step 2-2, traverse all data points, if a data point's eps neighborhood contains at least min_samples data points, then the point is marked as a core point; Step 2-3, starting from a core point, assign the core point and all points in its eps neighborhood to the same cluster; Recursively, if a point in a cluster is a core point, all points in its eps neighborhood are also added to the cluster; Repeat the above steps until all core points are assigned to a cluster; Step 2-4, mark boundary points and noise points: points that are assigned to a cluster but are not core points are marked as boundary points, and points that do not belong to any cluster are marked as noise points.

4. The semiconductor scratch automatic analysis system according to claim 1, characterized in that: in, The scratch defect analysis module includes calculating the distance from a point to a straight line and the angle of the scratch.

5. The semiconductor scratch automatic analysis system according to claim 4, characterized in that: in, The calculation process of the point-to-straight-line distance is as follows: Step 3-1, construct the formula for calculating the distance from a point to a straight line: Assuming that the straight line passes through points (x1, y1) and (x2, y2), the distance d from point (x0, y0) to the straight line is expressed by the following formula: Step 3-2, construct the vector: Constructing the direction vector is the vector from point (x1, y1) to point (x0, y0): Constructing point line vector is the vector from point (x1,y1) to point (x2,y2): Step 3-3, calculate the projection vector: Point Line Vector In the direction vector The projection vector on It is expressed by the formula as follows: in, It is the dot product of the point line vector and the direction vector, and the calculation formula is as follows: is the square magnitude of the direction vector, calculated as follows: <h2 style=";text-align:left;direction:ltr">(x2-x1)<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +(y2-y1)<h2 style=";text-align:left;direction:ltr"> 2 Then, the projection vector The calculation formula is as follows: Step 3-4, calculate the distance from the point to the straight line: The distance d from a point to a line is also called the point-line vector and the projection vector The difference in modulus length: After calculation, the distance d from the point to the straight line is expressed as: Where P x and P y They are the projection vectors The x and y components of .

6. The semiconductor scratch automatic analysis system according to claim 4, characterized in that: in, The calculation process of the scratch angle is as follows: Suppose all the points of the scratch are (x i ,y i ), where i = 1, 2, ..., n, where n is the total number of scratch points, and the scratch direction angle θ is expressed by the formula as follows: Where Δy=max(y i )-min(y i ) is the span of the scratch in the y direction, Δx=max(x i )-min(x i ) is the span of the scratch in the x direction, The above direction angle θ is expressed in radians. To convert it into degrees, the formula is as follows: The scratch angle output by the scratch defect analysis module is θ degree .

7. The semiconductor scratch automatic analysis system according to claim 1, characterized in that: Also includes: A machine information database module stores machine information, wherein the machine information includes machine Arm width; A machine information matching module, after the scratch result display module displays the analysis result, searches for matching corresponding machine information from the machine information database module; The matching result output module is used to output the matching result of the machine information matching module.

8. The semiconductor scratch automatic analysis system according to claim 7, characterized in that: Also includes: The machine information supplement module is used for users to input and supplement the machine information in the machine information database module.

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