Method for forming detection area, method for detecting surface of crystal grain, and method for judging hot spot of surface of crystal grain

Abstract

The application discloses a detection area forming method, a crystal grain surface detection method and a crystal grain surface hotspot judgment method. The detection area forming method is to select a characteristic area on an integrated circuit layout and form a detection area from each characteristic area; wherein the characteristic area is used to represent a local area with similar structure, and the size of the local area is in the range of 0.5 microns to 50 microns. The crystal grain surface detection method uses the detection area formed by the detection area forming method to perform shape detection on the crystal grain surface. The crystal grain surface hotspot judgment method judges whether there is a hotspot according to the result of the shape detection. By using the above method, compared with the prior art, the detection compression ratio is higher, and the detection means with high resolution is more conducive to popularization and application on the production line.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of detecting the surface of a die after CMP processing, and in particular to a method for forming a detection area, a method for detecting the surface of a die, and a method for determining hotspots on the surface of a die. BACKGROUND

[0002] Chemical mechanical polishing (CMP), also known as chemical mechanical planarization, is a key process for achieving global uniform planarization of a wafer in semiconductor manufacturing, and is the most frequently used process in manufacturing (about 25% of all processes), as shown in Figure 1 The purpose of the CMP process is to achieve planarization of the wafer surface. In the drawings of the present patent, the dark portions are semiconductor substrates, and the light portions are functional portions of metal material. In the present patent, the functional portion refers to a portion other than the semiconductor substrate. The most typical functional portion is of metal material, and can also include an insulating portion. The metal portion is often used to achieve the function of a circuit, such as connection and contact. The insulating portion is used for insulation and isolation, and is often formed by filling a trench of semiconductor material with insulating material.

[0003] Whether a CMP process has defects is mainly determined by whether there are hotspots on the die on the wafer. Hotspots will have a lasting impact on subsequent processes of semiconductor processing, and ultimately lead to a decrease in yield. Therefore, determining whether there are hotspots on the die after CMP processing is very important for improving yield. The generation of hotspots can be due to the design of the integrated circuit layout, or due to the material used in the CMP process or related parameters of the CMP process. Therefore, determining whether there are hotspots on the die after CMP processing is also very important for improving the design of the layout and adjusting the CMP process. In reality, there are mainly two manifestations of hotspots after the CMP process. One, as shown in Figure 2 , is erosion of the functional portion. Erosion occurs in the functional portion of metal material, and generally occurs along the width of the metal line. Therefore, the detection of erosion requires nanoscale detection of the shape (particularly the height) in the size range of nanometers or tens of nanometers. The other, as shown in Figure 3 , is dishing of the surface area. Dishing occurs in a surface area of micrometers or tens of micrometers, and is strongly related to the line width of the functional portion in the surface area and the density of the functional portion. Similarly, nanoscale detection of the shape is required.

[0004] In order to determine whether there is a hot spot on the die on the wafer, it is necessary to perform a topography detection (also known as roughness detection) on the surface of the die after each CMP process. The resolution of such detection should be in nanometers, and in order to achieve line efficiency, such detection should be completed within 4 hours.

[0005] In the prior art, a white light interferometer is the default option for performing surface topography detection of the die. Blunt et al. used a white light interferometer to measure the surface roughness of a semiconductor polished substrate and an epitaxial layer, and Zhang et al. also applied it to measure the step height of a sputtered functional layer. Studies have shown that although white light interferometric measurement basically meets the requirements in terms of longitudinal resolution (0.1 nanometer) and measurement time (about 8 hours for each die), its lateral resolution is in micrometers or slightly less than 1 micrometer, so the detection accuracy cannot meet the requirements. Moreover, when the thickness of the die to be measured is too small (less than 50 nanometers), the die itself will appear translucent, at which time the measurement result will be affected due to light scattering. To this end, a layer of material needs to be applied to the surface of the die to enable white light interferometric measurement, but at the same time the die will be damaged.

[0006] Atomic force microscopy (AFM) can achieve a spatial resolution of 1 nm in both the X-axis and Y-axis directions, and has a higher resolution in the Z-axis direction (height direction), which can provide a truly three-dimensional atomic-level surface image. At the same time, AFM does not require any special treatment of the sample, and the sample has a small chance of being damaged during preparation, so it can be used for non-destructive testing. As one of the powerful nanoscale resolution surface topography detection tools, AFM completely meets the resolution (accuracy) requirements of semiconductor die surface topography detection, and will not cause damage to the die during measurement. Xu et al. used AFM to non-destructively measure the density of etch pits generated after CMP of a GaN wafer, and Shi et al. also first realized in-situ observation of local CMP behavior on sapphire using AFM. However, the imaging speed of AFM is slow. For example, it takes about 5 minutes to image an area of 100 square micrometers with a lateral resolution of 10 nm, and it takes several weeks to several months to measure a typical die of 33x27mm 2 Therefore, it is difficult to popularize the use of detection means with high resolution for die surface topography detection on the production line. SUMMARY

[0007] The present application aims to overcome the above-mentioned defects or problems in the background art, and provides a detection area forming method, a die surface detection method, a die surface hot spot judgment method, a computer program, a computing device and a detection device, which have a higher detection compression ratio compared to the prior art, and are more conducive to popularizing the use of detection means with high resolution on the production line.

[0008] To achieve the above object, the following technical solutions are adopted:

[0009] The first technical solution relates to a detection area forming method, which is used to determine the detection area when performing surface detection on a wafer after CMP processing. The method selects a representative area on an integrated circuit layout and forms a detection area from each representative area. The representative area is used to represent a local area with similar structure, and the size of the local area in X and Y axes is in the range of 0.5-50 microns.

[0010] The second technical solution is based on the first technical solution, wherein the size of the local area in X and Y axes is in the range of 10-30 microns.

[0011] The third technical solution is based on the second technical solution, wherein the size of the local area in X and Y axes is 20 microns.

[0012] The fourth technical solution is based on the first technical solution, which includes the following steps: dividing the measured part on the integrated circuit layout into several local areas, and all local areas cover the measured part; the measured part covers all processing area groups, and each processing area group includes processing areas with similar structures; classifying all local areas based on the similarity of the structures to obtain local area groups; selecting at least one local area from each local area group as a representative area, and the set of all representative areas forms a detection area.

[0013] The fifth technical solution is based on the fourth technical solution, wherein adjacent local areas overlap with each other.

[0014] The sixth technical solution is based on the fifth technical solution, wherein the local areas are obtained by sliding a virtual window on the measured part.

[0015] The seventh technical solution is based on the fourth technical solution, wherein the measured part is determined by the following steps: performing edge diffusion on each processing area in the integrated circuit layout to form a corresponding diffusion area; classifying all diffusion areas based on the similarity of the structures to obtain diffusion area groups; selecting at least one diffusion area from each diffusion area group as a measured area, and the set of all measured areas forms the measured part.

[0016] The eighth technical solution is based on the seventh technical solution, wherein the structural similarity is rotational symmetry and / or mirror symmetry.

[0017] The ninth technical solution is based on the seventh technical solution, wherein the to-be-measured part has the shortest first path in each candidate to-be-measured part; the candidate to-be-measured part is a set of candidate to-be-measured regions, the candidate to-be-measured part covers all diffusion region groups and each diffusion region group has only a preset number of candidate to-be-measured regions; and the first path is the shortest one-way path in the candidate to-be-measured part that passes through the center points of all candidate to-be-measured regions.

[0018] The tenth technical solution is based on any one of the fourth to ninth technical solutions, wherein the detection region has the shortest second path in each candidate detection region; the candidate detection region is a set of candidate feature regions, the candidate detection region covers all local region groups and each local region group has only a preset number of candidate feature regions; and the second path is the shortest one-way path in the candidate detection region that passes through the center points of all candidate feature regions.

[0019] The eleventh technical solution relates to a grain surface detection method for detecting a grain surface after CMP processing, which is based on the detection region formation method according to any one of the first to tenth technical solutions to form a detection region and perform shape detection on the grain surface.

[0020] The twelfth technical solution is based on the eleventh technical solution, wherein the shape detection on the grain surface is performed by using an AFM.

[0021] The thirteenth technical solution is based on the twelfth technical solution, wherein the sizes of the X and Y axes of the local region are less than or equal to the sizes of the X and Y axes of the maximum imaging range of the AFM.

[0022] The fourteenth technical solution relates to a grain surface hotspot judgment method for judging whether a grain surface after CMP processing has a hotspot, which performs shape detection on the grain surface by using the grain surface detection method according to any one of the tenth to thirteenth technical solutions, and judges that the grain surface has a hotspot if there is erosion of the functional part and / or depression of the feature region in the detection result.

[0023] Compared with the prior art, the above-mentioned solutions have the following beneficial effects:

[0024] In the paper "Hotspot Prevention Using CMP Model in Design Implementation Flow" by Norma Rodriguez et al., it is pointed out that the planarization of CMP depends on the density and width of the metal part of the local region on the grain. At different densities and widths, different degrees of dish-shaped depression and erosion may occur.

[0025] In the paper "Hotspot detection and design recommendation using silicon calibrated CMP model" by Colin Hui et al., it is pointed out that in the CMP process, recess and erosion will occur, recess or erosion is formed in the lower layer, which leads to copper residue or copper accumulation in the upper layer. Recess and erosion are strongly dependent on the density and line width of the design pattern. Wide functions are more prone to recess than narrow functions.

[0026] From the above paper, it can be seen that the functional part structure of the local area on the surface of the wafer (reflected as a pattern structure on the integrated circuit layout) is a key factor affecting the hotspot distribution of the local area after the CMP process is completed. The structure of the functional part covers two important indicators of design pattern density and line width. Of course, the local area without a functional part should also be considered as a functional part structure. Local areas with different structures have different possibilities of forming hotspots during the CMP process.

[0027] The first technical solution is based on the law revealed in the above paper. The local area is represented by a representative area with similar structure, so that among a large number of local areas with similar structures, only a small number of representative areas need to be detected, which can represent the possibility of hotspots in these local areas, thereby greatly improving the detection compression ratio, providing a basis for shortening the detection time of the wafer, and more conducive to popularizing the detection means with high resolution on the production line.

[0028] In the first technical solution, the size of the local area is comparable to the size of the surface area where recess may occur, so it can cover the detection of both recess and erosion hotspots.

[0029] In the first technical solution, the integrated circuit layout refers to the design layout of the integrated circuit on the wafer. In some processes, multiple CMP processes need to be performed, and if each CMP process has a different integrated circuit topology, the integrated circuit layout refers to the design layout corresponding to this CMP process. If it is a planar semiconductor integrated circuit, the integrated circuit layout is the final design layout of the integrated circuit. In the first technical solution, the integrated circuit layout is used to select the detection area, which has the advantages of being more convenient and shorter detection time compared to dividing the local area based on the macro image formed by low resolution in the detection process.

[0030] The second and third technical solutions are the optimization of the size of the local area.

[0031] The fourth technical solution provides a specific way to determine the characterization region and then form the detection region. In the fourth technical solution, the measurement part covers all the processing region groups, and all the local regions cover the measurement part, which can ensure that the set of local regions used for similarity classification can cover all the processing regions of the structure type.

[0032] In the fifth technical solution, adjacent local regions overlap with each other, which can traverse all the functional part structures in the measurement part range, further reduce the influence of factors (such as starting position) related to local region division on the detection result, and facilitate the characterization region to better characterize the possibility of hot spot occurrence.

[0033] The sixth technical solution is a specific implementation of the fifth technical solution.

[0034] In the seventh technical solution, the measurement part is based on the diffusion region, which not only enables the measurement part to include all types of processing regions, but also can contain regions adjacent to the processing regions, and can more comprehensively express the structures of various local regions. Classify the diffusion regions based on structural similarity, and select at least one diffusion region in each diffusion region group as a measurement region to form a measurement part, which can reduce the area of the measurement part, reduce the number of local regions used for classification by using the repetition of the macrostructure of the grain surface, while ensuring that the functional part structures with different structural similarities are included in the measurement part, avoiding missed detection in the detection process.

[0035] In the eighth technical solution, the structural similarity is rotational symmetry and / or mirror symmetry, so it can cover all different functional part structures.

[0036] In the ninth technical solution, the measurement part has the shortest first path among the various alternative measurement parts, which can provide a basis for shortening the final detection path as much as possible under the premise of traversing all diffusion region groups, further shortening the detection time.

[0037] In the tenth technical solution, the detection region has the shortest second path among the various alternative detection regions, which can shorten the final detection path as much as possible under the premise of traversing all local region groups, further shortening the detection time.

[0038] The eleventh technical solution performs surface shape detection on the grain surface based on the detection region formed by the above detection region forming method, which can utilize high compression ratio to shorten the detection time of the grain, and is more conducive to popularizing high-resolution detection means on the production line.

[0039] The twelfth technical solution is a preferred implementation of the tenth technical solution, which uses AFM to perform surface shape detection on the grain, which not only ensures the resolution compared to using a white light interferometer, but also can perform non-destructive detection on the grain, and no longer needs to smear materials on the grain surface, avoiding damaging the detected grain.

[0040] In the thirteenth technical solution, the size of the local region is less than or equal to the size of the maximum imaging range of the AFM, so that the AFM only needs to move the probe when detecting the characterization region, without moving other parts, and without causing errors in the detection field of view due to the movement of other parts. The correspondence between the detected characterization region in the actual detection process and the characterization region on the integrated circuit layout can be ensured.

[0041] In the fourteenth technical solution, the grain surface is detected by using the above grain surface detection method, and it is judged whether there is a hot spot on the grain surface based on the detection result. Compared with the prior art, it can more quickly judge whether a hot spot exists, and is more conducive to popularizing the detection means with high resolution on the production line. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments, the following briefly introduces the drawings needed to be used:

[0043] Figure 1 It is a local longitudinal section schematic diagram of the wafer before and after the CMP process;

[0044] Figure 2 It is a local longitudinal section schematic diagram of the wafer containing an erosion hot spot;

[0045] Figure 3 It is a local longitudinal section schematic diagram of the wafer containing a recessed hot spot;

[0046] Figure 4 The integrated circuit layout in embodiment one is shown;

[0047] Figure 5 The diffusion region in embodiment one is shown;

[0048] Figure 6 The diffusion region group in embodiment one is shown;

[0049] Figure 7 The measurement part in embodiment one is shown;

[0050] Figure 8 The local region in embodiment one is shown;

[0051] Figure 9 The local region group in embodiment one is shown;

[0052] Figure 10 The detection path in embodiment one is shown;

[0053] Figure 11 The detection region and the detection path in embodiment one are shown;

[0054] Figure 12The integrated circuit layout in Embodiment 2 is shown;

[0055] Figure 13 The following is a typical local region of the local region group in Example 2;

[0056] Figure 14 The detection path in Example 2 is shown. Detailed Implementation

[0057] The technical solutions in the embodiments will now be described clearly and completely with reference to the accompanying drawings.

[0058] Example 1

[0059] See Figure 4 , Figure 4 The integrated circuit layout in Embodiment 1 is shown. In this embodiment, the integrated circuit layout refers to the design layout of the integrated circuit corresponding to the die. In some processes, multiple CMP processes are required. If each CMP process has a different integrated circuit topology, then the integrated circuit layout refers to the design layout corresponding to that CMP process. If it is a planar semiconductor integrated circuit, then the integrated circuit layout is the final design layout of the integrated circuit. Using the integrated circuit layout to select the detection area has advantages over forming a macroscopic image at low resolution and then dividing the local area based on the macroscopic image during the detection process, resulting in greater convenience and shorter detection time.

[0060] like Figure 4 As shown, in this embodiment, the integrated circuit board Figure X The dimension along the X-axis is 12.5 micrometers, and the dimension along the Y-axis is 8.5 micrometers. In this embodiment, there are six processing areas on the integrated circuit layout, namely A1 to A6. Each processing area includes only the functional part. That is, in this embodiment, the processing area does not include non-functional parts. The functional part in this embodiment is a metal part, specifically a copper layer. Among them, A1, A3, A4, and A6 have the same dimensions, with a dimension of 2 micrometers along the X-axis and a dimension of 3 micrometers along the Y-axis; A2 and A5 have the same dimensions, with a dimension of 6 micrometers along the X-axis and a dimension of 3 micrometers along the Y-axis. The distance between two adjacent processing areas along the X-axis is 0.25 micrometers, and the distance between two adjacent processing areas along the Y-axis is 0.5 micrometers. The distance from the processing area to the adjacent edge of the integrated circuit layout is 1 micrometer along the X-axis and also 1 micrometer along the Y-axis.

[0061] In this embodiment, based on the integrated circuit layout, the detection area E and the detection path F are formed through the following steps.

[0062] Step 1: edge diffusion is performed on each processing region in the integrated circuit layout to form diffusion regions, and the to-be-measured part is based on the diffusion regions, so that the to-be-measured part can include all types of processing regions, can contain regions adjacent to the processing regions, and can more comprehensively express the structures of various local regions. For details, see Figure 4 and Figure 5 . Figure 4 and Figure 5 The diffusion regions in this embodiment are shown. As shown in Figure 4 , Figure 4 the red box in FIG. 1C is the diffusion region formed after edge diffusion of the processing region A1. In this embodiment, each edge of the processing region is diffused outward by a distance of 1 micrometer. The specific way of setting the diffusion distance will be described in detail later. As shown in Figure 5 , the processing regions A1 to A6 form corresponding diffusion regions B1 to B6 after edge diffusion;

[0063] Step 2: classify all diffusion regions based on rotational symmetry and / or mirror symmetry to obtain diffusion region groups; in this embodiment, the classification method of the diffusion region groups is structural similarity, specifically rotational symmetry and mirror symmetry. In other embodiments, it can be rotational symmetry or mirror symmetry, and in some cases where the accuracy requirement is not high, it can also be classified based on structural similarity indicators such as Hamming distance. Classify the diffusion regions based on rotational symmetry and / or mirror symmetry, and select at least one diffusion region in each diffusion region group as a to-be-measured region to form a to-be-measured part, which can reduce the area of the to-be-measured part, reduce the number of local regions used for classification by using the repetition of the macrostructure of the crystal surface, and at the same time ensure that the structures of all functional parts are included in the to-be-measured part C, avoiding missing detection during detection. For details, see Figure 6 . Figure 6 The diffusion region groups in this embodiment are shown. As shown in Figure 6 , the number of diffusion region groups in this embodiment is two, which are BC1 and BC2. Among them, the diffusion region group BC1 is a set of diffusion regions B1, B3, B4 and B6, wherein B1 and B3 are mirror symmetric along the X-axis direction, B1 and B4 are mirror symmetric along the Y-axis direction, and B1 and B6 are 180-degree rotationally symmetric with each other. The diffusion region group BC2 is a set of diffusion regions B2 and B5, wherein B2 and B5 are mirror symmetric along the Y-axis direction. Any diffusion region in the diffusion region group BC1 is not mirror symmetric or rotationally symmetric or mirror symmetric and rotationally symmetric with any diffusion region in the diffusion region group BC2;

[0064] Step 3: selecting at least one diffusion region in each diffusion region group as a quasi-measuring region, and forming a quasi-measuring part C by all quasi-measuring regions. In this embodiment, one diffusion region in each diffusion region group is selected as a quasi-measuring region. In this embodiment, the quasi-measuring part C has the shortest first path in each candidate quasi-measuring part. The candidate quasi-measuring part is a set of candidate quasi-measuring regions, and the candidate quasi-measuring part covers all diffusion region groups and has a preset number of candidate quasi-measuring regions in each diffusion region group. The first path is the shortest one-way path in the candidate quasi-measuring part that passes through the center points of all candidate quasi-measuring regions. The quasi-measuring part C has the shortest first path in each candidate quasi-measuring part, which can provide a basis for shortening the final detection path F as much as possible under the premise of traversing all diffusion region groups, thereby shortening the detection time. In this embodiment, the preset number is 1. Specifically, this embodiment includes 8 candidate quasi-measuring parts, each of which is a set of two candidate quasi-measuring regions, and the two candidate quasi-measuring regions come from two diffusion region groups, respectively. The 8 candidate quasi-measuring parts are the set of B1 and B2, the set of B1 and B5, the set of B3 and B2, the set of B3 and B5, the set of B4 and B2, the set of B4 and B5, the set of B6 and B2, and the set of B6 and B5. In this embodiment, the first path is the path between the center points of the two candidate quasi-measuring regions in the candidate quasi-measuring part. When the number of diffusion region groups exceeds 3, the first path is the shortest one-way path in the candidate quasi-measuring part that passes through the center points of all candidate quasi-measuring regions. According to the above rule, the first path of the candidate quasi-measuring part containing B1 and B2 is the shortest. Of course, the first paths of the candidate quasi-measuring parts containing B3 and B2, B4 and B5, and B5 and B6 are also the shortest. The above four candidate quasi-measuring parts can be selected as the quasi-measuring part C. Referring to Figure 7 , Figure 7 The quasi-measuring part C in this embodiment is shown. As shown in Figure 7 , in this embodiment, the quasi-measuring part C is a set of quasi-measuring regions B1 and B2. It should be noted that in other embodiments, the quasi-measuring part C can also be a part of the integrated circuit layout that covers all processing region groups. The quasi-measuring part can also be the entire integrated circuit layout. Of course, if the entire integrated circuit design layout is selected as the quasi-measuring part C, the calculation time of forming the detection region E and the detection path F will be longer, and therefore it is not efficient;

[0065] Step 4: Divide the region of the part C to be tested into a plurality of local regions, all of which cover the part C to be tested; all of the local regions cover the part C to be tested, which can ensure that the set of local regions used for similarity classification can cover all types of processing regions. In this embodiment, adjacent local regions overlap each other, and in other embodiments, adjacent local regions can also abut each other. The adjacent local regions overlap each other, which can traverse all functional part structures in the part to be tested, further reduce the influence of factors related to local region division (such as starting position) on the detection result, and facilitate the characterization region to better characterize the possibility of hot spot occurrence.

[0066] Referring to Figure 7 and Figure 8 , Figure 7 and Figure 8 The local regions in this embodiment are shown. As shown in Figure 7 , in this embodiment, the size of the local region along the X-axis direction is 2 microns, and the size along the Y-axis direction is also 2 microns. Specifically, in this embodiment, the local region is obtained by sliding the virtual window of the red box shown in Figure 7 in the part C to be tested, and the size of the virtual window is the same as that of the local region. When sliding the virtual window, the step value is 1 micron each time. In order to be more efficient in detection, the size of the local region can be set to be between 0.5-50 microns, more preferably between 10 microns and 30 microns, and more preferably 20 microns. It should be noted that the size of the local region can or can not be related to the setting of the aforementioned diffusion distance. In order to improve efficiency, the diffusion distance can generally be set to be less than or equal to the size of the local region, and more preferably, the diffusion distance can be set to be the step value when sliding the window, that is, the size of the overlapping part of adjacent local regions. As shown in Figure 8 , according to the above-mentioned method of dividing the local region, the number of local regions in this embodiment is 40, which are respectively from D1 to D40;

[0067] Step 5: Classify all local regions based on the similarity of functional part structure in the local region to obtain local region groups. The similarity of functional part structure should at least include the similarity of the proportion of functional part in the area of the local region, the similarity of the line width of the functional part. The structure of the functional part refers to the shape of the functional sub-domain if the functional part only contains one functional sub-domain, the shape of each functional sub-domain and the positional relationship between the functional sub-domains if the functional part contains more than two functional sub-domains, and a separate local region group if the local region does not contain a functional part. In the classification based on the similarity of the functional part structure, one classification method that may result in a large number of local region groups is to classify based on rotational symmetry and / or mirror symmetry, that is, only local regions with rotational symmetry and / or mirror symmetry can be classified into a local region group. The embodiment adopts the classification method based on rotational symmetry and / or mirror symmetry. On this basis, local regions with rotational symmetry and / or mirror symmetry after a slight translation can also be classified into the same local region group. Specifically, refer to Figure 9 , Figure 9 Eight local region groups formed based on the above classification method in the embodiment are shown. Among them, the local region group DC1 contains the local region D1; the local region group DC2 contains the local regions D2, D5 to D9, D11 and D12; the local region group DC3 contains the local regions D3, D4 and D10; the local region group DC4 contains the local regions D12, D15 to D19, D22, D25 to D29; the local region group DC5 contains the local regions D13, D14, D20, D23, D24 and D30; the local region group DC6 contains the local regions D33, D34 and D40; the local region group DC7 contains the local regions D32 and D35 to D39; and the local region group DC8 contains the local region D31.

[0068] Step 6: Select at least one local region from each local region group to form a representative region, and the set of all representative regions forms a detection region E; in the embodiment, the detection region E has the second shortest path in the alternative detection regions; the alternative detection region is a set of alternative representative regions, the alternative detection region covers all local region groups and each local region group has and only has a preset number of alternative representative regions; the second path is the shortest one-way path in the alternative detection region passing through the center points of all alternative representative regions. The detection region has the second shortest path in the alternative detection regions, which can shorten the final detection path as much as possible under the premise of traversing all local region groups, and further shorten the detection time. In the embodiment, the preset number is 1. Refer to Figure 10 and Figure 11 , Figure 10 and Figure 11 The detection region E and the detection path F are shown. AsFigure 10 and Figure 11 As shown, in this embodiment, the detection region E includes eight representation regions, namely D1, D2, D3, D12, D23, D33, D32, and D31. These eight representation regions cover all eight local region groups, and each local region group has exactly one representation region. In this embodiment, the number of candidate representation regions is very large, which can be selected from... Figure 10 As can be seen intuitively, the second path of the detection area E ( Figure 10 The shortest path is the red path in the diagram. For example... Figure 11 As shown, the detection path F is obtained based on the second path of the detection area E. Specifically, in this embodiment, the detection path F can optionally be D1 sequentially passing through D2, D3, D12, D23, D33, D32 to D31.

[0069] In this embodiment, the above-described detection region formation method can be implemented by executing a computer program. In the computer program, the algorithms for generating diffusion region clusters from each diffusion region and for generating local region clusters from each local region can be one or more combinations of Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest, Convolutional Neural Network (CNN), Transfer Learning, and Reinforcement Learning algorithms. In this embodiment, Support Vector Machine (SVM) is selected. The algorithms for generating the target region based on each diffusion region cluster and for generating the detection region E based on each local region cluster can be one or more combinations of Genetic Algorithm, Dijkstra's Algorithm, Grid Algorithm, Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). Specifically, in this embodiment, a Genetic Algorithm is selected.

[0070] The embodiment also discloses a detection device, which comprises the detection apparatus, the computing apparatus and the control apparatus. The detection apparatus is an atomic force microscope (AFM) for detecting the surface shape of the crystal grain after CMP processing and outputting the surface shape detection result. Compared with the white light interferometer, the AFM can ensure the resolution and perform nondestructive detection on the crystal grain, so that the material on the surface of the crystal grain is not needed to be smeared, and the detected crystal grain is not damaged. In the embodiment, the X-axis direction size of the maximum imaging range of the AFM is 2 microns, and the Y-axis direction size is 2 microns. Therefore, in the embodiment, the size of the local region is equal to the size of the maximum imaging range of the AFM, and in other embodiments, the size of the local region can be set to be less than the size of the maximum imaging range of the AFM. The size of the local region is less than or equal to the size of the maximum imaging range of the AFM, so that the AFM only needs to move the probe when detecting the characterization region, without moving other parts, and the detection field of view is not affected by the movement of other parts. The correspondence between the detected characterization region and the characterization region on the integrated circuit layout in the actual detection process can be ensured. The computing apparatus comprises an input unit, an output unit, a storage unit and a computing unit. The input unit is used to obtain the integrated circuit layout, the output unit is used to output the detection region and the detection path, the storage unit is used to store the computer program, and the computing unit is used to call and execute the computer program to generate the detection region and the detection path based on the integrated circuit layout. The control apparatus is used to obtain the detection region and the detection path from the computing apparatus to control the detection apparatus to detect the surface shape of the crystal grain in the detection region according to the detection path. In the embodiment, the AFM detects the surface shape in the selected local region by moving the probe, and the movement of the probe can adopt the raster scanning mode or the line scanning mode. The raster scanning mode is adopted in the embodiment. In the embodiment, the movement of the AFM between the characterization regions is mainly realized by the displacement of other parts except the probe, for example, the movement of the cantilever or the support arm or other mechanisms provided with the probe along the X-axis and the Y-axis.

[0071] The embodiment also discloses a crystal grain surface hot spot judgment method for judging whether the crystal grain surface after CMP processing has a hot spot. Specifically, the crystal grain surface hot spot judgment method judges whether there is erosion of the functional part and / or depression of the characterization area based on the detection result output by the detection device. Optionally, if the height of the functional part in each characterization area is lower than the height of the semiconductor substrate and the height difference exceeds a first threshold value, it is judged that there is erosion of the functional part. The first threshold value can be set to 10 nanometers, or can be specifically set according to process requirements. If the overall height of the characterization area with the functional part is lower than the overall height of the characterization area without the functional part or with less functional part, and the height difference exceeds a second threshold value, it is judged that there is depression of the characterization area. The second threshold value can be set to 20-50 nanometers, or can be specifically set according to process requirements. As long as there is any one of erosion or depression, it is judged that the crystal grain surface has a hot spot. The crystal grain surface is detected by using the above-mentioned crystal grain surface detection method, and whether the crystal grain surface has a hot spot is judged based on the detection result. Compared with the prior art, the hot spot can be more quickly judged, and the detection method with high resolution can be more easily popularized and applied in the production line.

[0072] In the embodiment, the total area of the crystal grain is 106.25 square microns, the detection area E includes 8 characterization areas, and the area of each characterization area is 4 square microns. Therefore, the area of the detection area is 32 square microns, and the compression ratio is 3.32. Of course, the embodiment is only for illustrating the method of the patent. In actual application, due to the high repeatability of the processing area and the high similarity of the local area, the actual compression ratio is much larger than the compression ratio of the embodiment.

[0073] Embodiment two

[0074] Referring to Figure 12 , Figure 12 A schematic diagram of the integrated circuit layout of the crystal grain in embodiment two is shown. As Figure 12As shown, the integrated circuit layout in Example Two contains two types of processing regions, which are arranged along the X axis and repeated as a repeating unit. The entire integrated circuit layout has 48 repeating units along the X axis and 96 repeating units along the Y axis. In a repeating unit, the processing region on the left has a size of 10 microns along the X axis and a size of 10 microns along the Y axis, and contains both functional parts and non-functional parts. The functional parts are lighter in color and are copper layers, and the non-functional parts are darker in color and are semiconductor substrates. The functional parts include eight square rings nested with each other, each ring has a line width of 0.3 microns, and each ring is spaced apart from each other by 0.3 microns. The processing region on the right has a size of 9.9 microns along the X axis and a size of 10 microns along the Y axis, and also contains both functional parts and non-functional parts. The functional parts are lighter in color and are copper layers, and the non-functional parts are darker in color and are semiconductor substrates. The functional parts include only one square ring, which is sandwiched by several square non-semiconductor substrate sub-domains. Each square non-semiconductor substrate sub-domain has a size of 0.3 microns along the X axis and a size of 0.3 microns along the Y axis, and is spaced apart from each other by 0.3 microns along the X axis and by 0.3 microns along the Y axis. In each repeating unit, the spacing between the left and right processing regions is 0.3 microns. Along the X axis, the spacing between two repeating units is 0.3 microns. Along the Y axis, the spacing between two repeating units is 0.3 microns. Based on the above dimensions, in Example Two, each repeating unit has an area of 20120 square microns, there are a total of 4608 repeating units, and the entire integrated circuit layout has an area of approximately 92712960 square microns.

[0075] The detection regions and the detection path of Example Two can be obtained in the same way as Example One. Different from Example One, in Example Two, the diffusion distance is 5 microns, the size of the local region along the X axis is 10 microns, the size of the local region along the Y axis is 10 microns, and the step value is 5 microns. In this embodiment, the method for obtaining local region classes based on structural similarity is to classify local regions that have rotational symmetry and / or mirror symmetry after a slight translation into the same local region class. See Figure 13 , Figure 13 The representative regions of each local region class in this embodiment are shown. As shown in Figure 13 , there are 11 local region classes in this embodiment, which are DC01 to DC11, and the representative regions in each local region class are shown in Figure 13 . Figure 14 The detection path in this embodiment is shown. As shown in Figure 14 , the detection path in this embodiment is indicated by the red line. The corresponding detection regions are also determined by the set of representative regions.

[0076] The computing device for outputting the detection area and the detection path in the second embodiment is basically the same as that in the first embodiment, and the detection device for performing shape detection on the wafer after CMP processing is also basically the same as that in the first embodiment, except that the maximum imaging range of the AFM is 10 microns x 10 microns.

[0077] The wafer surface detection method and the wafer surface hotspot judgment method in the second embodiment are the same as those in the first embodiment.

[0078] In the second embodiment, the detection area includes 11 representative areas, each representative area has an area of 100 microns, the detection area has an area of 1100 square microns, and the detection compression ratio is 84284.5. Therefore, by using the detection area forming method of the present patent, a very large detection compression ratio can be achieved, which is conducive to greatly shortening the detection time and facilitating the popularization of high-resolution detection means on the production line.

[0079] As can be seen from the above embodiments, the representative areas are used to represent local areas with similar structures, so that among a large number of local areas with similar structures, only a small number of representative areas need to be detected, which can represent the possibility of hotspots in these local areas, thereby greatly improving the detection compression ratio and shortening the detection time of the wafer, and more conducive to the popularization of high-resolution detection means on the production line.

[0080] The above embodiments use integrated circuit layout to select the detection area, which has the advantages of more convenience and shorter detection time compared to forming a macro image at a low resolution during the detection process and then dividing local areas based on the macro image.

Claims

1. A method of forming a detection area for determining a detection area when performing surface inspection of a die after CMP processing, characterized by, Selecting a representation region on an integrated circuit layout and forming a detection region from the representation region; the representation region is used to represent a local region with similar structure, the size of the local region in X-axis and Y-axis is in the range of 0.5-50 microns; the detection region forming method comprises the following steps: Dividing a measurement part on an integrated circuit layout into a plurality of local regions, the measurement part covers all processing region groups, each processing region group comprises processing regions with similar structure, and all local regions cover the measurement part; Classifying all local regions based on structural similarity to obtain local region groups; Selecting at least one local region from each local region group as a representation region, and forming a detection region from the set of all representation regions.

2. The detection area formation method as described in claim 1, characterized in that, The size of the local region in X-axis and Y-axis is in the range of 10-30 microns.

3. The detection area formation method as described in claim 2, characterized in that, The size of the local region in X-axis and Y-axis is 20 microns.

4. The detection area formation method as described in claim 1, characterized in that, Adjacent local regions overlap with each other.

5. The detection area formation method as described in claim 4, characterized in that, The local region is obtained by sliding a virtual window on the measurement part.

6. The method for forming a detection area according to claim 1, wherein The measurement part is determined by the following steps: Edge diffusion is performed on each processing region in the integrated circuit layout to form a corresponding diffusion region; Classifying all diffusion regions based on structural similarity to obtain diffusion region groups; Selecting at least one diffusion region from each diffusion region group as a measurement region, and forming a measurement part from the set of all measurement regions.

7. The detection area formation method as described in claim 6, characterized in that, The structural similarity is rotational symmetry and / or mirror symmetry.

8. The detection area formation method as described in claim 6, characterized in that, The measurement part has the shortest first path in each alternative measurement part; the alternative measurement part is a set of alternative measurement regions, the alternative measurement part covers all diffusion region groups and each diffusion region group has only a predetermined number of alternative measurement regions; the first path is the shortest one-way path in the alternative measurement part that passes through the center points of all alternative measurement regions.

9. The method for forming a detection area as described in any one of claims 4 to 8, characterized in that, The detection region has the shortest second path in each alternative detection region; the alternative detection region is a set of alternative representation regions, the alternative detection region covers all local region groups and each local region group has only a predetermined number of alternative representation regions; the second path is the shortest one-way path in the alternative detection region that passes through the center points of all alternative representation regions.

10. A method for detecting a surface of a wafer after CMP processing, characterized by, Performing topography detection on a die surface based on the detection region formed by the detection region forming method of any one of claims 1-9.

11. The die surface inspection method according to Claim 10, wherein Performing topography detection on the die surface using AFM.

12. The die surface inspection method according to Claim 11, wherein The size of the local region in X-axis and Y-axis is less than or equal to the size of the maximum imaging range of AFM in X-axis and Y-axis.

13. A method for judging the presence of a hot spot on the surface of a wafer after CMP processing, characterized by: If there is erosion of the functional part and / or depression of the representation region in the detection result, it is determined that there is a hot spot on the die surface.

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