阅读说明：本技术 加工工艺参数确定方法、装置、存储介质及电子设备 (Machining process parameter determination method and device, storage medium and electronic equipment ) 是由 梁鹏程 于 2019-04-12 设计创作，主要内容包括：本公开涉及集成电路技术领域,具体涉及一种加工工艺参数确定方法、加工工艺参数确定装置、计算机可读存储介质及电子设备。本公开实施例提供的加工工艺参数确定方法包括：获取测试晶圆的测试图像；其中,所述测试图像包括所述测试晶圆上各个测试芯片的局部图像；将所述测试图像的各个局部图像与标准图像进行对比以得到对比结果,并将所述对比结果满足第一预设条件的测试晶圆作为有效晶圆；获取所述有效晶圆的测试数据；其中,所述测试数据包括与所述有效晶圆上各个测试芯片相对应的测试工艺参数和关键尺寸；根据所述关键尺寸与目标尺寸的关系确定用于作为加工工艺参数的测试工艺参数。该方法具有确定效率高、准确率好等优点。(The present disclosure relates to the field of integrated circuit technologies, and in particular, to a processing parameter determining method, a processing parameter determining apparatus, a computer-readable storage medium, and an electronic device. The method for determining the processing technological parameters provided by the embodiment of the disclosure comprises the following steps: acquiring a test image of a test wafer; the test images comprise local images of all test chips on the test wafer; comparing each local image of the test image with the standard image to obtain a comparison result, and taking the test wafer of which the comparison result meets a first preset condition as an effective wafer; acquiring test data of the effective wafer; the test data comprises test process parameters and critical dimensions corresponding to each test chip on the effective wafer; and determining a testing process parameter used as a processing process parameter according to the relation between the critical dimension and the target dimension. The method has the advantages of high determination efficiency, high accuracy and the like.)

1. A method for determining processing technological parameters is characterized by comprising the following steps:

acquiring a test image of a test wafer; the test images comprise local images of all test chips on the test wafer;

comparing each local image of the test image with the standard image to obtain a comparison result, and taking the test wafer of which the comparison result meets a first preset condition as an effective wafer;

acquiring test data of the effective wafer; the test data comprises test process parameters and critical dimensions corresponding to each test chip on the effective wafer;

and determining a testing process parameter used as a processing process parameter according to the relation between the critical dimension and the target dimension.

2. The method for determining parameters of machining process according to claim 1, wherein comparing each partial image of the test image with a standard image to obtain a comparison result comprises:

determining standard images respectively corresponding to the local images of the test image;

comparing the local image with a corresponding standard image to obtain the similarity of the local image and the standard image;

Marking the local image with the similarity larger than a first preset threshold as a target image;

and calculating the quantity proportion of the target image in the local image of the test image, and taking the quantity proportion as the comparison result.

3. The machining process parameter determination method according to claim 2, wherein the determining of the standard images respectively corresponding to the respective partial images of the test image includes:

identifying a base pattern in each partial image of the test image;

and determining a standard image corresponding to the local image according to the basic pattern.

4. The method of claim 3, wherein the base pattern comprises one or more of single lines, multiple lines, wavy lines, and circular holes.

5. The method for determining parameters of machining process according to claim 3, wherein comparing the local image with the corresponding standard image to obtain the similarity between the local image and the standard image comprises:

respectively collecting pattern data of basic patterns in the local image and the standard image;

and comparing the pattern data of the basic patterns in the local image and the standard image to obtain the similarity of the local image and the standard image.

6. The machining process parameter determination method according to claim 5, wherein when the base pattern is a single line, the single line includes a center line and edge lines on both sides of the center line, and the pattern data includes a line width of the center line, a line width of the edge lines, and an impurity percentage of the single line.

7. The machining process parameter determination method according to claim 5, wherein when the basic pattern is a multi-line, the multi-line includes a plurality of sub-lines, the sub-lines include a center line and edge lines on both sides of the center line, and the pattern data includes a line width of the center line, a line width of the edge lines, a percentage of impurities of the multi-line, and connection information of adjacent sub-lines.

8. The machining process parameter determination method according to claim 5, wherein when the basic pattern is a wavy line, the pattern data includes a distance between a crest and a trough in the wavy line and link information of adjacent wavy lines.

9. The machining-process parameter determining method according to claim 5, wherein when the basic pattern is a circular hole, the pattern data includes a length-direction aperture, a width-direction aperture of the circular hole, and connection information of adjacent circular holes.

10. The method for determining parameters of machining process according to claim 2, wherein comparing the local image with the corresponding standard image to obtain the similarity of the local image comprises:

inputting the local images and the corresponding standard images into a pre-trained machine learning model so as to output the similarity of the local images through the machine learning model.

11. The method for determining machining process parameters according to claim 2, wherein the first preset threshold is 90%.

12. The method for determining machining process parameters according to claim 2, wherein the first preset condition is that the quantity ratio is greater than a second preset threshold.

13. The method for determining machining process parameters of claim 12, wherein the second predetermined threshold is 50%.

14. The method of claim 2, further comprising:

determining test process parameters corresponding to each of the target images from the test data;

and determining the reference range of the processing technological parameter based on the testing technological parameter of the target image.

15. The processing parameter determination method according to any one of claims 1 to 14, wherein the test process parameters include defocus amount and exposure amount;

determining a test process parameter used as a processing process parameter according to the relationship between the critical dimension and the target dimension, wherein the determining comprises the following steps:

performing linear regression fitting on the exposure and the critical dimension for each defocus amount to obtain goodness of fit corresponding to each defocus amount;

taking the defocusing amount of which the goodness of fit meets a second preset condition as a target defocusing amount;

and based on the target defocusing amount, taking the test process parameter corresponding to the key dimension closest to the target dimension as a processing process parameter.

16. The method of claim 15, wherein the second predetermined condition is that the goodness-of-fit is greater than a third predetermined threshold.

17. The method for determining parameters of a machining process according to claim 15, wherein the second predetermined condition is that the goodness-of-fit is higher than that of any other defocus amount.

18. A machining process parameter determining apparatus, comprising:

An image acquisition module configured to acquire a test image of a test wafer; the test images comprise local images of all test chips on the test wafer;

the image comparison module is configured to compare each local image of the test image with a standard image to obtain a comparison result, and the test wafer of which the comparison result meets a preset condition is taken as an effective wafer;

a data acquisition module configured to acquire test data of the valid wafer; the test data comprises test process parameters and critical dimensions corresponding to each test chip on the effective wafer;

and the parameter determination module is configured to determine a test process parameter used as a processing process parameter according to the relation between the critical dimension and the target dimension.

19. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method for determining process parameters according to any one of claims 1 to 17.

20. An electronic device, comprising:

a processor;

a memory for storing executable instructions of the processor;

Wherein the processor is configured to perform the method of any of claims 1-17 via execution of the executable instructions.

Technical Field

The present disclosure relates to the field of integrated circuit technologies, and in particular, to a processing parameter determining method, a processing parameter determining apparatus, a computer-readable storage medium, and an electronic device.

Background

With the development of integrated circuit technology, the integration level of semiconductor chips is continuously improved, the Critical Dimension (CD for short) of the chips is also continuously reduced to the nanometer level, and the corresponding processing technology becomes more and more complex. In the large-scale production of chips, the guarantee of the uniformity and stability of the critical dimension has very important significance for stabilizing the yield of products.

In order to timely grasp and adjust the processing parameters of the chips to achieve parameter optimization and further improve the product yield, a processing test, such as a series of pre-test procedures, may be performed on the wafer before the large-scale production. After the wafer passes through the test machine and accomplishes the test, produced a large amount of test data and pictures, need contrast, the analysis through the mode of artifical screening at present in the actual working process, not only consume a large amount of human costs, all have great problems in the aspect of efficiency and the rate of accuracy that the technological parameter confirms moreover.

It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.

Disclosure of Invention

The present disclosure is directed to a machining process parameter determining method, a machining process parameter determining apparatus, a computer-readable storage medium, and an electronic device, so as to overcome technical problems of low efficiency and poor accuracy of determining machining process parameters due to limitations of related technologies, at least to a certain extent.

According to one aspect of the present disclosure, there is provided a machining process parameter determination method, characterized by comprising:

acquiring a test image of a test wafer; the test images comprise local images of all test chips on the test wafer;

comparing each local image of the test image with the standard image to obtain a comparison result, and taking the test wafer of which the comparison result meets a first preset condition as an effective wafer;

acquiring test data of the effective wafer; the test data comprises test process parameters and critical dimensions corresponding to each test chip on the effective wafer;

and determining a testing process parameter used as a processing process parameter according to the relation between the critical dimension and the target dimension.

In an exemplary embodiment of the present disclosure, the comparing each partial image of the test image with a standard image to obtain a comparison result includes:

Determining standard images respectively corresponding to the local images of the test image;

comparing the local image with a corresponding standard image to obtain the similarity of the local image and the standard image;

marking the local image with the similarity larger than a first preset threshold as a target image;

and calculating the quantity proportion of the target image in the local image of the test image, and taking the quantity proportion as the comparison result.

In an exemplary embodiment of the present disclosure, the determining the standard images respectively corresponding to the respective partial images of the test image includes:

identifying a base pattern in each partial image of the test image;

and determining a standard image corresponding to the local image according to the basic pattern.

In an exemplary embodiment of the present disclosure, the base pattern includes one or more of a single line, a multi-line, a wavy line, and a circular hole.

In an exemplary embodiment of the present disclosure, the comparing the local image and the corresponding standard image to obtain a similarity between the local image and the standard image includes:

respectively collecting pattern data of basic patterns in the local image and the standard image;

And comparing the pattern data of the basic patterns in the local image and the standard image to obtain the similarity of the local image and the standard image.

In an exemplary embodiment of the present disclosure, when the base pattern is a single line, the single line includes a center line and edge lines at both sides of the center line, and the pattern data includes a line width of the center line, a line width of the edge lines, and a percentage of impurities of the single line.

In an exemplary embodiment of the present disclosure, when the base pattern is a multi-line, the multi-line includes a plurality of sub-lines including a center line and edge lines at both sides of the center line, and the pattern data includes a line width of the center line, a line width of the edge lines, a percentage of impurities of the multi-line, and connection information of adjacent sub-lines.

In an exemplary embodiment of the present disclosure, when the base pattern is a wavy line, the pattern data includes distances of crests and troughs in the wavy line and connection information of adjacent wavy lines.

In an exemplary embodiment of the present disclosure, when the base pattern is a circular hole, the pattern data includes a length-direction aperture, a width-direction aperture of the circular hole, and connection information of adjacent circular holes.

In an exemplary embodiment of the present disclosure, the comparing the local image and the corresponding standard image to obtain the similarity of the local image includes:

inputting the local images and the corresponding standard images into a pre-trained machine learning model so as to output the similarity of the local images through the machine learning model.

In an exemplary embodiment of the present disclosure, the first preset threshold is 90%.

In an exemplary embodiment of the disclosure, the first preset condition is that the number ratio is greater than a second preset threshold.

In an exemplary embodiment of the present disclosure, the second preset threshold is 50%.

In an exemplary embodiment of the present disclosure, the method further comprises:

determining test process parameters corresponding to each of the target images from the test data;

and determining the reference range of the processing technological parameter based on the testing technological parameter of the target image.

In an exemplary embodiment of the present disclosure, the test process parameters include defocus and exposure;

determining a test process parameter used as a processing process parameter according to the relationship between the critical dimension and the target dimension, wherein the determining comprises the following steps:

Performing linear regression fitting on the exposure and the critical dimension for each defocus amount to obtain goodness of fit corresponding to each defocus amount;

taking the defocusing amount of which the goodness of fit meets a second preset condition as a target defocusing amount;

and based on the target defocusing amount, taking the test process parameter corresponding to the key dimension closest to the target dimension as a processing process parameter.

In an exemplary embodiment of the disclosure, the second preset condition is that the goodness-of-fit is greater than a third preset threshold.

In an exemplary embodiment of the present disclosure, the second preset condition is that the goodness of fit is higher than the goodness of fit of any other defocus amount.

According to one aspect of the present disclosure, there is provided a machining process parameter determining apparatus, characterized by comprising:

an image acquisition module configured to acquire a test image of a test wafer; the test images comprise local images of all test chips on the test wafer;

the image comparison module is configured to compare each local image of the test image with a standard image to obtain a comparison result, and the test wafer of which the comparison result meets a preset condition is taken as an effective wafer;

A data acquisition module configured to acquire test data of the valid wafer; the test data comprises test process parameters and critical dimensions corresponding to each test chip on the effective wafer;

and the parameter determination module is configured to determine a test process parameter used as a processing process parameter according to the relation between the critical dimension and the target dimension.

According to an aspect of the present disclosure, there is provided a computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the machining process parameter determination method as described in any one of the above.

According to one aspect of the present disclosure, there is provided an electronic device characterized by comprising a processor and a memory; wherein the memory is configured to store executable instructions of the processor, and the processor is configured to perform any one of the above-described machining process parameter determination methods via execution of the executable instructions.

In the machining process parameter determining method provided by the exemplary embodiment of the disclosure, by introducing comparison between the test image and the standard image in the test analysis process of the test data, a manual mode can be replaced, the measurement result can be quickly and intuitively processed, and the optimized machining process parameter can be accurately positioned and determined.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.

FIG. 1 schematically illustrates a flow chart of steps of a process parameter determination method in an exemplary embodiment of the present disclosure.

Fig. 2 shows a test image schematic of a test wafer composed of partial images of test chips.

Fig. 3 schematically illustrates a flow chart of a portion of the steps of a process parameter determination method in an exemplary embodiment of the present disclosure.

Fig. 4 schematically illustrates a flow chart of a portion of the steps of a process parameter determination method in an exemplary embodiment of the present disclosure.

Fig. 5 shows a schematic diagram of the identification and correspondence of the test image and the standard image.

Fig. 6 schematically illustrates a flow chart of a portion of the steps of a process parameter determination method in an exemplary embodiment of the present disclosure.

Fig. 7 shows a schematic view of a single line base pattern and its constituent parts.

FIG. 8 shows a multi-line base pattern and pattern data acquisition diagram.

Fig. 9 shows a wavy line base pattern and a pattern data acquisition diagram.

Fig. 10 shows a circular hole base pattern and pattern data acquisition diagram (hole width direction).

Fig. 11 shows a circular hole base pattern and pattern data acquisition diagram (hole length direction).

Fig. 12 schematically illustrates a flow chart of a portion of the steps of a process parameter determination method in an exemplary embodiment of the present disclosure.

Fig. 13 shows a schematic of a bosch curve plotted against test data.

FIG. 14 shows a linear regression fit for a defocus amount.

FIG. 15 shows a schematic of the comparison of test data to test images.

Fig. 16 shows a schematic diagram of the effect of viewing a partial image from a test image.

Fig. 17 is a schematic diagram showing a switching display manner of test data on a test image.

Fig. 18 schematically shows a block diagram of a processing parameter determination apparatus in an exemplary embodiment of the present disclosure.

FIG. 19 schematically illustrates a schematic diagram of a program product in an exemplary embodiment of the disclosure.

Fig. 20 schematically illustrates a module diagram of an electronic device in an exemplary embodiment of the present disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.

The exemplary embodiment of the present disclosure first provides a method for determining processing parameters, which can be mainly applied to the optimization of the processing parameters in the chip processing process. The method can be applied to a photoetching process of a wafer, and is used for determining process parameters such as optimized exposure (Dose) and defocus (Focus) of a photoetching machine.

As shown in fig. 1, the method for determining processing parameters according to the exemplary embodiment may mainly include the following steps:

s110, obtaining a test image of a test wafer; wherein the test image comprises a partial image of each test chip on the test wafer.

In order to determine the optimized processing parameters, a plurality of test wafers may be selected for processing test, and then a test image of each test wafer may be obtained. A plurality of test chips are distributed on one test wafer, and accordingly, a test image corresponding to one test wafer may also be composed of partial images of a plurality of test chips. For example, in this step, the image acquisition device may be used to acquire the local images of the test chips, and then the local images of the test chips are combined according to the position distribution of the test chips on the test wafer to obtain the test image of the test wafer. The image capturing device for capturing the image of the test chip may be any device capable of observing and acquiring a sharp image of the surface of the test chip, such as a scanning electron microscope or an atomic force microscope. Fig. 2 is a schematic diagram of a test image consisting of partial images corresponding to the physical location distribution of chips on a wafer.

And S120, comparing each local image of the test image with the standard image to obtain a comparison result, and taking the test wafer of which the comparison result meets a first preset condition as an effective wafer.

The present exemplary embodiment may provide a plurality of standard images (Golden images) having different patterns in advance, and the patterns in the standard images may cover basic graphic units constituting the chip integrated circuit, and may include a single-line standard Image, a multi-line standard Image, a wavy-line standard Image, a circular-hole standard Image, and the like, for example. The comparison result about the image similarity can be obtained by comparing each local image of the test image obtained in step S110 with the corresponding standard image, and the comparison result of each local image on each test image can be analyzed and integrated to obtain the comparison result of the corresponding test wafer. And screening according to the comparison result of each test wafer, so that one or more test wafers meeting the first preset condition can be selected from the plurality of test wafers, and the test wafers meeting the first preset condition can be used as effective wafers for subsequent analysis of test data. The tester may set different first preset conditions according to the test requirement, for example, the partial image similar to the standard image in the test image may exceed a certain number ratio, or the partial image similar to the standard image may be within a certain position range of the test image, which is not particularly limited in this exemplary embodiment.

S130, acquiring test data of the effective wafer; the test data includes test process parameters and critical dimensions corresponding to each test chip on the active wafer.

For the valid wafers determined in step S120, the step may obtain test data of each valid wafer. The test data of each active wafer includes test process parameters and critical dimensions corresponding to the test chips on the active wafer. Taking the photolithography process as an example, the test process parameters may be collected from a photolithography machine, and may include parameters such as exposure amount and defocus amount used in exposing each test chip at different positions on the effective wafer. The critical dimension can be collected on a measuring machine, for example, by image analysis using a scanning electron microscope. In other exemplary embodiments, the test data acquisition and the test image comparison may be performed simultaneously, and the disclosure is not limited thereto.

And S140, determining a testing process parameter used as a processing process parameter according to the relation between the critical dimension and the target dimension.

In this exemplary embodiment, a Target Value (Target Value) may be provided in advance to serve as a Target dimension corresponding to the critical dimension of the chip, and then the critical dimension of each test chip on the effective wafer obtained in step S130 is compared with the Target dimension, and the test process parameter used as the processing process parameter may be determined according to a relationship between the critical dimension and the Target dimension. When the number of the effective wafers is multiple, a plurality of testing process parameters are correspondingly determined, and the testing process parameters can be averaged to obtain the final processing process parameters.

In the machining process parameter determining method provided by the exemplary embodiment, the comparison between the test image and the standard image is introduced in the test analysis process of the test data, so that the measurement result can be quickly and intuitively processed in a manual mode instead, and the optimized machining process parameter can be accurately positioned and determined.

As shown in fig. 3, in another exemplary embodiment of the present disclosure, comparing each partial image of the test image with the standard image in step S120 to obtain a comparison result, the following steps may be further included:

and S310, determining standard images corresponding to the local images of the test image respectively.

In the step, standard images corresponding to the local images on the test image respectively can be determined by adopting image recognition and comparison modes. For example, a local image on the test image is recognized as a single-line pattern, the single-line standard image can be selected as the standard image corresponding to the local image.

And S320, comparing the local image with the corresponding standard image to obtain the similarity of the local image and the standard image.

Comparing each local image of the test image with the corresponding standard image can obtain the similarity between each local image and the corresponding standard image, and the comparison content can include, for example, the shape, the line width, the impurities, the angles, and the like. After comparison, each local image on a test image will correspond to a similarity. In some exemplary embodiments, the local images and the corresponding standard images may be input to a machine learning model trained in advance to output the similarity of the local images through the machine learning model. In addition, the machine learning model can be continuously optimized by collecting user feedback information so as to improve the accuracy of similarity calculation.

And S330, marking the local image with the similarity larger than a first preset threshold as a target image.

In order to evaluate the similarity degree between the local image and the standard image, the exemplary embodiment may provide a first preset threshold value as needed, and then the local image with the similarity degree greater than the first preset threshold value may be marked as the target image. For example, if the first preset threshold is set to 90%, the partial image with a similarity degree exceeding 90% with the corresponding standard image is marked as the target image, and the other partial images with a similarity degree not exceeding 90% are non-target images. In the present exemplary embodiment, the partial images in the test image may be combined in the form of an actual physical location distribution to form a visual image viewable by the test person. Accordingly, the target image and the non-target image can be displayed in a differentiated manner, for example, a green frame may be set at the position of the target image, and a yellow frame or a red frame may be set at the position of the non-target image. Of course, besides color-based identification, shape identification, text identification, or any other differentiation identification means may also be adopted, and this is not particularly limited in this exemplary embodiment.

And S340, calculating the quantity proportion of the target image in the local image of the test image, and taking the quantity proportion as a comparison result.

According to the marking result in step S330, in this step, the quantity ratio of the target image in all the local images in each test image may be calculated, and the quantity ratio is used as the comparison result of the current test image, and step S120 may further use the test wafer of which the quantity ratio satisfies the first preset condition as the valid wafer. For example, in some exemplary embodiments of the present disclosure, the first preset condition may be set that the ratio of the number of target images is greater than a second preset threshold, and the second preset threshold may be set to be 50%, for example. Assuming that 100 local images corresponding to the test chips are distributed on the test image of a certain test wafer, after the comparison in the above steps, it can be determined that 60 local images are target images, and the other 40 local images are non-target images, so that the percentage of the number of the target images in the test image is 60%. This quantity ratio is greater than the second predetermined threshold of 50%, so the test wafer can be regarded as a valid wafer. Assuming that 100 local images corresponding to the test chips are distributed on the test image of another test wafer, after the comparison in the above steps, it can be determined that 48 local images are target images, and the other 52 local images are non-target images, so that the percentage of the number of the target images in the test image is 48%. This quantity ratio is less than 50% of the second predetermined threshold, and therefore the test wafer needs to be excluded from the valid wafers.

In the method for determining processing parameters according to the exemplary embodiment, the number of test chips meeting the image contrast requirement in each test wafer can be determined by comparing the local image with the standard image, and effective wafers in the test wafers can be clearly divided and determined based on the ratio of the number, so that the efficiency and accuracy of determining the processing parameters can be improved.

As shown in fig. 4, in another exemplary embodiment of the present disclosure, step s310. determining standard images respectively corresponding to the respective partial images of the test image may further include the steps of:

and S410, identifying basic patterns in each local image of the test image.

The base pattern of the partial image may generally comprise one or more of single lines, multiple lines, wavy lines and circular holes. In order to improve the accuracy of image recognition and contrast, when acquiring a partial image, only one basic pattern can be included in one partial image by adjusting the image acquisition area and the image acquisition range. In addition, when the image recognition and comparison technology allows, a plurality of different types of basic patterns may be included in one partial image, and this exemplary embodiment is not particularly limited thereto.

Step S420, determining a standard image corresponding to the local image according to the basic pattern.

Based on the basic pattern of each partial image in the test image identified in step S410, this step can determine a standard image corresponding to each partial image. Taking fig. 5 as an example, through image recognition and comparison, the correspondence between each local image and each standard image such as a single-line standard image, a multi-line standard image, a wavy-line standard image, or a circular-hole standard image can be determined.

On the basis of this exemplary embodiment, step s320, comparing the local image with the corresponding standard image to obtain the similarity between the local image and the standard image, may further include the following steps as shown in fig. 6:

s610, respectively collecting pattern data of basic patterns in the local image and the standard image;

and S620, comparing the pattern data of the basic patterns in the local image and the standard image to obtain the similarity of the local image and the standard image.

The pattern data acquisition method will be described in detail below for different basic patterns.

As shown in fig. 7, when the base pattern is a single line, the single line may include a center line (a black line region located at the center in the drawing) and edge lines (white line regions located at both sides of the black line) located at both sides of the center line, and the pattern data includes a line width of the center line, a line width of the edge lines, and an impurity percentage of the single line.

As shown in fig. 8, when the base pattern is a multi-line, the multi-line includes a plurality of sub-lines. Similar to the single line pattern, the sub-lines include a center line and edge lines on both sides of the center line, and the pattern data includes line widths of the center line, line widths of the edge lines, impurity percentages of the multi-lines, and also includes connection information (Bridge) of adjacent sub-lines. In a local image with a multi-line basic pattern, when partial image deletion occurs to cause an incomplete image of a certain sub-line, for example, only the edge line of the sub-line on one side is acquired in the local image, and the center line of the word line and the edge line on the other side are lost, then the missing sub-line of the image can be ignored in pattern data acquisition.

As shown in fig. 9, when the basic pattern is a wavy line, pattern data may be obtained in a vertical projection manner, where the pattern data includes the distance between the peak and the trough in the wavy line and the connection information of adjacent wavy lines, and may further include data such as line width and space width.

As shown in fig. 10, when the base pattern is a circular hole, the pattern data includes a length-direction aperture and a width-direction aperture of the circular hole and connection information of adjacent circular holes. In order to ignore some interference (noise), when collecting pattern data of the circular hole pattern, the area where the circular hole is located may be vertically projected to determine the width-direction aperture of the circular hole, i.e., the hole width shown in the figure. After determining the hole width, as shown in fig. 11, the area where the circular hole is located may be rotated by 90 degrees, and then vertical projection is performed to determine the length-direction aperture of the circular hole, i.e., the hole length shown in the figure.

According to different types of basic patterns, pattern data in different forms can be collected, the similarity between various local images and corresponding standard images can be obtained adaptively, and the accuracy of similarity calculation is improved.

The processing parameter determination method provided by each exemplary embodiment of the present disclosure can be applied to integrated circuit photomask manufacturing and lithography processes, and the processing parameters and testing process parameters in the lithography process can include defocus and exposure. On this basis, step s140. determining the testing process parameters as the processing process parameters according to the relationship between the critical dimension and the target dimension may include the following steps as shown in fig. 12:

and S1210, aiming at each defocus amount, performing linear regression fitting on the exposure and the key size to obtain goodness of fit corresponding to each defocus amount.

In the present exemplary embodiment, a Bossung Curve (Bossung Curve) may be prepared as shown in fig. 13, the Curve being plotted with defocus (relative value, Focus) as abscissa and critical dimension (SEM CD) as ordinate, and different curves in the figure represent different exposure amounts (relative values). Based on the Bosang curve, the exposure and the key size under different defocusing amounts can be taken to perform linear regression fitting to obtain goodness of fit respectively corresponding to the defocusing amounts. For example, FIG. 14 shows the exposure and critical dimension when defocus (relative value) is-1, and the linear regression equation is obtained by fitting

y＝-1.5673x+45.9655

The goodness of fit R corresponding to the defocus amount can be calculated by using the following formula²Is 0.9641.

Wherein SSR stands for regression sum of squares, SSE for residual sum of squares, and SST for total sum of squares.

And S1220, taking the defocusing amount with the goodness of fit meeting the second preset condition as the target defocusing amount.

In step S1210, goodness of fit corresponding to each defocus amount can be obtained through fitting, and then the defocus amount whose goodness of fit satisfies the second preset condition can be used as the target defocus amount in this step. For example, the second preset condition may be that the goodness of fit of the defocus amount is greater than a third preset threshold, or that the goodness of fit of the defocus amount is higher than the goodness of fit of any other defocus amount, i.e., the highest value of all the goodness of fit.

And S1230, based on the target defocusing amount, taking the test process parameter corresponding to the critical dimension closest to the target dimension as the processing process parameter.

Based on the target defocus amount determined in step S1220, in this step, the critical dimension of each test chip under the target defocus amount may be compared with the target dimension, and the test process parameter corresponding to the critical dimension closest to the target dimension may be used as the processing process parameter. For example, when the defocus amount is-1, the highest goodness of fit 0.9641 can be obtained, and the defocus amount is the target defocus amount. If the preset target size is 42.60, the value closest to the critical dimension with the defocus relative value of-1, 42.64, can be selected, and then the exposure relative value for obtaining the critical dimension can be determined to be 1 from the test data. The defocusing amount actual value-0.01 and the exposure amount actual value 22.35 corresponding to the defocusing amount actual value are finally determined processing technological parameters, and the determined processing technological parameters serving as recommended parameters can be applied to the large-scale chip processing process.

When the actual values of the test data are combined with the test image, a map as shown in fig. 15 can be formed, in which the area covered by the dotted line is the area where the target image (for example, the partial image having a similarity of more than 90% with the standard image) is located. It can be seen that the target image is located substantially in the center of the test image, and the recommended parameter values are measured from a test chip located in the center of all target images for the critical dimension 42.64 corresponding to-0.01 and 22.35 exposures. In addition, in the machining process parameter determining method provided by the exemplary embodiment of the present disclosure, test process parameters corresponding to respective target images may also be determined from the test data; and then determining the reference range of the processing technological parameter based on the testing technological parameter of the target image. For example, the defocus amount reference range as in fig. 15 is-0.07 to 0.035, and the exposure amount reference range is 19.95 to 24.75.

In addition, the visual graphic program can be correspondingly configured for visually displaying the test image and the related analysis result of the test data. For example, as shown in fig. 16, by triggering a control at the position of a certain test chip in the test image, a local image corresponding to the test chip can be viewed. As shown in fig. 17, the critical dimension of each test chip and the similarity between the local image of each test chip and the corresponding standard image may also be displayed at the corresponding position of the test image by switching the image display mode. By providing various visual display modes, a more intuitive test analysis result viewing way can be provided for testers.

It should be noted that although the above exemplary embodiments describe the various steps of the methods of the present disclosure in a particular order, this does not require or imply that these steps must be performed in that particular order, or that all of the steps must be performed, to achieve the desired results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.

In an exemplary embodiment of the present disclosure, a machining process parameter determining apparatus is also provided. As shown in fig. 18, the machining process parameter determining apparatus 1800 according to the present exemplary embodiment may mainly include: an image acquisition module 1810, an image contrast module 1820, a data acquisition module 1830, and a parameter determination module 1840. The image acquisition module 1810 is configured to acquire a test image of a test wafer; the test images comprise local images of all test chips on the test wafer; the image comparison module 1820 is configured to compare each local image of the test image with a standard image to obtain a comparison result, and take the test wafer of which the comparison result meets a preset condition as an effective wafer; the data acquisition module 1830 is configured to acquire test data of the valid wafer; the test data comprises test process parameters and critical dimensions corresponding to each test chip on the effective wafer; the parameter determination module 1840 is configured to determine test process parameters for use as process parameters based on the relationship of the critical dimension to the target dimension.

The specific details of the processing parameter determining apparatus have been described in detail in the corresponding processing parameter determining method, and therefore are not described herein again.

It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.

In an exemplary embodiment of the present disclosure, there is also provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, may implement the machining process parameter determination method of the present disclosure as described above. In some possible embodiments, various aspects of the disclosure may also be implemented in the form of a program product comprising program code; the program product may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, or a removable hard disk, etc.) or on a network; when the program product is run on a computing device (which may be a personal computer, a server, a terminal apparatus, or a network device, etc.), the program code is configured to cause the computing device to perform the method steps in the above exemplary embodiments of the disclosure.

Referring to fig. 19, a program product 1900 for implementing the above method according to an embodiment of the present disclosure may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a computing device (e.g., a personal computer, a server, a terminal device, or a network device, etc.). However, the program product of the present disclosure is not limited thereto. In the exemplary embodiment, the computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium.

The readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

A readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the C language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's computing device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device over any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), etc.; alternatively, the connection may be to an external computing device, such as through the Internet using an Internet service provider.

In an example embodiment of the present disclosure, there is also provided an electronic device comprising at least one processor and at least one memory for storing executable instructions of the processor; wherein the processor is configured to perform the method steps in the above-described exemplary embodiments of the disclosure via execution of the executable instructions.

The electronic device 2000 in the present exemplary embodiment is described below with reference to fig. 20. The electronic device 2000 is only one example and should not bring any limitations to the functionality or scope of use of the embodiments of the present disclosure.

Referring to fig. 20, an electronic device 2000 is shown in the form of a general purpose computing device. The components of the electronic device 2000 may include, but are not limited to: at least one processing unit 2010, at least one storage unit 2020, a bus 2030 connecting various system components including the processing unit 2010 and the storage unit 2020, and a display unit 2040.

Wherein the storage unit 2020 stores program code which is executable by the processing unit 2010 such that the processing unit 2010 performs the method steps in the exemplary embodiments described above in the present disclosure.

The storage unit 2020 may include readable media in the form of volatile storage units, such as a random access storage unit 2021(RAM) and/or a cache storage unit 2022, and may further include a read-only storage unit 2023 (ROM).

The storage unit 2020 may also include a program/utility 2024 having a set (at least one) of program modules 2025, such program modules including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

Bus 2030 may be one or more of any of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 2000 may also communicate with one or more external devices 800 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that allow a user to interact with the electronic device 2000, and/or with any devices (e.g., router, modem, etc.) that allow the electronic device 2000 to communicate with one or more other computing devices. Such communication may occur over an input/output (I/O) interface 2050. Also, the electronic device 2000 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) through the network adapter 2060. As shown in fig. 20, the network adapter 2060 may communicate with the other modules of the electronic device 2000 via the bus 2030. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 2000, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

As will be appreciated by one skilled in the art, aspects of the present disclosure may be embodied as a system, method or program product. Accordingly, various aspects of the present disclosure may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software may be referred to herein generally as a "circuit," module "or" system.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments, and the features discussed in connection with the embodiments are interchangeable, if possible. In the above description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the embodiments of the disclosure may be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.