Collaborative optimization method based on nanoimprint and etching processes

Abstract

The invention relates to the technical field of semiconductors, in particular to a collaborative optimization method based on a nanoimprint and etching process, which comprises the following steps: determining an image state according to the etching integrity of a defect etching image and a regional heterogeneity coefficient, and determining an optimization analysis mode according to the image state; in the imprinting optimization analysis, an imprinting processing mode is determined according to an abnormal region coverage index and an abnormal similarity coefficient; in the etching optimization analysis, a region division mode is determined based on the variation regularity so as to obtain a plurality of sub-regions, sub-region categories are determined according to the correlation influence coefficient and the feature representation degree, and an etching processing mode is determined according to the lateral domain variation value; according to the invention, linkage optimization can be carried out on the etching process parameters and the imprinting process parameters so as to improve the process effect.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor technology, and in particular to a collaborative optimization method based on nanoimprinting and etching processes. Background Art

[0002] With the development of science and technology, semiconductor materials, as an important part of cutting-edge science and technology, have a great impact on the development of society. In the semiconductor manufacturing process, nanoimprinting and etching processes, as key technical links, still face many challenges and problems. However, the existing process optimization methods usually rely on macro process parameters and artificial rules, and the analysis depth of defect characteristics is insufficient. There are obvious limitations and it is difficult to meet the high-precision and high-efficiency manufacturing requirements. Therefore, how to optimize the process according to the defect characteristics in the etching image to improve the process effect is a technical problem that technicians in this field need to solve urgently.

[0003] Chinese patent publication number CN118888483A discloses an intelligent control method for TSV etching equipment, including: using preset etching process parameters to perform deep hole etching, and in the etching process, forming an etching depth sequence to obtain substrate component characteristic information; according to the substrate component characteristic information and the preset etching process parameters, obtaining the hole wall etching morphology, thereby obtaining the conductor deposition defect information and electrical connection quality attenuation information; according to the electrical connection quality attenuation information, making decisions to adjust the step size; adjusting and optimizing the preset etching process parameters to obtain the optimal etching process parameters; continuing etching control, and continuing to optimize and control the etching process parameters within the next preset time period. It can be seen that the above technical solution has the following problems: it mainly relies on preset etching process parameters and etching depth sequences. Although it can obtain substrate component characteristic information and hole wall etching morphology, it lacks in-depth analysis of defect characteristics, making it difficult to accurately identify and locate defects, and the optimization dimension is relatively single, mainly focusing on the adjustment of etching process parameters, lacking linkage optimization of imprinting process parameters, resulting in poor overall process optimization effect. Summary of the invention

[0004] To this end, the present invention provides a collaborative optimization method based on nanoimprinting and etching processes to overcome the problem that the optimization dimension in the prior art is relatively single, mainly focusing on the adjustment of etching process parameters, and lacking the linkage optimization of imprinting process parameters, resulting in poor overall effect of process optimization.

[0005] To achieve the above object, the present invention provides a collaborative optimization method based on nanoimprinting and etching process, comprising: Determine the image state according to the etching completeness and the regional heterogeneity coefficient of the defect etching image, and determine the optimization analysis method according to the image state, and the optimization analysis method is imprint optimization analysis or etching optimization analysis; In the imprint optimization analysis, the imprint processing method is determined according to the abnormal area coverage index and the abnormal similarity coefficient. The imprint processing method is to adjust the imprint glue spin coating time according to the abnormal evaluation index, or to determine the adjustment method according to the uniformity of the abnormal area and the misalignment deviation value; The adjustment method is to optimize the detection of the viscosity of the imprint glue, or to adjust the imprint pressure of each abnormal area according to the bias instability threshold; In the etching optimization analysis, the region division method is determined based on the variation regularity to obtain several sub-regions, the sub-region category is determined according to the correlation influence coefficient and the characteristic representativeness, and the etching treatment method is determined according to the lateral domain variation value. The etching treatment method is to adjust the etching gas ratio according to the sub-region comparison coefficient, or to adjust the etching time according to the residual turbulence value of the characteristic sub-region; The area division method is to divide according to the comprehensive evaluation value or the combination of associated domains, and the defect etching image is an etching image with an estimated defect coefficient greater than a preset estimated defect coefficient.

[0006] Furthermore, if the image state is that the etching completeness is less than a preset etching completeness or the regional heterogeneity coefficient is less than a preset regional heterogeneity coefficient, the optimization analysis method is imprint optimization analysis.

[0007] Further, if the image status is that the etching completeness is greater than or equal to the preset etching completeness and the regional heterogeneity coefficient is greater than or equal to the preset regional heterogeneity coefficient, the optimization analysis method is etching optimization analysis.

[0008] Further, if the abnormal area coverage index is greater than or equal to the preset abnormal area coverage index or the abnormal similarity coefficient is less than the preset abnormal similarity coefficient, the imprint processing method is to increase the imprint glue spin coating time according to the abnormal evaluation index; The increase value of the imprint glue spin coating time is positively correlated with the abnormal evaluation index.

[0009] Further, if the abnormal area coverage index is less than the preset abnormal area coverage index and the abnormal similarity coefficient is greater than or equal to the preset abnormal similarity coefficient, the imprint processing method is to determine the adjustment method according to the uniformity of the abnormal area and the misalignment deviation value; If the uniformity of the abnormal area is greater than or equal to the preset uniformity of the abnormal area and the displacement deviation value is greater than or equal to the preset displacement deviation value, the adjustment method is to optimize the detection of the viscosity of the embossing glue; If the uniformity of the abnormal area is less than the preset uniformity of the abnormal area or the displacement deviation value is less than the preset displacement deviation value, the adjustment method is to increase the imprint pressure of each abnormal area according to the bias instability threshold.

[0010] Furthermore, the viscosity of the embossing adhesive is optimized and tested, including: Detect the viscosity of the embossing glue; If the viscosity of the embossing glue is greater than or equal to the preset viscosity of the embossing glue, the viscosity of the embossing glue is reduced and adjusted; If the embossing adhesive viscosity is less than the preset embossing adhesive viscosity, the number of blank prints is increased and adjusted.

[0011] Furthermore, the regional division method is determined based on the mutation regularity, including: If the mutation regularity is greater than or equal to the preset mutation regularity, the area division method is to divide according to the comprehensive evaluation value; If the mutation regularity is less than the preset mutation regularity, the region division method is to divide according to the combination of associated domains.

[0012] Furthermore, the sub-region category is determined according to the correlation influence coefficient and the characteristic representativeness, including: A feature sub-region whose correlation influence coefficient is less than a preset correlation influence coefficient and whose feature representativeness is greater than or equal to a preset feature representativeness; A non-characteristic sub-region whose correlation influence coefficient is greater than or equal to a preset correlation influence coefficient or whose characteristic representativeness is less than a preset characteristic representativeness.

[0013] Further, if the lateral domain variation value is greater than or equal to the preset lateral domain variation value, the etching processing method is to reduce and adjust the etching gas ratio according to the sub-region comparison coefficient; The reduction value of the etching gas ratio is positively correlated with the sub-region comparison coefficient.

[0014] Furthermore, if the lateral domain variation value is less than the preset lateral domain variation value, the etching processing method is to increase and adjust the etching time according to the residual turbulence value of the characteristic sub-region; The increase in the etching time is positively correlated with the residual turbulence value of the characteristic sub-region.

[0015] Compared with the prior art, the beneficial effect of the present invention lies in that, in the technical scheme of the present invention, the image state is determined according to the etching completeness and regional heterogeneity coefficient of the defective etching image, and the overall quality of the etching image and the differences between different regions in the etching image are effectively reflected by the etching completeness and the regional heterogeneity coefficient, and then different optimization analysis methods are adaptively selected according to the image state, so that the selection of the optimization analysis method is more in line with the actual application scenario, and the root cause of the process problem can be quickly located, blind adjustment of parameters can be avoided, and the optimization efficiency is improved.

[0016] Furthermore, in the present invention, the abnormal area coverage index and the abnormal similarity coefficient are used to effectively reflect the degree of change of the abnormal area, and then different imprint processing methods are adaptively selected according to the abnormal area coverage index and the abnormal similarity coefficient, which can effectively locate the source of the problem and adjust the process parameters in a targeted manner.

[0017] Furthermore, in the present invention, the uniformity and position deviation of the abnormal area are effectively reflected by the uniformity of the abnormal area and the misalignment deviation value, and then different adjustment methods are adaptively selected according to the uniformity and the misalignment deviation value of the abnormal area, so that the selected adjustment method is more targeted at the defect characteristics, and the imprint pressure of each abnormal area is increased and adjusted according to the bias instability threshold, and the imprint pressure of the abnormal area can be adaptively adjusted according to the characteristics of different abnormal areas, and can adapt to complex process requirements, thereby improving process efficiency and enhancing process stability.

[0018] Furthermore, in the present invention, the regularity of defects is effectively reflected through the variation regularity, and different area division methods are adaptively selected based on the variation regularity, so that the selection of area division method is more in line with the actual application scenario, ensuring that the similarity of defect characteristics in sub-regions is high, and then the sub-region category is determined according to the correlation influence coefficient and the feature representativeness, which can effectively reflect the defect influence degree of the sub-region, and is conducive to targeted optimization of the etching process according to the sub-region characteristics, thereby improving the process effect.

[0019] Furthermore, in the present invention, the roughness of the edge of the abnormal area in the defect etching image is effectively reflected by the lateral domain variation value, and then different etching treatment methods are adaptively selected according to the lateral domain variation value, so that the selection of etching treatment method is more in line with the actual application scenario, and the etching gas proportion is reduced and adjusted according to the sub-region comparison coefficient. The sub-region comparison coefficient is used to effectively reflect the comparison degree of the characteristics of different categories of sub-regions, which can avoid over-etching or under-etching. The abnormal situation of the characteristic sub-region is effectively reflected by the residual turbulence value of the characteristic sub-region, and the etching time is increased and adjusted according to the residual turbulence value of the characteristic sub-region to ensure that the residual layer can be completely removed, thereby improving the quality and efficiency of the etching process. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the collaborative optimization method based on nanoimprinting and etching process of the present invention; Figure 2 A flow chart of the present invention for determining an optimized analysis method according to an image state; Figure 3 It is a flow chart of determining the imprint processing method according to the abnormal area coverage index and the abnormal similarity coefficient of the present invention; Figure 4 The present invention is a flow chart for determining an etching processing method according to a lateral domain change value. DETAILED DESCRIPTION

[0021] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0022] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0023] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0024] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0025] See also Figures 1 to 4 As shown, the present invention provides a collaborative optimization method based on nanoimprinting and etching process, comprising: Determine the image state according to the etching completeness and the regional heterogeneity coefficient of the defect etching image, and determine the optimization analysis method according to the image state, and the optimization analysis method is imprint optimization analysis or etching optimization analysis; In the imprint optimization analysis, the imprint processing method is determined according to the abnormal area coverage index and the abnormal similarity coefficient. The imprint processing method is to adjust the imprint glue spin coating time according to the abnormal evaluation index, or to determine the adjustment method according to the uniformity of the abnormal area and the misalignment deviation value; The adjustment method is to optimize the detection of the viscosity of the imprint glue, or to adjust the imprint pressure of each abnormal area according to the bias instability threshold; In the etching optimization analysis, the region division method is determined based on the variation regularity to obtain several sub-regions, the sub-region category is determined according to the correlation influence coefficient and the characteristic representativeness, and the etching treatment method is determined according to the lateral domain variation value. The etching treatment method is to adjust the etching gas ratio according to the sub-region comparison coefficient, or to adjust the etching time according to the residual turbulence value of the characteristic sub-region; The area division method is to divide according to the comprehensive evaluation value or the combination of associated domains, and the defect etching image is an etching image with an estimated defect coefficient greater than a preset estimated defect coefficient.

[0026] The application scenario of the present invention is to optimize the nanoimprinting and etching process according to the etching image, and the etching image is a SEM image of the etching substrate. The specific steps of preparing the etching substrate in the present invention include: 1. Using deionized water, acetone and isopropanol to clean the silicon wafer in sequence to remove surface pollutants; 2. Spin-coating the imprinting glue evenly on the surface of the substrate, and heating it on a hot plate at 90°C for 1 minute to remove the solvent, and preliminarily solidifying the imprinting glue; 3. After performing two air imprints on the template using the imprinting glue, the template with the nanowire array pattern is pressed into the imprinting glue, a pressure of 6 bar is applied, and the template is separated after the imprinting glue is completely solidified by ultraviolet light irradiation for 5 minutes; 4. Etching the substrate using a fluorine-based gas to obtain an etched substrate; this is content that has been mastered by those skilled in the art and will not be repeated here.

[0027] In the present invention, several historical records are correspondingly provided, and any one of the historical records records the estimated defect coefficient, etching completeness, regional heterogeneity coefficient, abnormal area coverage index, abnormal similarity coefficient, and dislocation deviation value, etc. in the historical process of optimizing the nanoimprint and etching process according to the etching image at least once, and each historical record corresponds to a qualified mark, which records whether the optimization of the nanoimprint and etching process according to the etching image meets the user's needs. The qualified mark can be recorded manually. It can be understood that the user can determine whether the optimization process of the nanoimprint and etching process according to the etching image meets the needs according to the self-set indicators. The self-set indicators can be but not limited to the misjudgment rate, which will not be repeated here. Among them, the misjudgment rate is the number of etching images whose estimated defect coefficient is greater than the preset estimated defect coefficient after the optimization analysis method is used for the nanoimprint and etching process; Estimated defect coefficient = regional heterogeneity coefficient - etching completeness. The value of the preset estimated defect coefficient can be determined by the user according to the actual application scenario. The greater the user's demand for improving the wafer etching accuracy, the smaller the value of the preset estimated defect coefficient. A preset estimated defect coefficient value is provided to detect the historical records of the user optimizing the nanoimprint and etching processes according to the wafer etching image, and the minimum value of the estimated defect coefficient corresponding to the historical records that can meet the user's needs is recorded as the preset estimated defect coefficient; Specifically, if the image state is that the etching completeness is less than the preset etching completeness or the regional heterogeneity coefficient is less than the preset regional heterogeneity coefficient, the optimization analysis method is imprint optimization analysis.

[0028] The image state includes a first image state and a second image state. The first image state is that the etching completeness is less than a preset etching completeness or the regional heterogeneity coefficient is less than a preset regional heterogeneity coefficient. The second image state is that the etching completeness is greater than or equal to the preset etching completeness and the regional heterogeneity coefficient is greater than or equal to the preset regional heterogeneity coefficient. The template with the nanowire array pattern includes but is not limited to a silicon template and a quartz template. The template with the nanowire array pattern used in the present invention is recorded as a target template, and the SEM image of the target template is recorded as a template image. The template image and the etching image are aligned by image processing software to ensure that the two are consistent in size and position. The image processing software includes but is not limited to ImageJ, Photoshop and Matlab. This is easy to understand for those skilled in the art and will not be described in detail. Etching completeness = the number of identical pixels in the etching image / the total number of pixels in the etching image. Pixels located at the same position and having the same pixel value in the template image and the etching image are recorded as identical pixels; The etching image is divided into a number of rectangular areas with the same area and shape, and the number of rectangular areas is positively correlated with the area of ​​the etching image; regional heterogeneity coefficient = average value of sub-heterogeneity coefficients corresponding to each rectangular area / standard deviation of sub-heterogeneity coefficients corresponding to each rectangular area, and sub-heterogeneity coefficient corresponding to a single rectangular area = standard deviation of pixel values ​​corresponding to each pixel point in the etching area in the rectangular area / maximum value of pixel values ​​corresponding to each pixel point in the etching area in the rectangular area; An area in the etching image that is located at the same position as a connected area in the template image whose pixel value is less than a preset pixel value is recorded as an etching area, and an area in the etching image that is located at the same position as a connected area in the template image whose pixel value is greater than or equal to the preset pixel value is recorded as a non-etching area. The value of the preset pixel value can be determined by the user according to the actual application scenario. The greater the user's demand for improving the accuracy of etching area determination, the smaller the value of the preset pixel value is. A value of the preset pixel value is provided, and the average value of the pixel values ​​corresponding to each pixel point in the template image located at the same position as the etching area in the historical record that can meet the user's needs is recorded as the preset pixel value; The pixel values ​​corresponding to each pixel in a single connected domain are the same, and the adjacent pixel values ​​corresponding to the pixel value are different from the pixel values ​​corresponding to the pixel value. The adjacent pixel points corresponding to a single pixel value are the pixel points adjacent to the connected region. The values ​​of the preset etching completeness and the preset regional heterogeneity coefficient can be determined by the user according to the actual application scenario. The larger the values ​​of the preset etching completeness and the preset regional heterogeneity coefficient are, the greater the user's need for imprint optimization analysis is. The historical records of imprint optimization analysis are detected, and the average value of the etching completeness corresponding to the historical records that can meet the user's needs is recorded as the preset etching completeness, and the average value of the regional heterogeneity coefficient corresponding to the historical records that can meet the user's needs is recorded as the preset regional heterogeneity coefficient.

[0029] Specifically, if the image status is that the etching completeness is greater than or equal to the preset etching completeness and the regional heterogeneity coefficient is greater than or equal to the preset regional heterogeneity coefficient, the optimization analysis method is etching optimization analysis.

[0030] Specifically, if the abnormal area coverage index is greater than or equal to the preset abnormal area coverage index or the abnormal similarity coefficient is less than the preset abnormal similarity coefficient, the imprint processing method is to increase the imprint glue spin coating time according to the abnormal evaluation index; The increase value of the imprint glue spin coating time is positively correlated with the abnormal evaluation index.

[0031] Among them, the pixels located at the same position in the template image and the etched image and with different pixel values ​​are recorded as abnormal pixels; all pixels in a single abnormal area are abnormal pixels, and the pixels adjacent to the abnormal area are not abnormal pixels; The abnormal area coverage index is the average value of the sub-coverage coefficients corresponding to each abnormal area. The sub-coverage coefficient corresponding to a single abnormal area is the number of etching areas passed by the abnormal area. The abnormal similarity coefficient = 1 / (the standard deviation of the area corresponding to each abnormal area + the standard deviation of the perimeter corresponding to each abnormal area); The values ​​of the preset abnormal area coverage index and the preset abnormality similarity coefficient can be determined by the user according to the actual application scenario. The smaller the value of the preset abnormal area coverage index and the larger the value of the preset abnormality similarity coefficient, the greater the user's demand for increasing the imprint glue spin coating time according to the abnormality evaluation index. A value of the preset abnormal area coverage index and the preset abnormality similarity coefficient is provided, and the historical records of the user increasing the imprint glue spin coating time according to the abnormality evaluation index are detected. The average value of the abnormal area coverage index corresponding to the historical records that can meet the user's needs is recorded as the preset abnormal area coverage index, and the average value of the abnormality similarity coefficient corresponding to the historical records that can meet the user's needs is recorded as the preset abnormality similarity coefficient; Abnormal evaluation index = abnormal area coverage index + abnormal similarity coefficient; the embossed glue spin coating time is the spin coating time required to evenly coat the embossed glue on the surface of the substrate. It can be understood that in the present invention, the substrate is rotated at high speed and the centrifugal force is used to evenly spread the embossed glue on the surface of the substrate to form a thin film with uniform thickness. The initial embossed glue spin coating time in the present invention is 30s.

[0032] Specifically, if the abnormal area coverage index is less than the preset abnormal area coverage index and the abnormal similarity coefficient is greater than or equal to the preset abnormal similarity coefficient, the imprint processing method is to determine the adjustment method according to the abnormal area uniformity and the misalignment deviation value; If the uniformity of the abnormal area is greater than or equal to the preset uniformity of the abnormal area and the displacement deviation value is greater than or equal to the preset displacement deviation value, the adjustment method is to optimize the detection of the viscosity of the embossing glue; If the uniformity of the abnormal area is less than the preset uniformity of the abnormal area or the displacement deviation value is less than the preset displacement deviation value, the adjustment method is to increase the imprint pressure of each abnormal area according to the bias instability threshold.

[0033] Among them, the uniformity of abnormal area = the average value of the variation reference value corresponding to each abnormal area / the average value of the area corresponding to each abnormal area; the variation reference value corresponding to a single abnormal area = |the area of ​​the area where the abnormal area and the etching area overlap in each analysis area - the total area of ​​the etching area in each analysis area| / the total area of ​​each analysis area, and the rectangular area with the abnormal area is recorded as the analysis area; The displacement deviation value = |abnormal area displacement coefficient - etching displacement value|, the abnormal area displacement coefficient is the average value of the first distance reference values ​​corresponding to each abnormal area, and the first distance reference value corresponding to a single abnormal area is the minimum value of the shortest distances from the abnormal area to each abnormal area other than the abnormal area; the etching displacement value is the average value of the second distance reference values ​​corresponding to each etching area, and the second distance reference value corresponding to a single etching area is the minimum value of the shortest distances from the etching area to each etching area other than the etching area; The values ​​of the preset abnormal area uniformity and the preset misalignment deviation value can be determined by the user according to the actual application scenario. The smaller the values ​​of the preset abnormal area uniformity and the preset misalignment deviation value are, the greater the user's demand for optimizing the detection of the embossing adhesive viscosity is. A value of the preset abnormal area uniformity and the preset misalignment deviation value is provided, and the historical records of optimizing the detection of the embossing adhesive viscosity are detected. The average value of the abnormal area uniformity corresponding to the historical records that can meet the user's needs is recorded as the preset abnormal area uniformity, and the average value of the misalignment deviation values ​​corresponding to the historical records that can meet the user's needs is recorded as the preset misalignment deviation value. When the imprint pressure of each abnormal area is increased and adjusted according to the bias instability threshold, the imprint pressure corresponding to a single abnormal area is positively correlated with the bias instability threshold corresponding to the abnormal area; The bias instability threshold corresponding to a single abnormal area = distance deviation value × pixel deviation value. For a single abnormal area, the abnormal area is recorded as a target abnormal area, the center of the circumscribed circle of the etched image is recorded as a reference point, and the shortest distance from the center of the circumscribed circle of the target abnormal area to the reference point is recorded as the distance deviation value. The pixel deviation value = |(the average value of the pixel values ​​corresponding to each pixel point in the target abnormal area × the standard deviation of the pixel values ​​corresponding to each pixel point in the target abnormal area) - (the average value of the pixel values ​​corresponding to each pixel point in the comparison area × the standard deviation of the pixel values ​​corresponding to each pixel point in the comparison area) |, the comparison area is an area in the template image that is located at the same position as the target abnormal area, and the printing pressure is the pressure applied when the template with a nanowire array pattern is pressed into the printing glue. The present invention adopts a piston device in a closed chamber to generate uniform pressure by gas compression, and controls the piston movement distance by partitioning to achieve pressure regulation in different areas. This is content that is easy for technicians in this field to understand and will not be described in detail.

[0034] Specifically, the optimization test for the viscosity of the embossing adhesive includes: Detect the viscosity of the embossing glue; If the viscosity of the embossing glue is greater than or equal to the preset viscosity of the embossing glue, the viscosity of the embossing glue is reduced and adjusted; If the embossing adhesive viscosity is less than the preset embossing adhesive viscosity, the number of blank prints is increased and adjusted.

[0035] The viscosity of the stamping glue is measured by a viscometer, and the value of the preset stamping glue viscosity can be determined by the user according to the actual application scenario. The larger the value of the preset stamping glue viscosity is, the greater the user's demand for increasing the number of blank prints is. The historical records of increasing the number of blank prints are detected, and the maximum value of the stamping glue viscosity corresponding to the historical records that can meet the user's needs is recorded as the preset stamping glue viscosity; When the viscosity of the embossing adhesive is adjusted to decrease, the decrease value of the viscosity of the embossing adhesive is positively correlated with the instability threshold, and the instability threshold is the average value of the pixel deviation values ​​corresponding to each abnormal area. When the viscosity of the embossing adhesive is adjusted to decrease, the viscosity of the embossing adhesive can be reduced by heating or adding a diluent. The specific method is not limited as long as it can meet the needs of users; When the number of blank printings is increased and adjusted, the increase value of the viscosity of the embossing glue is positively correlated with the instability threshold value, and the number of blank printings is the number of blank printings performed on the template using the embossing glue.

[0036] Specifically, the regional division method is determined based on the mutation regularity, including: If the mutation regularity is greater than or equal to the preset mutation regularity, the area division method is to divide according to the comprehensive evaluation value; If the mutation regularity is less than the preset mutation regularity, the region division method is to divide according to the combination of associated domains.

[0037] Among them, the regularity of anomaly variation = the dislocation coefficient of the abnormal area / (the standard deviation of the sub-variation coefficients corresponding to each abnormal area + the standard deviation of the overlap coefficients corresponding to each abnormal area), the sub-variation coefficient corresponding to a single abnormal area = the area of ​​the abnormal area / the domain variation coefficient corresponding to the abnormal area, the overlap coefficient corresponding to a single abnormal area is the number of etched areas overlapping with the abnormal area; the domain variation coefficient corresponding to a single abnormal area is the standard deviation of the domain variation distance reference values ​​corresponding to the edge pixels corresponding to the abnormal area, the edge pixels are the pixels located at the boundary of the abnormal area, and the domain variation distance reference value corresponding to a single edge pixel is the minimum value of the shortest distance from the edge pixel to each etched area; The value of the preset anomaly regularity can be determined by the user according to the actual application scenario. The larger the value of the preset anomaly regularity is, the greater the user's need for division according to the combination of associated domains is. The historical records of the user's division according to the combination of associated domains are detected, and the average value of the anomaly regularity corresponding to the historical records that can meet the user's needs is recorded as the preset anomaly regularity; When dividing according to the comprehensive evaluation value, the defect etching image is divided into n reference areas with the same area and shape. The shape of the reference area is a rectangle, and each reference area is recorded as a sub-area. There is a negative correlation between n and the comprehensive evaluation value. The comprehensive evaluation value = the degree of variation regularity × the average value of the area corresponding to each etching area; When dividing according to the association domain combination, the area corresponding to each association domain combination is recorded as a sub-area, a single association domain combination contains several reference areas, the association similarity corresponding to any two reference areas in the single association domain combination is greater than the preset association similarity, and the adjacent reference value corresponding to any reference area in the single association domain combination is greater than or equal to 1; The confirmation method of the adjacent reference value is that, for a single reference area in a single association domain combination, the reference area is recorded as a target reference area, and other reference areas in the association domain combination except the target reference area are recorded as analysis reference areas, and the adjacent reference value corresponding to the target reference area is the number of analysis reference areas adjacent to the target reference area; The confirmation method of the correlation similarity is that, for any two reference areas, the correlation similarity = 1 / the absolute value of the difference between the regional reference values ​​corresponding to the two reference areas, and the regional reference value corresponding to a single reference area = the average value of the pixel values ​​corresponding to the pixel points located in the abnormal area of ​​the reference area / the standard deviation of the pixel values ​​corresponding to the pixel points located in the abnormal area of ​​the reference area; The value of the preset association similarity can be determined by the user according to the actual application scenario. The greater the user's demand for improving the preparation effect, the greater the value of the preset association similarity. A value of the preset association similarity is provided, and the historical records divided according to the combination of association domains are detected. The average value of the association similarity corresponding to the historical records that can meet the user's needs is recorded as the preset association similarity.

[0038] Specifically, the sub-region categories are determined based on the correlation influence coefficient and the characteristic representativeness, including: A feature sub-region whose correlation influence coefficient is less than a preset correlation influence coefficient and whose feature representativeness is greater than or equal to a preset feature representativeness; A non-characteristic sub-region whose correlation influence coefficient is greater than or equal to a preset correlation influence coefficient or whose characteristic representativeness is less than a preset characteristic representativeness.

[0039] The confirmation method of the correlation influence coefficient is as follows: for a single sub-region, the sub-region is recorded as the target sub-region, and the sub-region adjacent to the target sub-region is recorded as the reference sub-region. The correlation influence coefficient corresponding to the target sub-region = (the maximum value of the sub-influence coefficients corresponding to each reference sub-region) / (the standard deviation of the sub-influence coefficients corresponding to each reference sub-region), and the sub-influence coefficient corresponding to a single reference sub-region = (the feature representativeness corresponding to the reference sub-region - the feature representativeness corresponding to the target sub-region); The feature representativeness corresponding to a single sub-region = the area of ​​the abnormal region in the sub-region × the average value of the pixel values ​​corresponding to the abnormal region in the sub-region; The values ​​of the preset association influence coefficient and the preset feature representativeness can be determined by the user according to the actual application scenario. The larger the value of the preset association influence coefficient and the smaller the value of the preset feature representativeness, the greater the user's need to determine the sub-region as a feature sub-region. A value of a preset association influence coefficient and a preset feature representativeness is provided, and the average value of the association influence coefficient corresponding to each feature sub-region in the historical records that can meet the user's needs is recorded as the preset association influence coefficient, and the average value of the feature representativeness corresponding to each feature sub-region in the historical records that can meet the user's needs is recorded as the preset feature representativeness.

[0040] Specifically, if the lateral domain variation value is greater than or equal to the preset lateral domain variation value, the etching processing method is to reduce and adjust the etching gas ratio according to the sub-region comparison coefficient; The reduction value of the etching gas ratio is positively correlated with the sub-region comparison coefficient.

[0041] Among them, the lateral domain variation value is the average value of the subdomain variation coefficient corresponding to each etching area. The confirmation method of the subdomain variation coefficient is that, for a single etching area, the etching area is recorded as the target etching area, the pixel point located at the center of the circumscribed circle of the target etching area is recorded as the target pixel point, each pixel point located at the boundary of the target etching area is recorded as a reference pixel point, and the line connecting each reference pixel point and the target pixel point is recorded as a reference line segment. The subdomain variation coefficient corresponding to the target etching area is the standard deviation of the line fluctuation value corresponding to each reference line segment in the target etching area. The line fluctuation value corresponding to a single reference line segment = the standard deviation of the pixel value corresponding to each pixel point in the reference line segment / the pixel gradient coefficient corresponding to the reference line segment. The pixel gradient coefficient corresponding to a single reference line segment = the pixel value corresponding to the reference pixel point in the reference line segment + the pixel value corresponding to the target pixel point in the reference line segment - 2 × the average value of the pixel value corresponding to each pixel point in the reference line segment; The value of the preset lateral domain variation value can be determined by the user according to the actual application scenario. The smaller the value of the preset lateral domain variation value is, the greater the user's need to reduce the etching gas ratio according to the sub-region comparison coefficient. A value of the preset lateral domain variation value is provided, and the historical records of the user reducing the etching gas ratio according to the sub-region comparison coefficient are detected, and the average value of the lateral domain variation values ​​corresponding to the historical records that can meet the user's needs is recorded as the preset lateral domain variation value; Sub-region comparison coefficient = average value of characteristic representativeness corresponding to each characteristic sub-region - average value of characteristic representativeness corresponding to each non-characteristic sub-region, etching gas ratio = CHF 3 Volume / CF 4The volume of the initial etching gas ratio in the present invention is 0.3.

[0042] Specifically, if the lateral domain variation value is less than the preset lateral domain variation value, the etching processing method is to increase the etching time according to the residual turbulence value of the characteristic sub-region; The increase in the etching time is positively correlated with the residual turbulence value of the characteristic sub-region.

[0043] The residual turbulence value of the characteristic sub-region is the average value of the characteristic representativeness corresponding to each characteristic sub-region, and the etching time is the duration of the substrate being acted upon by the gas during the etching process. In the present invention, the initial etching time is 5 minutes.

[0044] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0045] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A collaborative optimization method based on nanoimprinting and etching process, characterized in that: include: Determine the image state according to the etching completeness and the regional heterogeneity coefficient of the defect etching image, and determine the optimization analysis method according to the image state, and the optimization analysis method is imprint optimization analysis or etching optimization analysis; In the imprint optimization analysis, the imprint processing method is determined according to the abnormal area coverage index and the abnormal similarity coefficient. The imprint processing method is to adjust the imprint glue spin coating time according to the abnormal evaluation index, or to determine the adjustment method according to the uniformity of the abnormal area and the misalignment deviation value; The adjustment method is to optimize the detection of the viscosity of the imprint glue, or to adjust the imprint pressure of each abnormal area according to the bias instability threshold; In the etching optimization analysis, the region division method is determined based on the variation regularity to obtain several sub-regions, the sub-region category is determined according to the correlation influence coefficient and the characteristic representativeness, and the etching treatment method is determined according to the lateral domain variation value. The etching treatment method is to adjust the etching gas ratio according to the sub-region comparison coefficient, or to adjust the etching time according to the residual turbulence value of the characteristic sub-region; The area division method is to divide according to the comprehensive evaluation value or the combination of associated domains, and the defect etching image is an etching image with an estimated defect coefficient greater than a preset estimated defect coefficient.

2. The collaborative optimization method based on nanoimprinting and etching process according to claim 1, characterized in that: If the image status is that the etching completeness is less than the preset etching completeness or the regional heterogeneity coefficient is less than the preset regional heterogeneity coefficient, the optimization analysis method is the imprint optimization analysis.

3. The collaborative optimization method based on nanoimprinting and etching process according to claim 2, characterized in that: If the image status is that the etching completeness is greater than or equal to the preset etching completeness and the regional heterogeneity coefficient is greater than or equal to the preset regional heterogeneity coefficient, the optimization analysis method is etching optimization analysis.

4. The collaborative optimization method based on nanoimprinting and etching process according to claim 2, characterized in that: If the abnormal area coverage index is greater than or equal to the preset abnormal area coverage index or the abnormal similarity coefficient is less than the preset abnormal similarity coefficient, the imprint processing method is to increase the imprint glue spin coating time according to the abnormal evaluation index; The increase value of the imprint glue spin coating time is positively correlated with the abnormal evaluation index.

5. The collaborative optimization method based on nanoimprinting and etching process according to claim 4, characterized in that: If the abnormal area coverage index is less than the preset abnormal area coverage index and the abnormal similarity coefficient is greater than or equal to the preset abnormal similarity coefficient, the imprint processing method is to determine the adjustment method according to the uniformity of the abnormal area and the misalignment deviation value; If the uniformity of the abnormal area is greater than or equal to the preset uniformity of the abnormal area and the displacement deviation value is greater than or equal to the preset displacement deviation value, the adjustment method is to optimize the detection of the viscosity of the embossing glue; If the uniformity of the abnormal area is less than the preset uniformity of the abnormal area or the displacement deviation value is less than the preset displacement deviation value, the adjustment method is to increase the imprint pressure of each abnormal area according to the bias instability threshold.

6. The collaborative optimization method based on nanoimprinting and etching process according to claim 5, characterized in that: Optimized testing for embossing adhesive viscosity, including: Detect the viscosity of the embossing glue; If the viscosity of the embossing glue is greater than or equal to the preset viscosity of the embossing glue, the viscosity of the embossing glue is reduced and adjusted; If the embossing adhesive viscosity is less than the preset embossing adhesive viscosity, the number of blank prints is increased and adjusted.

7. The collaborative optimization method based on nanoimprinting and etching process according to claim 3, characterized in that: The regional division method is determined based on the mutation regularity, including: If the mutation regularity is greater than or equal to the preset mutation regularity, the area division method is to divide according to the comprehensive evaluation value; If the mutation regularity is less than the preset mutation regularity, the region division method is to divide according to the combination of associated domains.

8. The collaborative optimization method based on nanoimprinting and etching process according to claim 7, characterized in that: The sub-region category is determined based on the correlation influence coefficient and the characteristic representativeness, including: A feature sub-region whose correlation influence coefficient is less than a preset correlation influence coefficient and whose feature representativeness is greater than or equal to a preset feature representativeness; A non-characteristic sub-region whose correlation influence coefficient is greater than or equal to a preset correlation influence coefficient or whose characteristic representativeness is less than a preset characteristic representativeness.

9. The collaborative optimization method based on nanoimprinting and etching process according to claim 8, characterized in that: If the lateral domain variation value is greater than or equal to the preset lateral domain variation value, the etching processing method is to reduce and adjust the etching gas ratio according to the sub-region comparison coefficient; The reduction value of the etching gas ratio is positively correlated with the sub-region comparison coefficient.

10. The collaborative optimization method based on nanoimprinting and etching process according to claim 9, characterized in that: If the lateral domain variation value is less than the preset lateral domain variation value, the etching processing method is to increase and adjust the etching time according to the residual turbulence value of the characteristic sub-region; The increase in the etching time is positively correlated with the residual turbulence value of the characteristic sub-region.

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