CN114092490A - Method for obtaining diffraction near field distribution - Google Patents
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Abstract
The invention discloses a method for acquiring diffraction near field distribution, which belongs to the technical field of photoetching, and not only can improve the accuracy of a diffraction near field distribution calculation result, but also can reduce the calculation time. The method comprises the following steps: determining a target mask pattern of diffraction near-field distribution to be obtained, and dividing the target mask pattern according to pattern types to obtain a target division pattern forming the target mask pattern; determining a target nuclear matrix corresponding to the target segmentation graph, and obtaining a target area diffraction near-field distribution corresponding to the target segmentation graph according to the target segmentation graph and the target nuclear matrix; and splicing the diffraction near field distribution of all the target areas to obtain the diffraction near field distribution of the target mask pattern.
Description
Technical Field
The invention relates to the technical field of photoetching, in particular to a method for acquiring diffraction near-field distribution.
Background
As shown in fig. 1, photolithography is a very critical step in the fabrication of very large scale integrated circuits, and is used to transfer the pattern of circuit devices on a mask to a photoresist coated on a silicon wafer by using an optical projection method.
With the continuous development of integrated circuit technology, the size of circuit patterns on a chip is gradually reduced, the performance of the chip is improved while the density of transistors is improved, but great challenges are brought to the manufacturing of the chip. At present, the minimum distance between patterns is only tens of nanometers on advanced chips, and the mainstream lithography technology in the industry uses a light source wavelength of 193 nanometers (in the case of ArF deep ultraviolet lithography). At such dimensions, the diffraction effect of light can be very severe, and therefore, resolution enhancement techniques such as Optical Proximity Correction (OPC), Source Mask Optimization (SMO), etc. can be used to improve the quality of the lithographic pattern. Among these techniques, calculating the diffracted near-field distribution of the target mask pattern is a very important step.
Existing methods for calculating the diffraction near-field distribution can be classified into kirchhoff approximation, strict calculation of electromagnetic field, and fast calculation. Kirchhoff approximation uses a scalar model and assumes that the mask satisfies kirchhoff boundary conditions: in the light-transmitting area of the mask, the field distribution and the partial derivative thereof are completely the same as those in the absence of the mask; in the shaded areas of the mask, the field distribution and its partial derivative are completely zero. In this way, the near field distribution of the mask can be expressed as a two-dimensional amplitude transmittance function of the mask pattern. It can be seen that the kirchhoff approximation is simple and convenient, but the influence of the three-dimensional effect of the mask and the boundary thereof on the optical field is not considered. Therefore, the accuracy of the near-field distribution results obtained using kirchhoff approximation is low. Correspondingly, the electromagnetic field rigorous calculation method is used for calculating the diffraction near field of the thick mask by solving Maxwell equations. The near-field results obtained by using the method are theoretically completely accurate, but at the same time, the method has too long calculation time, so that the method cannot be applied to the calculation of large-scale diffraction near-field distribution.
The existing fast calculation method is based on convolution calculation, firstly, an electromagnetic field strict calculation method is used for calculating accurate near-field distribution corresponding to a part of graphs, and the accurate near-field distribution is used as a training set. And calculating to obtain a convolution kernel capable of reflecting the distribution relation between the graph and the corresponding near field by taking the training set as a standard. And finally, performing convolution on the whole graph by using a convolution core, calculating and restoring the mask graph point by point, and finally obtaining the near field distribution corresponding to the target graph. Compared with the accurate result of the strict calculation of the electromagnetic field, the result of the quick calculation method has smaller error, and the calculation speed is obviously improved.
In conclusion, the near-field distribution corresponding to the mask pattern can be quickly obtained by using the kirchhoff approximation method. However, as the chip manufacturing process decreases year by year, the pitch of the pattern to be etched is already smaller than the wavelength of the light source, which results in that kirchhoff approximation no longer holds, and the near-field distribution obtained by using the kirchhoff approximation has low accuracy. Accurate near-field distributions can be calculated by using an electromagnetic field rigorous calculation method, but the calculation amount is too large, so that the simulation of large-scale near-field distributions consumes intolerable calculation time. The existing fast calculation method is based on convolution calculation, firstly, an electromagnetic field strict calculation method is used for calculating accurate near-field distribution corresponding to a part of graphs, and the accurate near-field distribution is used as a training set. And calculating to obtain a convolution kernel capable of reflecting the distribution relation between the graph and the corresponding near field by taking the training set as a standard. And finally, carrying out convolution on the whole graph by using a convolution core to obtain the near field distribution corresponding to the target graph. Compared with the accurate result of strict calculation of the electromagnetic field, the result of the rapid calculation method has smaller error, and meanwhile, the calculation speed is greatly improved, so that the near-field distribution of a large-scale graph can be simulated with higher accuracy. On the one hand, however, the calculation accuracy and speed using the fast calculation method have room for further improvement; on the other hand, in the case of DUV lithography, since the pitch of the prepared pattern is much smaller than the wavelength of the light source, the diffraction effect of the near field distribution is significant, and a "ripple-like" field intensity distribution appears inside the pattern. As shown in fig. 2(a) and 2(b), when the image pixels corresponding to the convolution kernels are completely within a certain pattern in the calculation using the convolution method, the calculation result is not dependent on the positions thereof, and uniform intensity is obtained inside the pattern, so that the "ripple-like" field intensity distribution due to the diffraction effect cannot be restored, and it can be seen that the fluctuation of the field intensity distribution inside the pattern is not well restored.
Disclosure of Invention
In view of the foregoing analysis, the embodiments of the present invention are directed to providing a method for obtaining a diffracted near-field distribution, which can not only improve the accuracy of a result of calculating the diffracted near-field distribution, but also reduce the calculation time.
The invention discloses a method for acquiring diffraction near field distribution, which comprises the following steps:
determining a target mask pattern of diffraction near-field distribution to be obtained, and dividing the target mask pattern according to pattern types to obtain a target division pattern forming the target mask pattern;
determining a target nuclear matrix corresponding to the target segmentation graph, and obtaining a target area diffraction near-field distribution corresponding to the target segmentation graph according to the target segmentation graph and the target nuclear matrix; each row of elements in the target kernel matrix corresponds to a pixel position of the rearranged target segmentation graph and represents a weight of the near-field intensity corresponding to the pixel position;
and splicing the diffraction near field distribution of all the target areas to obtain the diffraction near field distribution of the target mask pattern.
Further, the pattern type is a manhattan pattern and includes at least one of a convex corner portion, a concave corner portion, a vertical side portion, and a horizontal side portion; correspondingly, the dividing the target mask pattern according to the pattern type to obtain a target division pattern forming the target mask pattern includes:
and dividing the target mask pattern according to at least one Manhattan pattern, wherein the obtained target division pattern comprises at least one of the convex corner part, the concave corner part, the vertical side part and the horizontal side part.
Further, the obtaining of the target area diffraction near-field distribution corresponding to the target segmentation graph according to the target segmentation graph and the target kernel matrix includes:
performing matrixing processing on the target segmentation graph to obtain a target segmentation graph matrix; the number of rows of the target segmentation graph matrix is 1, the number of columns is the total number of pixels corresponding to the target segmentation graph, and elements of the target segmentation graph matrix represent the positions of the pixels corresponding to the target segmentation graph, which are light-transmitting or light-shading;
calculating the diffraction near-field distribution of the target area corresponding to the target segmentation graph according to the following formula:
X1·A 1＝Y1
wherein X1 is the target segmentation pattern matrix, A1 is the target kernel matrix, and Y1 is the target region diffraction near-field distribution.
Further, before the step of determining a target mask pattern for which a diffracted near-field distribution is to be acquired, the method of acquiring a diffracted near-field distribution further includes:
determining whether a kernel matrix exists; each column of elements in the kernel matrix corresponds to a pixel position of the training set graph after rearrangement, and represents the weight of the near-field intensity corresponding to the pixel position;
and if the nuclear matrix exists, executing the target mask pattern for determining the diffraction near field distribution to be acquired, and the subsequent steps.
Further, the method for acquiring the diffraction near-field distribution further comprises the following steps:
if the kernel matrix does not exist, acquiring a training set graph, and calculating the overall reference diffraction near-field distribution of the training set graph;
dividing the training set pattern and the whole reference diffraction near-field distribution corresponding to the training set pattern to obtain a divided mask pattern and a reference diffraction near-field distribution of a region corresponding to the position of the mask pattern;
and calculating the nuclear matrix according to the mask pattern and the reference diffraction near field distribution.
Further, the calculating the kernel matrix according to the mask pattern and the reference diffracted near-field distribution includes:
performing matrixing processing on the mask graph to obtain a mask graph matrix; the number of lines of the mask pattern matrix is 1, the number of columns is the total number of pixels corresponding to the mask pattern, and elements of the mask pattern matrix represent that the pixel positions corresponding to the mask pattern are light-transmitting or light-shielding;
performing matrixing processing on the reference diffraction near-field distribution to obtain a reference diffraction near-field distribution matrix; the number of rows of the reference diffraction near-field distribution matrix is 1, the number of columns is the total number of pixels in an area corresponding to the position of the mask pattern, and elements of the reference diffraction near-field distribution matrix represent the near-field intensity of the pixel position;
the kernel matrix is calculated according to the following formula:
A＝(XTX)-1XTY
wherein A is the kernel matrix, X is the mask pattern matrix, XTA transposed matrix of X, (X)TX)-1Represents XTAnd the inverse of X and Y are the reference diffraction near field distribution matrix.
Further, obtaining the global reference diffraction near-field distribution comprises:
and calculating the whole reference diffraction near-field distribution according to an electromagnetic field strict calculation method.
Further, the kernel matrix is calculated according to the following formula:
A＝(XTX)-1XTY
wherein A is the kernel matrix, X is the mask pattern matrix, XTA transposed matrix of X, (X)TX)-1Represents XTThe inverse of X and Y are the reference diffraction near field distribution matrix, which comprises:
and calculating to obtain the kernel matrix by using a least square method according to the formula.
Further, the determining a target kernel matrix corresponding to the target segmentation graph includes:
and selecting a target core matrix corresponding to the target segmentation graph from the core matrices.
Further, the method for acquiring the diffraction near-field distribution further comprises the following steps:
if the mask pattern is a convex angle part or a concave angle part, carrying out external minimum square processing on the convex angle part or the concave angle part;
the image other than the convex corner portion or the concave corner portion in the smallest square is subjected to the marking process, and the marked image is not calculated.
Compared with the prior art, the invention can realize at least one of the following beneficial effects:
the method for acquiring the diffraction near field distribution provided by the embodiment of the invention determines a target mask pattern of the diffraction near field distribution to be acquired, and divides the target mask pattern according to the pattern type to obtain a target division pattern forming the target mask pattern; determining a target nuclear matrix corresponding to the target segmentation graph, and obtaining a target area diffraction near-field distribution corresponding to the target segmentation graph according to the target segmentation graph and the target nuclear matrix; and splicing the diffraction near field distribution of all the target areas to obtain the diffraction near field distribution of the target mask pattern. Not only can the accuracy of the diffraction near-field distribution calculation result be improved, but also the calculation time can be reduced.
In the invention, the technical schemes can be combined with each other to realize more preferable combination schemes. Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
FIG. 1 is a schematic illustration of a prior art lithography model;
FIG. 2(a) is a schematic illustration of the "moire effect" of the near field distribution due to diffraction;
FIG. 2(b) is a diagram illustrating the results of calculations using a prior art convolution method;
FIG. 3 is a schematic flow chart of a method for obtaining a diffraction near field distribution according to an embodiment of the present invention;
FIG. 4 is a schematic illustration of a convex portion and a concave portion in accordance with an embodiment of the present invention;
FIG. 5 is a schematic flow chart of a method for obtaining a diffractive near field distribution according to another embodiment of the present invention;
FIG. 6(a) is a schematic representation of the exact results obtained using electromagnetic field stringency calculations in an embodiment of the present invention;
FIG. 6(b) is a diagram illustrating simulation results obtained by using a conventional convolution method according to an embodiment of the present invention;
fig. 6(c) is a schematic diagram of simulation results obtained by using the method of the embodiment of the present invention in the embodiment of the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
A specific embodiment of the present invention discloses a method for obtaining a diffraction near field distribution, and a flowchart is shown in fig. 1, and includes the following steps:
step S1: determining a target mask pattern of the diffraction near-field distribution to be obtained, and dividing the target mask pattern according to the pattern type to obtain a target division pattern forming the target mask pattern.
Step S2: determining a target nuclear matrix corresponding to the target segmentation graph, and obtaining a target area diffraction near-field distribution corresponding to the target segmentation graph according to the target segmentation graph and the target nuclear matrix; each column of elements in the target kernel matrix corresponds to a pixel position of the rearranged target segmentation graph and represents a weight of the near-field intensity corresponding to the pixel position.
Step S3: and splicing the diffraction near field distribution of all the target areas to obtain the diffraction near field distribution of the target mask pattern.
In step S1, the pattern type may be a manhattan pattern and include at least one of a convex portion, a concave portion, a vertical side portion, and a horizontal side portion. As shown in fig. 4, the convex corner portion is marked by the dashed box 2 in fig. 4 and the concave corner portion is marked by the dashed box 1 in fig. 4.
Further, the dividing the target mask pattern according to the pattern type to obtain a target division pattern constituting the target mask pattern includes:
and dividing the target mask pattern according to at least one Manhattan pattern, wherein the obtained target division pattern comprises at least one of the convex corner part, the concave corner part, the vertical side part and the horizontal side part. Referring to the above description, the target mask pattern is divided by the convex corner portion, the concave corner portion, the vertical edge portion, and the horizontal edge portion.
The object of dividing the object mask patterns according to the pattern type is to make the object mask patterns independent from each other and be one of the above-mentioned convex corner portions, concave corner portions, vertical side portions, and horizontal side portions.
In step S2, the divided convex corner portion, concave corner portion, vertical edge portion, and horizontal edge portion have target kernel matrices corresponding to the respective portions. The target kernel matrix may be represented as (a 1)2,b12) Is represented by a12A line representing the total number of pixels corresponding to the target division pattern, b12And the columns represent the total number of pixels in the area corresponding to the mask pattern position, wherein the mask pattern is obtained by dividing the training set pattern, and the area corresponding to the mask pattern position corresponds to the reference diffraction near-field distribution.
The rearranged representation form of the target division pattern is a target division pattern matrix (1, a 1)2) I.e. 1 line a12Column, i-th column element in target kernel matrix and a12The pixel position of the ith column in each column corresponds to the near field intensity weight corresponding to the pixel position of the ith column, the near field intensity weight is obtained by training the nuclear matrix based on the reference diffraction near field distribution, and the reference diffraction near field distribution can be calculated by adopting the most accurate method for calculating the diffraction near field distribution.
The target area diffraction near-field distribution can be obtained by multiplying the target segmentation graph and the target nuclear matrix, namely the target area diffraction near-field distribution can be represented by the target area diffraction near-field distribution matrix and can be represented by the target area diffraction near-field distribution matrixIs (1, b 1)2) Where the elements represent near field intensity values at the pixel locations corresponding to the near field of diffraction of the target region.
Further, obtaining a target area diffraction near-field distribution corresponding to the target segmentation graph according to the target segmentation graph and the target kernel matrix, including:
performing matrixing processing on the target segmentation graph to obtain a target segmentation graph matrix; the number of rows of the target segmentation graph matrix is 1, the number of columns is the total number of pixels corresponding to the target segmentation graph, and elements of the target segmentation graph matrix represent the positions of the pixels corresponding to the target segmentation graph, which are light-transmitting or light-shading; taking the target segment as a square, the total number of pixels corresponding to the target segment can be represented by a1 × a 1.
The object segmentation graph matrix may be represented as (1, a 1)2) Wherein "1" represents line 1 and "a 12"indicates the number of columns as a12The element in the matrix may be 1 or 0, which respectively indicates that the target segmentation pattern is transparent or opaque (i.e. light is blocked) at the corresponding pixel position.
Calculating the diffraction near-field distribution of the target area corresponding to the target segmentation graph according to the following formula:
X1·A1＝Y1
wherein X1 is the target segmentation pattern matrix, A1 is the target kernel matrix, and Y1 is the target region diffraction near-field distribution. Y1 may be represented by a target area diffraction near field distribution matrix, where the elements represent near field intensity values for pixel locations corresponding to the target area diffraction near field. The target kernel matrix is of size (a 1)2,b12) Can be described with reference to the above embodiments.
Further, as shown in fig. 5, before the step of determining the target mask pattern for acquiring the diffracted near-field distribution, the method of acquiring the diffracted near-field distribution further includes:
determining whether a kernel matrix exists; each column of elements in the kernel matrix corresponds to a pixel position of the training set graph after rearrangement, and represents the weight of the near-field intensity corresponding to the pixel position; whether the kernel matrix exists or not can be understood as that if the weight of the near field intensity corresponding to the pixel position is obtained, the training of the kernel matrix is finished, the kernel matrix exists at the moment, and otherwise, the kernel matrix does not exist. The kernel matrix of the embodiment of the invention refers to a kernel matrix with trained weights.
The training set pattern can be understood as a set of standard patterns for training, is a manhattan pattern, comprises vertical rectangles and horizontal rectangles, can acquire the convex corners and concave corners of the pattern, can be formed at the junctions of the horizontal rectangles and the vertical rectangles, and selects points at certain intervals on the horizontal and vertical sides. Based on these points, the whole graph is decomposed and classified into four types of parts, namely a convex part, a concave part, a vertical part and a horizontal part.
Accurate near field distributions corresponding to these patterns can be calculated using electromagnetic field rigorous calculations.
The description of the core matrix may refer to the description of the target core matrix, and is not repeated.
And if the nuclear matrix exists, executing the target mask pattern for determining the diffraction near field distribution to be acquired, and the subsequent steps.
Further, the determining a target kernel matrix corresponding to the target segmentation graph includes:
and selecting a target core matrix corresponding to the target segmentation graph from the core matrices. For example, if the target segmentation graph is a reentrant corner portion, a kernel matrix corresponding to the reentrant corner portion is selected, and the kernel matrix is used as a target kernel matrix corresponding to the reentrant corner portion.
Further, the method for acquiring the diffraction near-field distribution further comprises the following steps:
if the kernel matrix does not exist, acquiring a training set graph, and calculating the overall reference diffraction near-field distribution of the training set graph; because the training set of patterns is a set of standard patterns, the overall standard diffraction near-field distribution corresponding to the set of standard patterns can be calculated at one time.
Dividing the training set pattern and the overall reference diffraction near-field distribution corresponding to the training set pattern to obtain a mask pattern after division and reference diffraction near-field distribution of a region corresponding to the position of the mask pattern; for the description of the training set pattern segmentation, reference may be made to the description of the mask pattern of the segmentation target, which is not described in detail.
And calculating the nuclear matrix according to the mask pattern and the reference diffraction near field distribution.
Further, the calculating the kernel matrix according to the mask pattern and the reference diffracted near-field distribution includes:
performing matrixing processing on the mask graph to obtain a mask graph matrix; the number of lines of the mask pattern matrix is 1, the number of columns is the total number of pixels corresponding to the mask pattern, and elements of the mask pattern matrix represent that the pixel positions corresponding to the mask pattern are light-transmitting or light-shielding; the mask pattern can be represented by an X and transformed into a line representation (1, a)2) The elements are 1 and 0, which indicate that the corresponding position of the mask pattern is transparent or opaque.
It should be noted that, if the mask pattern is a vertical side portion or a horizontal side portion, the total number of pixels corresponding to the vertical side portion or the horizontal side portion may be represented by the area of the minimum circumscribed rectangle or the minimum circumscribed square corresponding to the vertical side portion or the horizontal side portion, for example, the length is multiplied by the width.
Further, the method for acquiring the diffraction near-field distribution further comprises the following steps:
as shown in fig. 4, if the mask pattern is a convex angle portion or a concave angle portion, performing a minimum square circumscribing process on the convex angle portion or the concave angle portion;
the image other than the convex corner portion or the concave corner portion in the smallest square is subjected to the marking process, and the marked image is not calculated. Therefore, the image with the mark is not calculated any more, and the calculation resource is saved.
Performing matrixing processing on the reference diffraction near-field distribution to obtain a reference diffraction near-field distribution matrix; the number of rows of the reference diffraction near-field distribution matrix is 1, the number of columns is the total number of pixels in an area corresponding to the position of the mask pattern, and elements of the reference diffraction near-field distribution matrix represent the near-field intensity of the pixel position; and mask diagramThe shape position corresponding region is understood to be the region which has the same position as the mask pattern and has been subjected to calculation of the reference diffraction near field distribution and is transformed into a line representation, the size of the region is b x b, and Y is (1, b) after transformation2) A matrix of sizes.
The above a and b may be the same or different, and since the object of the present invention is to calculate the near field distribution by a given mask pattern, the area of the logically selected mask pattern should cover the area of the calculated near field distribution (i.e., preferably a > b). A is a kernel matrix corresponding to the pattern types (convex angle, concave angle, vertical edge, horizontal edge) in the mask pattern, and details are not repeated.
The kernel matrix is calculated according to the following formula:
A＝(XTX)-1XTY
wherein A is the kernel matrix, X is the mask pattern matrix, XTA transposed matrix of X, (X)TX)-1Represents XTAnd the inverse of X and Y are the reference diffraction near field distribution matrix.
Further, the kernel matrix can be calculated by using a least square method according to the formula. The least squares method is a conventional algorithm and is not specifically described.
Further, obtaining the global reference diffraction near-field distribution comprises:
and calculating the reference diffraction near-field distribution according to an electromagnetic field strict calculation method. The strict calculation method of the electromagnetic field is a method for calculating diffraction near-field distribution more accurately at present, and is not specifically described.
In step S3, the diffracted near-field distributions of the target mask patterns may be obtained by stitching all the diffracted near-field distributions of the target regions corresponding to the respective divided target patterns after division, according to the positions of the respective divided target patterns before division of the target mask patterns.
In the embodiment of the invention, the target segmentation graph is multiplied by the target kernel matrix, so that the problem that the corrugated field intensity distribution caused by the diffraction effect cannot be recovered can be well solved, the recovery accuracy is improved, and the calculation object is the kernel matrix instead of a large number of convolution kernels, so that the calculation amount is greatly reduced, and the calculation time can be further reduced.
By using the rapid algorithm for DUV photoetching diffraction near-field distribution, the calculation accuracy of the DUV photoetching near-field distribution can be greatly improved on the premise of ensuring higher calculation efficiency. Compared with the existing DDM algorithm, the method has the advantages that the operation time is reduced to a certain degree, and meanwhile, the absolute error can be reduced by tens of times. The method can be applied to the OPC process, improves the calculation efficiency and accuracy of diffraction near field distribution, finally improves the resolution of the photoetching pattern, and saves the calculation time, as shown in Table 1:
TABLE 1
As shown in fig. 6(a), 6(b) and 6(c), it can be determined that the effect of fig. 6(c) is closest to that of fig. 6(a) and much better than that of fig. 6 (b).
The method uses a kernel matrix method to replace the existing single convolution kernel method, and directly obtains the near field distribution of a region by multiplying the near field distribution once after obtaining the kernel matrix corresponding to the near field distribution by using a least square method, rather than using a convolution method to calculate the near field intensity of each point in the region one by one. The method using the nuclear matrix can well restore the 'corrugated' field intensity distribution caused by the diffraction effect, improve the accuracy of the calculation result and reduce the calculation time at the same time.
Compared with the prior art, the method for acquiring the diffraction near field distribution, provided by the embodiment of the invention, is used for determining the target mask pattern to be acquired with the diffraction near field distribution, and dividing the target mask pattern according to the pattern type to obtain the target division pattern forming the target mask pattern; determining a target nuclear matrix corresponding to the target segmentation graph, and obtaining a target area diffraction near-field distribution corresponding to the target segmentation graph according to the target segmentation graph and the target nuclear matrix; and splicing the diffraction near field distribution of all the target areas to obtain the diffraction near field distribution of the target mask pattern. Not only can the accuracy of the diffraction near-field distribution calculation result be improved, but also the calculation time can be reduced.
Those skilled in the art will appreciate that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program, which is stored in a computer readable storage medium, to instruct related hardware. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (10)
1. A method of obtaining a diffracted near-field distribution, comprising:
determining a target mask pattern of diffraction near-field distribution to be obtained, and dividing the target mask pattern according to pattern types to obtain a target division pattern forming the target mask pattern;
determining a target nuclear matrix corresponding to the target segmentation graph, and obtaining a target area diffraction near-field distribution corresponding to the target segmentation graph according to the target segmentation graph and the target nuclear matrix; each row of elements in the target kernel matrix corresponds to a pixel position of the rearranged target segmentation graph and represents a weight of the near-field intensity corresponding to the pixel position;
and splicing the diffraction near field distribution of all the target areas to obtain the diffraction near field distribution of the target mask pattern.
2. The method of acquiring a diffracted near-field distribution according to claim 1, wherein the pattern type is a manhattan pattern and includes at least one of a convex corner portion, a concave corner portion, a vertical side portion, and a horizontal side portion; correspondingly, the dividing the target mask pattern according to the pattern type to obtain a target division pattern forming the target mask pattern includes:
and dividing the target mask pattern according to at least one Manhattan pattern, wherein the obtained target division pattern comprises at least one of the convex corner part, the concave corner part, the vertical side part and the horizontal side part.
3. The method for obtaining a diffractive near field distribution according to claim 1, wherein the obtaining a diffractive near field distribution of a target area corresponding to the target segmentation pattern according to the target segmentation pattern and the target kernel matrix comprises:
performing matrixing processing on the target segmentation graph to obtain a target segmentation graph matrix; the number of rows of the target segmentation graph matrix is 1, the number of columns is the total number of pixels corresponding to the target segmentation graph, and elements of the target segmentation graph matrix represent the positions of the pixels corresponding to the target segmentation graph, which are light-transmitting or light-shading;
calculating the diffraction near-field distribution of the target area corresponding to the target segmentation graph according to the following formula:
X1·A1＝Y1
wherein X1 is the target segmentation pattern matrix, A1 is the target kernel matrix, and Y1 is the target region diffraction near-field distribution.
4. The method of acquiring a diffracted near-field distribution according to any one of claims 1 to 3, wherein, prior to the step of determining the target mask pattern for which a diffracted near-field distribution is to be acquired, the method of acquiring a diffracted near-field distribution further comprises:
determining whether a kernel matrix exists; each column of elements in the kernel matrix corresponds to a pixel position of the training set graph after rearrangement, and represents the weight of the near-field intensity corresponding to the pixel position;
and if the nuclear matrix exists, executing the target mask pattern for determining the diffraction near field distribution to be acquired, and the subsequent steps.
5. The method of acquiring a diffracted near-field distribution according to claim 4, further comprising:
if the kernel matrix does not exist, acquiring a training set graph, and calculating the overall reference diffraction near-field distribution of the training set graph;
dividing the training set pattern and the overall reference diffraction near-field distribution corresponding to the training set pattern to obtain a mask pattern after division and reference diffraction near-field distribution of a region corresponding to the position of the mask pattern;
and calculating the nuclear matrix according to the mask pattern and the reference diffraction near field distribution.
6. The method of obtaining a diffracted near-field distribution according to claim 5, wherein the calculating the kernel matrix from the mask pattern and the reference diffracted near-field distribution includes:
performing matrixing processing on the mask graph to obtain a mask graph matrix; the number of lines of the mask pattern matrix is 1, the number of columns is the total number of pixels corresponding to the mask pattern, and elements of the mask pattern matrix represent that the pixel positions corresponding to the mask pattern are light-transmitting or light-shielding;
performing matrixing processing on the reference diffraction near-field distribution to obtain a reference diffraction near-field distribution matrix; the number of rows of the reference diffraction near-field distribution matrix is 1, the number of columns is the total number of pixels in an area corresponding to the position of the mask pattern, and elements of the reference diffraction near-field distribution matrix represent the near-field intensity of the pixel position;
the kernel matrix is calculated according to the following formula:
A＝(XTX)-1XTY
wherein A is the kernel matrix, X is the mask pattern matrix, XTA transposed matrix of X, (X)TX)-1To representXTAnd the inverse of X and Y are the reference diffraction near field distribution matrix.
7. The method of acquiring a diffracted near-field distribution according to claim 5, wherein acquiring the overall reference diffracted near-field distribution comprises:
and calculating the whole reference diffraction near-field distribution according to an electromagnetic field strict calculation method.
8. The method of obtaining a diffracted near-field distribution according to claim 6, wherein the nuclear matrix is calculated according to the following formula:
A＝(XTX)-1XTY
wherein A is the kernel matrix, X is the mask pattern matrix, XTA transposed matrix of X, (X)TX)-1Represents XTThe inverse of X and Y are the reference diffraction near field distribution matrix, which comprises:
and calculating to obtain the kernel matrix by using a least square method according to the formula.
9. The method of obtaining a diffracted near-field distribution according to claim 4, wherein said determining a target kernel matrix corresponding to the target segmentation pattern comprises:
and selecting a target core matrix corresponding to the target segmentation graph from the core matrices.
10. The method of acquiring a diffracted near-field distribution according to claim 6, further comprising:
if the mask pattern is a convex angle part or a concave angle part, carrying out external minimum square processing on the convex angle part or the concave angle part;
the image other than the convex corner portion or the concave corner portion in the smallest square is subjected to the marking process, and the marked image is not calculated.
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