Method for determining holographic mask imaging data and holographic mask transmissivity

Abstract

The present application relates to the technical field of integrated circuit manufacturing, and discloses a holographic mask imaging data and a holographic mask transmittance determination method. The holographic mask imaging data determination method comprises the following steps: obtaining reconstruction light data and transmittance of the holographic mask; generating first light wave field data based on the reconstruction light data and the transmittance; sampling and processing the first light wave field data and preset first diffraction transfer data according to a preset sampling interval, to obtain first light wave field discrete data corresponding to the first light wave field data and first target diffraction transfer data corresponding to the first diffraction transfer data; and determining the holographic mask imaging data based on the convolution result between the first light wave field discrete data represented by a first matrix and the first target diffraction transfer data represented by a second matrix. The present application improves the determination efficiency of the holographic mask imaging data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of integrated circuit manufacturing, and in particular to a holographic mask imaging data and holographic mask transmittance determination method. BACKGROUND

[0002] Holographic lithography is to reconstruct the amplitude and phase information of integrated circuit patterns on a silicon wafer to obtain the required integrated circuit patterns by using the imaging principle of light diffraction. In theory, the image of the integrated circuit pattern on the silicon wafer should be completely consistent with the actual required integrated circuit pattern, but due to the influence of factors such as mask area size, light source, twin image, sampling interval, etc., it is difficult to determine the holographic mask imaging data at one time, and continuous iteration optimization is also needed, so the imaging efficiency of the holographic mask is very important. SUMMARY

[0003] Therefore, the holographic mask imaging data and holographic mask transmittance determination method is provided to solve the problem of difficult to quickly determine the holographic mask imaging data.

[0004] According to a first aspect, the holographic mask imaging data determination method provided by the embodiments of the present application comprises: obtaining reconstruction light data and transmittance of a holographic mask; generating first light wave field data based on the reconstruction light data and the transmittance; sampling and processing the first light wave field data and a preset first diffraction transfer data according to a preset sampling interval to obtain first light wave field discrete data corresponding to the first light wave field data and first target diffraction transfer data corresponding to the first diffraction transfer data; and determining the holographic mask imaging data based on the convolution result between the first light wave field discrete data represented by a first matrix and the first target diffraction transfer data represented by a second matrix.

[0005] The holographic mask imaging data determination method provided by the embodiments of the present application samples and processes the light wave field data and the first diffraction transfer data by the same sampling interval to obtain the second light wave field discrete data and the first target diffraction transfer data, and then represents the second light wave field discrete data by the first matrix and represents the first target diffraction transfer data by the second matrix to realize the convolution of the light wave distribution data and the first target diffraction transfer data and generate the holographic mask imaging data. With the help of the idea of piecewise convolution, the order matching of the light wave field discrete data and the target diffraction transfer data is ensured, and the determination efficiency of the holographic mask imaging data is improved on the premise of ensuring the calculation accuracy.

[0006] In a first implementation of the first aspect, the holographic mask imaging data is determined based on a convolution result between the first light field discrete data represented by the first matrix and the first target diffraction transfer data represented by the second matrix, and the holographic mask imaging data comprises: obtaining a first order of the first light field discrete data represented by the first matrix; dividing the first target diffraction transfer data represented by the second matrix according to the first order to obtain a plurality of diffraction transfer sub-matrices, the second order of the second matrix being greater than the first order; and expanding the first light field discrete data represented by the first matrix and the plurality of diffraction transfer sub-matrices to a target order to obtain a target light field matrix of the target order and a plurality of target diffraction transfer matrices; and performing convolution processing on the target light field matrix and the plurality of first target diffraction transfer matrices to obtain the holographic mask imaging data.

[0007] The holographic mask imaging data determination method provided by the embodiment of the present application expands the first matrix and the diffraction transfer sub-matrix to the target order to perform convolution on the target light field matrix and the first target diffraction transfer matrix of the same order, thereby avoiding the problem of order mismatch between the target light field matrix and the second matrix, and greatly reducing the memory requirement of the calculation process. By performing convolution processing on the target light field matrix of the same order and each first target diffraction transfer matrix, the segmented convolution of the target light field matrix and the target diffraction transfer matrix is realized, and the calculation complexity for the holographic mask imaging data can be optimized in combination with the fast Fourier transform, thereby improving the determination efficiency of the holographic mask imaging data.

[0008] In a second implementation of the first aspect, expanding the first light field discrete data represented by the first matrix to the target order to obtain the target light field matrix of the target order comprises: determining a first expansion order of the first matrix based on the target order and the first order; performing zero padding processing on an element position corresponding to the first expansion order to obtain a plurality of zero elements; and splicing the plurality of zero elements to the first matrix according to a preset splicing rule to obtain the target light field matrix, wherein the target light field matrix obtained after splicing has the target order.

[0009] In a third implementation form of the first aspect, in conjunction with the second implementation form of the first aspect, the first diffraction transfer sub-matrix is expanded to a target order to obtain a first target diffraction transfer matrix of the target order, including: determining a second expansion order of the first diffraction transfer sub-matrix based on the target order and the first order; extracting target elements corresponding to the second expansion order from the second matrix; and splicing the target elements to the first diffraction transfer sub-matrix according to the preset splicing rule to generate the first target diffraction transfer matrix, wherein the first target diffraction transfer sub-matrix obtained after splicing has the target order.

[0010] The method for determining holographic mask imaging data provided in the embodiments of the present application performs zero padding on the first matrix to obtain a target light wave field matrix of a target order, so as to convert cyclic convolution into linear convolution, and expands the expansion order of the first diffraction transfer sub-matrix according to target elements in the second matrix, so as to implement subsequent segmented convolution processing. Since the first matrix does not need to be zero-padded according to the order of the second matrix, the second matrix can adopt parallel means for each convolution after being divided into blocks, and the calculation time is further shortened.

[0011] In a fourth implementation form of the first aspect, in conjunction with any one of the first implementation form to the third implementation form of the first aspect, the method for determining holographic mask imaging data includes: performing fast Fourier transform on the target light wave field matrix to obtain a first transformed matrix; performing fast Fourier transform on each of the first target diffraction transfer matrices to obtain a plurality of second transformed matrices; multiplying the first transformed matrix with corresponding elements of each of the second transformed matrices in sequence to obtain a plurality of frequency domain matrices; performing inverse fast Fourier transform on each of the frequency domain matrices to obtain a plurality of third transformed matrices; extracting matrix elements corresponding to the first matrix from each of the third transformed matrices; and splicing the matrix elements to obtain the holographic mask imaging data.

[0012] The method for determining holographic mask imaging data provided in the embodiments of the present application determines holographic mask imaging data in conjunction with convolution theorem and fast Fourier transform means, and greatly reduces the calculation complexity of holographic mask imaging data.

[0013] According to a second aspect, embodiments of the present application provide a holographic mask transmittance determination method, comprising: acquiring integrated circuit topology data, illumination light data, and reference light data; generating second light wave field data based on the integrated circuit topology data and the illumination light data; sampling the second light wave field data and a preset second diffraction transfer data according to a preset sampling interval to obtain second light wave field discrete data corresponding to the second light wave field data and second target diffraction transfer data corresponding to the second diffraction transfer data; determining a convolution result between the second light wave field discrete data represented by a third matrix and the second target diffraction transfer data represented by a fourth matrix; and determining the holographic mask transmittance based on the convolution result and the reference light data.

[0014] In combination with the second aspect, in a first implementation of the second aspect, the holographic mask transmittance is determined based on the convolution result and the reference light data, comprising: coherently superimposing the convolution result and the reference light data to obtain light intensity superposition data; and determining the light intensity superposition data after normalization as the holographic mask transmittance.

[0015] The holographic mask transmittance determination method provided by the embodiments of the present application can determine the transmittance of the holographic mask through segmented convolution processing, can reduce the calculation complexity of the transmittance by combining the fast Fourier transform technology, and then further determines the holographic mask imaging data according to the transmittance data of the holographic mask, thereby saving the determination time of the holographic mask imaging data and improving the determination efficiency of the holographic mask imaging data.

[0016] According to a third aspect, embodiments of the present application provide a holographic mask imaging data determination device, comprising: a first acquisition module configured to acquire reconstruction light data and transmittance for a holographic mask; a first generation module configured to generate first light wave field data based on the reconstruction light data and the transmittance; a first sampling module configured to sample the first light wave field data and a preset first diffraction transfer data according to a preset sampling interval to obtain light wave distribution data corresponding to the first light wave field data and target diffraction transfer data corresponding to the first diffraction transfer data; and a first convolution module configured to determine holographic mask imaging data based on a convolution result between the light wave distribution data represented by a first matrix and the target diffraction transfer data represented by a second matrix.

[0017] According to a fourth aspect, an embodiment of the present application provides a holographic mask transmittance determination device, characterized in that comprising: a second acquisition module, configured to acquire integrated circuit topology data, illumination light data and reference light data; a second generation module, configured to generate second light wave field data based on the integrated circuit topology data and the illumination light data; a second sampling module, configured to sample the second light wave field data and a preset second diffraction transfer data according to a preset sampling interval, to obtain second light wave field discrete data corresponding to the second light wave field data, and second target diffraction transfer data corresponding to the second diffraction transfer data; a second convolution module, configured to determine a convolution result between the second light wave field discrete data represented by a third matrix and the second target diffraction transfer data represented by a fourth matrix; and a transmittance determination module, configured to determine the transmittance of the holographic mask based on the convolution result and the reference light data.

[0018] According to a fifth aspect, an embodiment of the present application provides an electronic device, comprising a memory and a processor, which are in communication connection with each other, and the memory stores computer instructions, and the processor executes the computer instructions to perform the holographic mask imaging data determination method of the first aspect or any of the embodiments of the first aspect, or perform the holographic mask transmittance determination method of the second aspect or the first embodiment of the second aspect.

[0019] According to a sixth aspect, an embodiment of the present application provides a computer readable storage medium, which stores computer instructions, and the computer instructions are used to make a computer execute the holographic mask imaging data determination method of the first aspect or any of the embodiments of the first aspect, or execute the holographic mask transmittance determination method of the second aspect or the first embodiment of the second aspect.

[0020] It should be noted that the corresponding beneficial effects of the holographic mask imaging data determination device, the holographic mask transmittance determination device, the electronic device and the computer readable storage medium provided by the embodiments of the present application are described in the corresponding content of the holographic mask imaging data determination method and the holographic mask transmittance determination method, and will not be described here. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0022] Figure 1A holographic lithography schematic diagram in the embodiment of the present application is shown;

[0023] Figure 2 A flow chart of a holographic mask imaging data determination method according to the embodiment of the present application is shown;

[0024] Figure 3 Another flow chart of a holographic mask imaging data determination method according to the embodiment of the present application is shown;

[0025] Figure 4 A second matrix partitioning schematic diagram in the embodiment of the present application is shown;

[0026] Figure 5 A first matrix expansion schematic diagram in the embodiment of the present application is shown;

[0027] Figure 6 A diffraction transfer sub-matrix expansion schematic diagram in the embodiment of the present application is shown;

[0028] Figure 7 A flow chart of a holographic mask transmittance determination method according to the embodiment of the present application is shown;

[0029] Figure 8 Another flow chart of a holographic mask transmittance determination method according to the embodiment of the present application is shown;

[0030] Figure 9 A structural block diagram of a holographic mask imaging data determination device according to the embodiment of the present application is shown;

[0031] Figure 10 A structural block diagram of a holographic mask transmittance determination device according to the embodiment of the present application is shown;

[0032] Figure 11 A hardware structure schematic diagram of an electronic device provided by the embodiment of the present application is shown. DETAILED DESCRIPTION

[0033] In order to make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0034] Holographic lithography is to reconstruct the amplitude and phase information of an integrated circuit pattern by using the diffraction imaging principle of light to obtain the required integrated circuit pattern. Figure 1The holographic lithography schematic diagram shown, R(x, y) is the reconstruction light, T(x, y) is the transmittance of the holographic mask, after the holographic mask is irradiated by the reconstruction light, the required integrated circuit pattern can be presented on the silicon wafer, thereby the photolithography process is completed. Therefore, the holographic mask is crucial for the holographic lithography.

[0035] At present, the holographic mask mainly uses the method provided by Gabor to calculate the transmittance T(x, y), and then determines the image presented by the holographic mask on the silicon wafer based on the transmittance. The expression for determining the transmittance T(x, y) is as follows:

[0036]

[0037] Wherein, Q(x0, y0) is an integrated circuit topological pattern, I(x0, y0) is irradiation light, H(x, y, x0, y0) is a diffraction transfer function, R * (x, y) is reference light, which is conjugate with the reconstruction light.

[0038] After the mask transmittance T(x, y) is calculated, the expression for determining the image presented by the holographic mask on the silicon wafer is as follows:

[0039]

[0040] Wherein, T(x, y) is the transmittance, H(x0, y0, x, y) is a diffraction transfer function, R(x, y) is the reconstruction light, which is conjugate with the reference light.

[0041] Wherein, the expression of the diffraction transfer function is as follows:

[0042]

[0043] Wherein, j is an imaginary unit, λ is the wavelength of light, and d is the distance between the image plane and the object plane.

[0044] In theory, based on the above method, the image of the integrated circuit pattern generated on the silicon wafer should be completely consistent with the actual required integrated circuit pattern, but the area of the mask is not infinite, and is affected by factors such as the size of the mask area, the light source and the sampling interval, so it is difficult to determine the holographic mask imaging data at one time, and continuous iteration optimization is also needed, therefore the mask imaging calculation efficiency is crucial.

[0045] Therefore, the holographic mask is crucial for the holographic lithography. Based on this, the technical scheme of the present application uses the same sampling interval for sampling processing, and combines matrix expression to realize the matching of light intensity information and diffraction transfer information, which is not limited by the area of the mask, and the holographic mask imaging data is generated through convolution processing, compared with the integral Riemann summation in the related art, the calculation time complexity is reduced, and the determination efficiency of the holographic mask imaging data is improved.

[0046] According to the embodiment of the present application, an embodiment of a holographic mask imaging data determination method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from here.

[0047] In the present embodiment, a holographic mask imaging data determination method is provided, which can be used in electronic devices such as mobile phones, tablets, computers, etc. Figure 2 is a flowchart of the holographic mask imaging data determination method according to the embodiment of the present application, as shown in Figure 2 The flowchart includes the following steps:

[0048] S11, obtaining reconstruction light data and transmittance for the holographic mask.

[0049] The reconstruction light data is the light data of the light irradiating the holographic mask, which can be determined based on the properties of the reconstruction light source. The transmittance is used to characterize the light transmittance of the reconstruction light passing through the holographic mask, which can be determined by the ratio of the light flux of the outgoing light to the light flux of the incident reconstruction light.

[0050] S12, generating first light wave field data based on the reconstruction light data and the transmittance.

[0051] The first light wave field data represents the light wave field generated by the reconstruction light passing through the holographic mask. Specifically, the first light wave field data U is the product of the reconstruction light data and the transmittance, and its expression is as follows:

[0052] U(x,y)=T(x,y)R(x,y)

[0053] Wherein, U(x,y) represents the first light wave field data; T(x,y) represents the transmittance corresponding to each data point; R(x,y) represents the reconstruction light data composed of each data point; (x,y) represents the coordinate information of each data point constituting the light wave field data, i.e. the plane coordinate of the holographic mask.

[0054] S13, sampling the first light wave field data and the preset first diffraction transfer data according to the preset sampling interval, respectively, to obtain the first light wave field discrete data corresponding to the first light wave field data, and the target diffraction transfer data corresponding to the first diffraction transfer data.

[0055] The preset first diffraction transfer data is determined according to the distance between the object plane of the integrated circuit and the imaging image plane. Specifically, the first diffraction transfer data H(x0,y0,x,y) can be defined as:

[0056]

[0057] The preset sampling interval is a preset sampling spatial interval for sampling processing. The electronic device performs sampling processing on the first light field data U(x, y) according to the preset sampling interval to obtain a discrete value corresponding to the first light field data U(x, y), which is the first light field discrete data corresponding to the first light field data.

[0058] Similarly, the first diffraction transfer data H(x0, y0) is sampled according to the preset sampling interval to obtain a discrete value corresponding to the first diffraction transfer data H(x0, y0), which is the target diffraction transfer data.

[0059] Then, H(x0, y0) = H(x0, y0, 0, 0) is obtained according to the first light field data and the first diffraction transfer data, and the holographic mask imaging data P(x0, y0) can be expressed as: P(x0, y0) = |U(x, y) * Hx0, y02. S14, based on the convolution result between the first light field discrete data represented by the first matrix and the first target diffraction transfer data represented by the second matrix, the holographic mask imaging data is determined.

[0060] The first light field discrete data is represented by a first matrix u, and the first target diffraction transfer data is represented by a second matrix h. The first matrix u and the second matrix h are subjected to fast Fourier transform based on the convolution theorem to reduce the calculation complexity, and then the corresponding elements of the data matrix after the fast Fourier transform are multiplied, and the fast inverse Fourier transform of the result is performed to obtain the holographic mask imaging data.

[0061] The determination method of the holographic mask imaging data provided in the embodiment samples the light field data and the first diffraction transfer data by the same sampling interval to obtain the second light field discrete data and the first target diffraction transfer data, and then represents the second light field discrete data by a first matrix and represents the first target diffraction transfer data by a second matrix to realize the convolution of the light field distribution data and the first target diffraction transfer data and generate the holographic mask imaging data. With the idea of piecewise convolution, the order matching of the light field discrete data and the target diffraction transfer data is ensured, and the determination efficiency of the holographic mask imaging data is improved on the premise of ensuring the calculation accuracy.

[0062] In the embodiment, a determination method of holographic mask imaging data is provided, which can be used in electronic devices such as mobile phones, tablet computers, computers, etc. Figure 3 The flowchart of the determination method of the holographic mask imaging data according to the embodiment of the present application is shown in FIG. 1, which includes the following steps: Figure 3

[0063] ​S21, obtain the reconstruction light data and the transmittance of the holographic mask. For details, refer to the related description of the above embodiments, which will not be repeated here.

[0064] S22, generate the first light wave field data based on the reconstruction light data and the transmittance. For details, refer to the related description of the above embodiments, which will not be repeated here.

[0065] S23, sample the first light wave field data and the preset first diffraction transfer data according to a preset sampling interval, to obtain the first light wave field discrete data corresponding to the first light wave field data, and the first target diffraction transfer data corresponding to the first diffraction transfer data. For details, refer to the related description of the above embodiments, which will not be repeated here.

[0066] S24, determine the holographic mask imaging data based on the convolution result between the first light wave field discrete data represented by the first matrix and the first target diffraction transfer data represented by the second matrix.

[0067] Specifically, the above step S24 can include:

[0068] S241, obtain the first order of the first light wave field discrete data represented by the first matrix.

[0069] The first matrix used to represent the first light wave field discrete data is composed of a number of rows and a number of columns of values, and the number of rows and the number of columns of the first matrix are the same. The first order represents the number of rows and the number of columns of the first matrix. Here, the number of rows (the number of columns) is the first order because the number of rows and the number of columns of the first matrix are the same.

[0070] S242, divide the first target diffraction transfer data represented by the second matrix according to the first order, to obtain a plurality of diffraction transfer sub-matrices.

[0071] Here, the second order of the second matrix is greater than the first order.

[0072] In holographic lithography, in order to improve the resolution, the area of the mask is usually set to be larger than the area of the required integrated circuit pattern. In theory, the larger the area of the mask, the better, and the order of the second transfer matrix is infinite. Therefore, in the actual process of determining the mask imaging data or the mask transmittance, the orders of the second matrix and the first matrix are usually different, and the first matrix or the second matrix can be segmented flexibly.

[0073] In order to avoid the mismatch between the orders of the first matrix and the second matrix, the method of piecewise convolution can be used to convolve the first matrix and the second matrix. Specifically, the electronic device can divide the second matrix according to the first order of the first matrix to obtain a plurality of diffraction transfer sub-matrices, and the order of each diffraction transfer sub-matrix is equal to the first order.

[0074] For example, the first order of the first matrix is N, the second order of the second matrix is M, the second matrix is divided according to the N order to obtain a plurality of N-order diffraction transfer sub-matrices, as shown in the following formula. Figure 4

[0075] It should be noted that the first matrix can also be divided into a plurality of L-order sub-matrices, and then the second matrix is divided according to the L order to obtain a plurality of L-order diffraction transfer sub-matrices.

[0076] S243, the first light wave field discrete data represented by the first matrix and the plurality of diffraction transfer sub-matrices are expanded to a target order to obtain a target light wave field matrix of the target order and a plurality of first target diffraction transfer matrices.

[0077] The target order is an order that can realize fast Fourier transform. Specifically, the target order is twice the first order, that is, the first order is N, and the target order is 2N. Thus, on the basis of realizing the segmented convolution processing, the memory burden of the electronic device is reduced, and parallel computing processing can be easily implemented, thereby improving the calculation speed of the holographic mask imaging data.

[0078] Of course, if the first matrix is divided into a plurality of L-order sub-matrices, and the second matrix is divided according to the L order to obtain a plurality of L-order diffraction transfer sub-matrices, the L-order sub-matrix in the first matrix can be expanded to 2L order, and the L-order diffraction transfer sub-matrix can be expanded to 2L order. That is, as long as the target order after expansion is twice the target order before expansion.

[0079] The electronic device respectively expands the order of the first matrix and each diffraction transfer sub-matrix. Specifically, the first matrix is expanded from the first order to the target order to obtain a target light wave field matrix corresponding to the first matrix, and each diffraction transfer sub-matrix is expanded from the first order to the target order to obtain a target diffraction transfer matrix corresponding to each diffraction transfer sub-matrix.

[0080] Specifically, the first light wave field discrete data represented by the first matrix is expanded to the target order to obtain a target light wave field matrix of the target order, including:

[0081] (1) Based on the target order and the first order, determining a first expansion order of the first matrix.

[0082] (2) Zero processing is performed on the element positions corresponding to the first expansion order to obtain a plurality of zero elements.

[0083] (3) The plurality of zero elements are spliced to the first matrix according to a preset splicing rule to obtain a target light wave field matrix, wherein the target light wave field matrix obtained after splicing has the target order.

[0084] ​The first expansion order is the difference between the target order and the first order. The matrix elements corresponding to the first expansion order are assigned values. Here, in order to avoid the influence of cyclic convolution on the accuracy of the convolution result, the element positions corresponding to the first expansion order can be zero-padded, thereby obtaining a plurality of zero elements. If the target order is 2N and the first order is N, the first expansion order is 2N-N=N, that is, after expansion, the expansion position elements are all 0.

[0085] The preset splicing rule is a rule for splicing a plurality of zero elements to the first matrix to make the first matrix have a target order. The preset splicing rule is pre-deployed in the electronic device, and the electronic device splices a plurality of zero elements to the first matrix according to the preset splicing rule to obtain a first target light wave field matrix having a target order.

[0086] Specifically, the preset splicing rule can be to uniformly splice zero elements to the right and below of the first matrix, for example, placing the first matrix at the top left corner, and uniformly splicing zero elements to the right and below of the first matrix to obtain a target light wave field matrix of order 2N, as shown in the left graph of FIG. 1. Figure 5 The preset splicing rule can be to uniformly splice zero elements to the left and above of the first matrix, for example, placing the first matrix at the bottom right corner, and uniformly splicing N zero elements to the left and above of the first matrix to obtain a target light wave field matrix of order 2N, as shown in the right graph of FIG. 1. Figure 5 The preset splicing rule can also be to uniformly splice zero elements around the first matrix, for example, uniformly zero-padded around the first matrix, then N zero elements need to be uniformly spliced around the first matrix to obtain a target light wave field matrix of order 2N, as shown in the middle graph of FIG. 1. Figure 5 Here, the preset splicing rule is not specifically limited, as long as it can expand the first matrix to the target order using zero elements.

[0087] Each first diffraction transfer sub-matrix generates a first target diffraction transfer matrix in the same way, which is described below with reference to one first diffraction transfer sub-matrix. Specifically, the steps of expanding the first diffraction transfer sub-matrix into the first target diffraction transfer matrix include:

[0088] (1) Based on the target order and the first order, determine the second expansion order of the first diffraction transfer sub-matrix.

[0089] (2) Extract target elements corresponding to the second expansion order from the second matrix.

[0090] (3) Splice the target elements to the first diffraction transfer sub-matrix according to the preset splicing rule to generate the first target diffraction transfer matrix, wherein the first target diffraction transfer sub-matrix obtained after splicing has a target order.

[0091] The second expansion order is the difference between the target order and the first order, that is, the second expansion order is the same as the first expansion order. According to the principle of segmented convolution, the element positions corresponding to the second expansion order can be assigned values according to the values of the positions corresponding to the second matrix, thereby obtaining a plurality of assigned elements.

[0092] If the target order is 2N and the first order is N, then the second expansion order is 2N-N=N, that is, in addition to the data of the first diffraction transfer sub-matrix itself, the remaining position elements are extracted from the second matrix.

[0093] Here, the preset splicing rule for splicing a plurality of target elements to the first diffraction transfer sub-matrix to make the first diffraction transfer sub-matrix have the target order is the same as the preset splicing rule for splicing a plurality of zero elements to the first matrix to make the first matrix have the target order, that is, the preset splicing rules used for expanding the first matrix and the first diffraction transfer sub-matrix are consistent.

[0094] Specifically, the preset splicing rule is pre-deployed in the electronic device, and the electronic device can splice a plurality of target elements to the first diffraction transfer sub-matrix according to the preset splicing rule to obtain a corresponding first target diffraction transfer matrix. For example, the preset splicing rule is to uniformly splice the target elements to the right and below of the first diffraction transfer sub-matrix, the first diffraction transfer sub-matrix is placed in the upper left corner, the target elements corresponding to the positions are extracted from the second matrix, and the extracted target elements are spliced to the right and below of the first diffraction transfer sub-matrix to obtain a 2N-order first target diffraction transfer matrix, as shown in the left graph of FIG. 2. Figure 6

[0095] For example, the preset splicing rule is to uniformly splice the target elements to the four sides of the first diffraction transfer sub-matrix, and the extracted target elements are uniformly spliced to the four sides of the first diffraction transfer sub-matrix to obtain a 2N-order first target diffraction transfer matrix, as shown in the middle graph of FIG. 2. Figure 6

[0096] For example, the preset splicing rule is to uniformly splice the target elements to the left and above of the first diffraction transfer sub-matrix, the first diffraction transfer sub-matrix is placed in the lower right corner, and the extracted target elements are uniformly spliced to the left and above of the first diffraction transfer sub-matrix to obtain a 2N-order first target diffraction transfer matrix, as shown in the right graph of FIG. 2. Figure 6

[0097] ​​​In the above embodiments, the target light wave field matrix of the target order is obtained by zero-padding the first matrix, without the need to zero-padding the second matrix, thus reducing the memory requirement of the holographic mask imaging data. The expansion order of the first diffraction transfer sub-matrix is ​​expanded by the target elements in the second matrix to realize the subsequent segmented convolution processing. This makes it easier to perform parallel convolution on each convolution after the first target diffraction transfer data represented by the second matrix is ​​divided into blocks, which greatly shortens the computation time.

[0098] S244, the target light wave field matrix and multiple first target diffraction transfer matrices are convolved to obtain holographic mask imaging data.

[0099] Based on the piecewise convolution method, the target light wave field matrix is ​​sequentially convolved with each of the first target diffraction transfer matrices to obtain multiple convolution results. Then, the corresponding holographic mask imaging data is extracted from these multiple convolution result matrices.

[0100] Specifically, step S244 above may include:

[0101] (1) Perform a fast Fourier transform on the target light wave field matrix to obtain the first transformation matrix.

[0102] (2) Perform a fast Fourier transform on each of the first target diffraction transfer matrices to obtain multiple second transformation matrices.

[0103] (3) Multiply the first transformation matrix by the corresponding elements of each of the second transformation matrices in turn to obtain multiple frequency domain matrices.

[0104] (4) Perform fast inverse Fourier transform on each frequency domain matrix to obtain multiple third transform matrices.

[0105] (5) Extract the matrix elements corresponding to the first matrix from each third transformation matrix.

[0106] (6) The matrix elements are spliced ​​together to obtain holographic mask imaging data.

[0107] Let the first matrix be u, and its corresponding target light wave field matrix be u′; let the second matrix be h, which corresponds to multiple first diffraction transfer submatrices h. ij The first diffraction transfer submatrix h ij The corresponding target diffraction transfer matrix is ​​h′ ij Where i,j are the two-dimensional indices of the first diffraction transfer submatrix, such as... Figure 4 As shown.

[0108] Using the target light wave field matrix u′ and any first target diffraction transfer matrix h′ ijThe convolution processing is explained. Specifically, the electronic device target light wave field matrix u' is subjected to fast Fourier transform to obtain a first transform matrix The first target diffraction transfer matrix h' ij is subjected to fast Fourier transform to obtain a second transform matrix The Hadamard product of the first transform matrix and the second transform matrix is calculated to obtain a matrix Then, the matrix is subjected to inverse fast Fourier transform to obtain a third transform matrix

[0109] Based on the relationship between the first matrix and the target light wave field matrix, the position of the first matrix in the target light wave field matrix can be determined, and the matrix elements corresponding to the first matrix are extracted from the third transform matrix P ij , according to the position. For example, if the first matrix is an N-order matrix located at the upper left corner of the target light wave field matrix, the matrix elements from the 0th row to the Nth row and from the 0th column to the Nth column are extracted from the third transform matrix P ij . For example, if the first matrix is an N-order matrix located at the center of the target light wave field matrix, the matrix elements from the N / 2th row to the 3N / 2th row and from the N / 2th column to the 3N / 2th column are extracted from the third transform matrix P ij . For example, if the first matrix is an N-order matrix located at the lower right corner of the target light wave field matrix, the matrix elements from the Nth row to the 2Nth row and from the Nth column to the 2Nth column are extracted from the third transform matrix P ij .

[0110] The target light wave field matrix and each first target diffraction transfer matrix are subjected to convolution processing in the same way, which will not be repeated here. Based on the above method, the corresponding matrix elements can be extracted from each third transform matrix, and then the extracted matrix elements are spliced according to the two-dimensional index i, j of the first diffraction transfer sub-matrix to obtain the final holographic mask imaging data P.

[0111] The holographic mask imaging data is determined based on the segmented convolution principle and the fast Fourier transform method, which greatly reduces the calculation complexity of the holographic mask imaging data.

[0112] S25, based on the holographic mask data, the holographic mask imaging is performed on the silicon wafer to obtain an integrated circuit pattern.

[0113] The electronic device performs imaging processing according to the determined holographic mask imaging data to generate an integrated circuit pattern on the silicon wafer, thereby realizing holographic lithography of integrated circuits.

[0114] The method for determining holographic mask imaging data provided by the embodiment realizes segmented convolution of the target light wave field matrix and the first target diffraction transfer matrix, optimizes the calculation complexity of the holographic mask imaging data, and improves the determination efficiency of the holographic mask imaging data. Then, imaging of the holographic mask is performed according to the holographic mask data, integrated circuit patterns are generated on the silicon wafer, and the accuracy and efficiency of the holographic lithography are ensured.

[0115] Based on the above, the holographic mask mainly calculates the transmittance T(x,y) provided by Gabor, and then determines the image of the holographic mask on the silicon wafer based on the transmittance. Specifically, the expression of the transmittance T(x,y) is as follows:

[0116]

[0117] Wherein, Q(x0,y0) is an integrated circuit topological pattern, I(x0,y0) is an illumination light, H(x,y,x0,y0) is a diffraction transfer function, R * (x,y) is a reference light, which is conjugate with the reconstructed light. However, with the influence of factors such as the size of the mask area, the light source, the sampling interval, etc., continuous iteration optimization is required, the calculation of the transmittance T(x,y) is also time-consuming, which affects the determination efficiency of the holographic mask transmittance, and thus affects the determination efficiency of the subsequent holographic mask imaging data.

[0118] Through comparative analysis, the key calculation part of the holographic mask transmittance T(x,y) is similar to the holographic mask imaging data P(x0,y0) Based on this, the same concept as described in the above embodiment for determining the holographic mask imaging data can be used to quickly determine the holographic mask transmittance.

[0119] According to the embodiment of the holographic mask transmittance determination method provided by the embodiment of the present application, it should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a group of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from here.

[0120] A holographic mask transmittance determination method is provided in the embodiment, which can be used in electronic devices such as mobile phones, tablet computers, computers, etc. Figure 7 The flowchart of the holographic mask transmittance determination method according to the embodiment of the present application is shown in the flowchart as Figure 7 The flowchart includes the following steps:

[0121] In step S31, the integrated circuit topological data, the irradiation light data and the reference light data are acquired.

[0122] The integrated circuit topological data is used to represent the graph data of the integrated circuit topological structure, and can be determined based on the integrated circuit graph formed on the silicon wafer. The irradiation light data is the light data of the light source irradiating the integrated circuit, which can be determined based on the properties of the irradiation light source. The reference light data is conjugate to the reconstructed light data.

[0123] In step S32, the second light wave field data is generated based on the integrated circuit topological data and the irradiation light data.

[0124] The second light wave field data represents the light wave field generated by the light source irradiating the integrated circuit. Specifically, the second light wave field data U2 is the product of the irradiation light data and the integrated circuit topological data, and the expression is as follows:

[0125] U2(x0, y0) = Q(x0, y0)I(x0, y0)

[0126] Wherein, U2(x0, y0) represents the first light wave field data; Q(x0, y0) represents the integrated circuit topological data; I(x0, y0) represents the irradiation light data composed of each data point; (x0, y0) represents the coordinate information of each data point constituting the second light wave field data, that is, the coordinate information of the plane where the integrated circuit is located.

[0127] In step S33, the second light wave field data and the preset second diffraction transfer data are sampled at a preset sampling interval to obtain the second light wave field discrete data corresponding to the second light wave field data and the second target diffraction transfer data corresponding to the second diffraction transfer data.

[0128] The preset second diffraction transfer data is determined according to the distance between the object surface where the integrated circuit is located and the imaging image surface. Specifically, the second diffraction transfer data H2(x, y, x0, y0) can be defined as:

[0129]

[0130] Wherein, (x, y, x0, y0) represents the coordinate information of each data point of the diffraction transfer data.

[0131] The preset sampling interval is a preset sampling space interval for sampling processing. The electronic device samples the second light wave field data U2(x0, y0) at the preset sampling interval to obtain the discrete value corresponding to the second light wave field data U2(x0, y0), which is the second light wave field discrete data corresponding to the second light wave field data.

[0132] Similarly, the second diffraction transfer data H2(x, y, x0, y0) is sampled according to a preset sampling interval to obtain discrete values corresponding to the second diffraction transfer data H2(x, y, x0, y0), i.e., second target diffraction transfer data.

[0133] In step S34, a convolution result between the second light field discrete data represented by the third matrix and the second target diffraction transfer data represented by the fourth matrix is determined.

[0134] Let H2(x, y) = H2(x, y, 0, 0), then according to the second light field data and the second diffraction transfer data, the holographic mask transmittance T(x, y) can be expressed as:

[0135] T(x, y) = |U2(x0, y0) * H2(x, y) + R * (x, y)| 2

[0136] It can be seen that the expression form of the first part U2(x0, y0) * H2(x, y) of the transmittance T(x, y) is similar to the convolution expression form of the holographic mask imaging data P(x0, y0), and the result of U2(x0, y0) * H(x, y) can be determined in the same way as the holographic mask imaging data P(x0, y0).

[0137] Specifically, the second light field discrete data is represented by a third matrix u2, and the second target diffraction transfer data is represented by a fourth matrix h2. The third matrix u2 and the fourth matrix h2 are subjected to fast Fourier transform based on the convolution theorem to reduce the calculation complexity, and then the corresponding elements of the data matrix after the fast Fourier transform are multiplied. The convolution result of U2(x0, y0) * H2(x, y) can be obtained by subjecting the multiplication result to inverse fast Fourier transform. The specific convolution processing manner is the same as the way of determining U(x, y) * H(x0, y0) in the above embodiment, which will not be described here.

[0138] In step S35, the holographic mask transmittance is determined based on the convolution result and the reference light data.

[0139] After obtaining the convolution result, the convolution result and the reference light data are processed in combination with the expression of the transmittance T(x, y), and the corresponding holographic mask transmittance can be calculated.

[0140] The holographic mask transmittance determination method provided by the embodiment can determine the transmittance of the holographic mask through segmented convolution processing, can reduce the calculation complexity of the transmittance in combination with the fast Fourier transform technology, and then further determines the holographic mask imaging data according to the transmittance data of the holographic mask, thereby saving the determination time of the holographic mask imaging data and improving the determination efficiency of the holographic mask imaging data

[0141] As an optional implementation, as shown in Figure 8 The step S35 can include the following steps:

[0142] In step S351, the convolution result is coherently superimposed with the reference light data to determine light intensity data.

[0143] The coherent superposition refers to superimposing the phase and amplitude of the convolution result and the reference light data to achieve a strengthened interference effect and obtain corresponding superposition data. The superposition data is determined in the following manner: U2(x,y)*H2(x,y)+R * (x,y). The square value of the superposition data is determined as the light intensity data, that is, |U2(x,y)*H2x,y+R*(x,y)2. * 2 .

[0144] In step S352, the light intensity data is normalized to obtain the holographic mask transmittance.

[0145] Since the holographic mask transmittance is in the range of 0-1, after obtaining the light intensity data |U2(x,y)*H2x,y+R*(x,y)2, the light intensity data is normalized to make the value thereof in the range of 0-1, and the value after the normalization processing is determined as the transmittance T of the holographic mask.

[0146] The holographic mask transmittance determination method provided by the embodiment determines the transmittance of the holographic mask through convolution processing, reduces the calculation complexity of the transmittance, and improves the determination efficiency of the holographic mask transmittance.

[0147] In the embodiment, a holographic mask imaging data determination device is also provided, which is used to implement the above embodiments and preferred embodiments and has been described above. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware or a combination of software and hardware is also possible and is contemplated.

[0148] The holographic mask imaging data determination device provided by the embodiment includes: Figure 9 as shown, including:

[0149] ​The first acquisition module 41 is configured to acquire reconstruction light data and transmittance of the holographic mask.

[0150] The first generation module 42 is configured to generate first light wave field data based on the reconstruction light data and the transmittance.

[0151] The first sampling module 43 is configured to sample the first light wave field data and the preset first diffraction transfer data respectively according to a preset sampling interval, to obtain first light wave field discrete data corresponding to the first light wave field data and first target diffraction transfer data corresponding to the first diffraction transfer data.

[0152] The first convolution module 44 is configured to determine the holographic mask imaging data based on a convolution result between the light wave distribution data represented by the first matrix and the first target diffraction transfer data represented by the second matrix.

[0153] Optionally, the first convolution module 44 can include:

[0154] The first acquisition sub-module is configured to acquire a first order of the first light wave field discrete data represented by the first matrix.

[0155] The division sub-module is configured to divide the first target diffraction transfer data represented by the second matrix according to the first order, to obtain a plurality of first diffraction transfer sub-matrices.

[0156] The expansion sub-module is configured to expand the first light wave field discrete data represented by the first matrix and the plurality of first diffraction transfer sub-matrices to a target order, to obtain a target light wave field matrix of the target order and a plurality of first target diffraction transfer matrices.

[0157] The convolution sub-module is configured to perform convolution processing on the target light wave field matrix and the plurality of first target diffraction transfer matrices respectively, to obtain the holographic mask imaging data.

[0158] Optionally, the expansion sub-module is specifically configured to: determine a first expansion order of the first matrix based on the target order and the first order; perform zero padding processing on an element position corresponding to the first expansion order, to obtain a plurality of zero elements; and splice the plurality of zero elements to the first matrix according to a preset splicing rule, to obtain the target light wave field matrix, wherein the target light wave field matrix obtained after splicing has the target order.

[0159] Optionally, the expansion sub-module is specifically configured to: determine a second expansion order of the first diffraction transfer sub-matrix based on the target order and the first order; extract target elements corresponding to the second expansion order from the second matrix; and splice the target elements to the first diffraction transfer sub-matrix according to a preset splicing rule, to generate the first target diffraction transfer matrix, wherein the first target diffraction transfer sub-matrix obtained after splicing has the target order.

[0160] Optionally, the convolution submodule is specifically configured to: perform fast Fourier transform on the target light wave field matrix to obtain a first transform matrix; perform fast Fourier transform on each first target diffraction transfer matrix to obtain a plurality of second transform matrices; multiply the first transform matrix with corresponding elements of each second transform matrix in sequence to obtain a plurality of frequency domain matrices; perform inverse fast Fourier transform on each frequency domain matrix to obtain a plurality of third transform matrices; extract matrix elements corresponding to the first matrix from each third transform matrix; and splice the matrix elements to obtain the holographic mask imaging data.

[0161] The holographic mask imaging data determination device provided by the embodiment of the present application samples the light wave field data and the first diffraction transfer data with the same sampling interval to obtain second light wave field discrete data and first target diffraction transfer data, then uses a first matrix to represent the second light wave field discrete data and uses a second matrix to represent the first target diffraction transfer data, so as to realize convolution of the light wave distribution data and the first target diffraction transfer data and generate the holographic mask imaging data. Thus, the order matching of the light wave field discrete data and the target diffraction transfer data can be ensured by means of the idea of piecewise convolution, and the determination efficiency of the holographic mask imaging data is improved on the premise of ensuring the calculation accuracy.

[0162] In the embodiment, a holographic mask transmittance determination device is also provided, which is configured to implement the above-mentioned embodiments and preferred embodiments, and will not be described herein. As used in the following, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware or a combination of software and hardware is also possible and is contemplated.

[0163] The holographic mask transmittance determination device provided by the embodiment includes: Figure 10 as shown in the figure, comprising:

[0164] The second acquisition module 51 is configured to acquire integrated circuit topology data, illumination light data and reference light data.

[0165] The second generation module 52 is configured to generate second light wave field data based on the integrated circuit topology data and the illumination light data.

[0166] The second sampling module 53 is configured to sample the second light wave field data and preset second diffraction transfer data according to a preset sampling interval to obtain second light wave field discrete data corresponding to the second light wave field data and second target diffraction transfer data corresponding to the second diffraction transfer data.

[0167] The second convolution module 54 is configured to determine a convolution result between the second light wave field discrete data represented by the third matrix and the second target diffraction transfer data represented by the fourth matrix.

[0168] The transmittance determination module 55 is configured to determine the transmittance of the holographic mask based on the convolution result and the reference light data.

[0169] Optionally, the transmittance determination module 55 can include:

[0170] The coherent processing submodule is configured to coherently superimpose the convolution result and the reference light data to determine light intensity data.

[0171] The determination submodule is configured to normalize the light intensity data to obtain the transmittance of the holographic mask.

[0172] Further function descriptions of the above modules and submodules are the same as those of the corresponding embodiments, and will not be repeated here.

[0173] The holographic mask transmittance determination apparatus in the embodiment is presented in the form of functional units, and the units herein refer to ASIC circuits, processors and memories for executing one or more software or fixed programs, and / or other devices that can provide the above functions.

[0174] The holographic mask transmittance determination apparatus provided by the embodiment determines the transmittance of the holographic mask through segmented convolution processing, can reduce the calculation complexity of the transmittance by combining the fast Fourier transform technology, and then further determines the holographic mask imaging data according to the transmittance data of the holographic mask, thereby saving the determination time of the holographic mask imaging data and improving the determination efficiency of the holographic mask imaging data.

[0175] The embodiment of the application further provides an electronic device having the holographic mask imaging data determination apparatus shown in the above Figure 9 , or the holographic mask transmittance determination apparatus shown in the above Figure 10 .

[0176] Please refer to Figure 11 , Figure 11 is a structural schematic diagram of an electronic device provided by an optional embodiment of the application, as shown in Figure 11As shown, the electronic device can include at least one processor 601, such as a central processing unit (CPU), at least one communication interface 603, a memory 604, and at least one communication bus 602. The communication bus 602 is used to realize the connection and communication between the components. The communication interface 603 can include a display, a keyboard, and can also include a standard wired interface and a wireless interface. The memory 604 can be a high-speed volatile random access memory (RAM), and can also be a non-volatile memory, such as at least one disk memory. The memory 604 can also be at least one storage device located away from the aforementioned processor 601. The processor 601 can be combined with Figure 9 or Figure 10 The described device, the memory 604 stores an application program, and the processor 601 calls the program code stored in the memory 604 to execute any of the above method steps.

[0177] The communication bus 602 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The communication bus 602 can be divided into an address bus, a data bus, and a control bus. For ease of representation, Figure 11 Only one thick line is used in the figure, but it does not mean that there is only one bus or only one type of bus.

[0178] The memory 604 can include volatile memory, such as random access memory (RAM); the memory can also include non-volatile memory, such as flash memory, a hard disk drive (HDD) or a solid-state drive (SSD); the memory 604 can also include a combination of the above types of memory.

[0179] The processor 601 can be a central processing unit (CPU), a network processor (NP), or a combination of a CPU and an NP.

[0180] The processor 601 can further include a hardware chip. The hardware chip can be an application-specific integrated circuit (ASIC), a programmable logic device (PLD) or a combination thereof. The PLD can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL) or any combination thereof.

[0181] Optionally, the memory 604 is further configured to store program instructions. The processor 601 can invoke the program instructions to implement the method for determining holographic mask imaging data as shown in the above embodiments of the present application, or implement the method for determining holographic mask transmittance as shown in the above embodiments.

[0182] The embodiments of the present application further provide a non-transitory computer storage medium, which stores computer executable instructions. The computer executable instructions can execute the method for determining holographic mask imaging data in any of the above method embodiments, or the method for determining holographic mask transmittance in any of the above method embodiments. The storage medium can be a disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory, a Hard Disk Drive (HDD) or a Solid-State Drive (SSD), etc. The storage medium can also include a combination of the above types of memories.

[0183] Although the embodiments of the present application are described in conjunction with the accompanying drawings, various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application, and such modifications and changes are intended to fall within the scope of the appended claims.

Claims

1. A method of determining holographic mask imaging data, characterized by, The method comprises: acquiring reconstruction light data and transmittance of a holographic mask; generating first light wave field data based on the reconstruction light data and the transmittance; sampling the first light wave field data and preset first diffraction transfer data at preset sampling intervals to obtain first light wave field discrete data corresponding to the first light wave field data and first target diffraction transfer data corresponding to the first diffraction transfer data; determining holographic mask imaging data based on a convolution result between the first light wave field discrete data represented by a first matrix and the first target diffraction transfer data represented by a second matrix.

2. The method of claim 1, wherein, The method of determining the holographic mask imaging data based on the convolution result between the first light wave field discrete data represented by the first matrix and the first target diffraction transfer data represented by the second matrix comprises: acquiring a first order of the first light wave field discrete data represented by the first matrix; dividing the first target diffraction transfer data represented by the second matrix according to the first order to obtain a plurality of diffraction transfer sub-matrices, wherein a second order of the second matrix is greater than the first order; expanding the first light wave field discrete data represented by the first matrix and the plurality of diffraction transfer sub-matrices to a target order to obtain a target light wave field matrix of the target order and a plurality of first target diffraction transfer matrices; performing convolution processing on the target light wave field matrix and the plurality of first target diffraction transfer matrices to obtain the holographic mask imaging data.

3. The method of claim 2, wherein, The method of expanding the first light wave field discrete data represented by the first matrix to the target order to obtain the target light wave field matrix of the target order comprises: determining a first expansion order of the first matrix based on the target order and the first order; performing zero element filling processing on an element position corresponding to the first expansion order to obtain a plurality of zero elements; splicing the plurality of zero elements to the first matrix according to a preset splicing rule to obtain the target light wave field matrix, wherein the target light wave field matrix obtained after splicing has the target order.

4. The method of claim 3, wherein, The method of expanding the diffraction transfer sub-matrix to the target order to obtain the first target diffraction transfer matrix of the target order comprises: determining a second expansion order of the diffraction transfer sub-matrix based on the target order and the first order; extracting target elements corresponding to the second expansion order from the second matrix; splicing the target elements to the diffraction transfer sub-matrix according to the preset splicing rule to generate the first target diffraction transfer matrix, wherein the first target diffraction transfer matrix obtained after splicing has the target order.

5. The method according to any one of claims 2-4, characterized in that, The method of performing convolution processing on the target light wave field matrix and the plurality of first target diffraction transfer matrices to obtain the holographic mask imaging data comprises: performing fast Fourier transform on the target light wave field matrix to obtain a first transform matrix; performing fast Fourier transform on each of the first target diffraction transfer matrices to obtain a plurality of second transform matrices; multiplying the first transform matrix with corresponding elements of each of the second transform matrices in sequence to obtain a plurality of frequency domain matrices; performing inverse fast Fourier transform on each of the frequency domain matrices to obtain a plurality of third transform matrices; extracting matrix elements corresponding to the first matrix from each of the third transform matrices; splicing the matrix elements to obtain the holographic mask imaging data.

6. A method for determining a transmittance of a holographic mask, comprising: obtaining integrated circuit topological data, illumination light data, and reference light data; generating second light wave field data based on the integrated circuit topological data and the illumination light data; sampling the second light wave field data and preset second diffraction transfer data according to a preset sampling interval to obtain second light wave field discrete data corresponding to the second light wave field data and second target diffraction transfer data corresponding to the second diffraction transfer data; determining a convolution result between the second light wave field discrete data represented by a third matrix and the second target diffraction transfer data represented by a fourth matrix; determining the transmittance of the holographic mask based on the convolution result and the reference light data.

7. The method of claim 6, wherein, The determining of the transmittance of the holographic mask based on the convolution result and the reference light data comprises: coherently superimposing the convolution result and the reference light data to determine light intensity data; normalizing the light intensity data to obtain the transmittance of the holographic mask.

8. An apparatus for determining hologram mask imaging data, characterized by The method comprises: a first obtaining module configured to obtain reconstruction light data and a transmittance for a holographic mask; a first generating module configured to generate first light wave field data based on the reconstruction light data and the transmittance; a first sampling module configured to sample the first light wave field data and preset first diffraction transfer data according to a preset sampling interval to obtain first light wave field discrete data corresponding to the first light wave field data and first target diffraction transfer data corresponding to the first diffraction transfer data; a first convolution module configured to determine holographic mask imaging data based on a convolution result between the first light wave field discrete data represented by a first matrix and the first target diffraction transfer data represented by a second matrix.

9. An apparatus for determining the transmissivity of a holographic mask, characterized in that The method comprises: a second obtaining module configured to obtain integrated circuit topological data, illumination light data, and reference light data; a second generating module configured to generate second light wave field data based on the integrated circuit topological data and the illumination light data; a second sampling module configured to sample the second light wave field data and preset second diffraction transfer data according to a preset sampling interval to obtain second light wave field discrete data corresponding to the second light wave field data and second target diffraction transfer data corresponding to the second diffraction transfer data; a second convolution module configured to determine a convolution result between the second light wave field discrete data represented by a third matrix and the second target diffraction transfer data represented by a fourth matrix; a transmittance determining module configured to determine the transmittance of the holographic mask based on the convolution result and the reference light data.

10. An electronic device, comprising: The method comprises: A memory and a processor, which are in communication connection with each other, the memory has stored computer instructions, and the processor executes the computer instructions to perform the method for determining the holographic mask imaging data according to any one of claims 1-5, or to perform the method for determining the transmittance of the holographic mask according to claim 6 or 7.

11. A computer readable storage medium, characterized in that, The computer readable storage medium has stored computer instructions for causing a computer to perform the method for determining the holographic mask imaging data according to any one of claims 1-5, or to perform the method for determining the transmittance of the holographic mask according to claim 6 or 7.

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