CN113703277A - Pattern correction method - Google Patents
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- G03F1/00—Originals for photomechanical production of textured or patterned surfaces, e.g., masks, photo-masks, reticles; Mask blanks or pellicles therefor; Containers specially adapted therefor; Preparation thereof
- G03F1/36—Masks having proximity correction features; Preparation thereof, e.g. optical proximity correction [OPC] design processes
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- G03F7/70—Microphotolithographic exposure; Apparatus therefor
- G03F7/70425—Imaging strategies, e.g. for increasing throughput or resolution, printing product fields larger than the image field or compensating lithography- or non-lithography errors, e.g. proximity correction, mix-and-match, stitching or double patterning
- G03F7/70433—Layout for increasing efficiency or for compensating imaging errors, e.g. layout of exposure fields for reducing focus errors; Use of mask features for increasing efficiency or for compensating imaging errors
- G03F7/70441—Optical proximity correction [OPC]
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Abstract
A pattern correction method, comprising: providing a bottom layer structure, wherein the bottom layer structure comprises a plurality of conducting layers which are arranged in parallel; providing a layout to be corrected, wherein the layout to be corrected is provided with a plurality of patterns to be corrected, and the layout to be corrected is used for forming a patterning layer on the bottom layer structure; acquiring first information through the graph to be corrected; acquiring second information through the conductive layer; acquiring etching deviation according to the first information and the second information; and correcting the pattern to be corrected according to the etching deviation to obtain a target pattern. The target graph obtained by the method is high in graph accuracy.
Description
Technical Field
The invention relates to the technical field of semiconductor manufacturing, in particular to a pattern correction method.
Background
In order to transfer the pattern from the reticle to the surface of the silicon wafer, an exposure step, a development step performed after the exposure step, and an etching step performed after the development step are generally required. In the exposure step, light irradiates on a silicon wafer coated with photoresist through a light-transmitting area in a mask plate, and the photoresist undergoes a chemical reaction under the irradiation of the light; in the developing step, photoetching patterns are formed by utilizing the different dissolution degrees of photosensitive and non-photosensitive photoresist to a developer, so that the mask pattern is transferred to the photoresist; in the etching step, the silicon wafer is etched based on the photoetching pattern formed by the photoetching adhesive layer, and the pattern of the mask is further transferred to the silicon wafer.
In semiconductor manufacturing, as the design size is continuously reduced and the design size is closer to the limit of the lithography imaging system, the diffraction Effect of light becomes more and more obvious, which causes the Optical image degradation of the design pattern, the actual formed lithography pattern is seriously distorted relative to the pattern on the mask, and the actual pattern and the design pattern formed by lithography on the silicon wafer are different, and this phenomenon is called Optical Proximity Effect (OPE).
In order to correct for Optical Proximity effects, an Optical Proximity Correction (OPC) is generated. The core idea of optical proximity correction is to establish an optical proximity correction model based on consideration of counteracting optical proximity effect, and design a photomask pattern according to the optical proximity correction model, so that although the optical proximity effect occurs to the photomask pattern corresponding to the photoetched photoetching pattern, the counteraction of the phenomenon is considered when the photomask pattern is designed according to the optical proximity correction model, and therefore, the photoetched photoetching pattern is close to a target pattern actually expected by a user. Etch bias is one of the parameters of optical proximity correction.
However, the dimension error between the lithography pattern obtained by correcting the mask pattern and performing exposure and the target pattern is large due to the etching deviation obtained by the conventional method for obtaining the etching deviation.
Disclosure of Invention
The invention provides a pattern correction method for reducing the dimension error between a photoetching pattern and a target pattern.
In order to solve the above technical problem, a technical solution of the present invention provides a method for correcting a pattern, including: providing a bottom layer structure, wherein the bottom layer structure comprises a plurality of conducting layers which are arranged in parallel; providing a layout to be corrected, wherein the layout to be corrected is provided with a plurality of patterns to be corrected, and the layout to be corrected is used for forming a patterning layer on the bottom layer structure; acquiring first information through the graph to be corrected; acquiring second information through the conductive layer; acquiring etching deviation according to the first information and the second information; and correcting the pattern to be corrected according to the etching deviation to obtain a target pattern.
Optionally, the method for obtaining the etching deviation according to the first information and the second information includes: acquiring a convolutional neural network model; and processing the first information and the second information by adopting a convolutional neural network model to obtain etching deviation.
Optionally, the method for processing the first information and the second information to obtain the etching deviation includes: acquiring an input vector v according to the first information and the second information; and processing the input vector v by adopting a convolutional neural network model to obtain the etching deviation.
Optionally, the method for obtaining the input vector v according to the first information and the second information includes: obtaining a first vector v according to the first information0(ii) a Acquiring a second vector v according to the second informationg(ii) a The first vector v0And a second vector vgConnecting in series to obtain input vector v ═ v0+vg。
Optionally, the graph to be corrected includes: opposing first and second short edges LE1, LE 2; and a second edge perpendicular to the first short edge LE1 and the second short edge LE2, and two ends of the second edge are respectively connected with the first short edge LE1 and the second short edge LE 2.
Optionally, the method for obtaining the first information of the graph to be corrected includes: acquiring a first dimension CD, wherein the first dimension CD is the side length of the first short edge LE1 and the second short edge LE 2; acquiring a second size L, wherein the second size L is the side length of the second edge and is larger than the first size CD; and acquiring a first space, wherein the first space is the space between adjacent parallel graphs to be corrected.
Optionally, a first vector v is obtained0The method comprises the following steps: obtaining a first vector v according to the first dimension CD, the second dimension L and the first space0(ii) a The first vector v0＝[CD，space，L]。
Optionally, the method for obtaining the first information of the graph to be corrected further includes: dividing the second edge into a plurality of line segments; obtaining a second distance dist1, where the second distance dist1 is a distance between a midpoint of any one of the line segments and the first short edge LE 1; obtaining a third distance dist2, where the third distance dist2 is a distance between a midpoint of any one of the line segments and the second short edge LE 2; the segment type includes: the first type of line segment is a line segment connected with the first short edge LE1 or the second short edge LE2, the second type of line segment is a line segment which is not connected with the first short edge LE1 and the second short edge LE2, and the third type of line segment comprises the first short edge LE1 and the second short edge LE 2.
Optionally, the method for obtaining the first vector v0 further includes: acquiring a first vector v according to the first size CD, the second size L, the first space, the line segment type, the second space dist1 and the third space dist20Said first vector v0＝[CD，space，L，type，dist1，dist2]。
Optionally, the method for acquiring the second information of the conductive layer includes: acquiring a density function P (x, y) of the conducting layer in the bottom layer structure; providing a first Gaussian function GS1A second Gaussian function GS2A third Gaussian function GS3And a fourth Gaussian function GS4Said first Gaussian function GS1Having a first variance S1, the second Gaussian function GS2Having a second variance S2, the third Gaussian function GS3Having a third variance S3, the fourth Gaussian function GS4A fourth difference S4, the first variance S1, the second variance S2, the third variance S3 and the fourth variance S4 being different from each other; performing first Gaussian convolution processing on the density function P (x, y) to obtain a first Gaussian convolution function Q1(x,y)，
Performing second Gaussian convolution processing on the density function P (x, y) to obtain a second GaussianConvolution function Q2(x,y)，
Performing a third Gaussian convolution processing on the density function P (x, y) to obtain a third Gaussian convolution function Q3(x,y)，
Performing a fourth Gaussian convolution processing on the density function P (x, y) to obtain a fourth Gaussian convolution function Q4(x,y)，
Acquiring a midpoint coordinate (x ', y') of any line segment; obtaining second information from the midpoint coordinates (x ', y'), the second information comprising a first Gaussian convolution function Q1(x, y) function value at midpoint (x ', y') of line segment
Second gaussian convolution function Q2(x, y) function value at midpoint (x ', y') of line segment
Third Gaussian convolution function Q3(x, y) function value at midpoint (x ', y') of line segment
And a fourth Gaussian convolution function Q4(x, y) function value at midpoint (x ', y') of line segment
Optionally, the method for obtaining the density function P (x, y) of the conductive layer includes: establishing a two-dimensional coordinate system (X, Y) on the plane of the surface of the underlying structure; acquiring a coordinate range set of the outline of the conducting layer in the two-dimensional coordinate system; and acquiring a density function P (x, y) of the conducting layer according to the coordinate range set of the conducting layer.
Optionally, P (x, y) is 0 or P (x, y) is 1.
Optionally, when any coordinate (x, y) in the two-dimensional coordinate system is located in the coordinate range of the conductive layer, P (x, y) is 1; when any coordinate (x, y) in the two-dimensional coordinate system is located outside the coordinate range of the conductive layer, P (x, y) is 0.
Optionally, a second vector v is obtainedgThe method comprises the following steps: according to a first Gaussian convolution function Q1(x, y) function value at midpoint (x ', y') of line segment
Second gaussian convolution function Q2(x, y) function value at midpoint (x ', y') of line segment
Third Gaussian convolution function Q3(x, y) function value at midpoint (x ', y') of line segment
And a fourth Gaussian convolution function Q4(x, y) function value at midpoint (x ', y') of line segment
Obtaining a second vector vgSaid second vector
Optionally, the method for obtaining the convolutional neural network model includes: providing an initial convolutional neural network model; providing a plurality of groups of training samples, wherein the training samples comprise information of a plurality of patterns to be corrected and corresponding target etching deviation; acquiring an input vector according to the information of a plurality of graphs to be corrected; inputting the input vector into an initial convolutional neural network model for iterative training to obtain the convolutional neural network model.
Optionally, the method for inputting the input vector into the initial convolutional neural network model for iterative training includes: inputting the input vector into an initial convolutional neural network model, and outputting a predicted etching deviation; acquiring a difference value between the predicted etching deviation and the target etching deviation; judging whether the difference value is within a preset range; if the difference value exceeds the preset range, continuing to carry out iterative training on the initial convolutional neural network model by using the training sample until the range of the difference value is within the preset range; and if the difference value is within the preset range, obtaining the convolutional neural network model.
Optionally, the number of the training samples is greater than 1 ten thousand groups.
Optionally, the convolutional neural network model includes: the input layer is used for vectorizing the input first information and the input second information to obtain an input vector; the hidden layer is used for classifying the input vectors; and the output layer is used for outputting the classification processing result.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
by acquiring the first information of the graph to be corrected and the second information of the underlying structure and acquiring the etching deviation according to the first information and the second information, the accurate rate of the acquired etching deviation is improved, and the graph to be corrected is corrected by adopting the etching deviation, so that the correction effect of acquiring the target graph is better.
Further, a convolutional neural network model is obtained, and etching deviation is obtained from the convolutional neural network model according to the first information and the second information, so that the precision rate of the etching deviation is improved.
Drawings
FIGS. 1 and 2 are a schematic cross-sectional structure and a top view of a semiconductor structure in an embodiment of the invention;
FIGS. 3 and 4 are schematic diagrams of layouts to be corrected in the embodiment of the present invention;
FIG. 5 is a flowchart illustrating steps for obtaining second information according to an embodiment of the present invention;
FIG. 6 is a flowchart illustrating steps of obtaining a convolutional neural network model according to an embodiment of the present invention.
Detailed Description
As described in the background art, the size error between the lithography pattern and the target pattern obtained by performing exposure after correcting the mask pattern using the etching bias obtained by the conventional method for obtaining the etching bias is large.
Specifically, in the back-end process of the semiconductor structure, when the etching deviation is adopted to correct the optical proximity effect of the etching graph of the metal layer, only the influence of the line width of the metal layer and the distance between adjacent metal layers on the etching deviation is considered, an OPC engineer establishes an etching deviation table, and the etching deviation of a specific segment is a function of the related line width and the distance. The method only considers the very limited information of the current metal layer, completely ignores the shape of the bottom layer, actually has a plurality of metal connecting layers on the bottom layer, the metal layer is partially or completely positioned on the metal connecting layers, in the actual etching process, the etching process is influenced by the material of the bottom layer, and the etching rates of the part of the metal layer positioned on the metal connecting layers are different from those of the part of the metal layer not positioned on the metal connecting layers, thereby influencing the size of the formed metal layer. Therefore, the etching bias calculated based on the above-described bias table may not be accurately corrected.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
Fig. 1 and 2 are a schematic cross-sectional structure diagram and a top view of a semiconductor structure in an embodiment of the invention.
Referring to fig. 1 and 2, fig. 1 is a schematic cross-sectional view along direction AA' of fig. 2, and fig. 2 is a top view of fig. 1, providing a bottom structure including a plurality of conductive layers 103 arranged in parallel.
Establishing a two-dimensional coordinate system (X, Y) in a plane of the substructure surface in a first direction X and a second direction Y parallel to the substructure surface, the profile of the conductive layer 103 having a set of coordinate ranges U in the two-dimensional coordinate system (X, Y), the first direction X and the second direction Y being perpendicular to each other.
In this embodiment, the bottom layer structure further includes: a substrate 100; a device layer 101 on the substrate 100, the device layer 101 including an isolation structure (not shown) and a device structure (not shown) within the isolation structure, the device structure including a transistor, a diode, a transistor, a capacitor, an inductor, a conductive structure, or the like; a dielectric layer 102 on the device layer 101; a conductive layer 103 within the dielectric layer 102, the conductive layer 103 being electrically connected to the device structure.
In this embodiment, the material of the substrate 100 is silicon.
In other embodiments, the substrate material comprises silicon carbide, silicon germanium, a multi-component semiconductor material of group iii-v elements, silicon-on-insulator (SOI), or germanium-on-insulator (GOI). The multielement semiconductor material formed by III-V group elements comprises InP, GaAs, GaP, InAs, InSb, InGaAs or InGaAsP.
The material of the isolation structure comprises a dielectric material comprising a combination of one or more of silicon oxide, silicon nitride, silicon oxynitride, aluminum oxide, aluminum nitride, silicon carbonitride, and silicon oxycarbonitride. The material of the dielectric layer 102 comprises a dielectric material comprising one or more of silicon oxide, silicon nitride, silicon oxynitride, aluminum oxide, aluminum nitride, silicon carbonitride, and silicon oxycarbonitride. The material of the conductive layer 103 includes a metal including a combination of one or more of copper, aluminum, tungsten, cobalt, and titanium nitride.
In this embodiment, the material of the isolation structure includes silicon oxide; the material of the dielectric layer 102 includes silicon oxide.
Fig. 3 and 4 are schematic diagrams of a layout to be corrected in the embodiment of the present invention.
Referring to fig. 3, a layout 200 to be corrected is provided, where the layout 200 to be corrected has a plurality of patterns 201 to be corrected, and the layout 200 to be corrected is used to form a patterning layer on the underlying structure.
The plurality of patterns 201 to be corrected are arranged in parallel along a first direction X parallel to the layout to be corrected, and the plurality of patterns 201 to be corrected are arranged in parallel along a second direction Y parallel to the layout to be corrected.
The adjacent parallel patterns 201 to be corrected have a space therebetween.
In this embodiment, the layout 200 to be corrected and the underlying structure share the same coordinate system.
Referring to fig. 4, fig. 4 is an enlarged schematic view of any one of the to-be-corrected patterns 201 in fig. 3.
The graph 201 to be corrected includes: opposing first and second short edges LE1, LE 2; and a second edge (not labeled) perpendicular to the first short edge LE1 and the second short edge LE2 and having two ends respectively connected with the first short edge LE1 and the second short edge LE 2.
The side length of the first short side LE1 and the second short side LE2 is a first size CD, the side length of the second side is a second size L, and the second size L is larger than the first size CD.
Next, first information is obtained according to the graph 201 to be corrected.
With continued reference to fig. 3 and fig. 4, first information is obtained through the to-be-corrected graph 201.
The method for acquiring the first information of the graph 201 to be corrected includes: acquiring a first dimension CD, wherein the first dimension CD is the side length of the first short edge LE1 and the second short edge LE 2; acquiring a second size L, wherein the second size L is the side length of the second edge and is larger than the first size CD; and acquiring a first space, wherein the first space is the space between adjacent parallel graphs to be corrected.
The first information includes a first size CD, a second size L, and a first space.
Next, a first vector v is obtained according to the first information0。
Obtaining a first vector v0The method comprises the following steps: obtaining a first vector v according to the first dimension CD, the second dimension L and the first space0(ii) a The first vector v0＝[CD，space，L]。
With continuing reference to fig. 3 and fig. 4, in the present embodiment, the method further includes: the second edge is divided into a plurality of line segments.
The segment type includes: the first type of line segment N1, the second type of line segment N2 or the third type of line segment, the first type of line segment is a line segment which is connected with the first short edge LE1 or the second short edge LE2, the second type of line segment is a line segment which is not connected with the first short edge LE1 and the second short edge LE2, and the third type of line segment includes the first short edge LE1 and the second short edge LE 2.
The first line segment N1 has a first midpoint C1, and the first midpoint C1 has coordinates of (x '1, y' 1); the second type line segment N2 has a second midpoint C2, the second midpoint C2 has coordinates of (x '2, y' 2); the first short edge LE1 or the second short edge LE2 has a third midpoint C3, and the coordinates of the third midpoint C3 are (x '3, y' 3).
The method for acquiring the first information of the graph 201 to be corrected further includes: obtaining a second distance dist1, where the second distance dist1 is a distance between a midpoint C1, C2, or C3 of any of the line segments and the first short edge LE 1; obtaining a third distance dist2, where the third distance dist2 is a distance between a midpoint C1, C2, or C3 of any of the line segments and the second short edge LE 2; acquiring the segment type, wherein the segment type comprises a first type segment N1, a second type segment N2 or a third type segment, and the third type segment comprises the first short edge LE1 and a second short edge LE 2.
In this embodiment, the first information includes a first size CD, a second size L, a first space, a second space dist1, a third space dist2, and a segment type.
The first information comprises a first size CD, a second size L and a first space, and also comprises a second space dist1, a third space dist2 and a line segment type, so that the content of the first information is richer, and when the etching deviation is obtained through the first information and the second information, the etching deviation is more accurate.
In other embodiments, the first information can exclude the second and third pitches dist1, dist2 and segment type.
Next, second information is acquired from the conductive layer 103.
Fig. 5 is a flowchart illustrating steps for acquiring the second information according to an embodiment of the present invention.
Referring to fig. 5, the second information is obtained through the conductive layer 103, and the step of obtaining the second information of the conductive layer 103 is as follows:
s10: acquiring a density function P (x, y) of the conducting layer in the bottom layer structure;
s20: providing a first Gaussian function GS1A second Gaussian function GS2A third Gaussian function GS3And a fourth Gaussian function GS4Said first Gaussian function GS1Having a first variance S1, the second Gaussian function GS2Having a second variance S2, the third Gaussian function GS3Having a third variance S3, the fourth Gaussian function GS4A fourth difference S4, the first variance S1, the second variance S2, the third variance S3 and the fourth variance S4 being different from each other;
s30: performing first Gaussian convolution processing on the density function P (x, y) to obtain a first Gaussian convolution function Q1(x,y)，
S40: performing second Gaussian convolution processing on the density function P (x, y) to obtain a second Gaussian convolution function Q2(x,y)，
S50: performing a third Gaussian convolution processing on the density function P (x, y) to obtain a third Gaussian convolution function Q3(x,y)，
S60: performing a fourth Gaussian convolution processing on the density function P (x, y) to obtain a fourth Gaussian convolution function Q4(x,y)，
S70: acquiring a midpoint coordinate (x ', y') of any line segment;
s80: obtaining second information from said midpoint coordinates (x ', y'), saidThe second information includes a first Gaussian convolution function Q1(x, y) function value at midpoint (x ', y') of line segment
Second gaussian convolution function Q2(x, y) function value at midpoint (x ', y') of line segment
Third Gaussian convolution function Q3(x, y) function value at midpoint (x ', y') of line segment
And a fourth Gaussian convolution function Q4(x, y) function value at midpoint (x ', y') of line segment
Please refer to step S10: and acquiring a density function P (x, y) of the conducting layer in the bottom layer structure.
The method for acquiring the density function P (x, y) of the conducting layer comprises the following steps: the density function P (x, y) of the conductive layer 103 is obtained from a set U of coordinate ranges of the profile of the conductive layer 103.
In this embodiment, P (x, y) is 0 or P (x, y) is 1.
When any coordinate (X, Y) in a two-dimensional coordinate system (X, Y) is located in a coordinate range set U of the outline of the conductive layer 103, P (X, Y) is 1; when any coordinate (X, Y) in the two-dimensional coordinate system (X, Y) is located outside the coordinate range set U of the outline of the conductive layer 103, P (X, Y) is 0.
Please refer to step S20: providing a first Gaussian function GS1A second Gaussian function GS2A third Gaussian function GS3And a fourth Gaussian function GS4Said first Gaussian function GS1Having a first variance S1, the second Gaussian function GS2Having a second variance S2, the third Gaussian function GS3Having a third variance S3, the fourth Gaussian function GS4Has a fourth difference S4, the first variance S1, the second variance S2 and the third variance S3And the fourth difference S4 are different from each other.
The first Gaussian function GS1A second Gaussian function GS2A third Gaussian function GS3And a fourth Gaussian function GS4The density function P (x, y) can be subjected to Gaussian convolution processing by taking the first variance S1, the second variance S2, the third variance S3 and the fourth variance S4 as different diffusion radii, so that the density function P (x, y) can be fuzzified, and the influence of an underlying structure on the etching of an upper layer pattern under different physical states of ion bombardment in the actual etching process can be simulated.
The larger the values of the first variance S1, the second variance S2, the third variance S3 and the fourth variance S4 are, the larger the diffusion radius of the convolution processing performed on the density function P (x, y) is, and the higher the degree of the blurring processing performed on the density function P (x, y) is, so that the influence of the underlying structure on the etching of the upper layer pattern can be simulated in different physical states of ion bombardment.
Please refer to steps S30, S40, S50 and S60: performing first Gaussian convolution processing on the density function P (x, y) to obtain a first Gaussian convolution function Q1(x,y)，
Performing second Gaussian convolution processing on the density function P (x, y) to obtain a second Gaussian convolution function Q2(x,y)，
Performing a third Gaussian convolution processing on the density function P (x, y) to obtain a third Gaussian convolution function Q3(x,y)，
Performing a fourth Gaussian convolution processing on the density function P (x, y) to obtain a fourth Gaussian convolution function Q4(x,y)，
For the density function P (x, y)Performing a first Gaussian convolution process
Obtaining a first Gaussian convolution function Q1(x,y)，
Namely, on the basis of the underlying structure, the density function P (x, y) is fuzzified by taking the first variance S1 as a diffusion radius so as to simulate the influence of the underlying structure on the etching of the upper layer pattern in the actual etching process.
Performing a second Gaussian convolution process on the density function P (x, y)
Obtaining a second Gaussian convolution function Q2(x,y)，
Namely, on the basis of the underlying structure, the density function P (x, y) is fuzzified by taking the second variance S2 as a diffusion radius so as to simulate the influence of the underlying structure on the etching of the upper layer pattern in the actual etching process.
Performing a third Gaussian convolution process on the density function P (x, y)
Obtaining a third Gaussian convolution function Q3(x,y)，
Namely, on the basis of the underlying structure, the third variance S3 is used as a diffusion radius, and the density function P (x, y) is subjected to fuzzification processing so as to simulate the influence of the underlying structure on the etching of the upper layer pattern in the actual etching process.
Performing a fourth Gaussian convolution process on the density function P (x, y)
Obtaining a fourth Gaussian convolution function Q4(x,y)，
Namely, on the basis of the underlying structure, the fourth variance S4 is used as a diffusion radius, and the density function P (x, y) is fuzzified to simulate the influence of the underlying structure on the etching of the upper layer pattern in the actual etching process.
Please refer to step S70: the midpoint coordinates (x ', y') of any line segment are acquired.
Any line segment comprises a first line segment N1, a second line segment N2 or a third line segment, the first line segment is a line segment which is connected with the first short edge LE1 or the second short edge LE2 in a plurality of line segments, the second line segment is a line segment which is not connected with the first short edge LE1 and the second short edge LE2 in the plurality of line segments, and the third line segment comprises the first short edge LE1 and the second short edge LE 2.
The midpoint coordinates of any line segment include: coordinates (x '1, y' 1) of a first midpoint C1 of the first type line segment N1; coordinates (x '2, y' 2) of a second midpoint C2 of the second type line segment N2; coordinates (x '3, y' 3) of the third midpoint C3 of the first short edge LE1 or the second short edge LE 2.
Please refer to step S80: obtaining second information from the midpoint coordinates (x ', y'), the second information comprising a first Gaussian convolution function Q1(x, y) function value at midpoint (x ', y') of line segment
Second gaussian convolution function Q2(x, y) function value at midpoint (x ', y') of line segment
Third Gaussian convolution function Q3(x, y) function value at midpoint (x ', y') of line segment
And a fourth Gaussian convolution function Q4(x, y) function value at midpoint (x ', y') of line segment
In the present embodiment, the coordinates of the midpoint of the line segment include (x '1, y' 1), (x '2, y' 2), or (x '3, y' 3).
And after the first information and the second information are obtained, obtaining the etching deviation according to the first information and the second information.
By acquiring first information of a graph to be corrected, acquiring second information of the underlying structure and acquiring etching deviation according to the first information and the second information, the accuracy rate of the acquired etching deviation is improved.
The method for acquiring the etching deviation according to the first information and the second information comprises the following steps: acquiring a convolutional neural network model; and processing the first information and the second information by adopting a convolutional neural network model to obtain etching deviation.
The method for processing the first information and the second information to obtain the etching deviation comprises the following steps: acquiring an input vector v according to the first information and the second information; and processing the input vector v by adopting a convolutional neural network model to obtain the etching deviation.
The method for acquiring the input vector v according to the first information and the second information comprises the following steps: obtaining a first vector v according to the first information0(ii) a Acquiring a second vector v according to the second informationg(ii) a The first vector v0And a second vector vgConnecting in series to obtain input vector v ═ v0+vg。
In this embodiment, the first information includes a first size CD, a second size L, a first space, a second space dist1, a third space dist2, and a segment type.
Obtaining a first vector v0The method comprises the following steps: acquiring a first vector v according to the first size CD, the second size L, the first space, the line segment type, the second space dist1 and the third space dist20Said first vector v0＝[CD，space，L，type，dist1，dist2]。
The second information comprises a first Gaussian convolution function Q1(x, y) at midpoint (x 'of line segment'Y') function value
Second gaussian convolution function Q2(x, y) function value at midpoint (x ', y') of line segment
Third Gaussian convolution function Q3(x, y) function value at midpoint (x ', y') of line segment
And a fourth Gaussian convolution function Q4(x, y) function value at midpoint (x ', y') of line segment
Obtaining a second vector vgThe method comprises the following steps: according to a first Gaussian convolution function Q1(x, y) function value at midpoint (x ', y') of line segment
Second gaussian convolution function Q2(x, y) function value at midpoint (x ', y') of line segment
Third Gaussian convolution function Q3(x, y) function value at midpoint (x ', y') of line segment
And a fourth Gaussian convolution function Q4(x, y) function value at midpoint (x ', y') of line segment
Obtaining a second vector vgSaid second vector
Obtaining a second vector vgThereafter, the first vector v is measured0And a second vector vgConnecting in series to obtain input vector v ═ v0+vg。
I.e. the input vector
And processing the first information and the second information by adopting a convolutional neural network model to obtain etching deviation, so that the precision rate of the etching deviation is improved.
FIG. 6 is a flowchart illustrating steps of obtaining a convolutional neural network model according to an embodiment of the present invention.
Referring to fig. 6, the step of obtaining the convolutional neural network model includes:
s100: providing an initial convolutional neural network model;
s200: providing a plurality of groups of training samples, wherein the training samples comprise information of a plurality of patterns to be corrected and corresponding target etching deviation;
s300: acquiring an input vector according to the information of a plurality of graphs to be corrected;
s400: inputting the input vector into an initial convolutional neural network model for iterative training to obtain the convolutional neural network model.
The method for inputting the input vector into the initial convolutional neural network model for iterative training comprises the following steps: inputting the input vector into an initial convolutional neural network model, and outputting a predicted etching deviation; acquiring a difference value between the predicted etching deviation and the target etching deviation; judging whether the difference value is within a preset range; if the difference value exceeds the preset range, continuing to carry out iterative training on the initial convolutional neural network model by using the training sample until the range of the difference value is within the preset range; and if the difference value is within the preset range, obtaining the convolutional neural network model.
In this embodiment, the number of training samples is greater than 1 ten thousand groups.
In this embodiment, the convolutional neural network model includes: the input layer is used for vectorizing the input first information and the input second information to obtain an input vector; the hidden layer is used for classifying the input vectors; and the output layer is used for outputting the classification processing result.
After the etching deviation is obtained, the pattern 201 to be corrected is corrected according to the etching deviation, and a target pattern is obtained.
The process of correcting the pattern 201 to be corrected to obtain the target pattern is a common optical proximity correction process, and is not described herein again.
In summary, by acquiring first information of a pattern to be corrected, acquiring second information of the underlying structure, and acquiring an etching deviation according to the first information and the second information, the accuracy rate of the acquired etching deviation is improved, and the correction effect of acquiring a target pattern by correcting the pattern to be corrected by using the etching deviation is better.
Although the present invention is disclosed above, the present invention is not limited thereto. Various changes and modifications may be effected therein by one skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (18)
1. A method of pattern correction, comprising:
providing a bottom layer structure, wherein the bottom layer structure comprises a plurality of conducting layers which are arranged in parallel;
providing a layout to be corrected, wherein the layout to be corrected is provided with a plurality of patterns to be corrected, and the layout to be corrected is used for forming a patterning layer on the bottom layer structure;
acquiring first information through the graph to be corrected;
acquiring second information through the conductive layer;
acquiring etching deviation according to the first information and the second information;
and correcting the pattern to be corrected according to the etching deviation to obtain a target pattern.
2. The pattern correction method according to claim 1, wherein the method of obtaining the etching deviation based on the first information and the second information includes: acquiring a convolutional neural network model; and processing the first information and the second information by adopting a convolutional neural network model to obtain etching deviation.
3. The pattern correction method according to claim 2, wherein the method of processing the first information and the second information to obtain the etching deviation comprises: acquiring an input vector v according to the first information and the second information; and processing the input vector v by adopting a convolutional neural network model to obtain the etching deviation.
4. The pattern correction method according to claim 3, wherein the method of obtaining the input vector v based on the first information and the second information comprises: obtaining a first vector v according to the first information0(ii) a Acquiring a second vector v according to the second informationg(ii) a The first vector v0And a second vector vgConnecting in series to obtain input vector v ═ v0+vg。
5. The pattern correction method according to claim 1, wherein the pattern to be corrected includes: opposing first and second short edges LE1, LE 2; and a second edge perpendicular to the first short edge LE1 and the second short edge LE2, and two ends of the second edge are respectively connected with the first short edge LE1 and the second short edge LE 2.
6. The pattern correction method according to claim 5, wherein the method of acquiring the first information of the pattern to be corrected includes: acquiring a first dimension CD, wherein the first dimension CD is the side length of the first short edge LE1 and the second short edge LE 2; acquiring a second size L, wherein the second size L is the side length of the second edge and is larger than the first size CD; and acquiring a first space, wherein the first space is the space between adjacent parallel graphs to be corrected.
7. The pattern correction method according to claim 6, characterized in that the first vector v is obtained0The method comprises the following steps: obtaining a first vector v according to the first dimension CD, the second dimension L and the first space0(ii) a The first vector v0＝[CD，space，L]。
8. The pattern correction method according to claim 6, wherein the method of acquiring the first information of the pattern to be corrected further comprises: dividing the second edge into a plurality of line segments; obtaining a second distance dist1, where the second distance dist1 is a distance between a midpoint of any one of the line segments and the first short edge LE 1; obtaining a third distance dist2, where the third distance dist2 is a distance between a midpoint of any one of the line segments and the second short edge LE 2; the segment type includes: the first type of line segment is a line segment connected with the first short edge LE1 or the second short edge LE2, the second type of line segment is a line segment which is not connected with the first short edge LE1 and the second short edge LE2, and the third type of line segment comprises the first short edge LE1 and the second short edge LE 2.
9. The pattern correction method according to claim 8, characterized in that the first vector v is obtained0The method of (2) further comprises: acquiring a first vector v according to the first size CD, the second size L, the first space, the line segment type, the second space dist1 and the third space dist20Said first vector v0＝[CD，space，L，type，dist1，dist2]。
10. The pattern correction method according to claim 9, wherein the method of acquiring the second information of the conductive layer comprises: acquiring a density function P (x, y) of the conducting layer in the bottom layer structure; providing a first Gaussian function GS1A second Gaussian function GS2A third Gaussian function GS3And a fourth Gaussian function GS4Said first Gaussian function GS1Having a first variance S1, the second Gaussian function GS2Having a second variance S2, the third Gaussian function GS3Having a third variance S3, the fourth Gaussian function GS4A fourth difference S4, the first variance S1, the second variance S2, the third variance S3 and the fourth variance S4 being different from each other; performing first Gaussian convolution processing on the density function P (x, y) to obtain a first Gaussian convolution function Q1(x,y)，
Performing second Gaussian convolution processing on the density function P (x, y) to obtain a second Gaussian convolution function Q2(x,y)，
Performing a third Gaussian convolution processing on the density function P (x, y) to obtain a third Gaussian convolution function Q3(x,y)，
Performing a fourth Gaussian convolution processing on the density function P (x, y) to obtain a fourth Gaussian convolution function Q4(x,y)，
Acquiring a midpoint coordinate (x ', y') of any line segment; obtaining second information from the midpoint coordinates (x ', y'), the second information comprising a first Gaussian convolution function Q1(x, y) function value at midpoint (x ', y') of line segment
Second gaussian convolution function Q2(x, y) function value at midpoint (x ', y') of line segment
Third Gaussian convolution function Q3(x, y) function value at midpoint (x ', y') of line segment
And a fourth Gaussian convolution function Q4(x, y) function value at midpoint (x ', y') of line segment
11. The pattern correction method according to claim 10, wherein the method of obtaining the density function P (x, y) of the conductive layer comprises: establishing a two-dimensional coordinate system (X, Y) on the plane of the surface of the underlying structure; acquiring a coordinate range set of the outline of the conducting layer in the two-dimensional coordinate system; and acquiring a density function P (x, y) of the conducting layer according to the coordinate range set of the conducting layer outline.
12. The pattern correction method according to claim 11, wherein P (x, y) is 0 or P (x, y) is 1.
13. The pattern correction method according to claim 12, wherein when any coordinate (x, y) in the two-dimensional coordinate system is located within the coordinate range of the conductive layer, P (x, y) ═ 1; when any coordinate (x, y) in the two-dimensional coordinate system is located outside the coordinate range of the conductive layer, P (x, y) is 0.
14. The pattern correction method according to claim 10, characterized in that the second vector v is acquiredgThe method comprises the following steps: according to a first Gaussian convolution function Q1(x, y) function value at midpoint (x ', y') of line segment
Second gaussian convolution function Q2(x, y) function value at midpoint (x ', y') of line segment
Third Gaussian convolution function Q3(x, y) at midpoint (x 'of line segment'Y') function value
And a fourth Gaussian convolution function Q4(x, y) function value at midpoint (x ', y') of line segment
Obtaining a second vector vgSaid second vector
15. The pattern correction method according to claim 2, wherein the method of obtaining the convolutional neural network model comprises:
providing an initial convolutional neural network model;
providing a plurality of groups of training samples, wherein the training samples comprise information of a plurality of patterns to be corrected and corresponding target etching deviation;
acquiring an input vector according to the information of a plurality of graphs to be corrected;
inputting the input vector into an initial convolutional neural network model for iterative training to obtain the convolutional neural network model.
16. The pattern correction method of claim 15, wherein the method of inputting the input vector into an initial convolutional neural network model for iterative training comprises: inputting the input vector into an initial convolutional neural network model, and outputting a predicted etching deviation; acquiring a difference value between the predicted etching deviation and the target etching deviation; judging whether the difference value is within a preset range; if the difference value exceeds the preset range, continuing to carry out iterative training on the initial convolutional neural network model by using the training sample until the range of the difference value is within the preset range; and if the difference value is within the preset range, obtaining the convolutional neural network model.
17. A pattern correction method as described in claim 15, wherein the number of said training samples is more than 1 ten thousand sets.
18. The pattern correction method according to claim 2, wherein the convolutional neural network model comprises: the input layer is used for vectorizing the input first information and the input second information to obtain an input vector;
the hidden layer is used for classifying the input vectors;
and the output layer is used for outputting the classification processing result.
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