CN109426067B - Modeling method for optical proximity correction and graph weight generation method for optical proximity correction - Google Patents
Modeling method for optical proximity correction and graph weight generation method for optical proximity correction Download PDFInfo
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- G—PHYSICS
- G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
- G03F—PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES, e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTOR DEVICES; MATERIALS THEREFOR; ORIGINALS THEREFOR; APPARATUS SPECIALLY ADAPTED THEREFOR
- 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
Abstract
The invention provides a modeling method for optical proximity correction and a graph weight generation method for the same. The optical proximity correction modeling method comprises the following steps: constructing a measurement matrix based on a plurality of measurement values of a plurality of measurement graphs in a measurement file; performing characteristic decomposition on the measurement matrix to obtain a characteristic value; and generating respective weights for the plurality of metrology graphics based on the feature values. The graph weight generation method for optical proximity correction modeling provided by the invention sets the weights of various measured graphs in a measurement file in a quantitative mode, and can be closer to the optimal weight compared with the traditional qualitative weight setting mode, so that the iteration times required in the modeling process of optical proximity correction based on the graph weight generation method are obviously reduced, the modeling time is shortened, and the modeling efficiency and the model accuracy are improved.
Description
Technical Field
The invention relates to the technical field of Optical Proximity Correction (OPC), in particular to a modeling method of OPC and a graph weight generation method used for the OPC.
Background
As integrated circuits become more complex, feature sizes become smaller, which makes specification requirements for the accuracy of OPC modeling more stringent. One key step in OPC modeling is to optimize the OPC model using weighted metrology files.
In the conventional process, the weights of various measurement graphs in the weighted measurement files are set based on a qualitative method, that is, based on the classification and understanding degree of the graphs by engineers and engineering experience. To achieve better weight setting, a large number of iterations of the "model optimization-verification-weight adjustment" process are required, which is very time consuming and inefficient.
Disclosure of Invention
In view of the above problems, in one aspect, the present invention provides a method for generating a graph weight for optical proximity correction modeling, the method including: constructing a measurement matrix based on a plurality of measurement values of a plurality of measurement graphs in a measurement file; performing characteristic decomposition on the measurement matrix to obtain a characteristic value; and generating respective weights for the plurality of metrology graphics based on the feature values.
In an embodiment of the present invention, the constructing the measurement matrix based on the respective measurement values of the measurement graphs in the measurement file includes: selecting n graphs from the measurement file, wherein n is a natural number greater than 1; for each of the n patterns, selecting n measurement values; and based on the selected n 2Each measurement constructs a square matrix as the measurement matrix.
In one embodiment of the invention, the selection is based on n2Constructing a square matrix of measurements as the metrology matrix comprises: and filling the n measured values of each graph into a matrix in a row form.
In an embodiment of the present invention, the generating weights for each of the plurality of metrology graphics based on the feature values comprises: if the generated characteristic value is a real number, directly taking the characteristic value as the weight; if the generated eigenvalue is a complex number, then the real part of the complex number is taken or the complex number is modulo as the weight.
In one embodiment of the invention, the method further comprises: and qualitatively fine-tuning the weights as required to generate respective optimal weights for the multiple measurement graphs in a quantitative and qualitative combined manner.
In one embodiment of the invention, the qualitative fine tuning comprises: and performing weight fine adjustment based on at least one of the type of the measurement graph, the design rule and the engineering experience.
In one embodiment of the present invention, the plurality of metrology patterns include different sizes of metrology patterns of the same category and/or different categories of metrology patterns.
In one embodiment of the present invention, the metrology pattern is a pattern designed on a test reticle, and the measurement value corresponding to each metrology pattern is a measurement value that the metrology pattern exposes to a wafer.
In one embodiment of the invention, the metrology pattern comprises a cross-pitch pattern and/or a two-dimensional pattern.
In another aspect, the present invention provides a method for modeling optical proximity correction, the method including: optimizing an optical proximity correction model based on a weighted measurement file until the model meets the specification, wherein the weighted measurement file is a measurement file containing respective weights of various measurement graphs in the measurement file, and the respective weights of the various measurement graphs are generated based on any one of the graph weight generation methods for optical proximity correction modeling.
The graph weight generation method for optical proximity correction modeling provided by the invention sets the weights of various measured graphs in a measurement file in a quantitative mode, and can be closer to the optimal weight compared with the traditional qualitative weight setting mode, so that the iteration times required in the modeling process of optical proximity correction based on the graph weight generation method are obviously reduced, the modeling time is shortened, and the modeling efficiency and the model accuracy are improved.
Drawings
The following drawings of the invention are included to provide a further understanding of the invention. The drawings illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.
In the drawings:
FIG. 1 shows a schematic flow diagram of a method of generating graphical weights for optical proximity correction modeling according to an embodiment of the present invention;
FIGS. 2A-2E illustrate a comparison of fitting errors based on a graph weight generation method for optical proximity correction modeling and based on a qualitative weight setting method according to an embodiment of the present invention; and
FIG. 3 shows a schematic flow chart of a modeling method of optical proximity correction according to an embodiment of the present invention.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present invention. It will be apparent, however, to one skilled in the art, that the present invention may be practiced without one or more of these specific details. In other instances, well-known features have not been described in order to avoid obscuring the invention.
It is to be understood that the present invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of the associated listed items.
In order to provide a thorough understanding of the present invention, detailed steps and detailed structures will be set forth in the following description in order to explain the present invention. The following detailed description of the preferred embodiments of the invention, however, the invention is capable of other embodiments in addition to those detailed.
As previously mentioned, the continuous reduction in feature size makes the specification requirements for the accuracy of OPC modeling increasingly stringent. For example, there are very strict specifications for the accuracy of OPC modeling for semiconductor manufacturing processes of 14 nanometers (nm) and below. Therefore, higher requirements are placed on OPC modeling.
One key step in OPC modeling is to optimize the OPC model using weighted metrology files. The weighted measurement file is a measurement file in which weights are set for various measurement graphs in the measurement file. The weights of various metrology patterns are set because the objective function of OPC modeling is: delt (CDm-CDs) - >0 (where CDm represents the measured wafer size and CDs represents the simulated fit size), while generally, the convergence of thousands of measurement points in a metrology file is not consistent due to several factors, and certain patterns have some priority for models. Therefore, the purpose of setting the weight is mainly to increase the weight of the graph with high priority in the model and optimize the convergence rate. Setting the weights of various metrology patterns using a qualitative approach requires a large number of iterations of the model optimization-verification-weight adjustment process, which is time consuming and inefficient.
Therefore, the present invention provides a graph weight generation method for optical proximity correction modeling, which is described in detail below with reference to specific embodiments in conjunction with the accompanying drawings.
FIG. 1 shows a schematic flow diagram of a method for generating graph weights for OPC modeling according to an embodiment of the present invention. As shown in fig. 1, the method 100 for generating the figure weight for optical proximity correction modeling includes the following steps:
In step S110, a measurement matrix is constructed based on a plurality of measurement values of each of the plurality of measurement patterns in the measurement file.
In an embodiment of the present invention, the measurement file may be a file containing information related to a series of measurement points (gauge). The measurement point can be understood as a vector to indicate the measurement position. For example, a line width is measured across which the start and end points of the vector need to be located. During the OPC modeling process, the software can locate and simulate the pattern by reading each measurement point.
In a metrology file, each metrology point may include a set of metrology graphics and a set of measurement values corresponding to the set of metrology graphics. The relevant information contained in the metrology file is understood below in conjunction with Table 1.
TABLE 1
| Gauge ID | Gauge name | Measured value | Weight of |
| 1 | {G1} | {M1} | W1 |
| 2 | {G2} | {M2} | W2 |
| 3 | {G3} | {M3} | W3 |
| 4 | {G4} | {M4} | W4 |
| 5 | {G5} | {M5} | W5 |
| 6 | {G6} | {M6} | W6 |
| 7 | {G7} | {M7} | W7 |
| 8 | {G8} | {M8} | W8 |
| … | … | … | … |
| … | … | … | … |
| n | {Gn} | {Mn} | Wn |
As shown in Table 1, each metrology point may include a set of metrology graphs { Gn }, where n is a natural number greater than 1, and a set of measurement values { Mn } corresponding to the set of metrology graphs { Gn }. { Gn } may represent a given set of graphics types, such as cross-pitch (through pitch), two-dimensional graphics, and so on. Accordingly, { Mn } may represent a combination of measured values corresponding to { Gn }. Here, the pitch in the through pitch may be understood as a pitch + space (where line is a line width and space is a distance between line widths), and the through pitch may be understood as an array in which the width of line (or space) is fixed and the space (or line) is sequentially changed.
In an embodiment of the present invention, the measurement patterns may be measurement patterns of the same type with different sizes. For example, { G1} may represent a set of through pins of size 20nm, while { G2} may represent a set of through pins of size 80 nm. In addition, the measurement patterns may be different types of measurement patterns.
In an embodiment of the present invention, the metrology pattern may be a pattern designed on the test reticle, and the measurement value corresponding to each metrology pattern is the measurement value that the metrology pattern exposes to the wafer.
With continued reference to table 1, for each metrology pattern { Gn }, table 1 also shows its corresponding weights Wn, which are to be generated by the pattern weight generation method 100 for OPC modeling according to an embodiment of the present invention, which will be described later.
Continuing now with reference to FIG. 1, in an embodiment of the present invention, the step of constructing a metrology matrix based on the plurality of measurement values of the plurality of metrology patterns in the metrology file described in step S110 may further comprise: selecting n graphs from the measurement file, wherein n is a natural number greater than 1; for each of the n patterns, selecting n measurement values; and based on the selected n 2A square matrix is constructed for each measurement as the measurement matrix. For example, the constructed measurement matrix M may be represented by the following formula (1):
wherein M1-Mn represents the type of gauge, for example, M1 may represent a thread. M11-M1 n represent n measurements in the through pitch of M1. In equation (1), n measurement values of each pattern are filled in a matrix in the form of rows, which is merely exemplary. In other examples, the n measurements for each pattern may be populated into the matrix in columns as well.
In step S120, the measurement matrix is subjected to eigen decomposition to obtain eigenvalues.
With continued reference to the above example, the metrology matrix M constructed in step S110 may be characterized as represented by equations (2) and (3) below:
Mx＝λx (2)
λ＝diag(λ1,λ2,...,λn) (3)
where x denotes a feature vector and λ denotes a feature value. Equation (3) represents a symmetric matrix of eigenvalue representation. Equation (2) can be understood as a linear transformation in which M acts on the feature vector x, equivalent to a linear transformation in which a matrix expressed by equation (3) acts on x. Each lambda value can be viewed as the contribution strength of the corresponding vector in M.
In step S130, weights for the plurality of metrology patterns are generated based on the feature values.
In an embodiment of the present invention, if the generated eigenvalue is a real number, the generated eigenvalue may be directly used as the weight of each of the plurality of metrology graphs. If the generated characteristic value is a complex number, the real part of the complex number can be taken or the modulus of the complex number can be taken as the weight of each of the plurality of measurement patterns.
In the embodiment of the present invention, since typical patterns (i.e. various measurement patterns) are placed together in a linear space (i.e. measurement matrix) to measure, so that the data which are not related to each other appears to have some degree of correlation, and the decomposed characteristic quantity represents the macroscopic characteristics of the modeling data in the linear transformation to some extent, the quantized values which are the most initial weights can be referred to, because the characteristic value obtained by decomposition represents the vibration (complex real part) and rotation (complex imaginary part) of the linear transformation. Therefore, the graph weight generating method 100 for OPC modeling provided by the present invention can set the weights of various measurement graphs in a measurement file in a quantitative manner.
In addition, in a further embodiment, the graph weight generation method 100 for optical proximity correction modeling may further include the following steps (not shown in fig. 1): and carrying out qualitative fine adjustment on the weights according to the requirement so as to realize that the optimal weights for the various measurement graphs are generated in a quantitative and qualitative combined manner. Illustratively, qualitative fine tuning may include: and performing weight fine adjustment based on at least one of the type of the measurement graph, the design rule and the engineering experience.
Based on the aforementioned quantitatively generated weights, combined with qualitative fine-tuning, the resulting weight values will be closer to the "optimal" weight settings than if only qualitative approaches were used. This is because the modeling process usually requires a series of iteration cycles to obtain a more ideal model result. Each iteration loop mainly needs to fine-tune the weight, and the finally optimized model corresponds to a weighted gauge file close to the optimization. In practice, the number of iterative cycles may be many hundreds, and few tens. If only a qualitative method is adopted to set the weight, since each group of data in the gauge file seems to have no correlation, an engineer mainly sets an initial value to start operation according to the classification and understanding degree of the graph (such as simple one-dimensional graph and two-dimensional graph) and engineering experience, and has a certain deviation from the optimal weight value, for example, the one-dimensional graph or the two-dimensional graph needs to be further differentiated and needs to be iteratively converged. The graph weight generation method for optical proximity correction modeling provided by the invention adds a quantization method in the initial weight setting, is closer to the optimal setting, can reduce the number of iterative cycles, and shortens the modeling time.
The benefits of the graph weight generation method for OPC modeling provided by the present invention are described below by way of example with reference to FIGS. 2A-2E.
In the example shown in fig. 2A to 2E, taking a 14 nm FIN layer (i.e., a mask including finfets) as an example, assuming that 5 typical sets of metrology patterns (G1, G2, G3, G4, and G5, respectively) are selected to construct a 5 × 5 matrix, and initial weight values for (G1, G2, G3, G4, and G5) are set to (1,1,0.7,0.2,0.1), respectively, based on eigenvalues obtained by eigen decomposition. After 1 iteration, the fitting errors of G1, G2, G3, G4, and G5 are shown in the diagrams of fig. 2A, 2B, 2C, 2D, and 2E, respectively, which are composed of small diamonds. The schematic diagrams of fig. 2A, 2B, 2C, 2D, and 2E with small squares represent the results of fitting errors of G1, G2, G3, G4, and G5 after 50 iterations after setting the initial weight values of (G1, G2, G3, G4, G5) to (1,1,1,0.5,0.5) only by a qualitative method.
As can be seen from fig. 2A to 2E, the fitting error of the initial weight generated by the graph weight generation method for optical proximity correction modeling according to the embodiment of the present invention after 1 iteration is very close to the fitting error of the initial weight set by the qualitative method after 50 iterations. Obviously, to obtain an ideal model, the number of iterations can be significantly reduced by using the graph weight generation method for optical proximity correction modeling according to the embodiment of the invention.
Based on the above description, the graph weight generation method for optical proximity correction modeling provided by the invention sets the weights of various measured graphs in a measurement file in a quantitative manner, and compared with the traditional qualitative weight setting manner, the method can be closer to the optimal weights, so that the iteration times required in the modeling process of the optical proximity correction based on the graph weight generation method are obviously reduced, the modeling time is shortened, and the modeling efficiency and the model accuracy are improved.
According to another aspect of the present invention, there is provided a method of modeling optical proximity correction, the method including: and optimizing the optical proximity correction model based on the weighted measurement file until the model meets the specification, wherein the weighted measurement file is a measurement file containing respective weights of various measurement graphs in the measurement file, and the respective weights of the various measurement graphs are generated based on the graph weight generation method for optical proximity correction modeling. The modeling method of optical proximity correction according to an embodiment of the present invention is further described below with reference to fig. 3.
FIG. 3 shows a schematic flow chart of a method 300 for modeling optical proximity correction according to an embodiment of the present invention. As shown in FIG. 3, the modeling method 300 for optical proximity correction includes the following steps:
In step S310, the optical model is optimized.
In step S320, the OPC model is optimized by a method combining quantification and qualitative.
In step S330, it is verified whether the model meets the specification, and if so, it proceeds to step S340; if not, go back to step S320.
In step S340, a model is output.
The steps S310, S330 and S340 are familiar to those skilled in the art in OPC modeling, and are not described herein again. The quantitative method in step S320 is the aforementioned qualitative method, that is, the aforementioned qualitative method according to the embodiment of the present invention, and the qualitative method is the aforementioned qualitative method (of course, as mentioned above, the method for generating the graph weight for optical proximity correction modeling according to the embodiment of the present invention may also include a step of combining quantification and qualification), and a person skilled in the art may refer to the method for generating the graph weight for optical proximity correction modeling according to the embodiment of the present invention described above with reference to fig. 1 to understand the operation of step S320, and for brevity, no further description is given here. In addition, step S320 may also optimize the OPC model only by a quantitative method.
Based on the above description, the optical proximity correction modeling method according to the embodiment of the present invention sets the weights of various measurement graphs in the measurement file in a quantitative manner (or a quantitative and qualitative manner combined), and compared with the conventional qualitative weight setting manner, the method can be closer to the optimal weight, so that the number of iterations required in the modeling process is significantly reduced, the modeling time is shortened, and the modeling efficiency and the model accuracy are improved.
Although the foregoing example embodiments have been described with reference to the accompanying drawings, it is to be understood that the foregoing example embodiments are merely illustrative and are not intended to limit the scope of the invention thereto. Various changes and modifications may be effected therein by one of ordinary skill in the pertinent art without departing from the scope or spirit of the present invention. All such changes and modifications are intended to be included within the scope of the present invention as set forth in the appended claims.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the method of the present invention should not be construed to reflect the intent: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
It will be understood by those skilled in the art that all of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where such features are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.
The above description is only for the specific embodiment of the present invention or the description thereof, and the protection scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and the changes or substitutions should be covered within the protection scope of the present invention. The protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. A method for generating a graph weight for optical proximity correction modeling, the method comprising:
constructing a measurement matrix based on a plurality of measurement values of a plurality of measurement graphs in a measurement file, so that the plurality of measurement graphs are in the same linear space, wherein the measurement graphs are graphs designed on a test photomask, and the measurement value corresponding to each measurement graph is the measurement value of the measurement graph exposed to a wafer;
performing eigen decomposition on the measurement matrix to obtain eigenvalues, wherein decomposed eigenvalues can be used for representing macroscopic features of the optical proximity correction modeling data in the linear transformation, and the eigenvalues represent vibration and rotation of the linear transformation;
generating respective weights for the plurality of metrology graphs based on the feature values, including: if the generated characteristic value is a real number, directly using the characteristic value as the weight; if the generated eigenvalue is a complex number, then the real part of the complex number is taken or the complex number is modulo as the weight.
2. The method of claim 1, wherein constructing a metrology matrix based on a plurality of measurements from a plurality of metrology profiles in a metrology file comprises:
Selecting n graphs from the measurement file, wherein n is a natural number greater than 1;
for each of the n patterns, selecting n measurements; and
based on the selected n2Each measurement constructs a square matrix as the measurement matrix.
3. The method of claim 2, wherein the selecting n is based on2Constructing a square matrix of measurements as the metrology matrix comprises:
and filling the n measured values of each graph into a matrix in a row form.
4. The method of claim 1, further comprising:
and qualitatively fine-tuning the weights as required to generate respective optimal weights for the multiple measurement graphs in a quantitative and qualitative combined manner.
5. The method of claim 4, wherein the qualitative fine tuning comprises: and performing weight fine adjustment based on at least one of the type of the measurement graph, the design rule and the engineering experience.
6. The method of claim 1, wherein the plurality of metrology patterns comprises different sizes of metrology patterns of the same type and/or different types of metrology patterns.
7. The method of claim 1, wherein the metrology pattern comprises a cross-pitch pattern and/or a two-dimensional pattern.
8. A method for modeling optical proximity correction, the method comprising:
optimizing an optical proximity correction model based on a weighted measurement file until the model meets the specification, wherein the weighted measurement file is a measurement file containing respective weights of various measurement graphs in the measurement file, and the respective weights of the various measurement graphs are generated based on the graph weight generation method for optical proximity correction modeling in any one of claims 1 to 7.
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