Modeling method

Abstract

The invention provides a modeling method, and the method comprises the steps: obtaining a data source which comprises a photomask related initial parameter, GDS data and Gauge data; presetting the related initial parameters of the photomask, and setting related parameters of a machine; performing optimization calculation on the photomask related initial parameters to obtain photomask optimization parameters, and generating point light intensity and a Gauge key size; optimizing the photomask optimization parameter, the point light intensity and the Gauge key size by adopting a simulated annealing algorithm; and outputting a complex graph contour, and performing graph overlay processing on the obtained complex graph contour and a picture acquired by a line width scanner to evaluate whether the graph contours of the complex graph contour and the picture are matched or not. According to the invention, the problem that the tapeout period of an existing product and the delivery period of a photomask are relatively long is solved.

Description

Technical Field

[0001] The invention relates to the technical field of semiconductor manufacturing, and in particular to a modeling method. Background Art

[0002] With the development of advanced process technology, the lithography process window is getting smaller and smaller. In particular, the yield loss caused by the optical proximity effect has become a key factor restricting the lithography process. Entering advanced process nodes, the commonly used modeling method of OPC (Optical Proximity Correction, OPC) is still based on the method of combining pure optical model and process model data to compensate and correct the mask. Specifically, the pure optical model used in the current lithography machine exposure process has been fully described by the Hopkins equation, while the process model is a method of measuring the key dimension data on the actual silicon wafer through experimental methods, and then performing trend fitting to predict the lithography process in advance.

[0003] Strictly speaking, the problem of lithography imaging simulation is actually a problem of projection imaging of partially coherent light in an imaging system with aberrations. When entering advanced process nodes, it is difficult to systematically calibrate the OPC model using the existing pure optical model combined with the process data simulation method. Although a more accurate model can be obtained through regression search, the process of model regression search for the optimal solution is very long and the accuracy of the model needs to be repeatedly corrected, which increases the product tapeout cycle and mask delivery cycle. Summary of the invention

[0004] In view of the above-mentioned shortcomings of the prior art, an object of the present invention is to provide a modeling method for solving the problem of long tapeout cycle and mask delivery cycle of existing products.

[0005] To achieve the above object and other related objects, the present invention provides a modeling method, which comprises:

[0006] Acquire a data source, wherein the data source includes mask-related initial parameters, GDS data, and Gauge data;

[0007] Presetting the initial parameters related to the mask and setting the machine related parameters;

[0008] Optimizing and calculating the initial parameters related to the mask to obtain optimized parameters of the mask, and generating point intensity and Gauge key dimensions;

[0009] Using a simulated annealing algorithm to optimize the mask optimization parameters, the point light intensity and the Gauge key size;

[0010] The complex graphic outline is output, and the obtained complex graphic outline is subjected to graphic overlay processing with the image captured by the line width scanner to evaluate whether the graphic outlines between the two are consistent.

[0011] Optionally, the mask-related initial parameters include: mask material, reflection coefficient, refraction coefficient, mask curvature and mask pattern linear offset.

[0012] Optionally, when optimizing and calculating the initial parameters related to the mask to obtain the optimized parameters of the mask, it is necessary to set the optimization range of the initial parameters of the mask and the step length of each calculation step.

[0013] Optionally, the machine-related parameters include baking temperature and exposure step length.

[0014] Optionally, the method for generating point light intensity includes: calculating the light intensity at each Gauge point in the GDS layout, and outputting the result.

[0015] Optionally, the method for generating the critical dimension of the Gauge includes: obtaining the critical dimension by simulating the light intensity on each Gauge through an optimization method.

[0016] Optionally, the method of optimizing the mask optimization parameters, the point light intensity and the Gauge critical dimension by using a simulated annealing algorithm includes:

[0017] Set optimization space;

[0018] Set the initial solution for optimization;

[0019] Set the objective function.

[0020] Optionally, the set optimization space refers to the optimized numerical range for the focusing accuracy parameters, focusing position parameters, mask optimization parameters, thermal diffusion effect parameters in the photoresist effect, rounded corner blunting effect parameters, line end shortening effect parameters, photoresist reflection coefficient parameters, refractive coefficient parameters, and photoresist thickness parameters of the lithography machine.

[0021] Optionally, the initial solution for setting the optimization refers to setting initial values ​​for the focusing accuracy parameters, focusing position parameters, mask optimization parameters, thermal diffusion effect parameters in the photoresist effect, rounded corner blunting effect parameters, line end shortening effect parameters, reflection coefficient parameters, refractive coefficient parameters, and photoresist thickness parameters of the lithography machine.

[0022] Optionally, the objective function is to make the set initial solution optimal in a local range and optimal in a global range.

[0023] Optionally, the GDS data is data obtained by reading a GDS file, and the GDS file is a layout file.

[0024] Optionally, the Gauge data is data obtained by reading a Gauge file, and the Gauge file is used to calculate the light intensity of a layout coordinate position recorded in the layout.

[0025] As described above, the modeling method of the present invention can significantly shorten the time for searching the optimal solution of the OPC model, and shorten the product tapeout cycle and the mask delivery cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 Shown is a flow chart of the modeling method of the present invention.

[0027] Figure 2 Shown is a graph of experimental results obtained according to the method provided by the present invention. DETAILED DESCRIPTION

[0028] The following describes the embodiments of the present invention through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention.

[0029] See also Figure 1 to Figure 2 It should be noted that the illustrations provided in this embodiment are only schematic illustrations of the basic concept of the present invention. Although the illustrations only show components related to the present invention and are not drawn according to the number, shape and size of components in actual implementation, the form, quantity and proportion of each component in actual implementation may be changed arbitrarily, and the component layout may also be more complicated.

[0030] like Figure 1 As shown, this embodiment provides a modeling method, the method comprising:

[0031] Acquire a data source, wherein the data source includes mask-related initial parameters, GDS data, and Gauge data;

[0032] Presetting the initial parameters related to the mask and setting the machine related parameters;

[0033] Optimizing and calculating the initial parameters related to the mask to obtain optimized parameters of the mask, and generating point intensity and Gauge key dimensions;

[0034] Using a simulated annealing algorithm to optimize the mask optimization parameters, the point light intensity and the Gauge key size;

[0035] The complex graphic outline is output, and the obtained complex graphic outline is subjected to graphic overlay processing with the image captured by the line width scanner to evaluate whether the graphic outlines between the two are consistent.

[0036] Specifically, the mask-related initial parameters include: mask material, reflection coefficient, refraction coefficient, mask curvature and mask pattern linear offset.

[0037] Specifically, when optimizing and calculating the initial parameters related to the mask to obtain the optimized parameters of the mask, it is necessary to set the optimization range of the initial parameters of the mask and the step length of each calculation step.

[0038] Specifically, the machine-related parameters include baking temperature and exposure step length.

[0039] Specifically, the method for generating point light intensity includes: calculating the light intensity at each Gauge point in the GDS layout and outputting the result.

[0040] Specifically, the method for generating the critical dimension of the Gauge includes: obtaining the critical dimension by simulating the light intensity on each Gauge through an optimization method.

[0041] Specifically, the method of optimizing the mask optimization parameters, the point light intensity and the Gauge key size by using the simulated annealing algorithm includes: setting an optimization space; setting an initial solution for optimization; and setting an objective function.

[0042] Specifically, the setting of the optimization space refers to setting the optimized numerical range for the focusing accuracy parameters, focusing position parameters, mask optimization parameters, thermal diffusion effect parameters in the photoresist effect, rounded corner blunting effect parameters, line end shortening effect parameters, photoresist reflection coefficient parameters, refractive coefficient parameters, and photoresist thickness parameters of the lithography machine.

[0043] Specifically, the initial solution for setting the optimization refers to setting initial values ​​for the focusing accuracy parameters, focusing position parameters, mask optimization parameters, thermal diffusion effect parameters in the photoresist effect, rounded corner blunting effect parameters, line end shortening effect parameters, photoresist reflection coefficient parameters, refractive coefficient parameters, and photoresist thickness parameters of the lithography machine.

[0044] Specifically, the objective function should make the set initial solution optimal in a local range and optimal in a global range.

[0045] Specifically, the GDS data is data obtained by reading a GDS file, and the GDS file is a layout file.

[0046] Specifically, the Gauge data is data obtained by reading a Gauge file, and the Gauge file is used to calculate the light intensity at the layout coordinate position recorded in the layout.

[0047] like Figure 2 As shown, in this embodiment, by monitoring the contours of 8 graphics that are prone to weak points, it is found that the complex graphics fast OPC modeling using the layout contour adopted in this embodiment (such as Figure 2 The model method established by this embodiment is obviously superior to the traditional OPC modeling method, and the red figure can clearly predict the bridging phenomenon on the actual wafer. The key dimension values ​​simulated by the method provided by this embodiment are shown in the following table.

[0048]

[0049] In summary, the modeling method of the present invention can significantly shorten the time of searching the optimal solution of the OPC model, shorten the product tapeout cycle and the mask delivery cycle. Therefore, the present invention effectively overcomes various shortcomings of the prior art and has high industrial utilization value.

[0050] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A modeling method, characterized in that: The method comprises: Acquire a data source, wherein the data source includes mask-related initial parameters, GDS data, and Gauge data; Presetting the initial parameters related to the mask and setting the machine related parameters; Optimizing and calculating the initial parameters related to the mask to obtain optimized parameters of the mask, and generating point intensity and Gauge key dimensions; Using a simulated annealing algorithm to optimize the mask optimization parameters, the point light intensity and the Gauge key size; The complex graphic outline is output, and the obtained complex graphic outline is subjected to graphic overlay processing with the image captured by the line width scanner to evaluate whether the graphic outlines between the two are consistent.

2. The modeling method according to claim 1, characterized in that: The mask-related initial parameters include: mask material, reflection coefficient, refraction coefficient, mask curvature and mask pattern linear offset.

3. The modeling method according to claim 2, characterized in that: When optimizing and calculating the initial parameters related to the mask to obtain the optimized parameters of the mask, it is necessary to set the optimization range of the initial parameters of the mask and the step length of each calculation step.

4. The modeling method according to claim 1, characterized in that: The machine-related parameters include baking temperature and exposure step length.

5. The modeling method according to claim 1, characterized in that: The method for generating point light intensity includes: calculating the light intensity at each Gauge point in the GDS layout and outputting the result.

6. The modeling method according to claim 1, characterized in that: The method for generating the critical dimension of the Gauge includes: obtaining the critical dimension by simulating the light intensity on each Gauge through an optimization method.

7. The modeling method according to claim 1, characterized in that: The method of optimizing the mask optimization parameters, the point light intensity and the Gauge critical size by using a simulated annealing algorithm includes: Set optimization space; Set the initial solution for optimization; Set the objective function.

8. The modeling method according to claim 7, characterized in that: The set optimization space refers to the optimized numerical range for the focusing accuracy parameters, focusing position parameters, mask optimization parameters, thermal diffusion effect parameters in the photoresist effect, rounded corner blunting effect parameters, line end shortening effect parameters, photoresist reflection coefficient parameters, refractive index parameters, and photoresist thickness parameters of the lithography machine.

9. The modeling method according to claim 7, characterized in that: The initial solution for setting the optimization refers to setting initial values ​​for the focusing accuracy parameters, focusing position parameters, mask optimization parameters, thermal diffusion effect parameters in the photoresist effect, rounded corner blunting effect parameters, line end shortening effect parameters, photoresist reflection coefficient parameters, refractive coefficient parameters, and photoresist thickness parameters of the lithography machine.

10. The modeling method according to claim 7, characterized in that: The objective function is to make the set initial solution optimal in the local range and optimal in the global range.

11. The modeling method according to claim 1, characterized in that: The GDS data is data obtained by reading a GDS file, and the GDS file is a layout file.

12. The modeling method according to claim 1, characterized in that: The Gauge data is data obtained by reading a Gauge file, and the Gauge file is used to calculate the light intensity at the layout coordinate position recorded in the layout.

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