CN111708255A - Method for forming SSA table of OPC - Google Patents

Method for forming SSA table of OPC Download PDF

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Publication number
CN111708255A
CN111708255A CN202010563173.XA CN202010563173A CN111708255A CN 111708255 A CN111708255 A CN 111708255A CN 202010563173 A CN202010563173 A CN 202010563173A CN 111708255 A CN111708255 A CN 111708255A
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value
edge
opc
dimensional
values
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CN111708255B (en
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冯佳计
金晓亮
袁春雨
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Shanghai Huahong Grace Semiconductor Manufacturing Corp
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Shanghai Huahong Grace Semiconductor Manufacturing Corp
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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
    • G03F7/00—Photomechanical, e.g. photolithographic, production of textured or patterned surfaces, e.g. printing surfaces; Materials therefor, e.g. comprising photoresists; Apparatus specially adapted therefor
    • 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]
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

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  • General Physics & Mathematics (AREA)
  • Image Generation (AREA)
  • Testing Or Measuring Of Semiconductors Or The Like (AREA)

Abstract

The invention discloses a method for forming an SSA table of OPC, which comprises the following steps: firstly, carrying out OPC on an original layer graph to obtain a simulation layer graph; step two, carrying out dimension division on each side of the original layer graph and dividing each side into a one-dimensional side and a two-dimensional side; step three, extracting the deviation value of each one-dimensional side and obtaining a group of corresponding (W, S, B) measurement values; step four, carrying out (W, S) interval division; step five, calculating the interval distribution of the deviation values, comprising the following steps: and calculating the final value of B in each (W, S) interval according to the distribution of the (W, S, B) measured values of each one-dimensional side in each (W, S) interval and forming an SSA table. The invention can obtain the SSA table which can be suitable for various structures at the same time in a short time; and whether bug occurs in the OPC operation or not can be judged, and whether and how the SSA table needs to be improved are judged.

Description

Method for forming SSA table of OPC
Technical Field
The present invention relates to a method for manufacturing a semiconductor integrated circuit, and more particularly, to a method for forming a selected binary error Correction (SSA) table for Optical Proximity Correction (OPC).
Background
In semiconductor lithography processes, in order to raise the resolution limit, OPC techniques need to be used. The OPC correction method based on the model is to expose the substrate of actual photoetching under the optimal photoetching conditions, collect the photoetching critical dimension values of various test patterns, establish a photoetching process model according to the measured values, and simulate the whole photoetching process including the exposure and development processes. And then, changing the line width size of the pattern on the photoetching plate according to model calculation to achieve the required line width size on a wafer such as a silicon wafer.
However, since the OPC model is obtained under one substrate condition, the actual pattern on the silicon wafer obtained after running OPC often differs according to the substrate. In order to overcome the deviation of the post-OPC pattern caused by the deviation of the substrate condition, an SSA table needs to be introduced, and OPC is run in conjunction with the SSA table. In the existing method, after the OPC is executed on the corresponding graphic layer, the SSA table needs to obtain the SSA table by measuring the difference between the actual graphic after the OPC and the target graphic, and for a certain graphic layer, the corresponding substrate is a substrate including a front graphic layer, and as the process becomes more and more complex, the calculation of the SSA table becomes more and more complex. In the development process of the OPC new process, along with the more and more complicated structure of the main chip, it is difficult to determine an SSA table applicable to various main chips from the beginning, especially, a metal layer process, the structure is complicated and varied, and it is necessary to continuously test and verify to obtain an appropriate SSA table applicable to various structures. Even the OPC often has problems in operation due to system or software bugs (bugs), and the OPC is not completely operated according to the SSA table, so that the OPC has risks (risk). The common practice of the current price section is to gradually perfect the SSA table by continuously accumulating the product number in the later period and solving problems, and the SSA table is long in time consumption and risky.
Disclosure of Invention
The invention aims to provide a method for forming an OPC SSA table, which can obtain the SSA table suitable for various structures simultaneously in a short time; and whether bug occurs in the OPC operation or not can be judged, and whether and how the SSA table needs to be improved are judged.
In order to solve the above technical problem, the method for forming the SSA table of the OPC provided by the present invention includes the steps of:
firstly, OPC is carried out on an original layer graph to obtain a simulation layer graph after OPC, and the simulation layer graph and the corresponding original layer graph are overlapped together.
Step two, performing dimension division on each edge of the original layer graph, wherein the dimension division divides the corresponding edge into a one-dimensional edge and a two-dimensional edge; the dimension differentiation includes:
and calculating the position difference values of the edges of the original layer graph and the simulated layer graph at a plurality of positions.
The position difference value has a positive value and a negative value, the position difference value is a positive value when the edge of the simulated layer pattern is positioned outside the edge of the original layer pattern, and the position difference value is a negative value when the edge of the simulated layer pattern is positioned inside the edge of the original layer pattern.
And distinguishing whether the corresponding side is a one-dimensional side or a two-dimensional side according to the calculated position difference, wherein the side of which the difference between the maximum position difference and the minimum position difference at each position is greater than a specified layer and the minimum position difference is a negative value is defined as the two-dimensional side, and the side of which the difference between the maximum position difference and the minimum position difference at each position is less than or equal to the specified layer is defined as the one-dimensional side.
Extracting the deviation value of each one-dimensional edge and obtaining a group of corresponding (W, S, B) measured values, wherein W represents the width of the original layer graph corresponding to the one-dimensional edge, S represents the distance between the original layer graphs corresponding to the one-dimensional edge, and B represents the deviation value (bias) of the one-dimensional edge; the deviation value of the one-dimensional edge is obtained from the position difference value at each position of the one-dimensional edge.
Step four, carrying out (W, S) interval division, comprising:
taking W as an abscissa and the value of W ranges from 0 to Wmax, wherein Wmax represents the maximum value of W of all the one-dimensional sides.
And taking S as a vertical coordinate, wherein the value range of S is 0 to Smax, and Smax represents the maximum value in S of all the one-dimensional sides.
Dividing the abscissa at equal intervals of a pitch Δ W and the ordinate at equal intervals of a pitch Δ S to form a plurality of the (W, S) sections.
Step five, calculating the interval distribution of the deviation values, comprising the following steps:
and B corresponding to the one-dimensional side is placed in the corresponding (W, S) interval according to the (W, S) interval to which W and S belong in the (W, S, B) measured value of each one-dimensional side.
After all the B of the one-dimensional side are placed in the corresponding (W, S) intervals, each (W, S) interval comprises a plurality of corresponding B.
And calculating a final value of one B according to a plurality of Bs in each (W, S) interval and forming a corresponding final value of (W, S, B).
SSA tables are formed from all of the (W, S, B) final values.
A further improvement is that in the dimension division in the step two, firstly, the edge of the original layer graph needs to be divided into a plurality of segmented edges, a position line is placed in the center of each segmented edge, and the position line is vertically intersected with the edge of the original layer graph and is intersected with the edge of the simulated layer graph; then, the corresponding position lines are placed at the positions with equal intervals except the center position of each segmented side.
And reading the position difference corresponding to each position line.
And sequentially judging whether each segmented edge is a one-dimensional edge or a two-dimensional edge according to the position difference value.
In a further improvement, if each of the segmented sides of the original layer pattern is a one-dimensional side, the sides of the entire original layer pattern are merged into one-dimensional side.
If each of the segment sides of the edge of the original layer pattern includes a one-dimensional side and a two-dimensional side, the one-dimensional side corresponds to the segment side.
In a further improvement, in step three, in the (W, S, B) measurement values: and taking the minimum value of the W when the original layer graph corresponding to the one-dimensional edge has different W at each position.
In a further improvement, in step three, in the (W, S, B) measurement values:
the deviation value of the one-dimensional edge is taken as the average value of the position difference values at each position of the one-dimensional edge.
Or, the deviation value of the one-dimensional edge is taken as the position difference value at the midpoint of the one-dimensional edge.
In a further improvement, in the first step, the original layer pattern includes a plurality of patterns.
In a further improvement, the original layer patterns comprise all patterns required in an OPC process.
In a further improvement, in step three, forming a statistical distribution map composed of the (W, S, B) measured values corresponding to all the original layer patterns.
And W is used as an abscissa, S is used as an ordinate and B is used as a value in the statistical distribution map.
In the fourth step, Δ W and Δ S are changed with the change of the process node, and the smaller the process node is, the smaller Δ W and Δ S are.
A further improvement is that in step four, as W and S increase, the corresponding Δ W and Δ S increase.
In a further improvement, in step five, a method for calculating a final value of B in each of the (W, S) intervals is:
and when the B values are distributed in a concentrated way in all the B values in the (W, S) interval, taking the average value of the B values at the concentrated distribution or the single B value at the concentrated distribution as the final value of the B.
And taking the average value of all B values in the (W, S) interval as the final value of B when the B values are uniformly distributed in percentage.
And in all the B values in the (W, S) interval, when the B values are uniformly distributed in proportion and simultaneously have a centrally distributed maximum B value or a centrally distributed minimum B value, the maximum B value or the minimum B value of the centrally distributed is removed, and then the average value of all the B values is taken as the final value of B.
And the further improvement is that after the fifth step is finished, the fifth step further comprises the steps of carrying out OPC on various graph structures according to the obtained SSA table and judging whether the OPC result meets the requirement or not.
In a further improvement, whether the OPC result meets the requirements is judged according to the process requirements and the initial offset value rule.
The further improvement is that the method also comprises the step of judging whether the SSA table needs to be improved according to the OPC result.
The further improvement is that OPC target values of various structures are compared, and whether bug occurs during OPC operation according to the SSA table is judged.
Unlike the prior art, which requires measuring an actual pattern formed on a wafer after running OPC to obtain a deviation between the actual pattern and a target pattern and thus obtain an SSA table, the present invention does not require obtaining an actual pattern after OPC, but the simulation layer graph is obtained by running OPC, the simulation layer graph is used for replacing the actual graph, the method comprises the steps of analyzing and measuring a simulation layer graph and an original layer graph, namely a target graph required by design, wherein the steps comprise distinguishing a one-dimensional side from a two-dimensional side, measuring a deviation value of the one-dimensional side and obtaining a group of corresponding (W, S, B) measured values, then carrying out (W, S) interval division and carrying out interval distribution calculation of the deviation value to obtain the SSA table.
On the basis of obtaining the SSA table, the invention can carry out OPC on various graphic structures according to the SSA table and judge whether the OPC result meets the requirement, thereby judging whether bug occurs during OPC operation according to the SSA table; and judging whether the SSA table needs to be improved according to the OPC result and if the SSA table needs to be improved, for example, when the corresponding graph structure is added in a new process and the OPC result is not good, the SSA table needs to be improved, and at the moment, the method only aims at the newly added graph structure to improve the SSA table.
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The invention is described in further detail below with reference to the following figures and detailed description:
FIG. 1 is a flow chart of a method for forming an SSA table for OPC according to an embodiment of the present invention;
FIG. 2A is a diagram of an original layer in step one of the method according to the embodiment of the present invention;
FIG. 2B is a diagram illustrating a superposition of an original layer diagram and a simulated layer diagram in step one of the method according to the embodiment of the present invention;
FIG. 2C is a graph corresponding to the dimension differentiation in step two of the method according to the embodiment of the present invention;
FIG. 2D is a graph of the deviation of the one-dimensional edge extracted in step three of the method of the embodiment of the present invention;
FIG. 2D1 is a statistical distribution diagram of the (W, S, B) measurements taken for each one-dimensional side in step three of the method of the present invention;
FIG. 2E shows the corresponding coordinates before the division of the (W, S) interval in step four of the method of the embodiment of the present invention;
FIG. 2E1 shows the coordinates of the (W, S) interval in step four of the method according to the present invention after division;
FIG. 2F is a schematic diagram of the distribution of the measured values of B in the coordinates after the (W, S) interval is divided in step five of the method according to the embodiment of the present invention;
FIG. 2F1 is a histogram of three distributions of measured values of B in each (W, S) interval of FIG. 2F;
FIG. 2F2 is a pie chart of a first distribution of measured values of B in each (W, S) interval of FIG. 2F 1;
FIG. 2F3 is a pie chart of a second distribution of measured values of B in each (W, S) interval of FIG. 2F 1;
FIG. 2F4 is a pie chart of a third distribution of measured values for B in each (W, S) interval of FIG. 2F 1;
FIG. 2G is a statistical distribution graph of the final values of (W, S, B) obtained in step five of the method according to the embodiment of the present invention.
Detailed Description
FIG. 1 is a flow chart of a method for forming an SSA table of OPC according to an embodiment of the present invention; the method for forming the SSA table of the OPC in the embodiment of the invention comprises the following steps:
step one, as shown in fig. 2A, is an original layer pattern 2 in step one of the method according to the embodiment of the present invention, where a layout 1 includes a plurality of the original layer patterns 2, and the original layer patterns 2 include all patterns required in an OPC process.
The original layer graph 2 is a design graph and is also a target graph which needs to be achieved finally. Fig. 2A shows 3 line patterns corresponding to the marks L1, L2, and L3, and further includes space patterns between the line patterns as shown by marks S1 and S2.
As shown in fig. 2B, it is a superimposed graph of the original layer graph 2 and the simulation layer graph 3 in step one of the method according to the embodiment of the present invention; and carrying out OPC on the original layer graph 2 to obtain an OPC simulated layer graph 3, wherein the simulated layer graph 3 and the corresponding original layer graph 2 are overlapped together. It can be seen that the simulated layer pattern 3 and the original layer pattern 2 have a deviation.
And step two, carrying out dimension division on each side of the original layer graph 2, wherein the dimension division divides the corresponding side into a one-dimensional (1D) side and a two-dimensional (2D) side.
As shown in fig. 2C, it is a graph corresponding to the dimension differentiation in step two of the method according to the embodiment of the present invention, and fig. 2C is separately illustrated by the original layer graph corresponding to the mark 2a and the simulation layer graph corresponding to the mark 3 a; the dimension differentiation includes:
position differences at a plurality of positions corresponding to the edges of the original layer pattern 2a and the simulated layer pattern 3a are calculated.
The position difference value has a positive value and a negative value, and is a positive value when the edge of the simulation layer pattern 3 is positioned outside the edge of the original layer pattern 2, and is a negative value when the edge of the simulation layer pattern 3 is positioned inside the edge of the original layer pattern 2.
And distinguishing whether the corresponding side is a one-dimensional side or a two-dimensional side according to the calculated position difference, wherein the side of which the difference between the maximum position difference and the minimum position difference at each position is greater than a specified layer and the minimum position difference is a negative value is defined as the two-dimensional side, and the side of which the difference between the maximum position difference and the minimum position difference at each position is less than or equal to the specified layer is defined as the one-dimensional side.
In the embodiment of the present invention, in the dimension division, firstly, the edge of the original layer graph 2 needs to be divided into a plurality of segment edges, a position (site) line 4 is placed at the center of each segment edge, and the position line 4 is vertically intersected with the edge of the original layer graph 2 and is intersected with the edge of the simulation layer graph 3; thereafter, the corresponding position lines 4 are placed at equally spaced positions other than the center position of each of the segment sides.
And reading the position difference corresponding to each position line 4.
And sequentially judging whether each segmented edge is a one-dimensional edge or a two-dimensional edge according to the position difference value.
In fig. 2C, 3 of said segmented edges corresponding to labels 5a, 5b and 5C are shown, wherein the segmented edge corresponding to label 5a is a one-dimensional edge; the segment edges corresponding to markers 5b and 5c are both two-dimensional edges.
If each of the segmented sides of the original layer pattern 2 is a one-dimensional side, the sides of the entire original layer pattern 2 are merged into a one-dimensional side, and the sides corresponding to the labels 5a1 and 5a2 in fig. 2D are all one-dimensional sides.
Extracting the deviation value of each one-dimensional side and obtaining a group of corresponding (W, S, B) measured values, wherein W represents the width of the original layer graph 2 corresponding to the one-dimensional side, S represents the distance between the original layer graphs 2 corresponding to the one-dimensional side, and B represents the deviation value (bias) of the one-dimensional side; the deviation value of the one-dimensional edge is obtained from the position difference value at each position of the one-dimensional edge.
As shown in fig. 2D, it is a graph of the deviation value of the one-dimensional edge extracted in the third step of the method according to the embodiment of the present invention; 2 of said original layer patterns corresponding to labels 2b and 2c, and two of said simulated layer patterns corresponding to labels 3b and 3c are shown in FIG. 2D; the one-dimensional side corresponding to the simulated layer pattern 3c is denoted by a reference 5a1, and the one-dimensional side corresponding to the simulated layer pattern 3b is denoted by a reference 5a 2. The one-dimensional side 5a1 is also denoted by e1, and the one-dimensional side 5a2 is also denoted by e 2.
Among the (W, S, B) measurements: and taking the minimum value of the W when the original layer graph 2 corresponding to the one-dimensional edge has different W at each position. For example, e1 includes two values of W, W101 and W102 respectively, and W101 is smaller than W102, where W101 is taken. The W value of e2 only includes one value, namely W103, so the W value of e2 can only be W103.
The one-dimensional sides e1 and e2 are two adjacent sides, and the two sides have equal S values and are directly represented by S.
Among the (W, S, B) measurements:
and taking the deviation value of the one-dimensional edge as the average value of the position difference values at each position of the one-dimensional edge, wherein the position difference value is the value measured in the second step. Or, the deviation value of the one-dimensional edge is taken as the position difference value at the midpoint of the one-dimensional edge.
Step three, forming a statistical distribution map consisting of the (W, S, B) measured values corresponding to all the original layer patterns 2. FIG. 2D1 shows a statistical distribution chart 101 of the (W, S, B) measured values extracted from each one-dimensional side in step three of the method according to the embodiment of the present invention; in the statistical distribution graph 101, W is used as an abscissa, S is used as an ordinate, and B is used as a value; in fig. 2D1, the values of B are represented in different colors and in different shades after printing in black and white.
Step four, carrying out (W, S) interval division, comprising:
as shown in fig. 2E, the coordinates before the division of the (W, S) interval in step four of the method according to the embodiment of the present invention are shown; taking W as an abscissa and the value of W ranges from 0 to Wmax, wherein Wmax represents the maximum value of W of all the one-dimensional sides. And taking S as a vertical coordinate, wherein the value range of S is 0 to Smax, and Smax represents the maximum value in S of all the one-dimensional sides.
As shown in fig. 2E1, the coordinates are obtained after the (W, S) interval in step four of the method according to the embodiment of the present invention is divided; dividing the abscissa at equal intervals of a pitch Δ W and the ordinate at equal intervals of a pitch Δ S to form a plurality of the (W, S) sections; it can be seen that dividing the abscissa by the equal interval of the pitch Δ W can obtain the corresponding scales W1, W2, W3, etc., and dividing the ordinate by the equal interval of the pitch Δ S can obtain the corresponding scales S1, S2, S3, etc., and the region formed by intersecting the scale lines shown in fig. 2E1 is the (W, S) section.
In the embodiment of the invention, the Δ W and the Δ S change along with the change of the process nodes, and the smaller the process nodes are, the smaller the corresponding Δ W and Δ S are.
As W and S increase, the graph tends to be more and more ISO-graph, and the corresponding Δ W and Δ S can increase.
Step five, calculating the interval distribution of the deviation values, comprising the following steps:
fig. 2F is a schematic diagram illustrating a distribution of the measured values of B in the coordinates after the (W, S) interval is divided in step five of the method according to the embodiment of the present invention; and B corresponding to the one-dimensional side is placed in the corresponding (W, S) interval according to the (W, S) interval to which W and S belong in the (W, S, B) measured value of each one-dimensional side.
After all the B of the one-dimensional side are placed in the corresponding (W, S) intervals, each (W, S) interval comprises a plurality of corresponding B. Fig. 2F shows only a plurality of B values included in the (W, S) section corresponding to 0 to W1 and 0 to S1.
And calculating a final value of one B according to a plurality of Bs in each (W, S) interval and forming a corresponding final value of (W, S, B). In an embodiment of the present invention, a method for calculating a final value of B in each of the (W, S) intervals includes:
as shown in fig. 2F1, is a histogram of the three distributions of measured values of B in each (W, S) interval in fig. 2F; and when the B values are distributed in a concentrated way in all the B values in the (W, S) interval, taking the average value of the B values at the concentrated distribution or the single B value at the concentrated distribution as the final value of the B. In this case, since the number of B values corresponding to the marker 201a is an absolute majority corresponding to the category 1 corresponding to the dashed box 201 in fig. 2F1, a single B value or an average value of B corresponding to the marker 201a is directly used as the final value of B. As shown in fig. 2F2, it is a pie chart of category 1, which is the first distribution of the measured values of B in each (W, S) interval in fig. 2F 1.
And taking the average value of all B values in the (W, S) interval as the final value of B when the B values are uniformly distributed in percentage. At this point, corresponding to category 2 for dashed box 202 in FIG. 2F 1; as shown in fig. 2F3, it is a pie chart of category 2, which is the second distribution of the measured values of B in each (W, S) interval in fig. 2F 1.
And in all the B values in the (W, S) interval, when the B values are uniformly distributed in proportion and simultaneously have a centrally distributed maximum B value or a centrally distributed minimum B value, the maximum B value or the minimum B value of the centrally distributed is removed, and then the average value of all the B values is taken as the final value of B. At this time, corresponding to category 3 corresponding to the dashed line box 203 in fig. 2F1, the average value of the other B values after the B values corresponding to the markers 203a and 203B are removed is defined as the final value of B, because the B maximum value of the concentrated distribution corresponding to the marker 203a and the B minimum value of the concentrated distribution corresponding to the marker 203B. As shown in fig. 2F4, is a pie chart for category 3, which is the third distribution of the measured values for B in each (W, S) interval in fig. 2F 1.
SSA tables are formed from all of the (W, S, B) final values. As shown in fig. 2G, it is a statistical distribution graph 102 of the final values of (W, S, B) obtained in step five of the method according to the embodiment of the present invention, where the statistical distribution graph 102 corresponds to the SSA table. In the statistical distribution graph 102, W is used as an abscissa, S is used as an ordinate, B is used as a value, the value of B is represented by different colors, and is represented by different gray scales after being printed as black and white. According to the OPC operation rule, one of the (W, S) sections can have only one B value, so that each of the (W, S) sections in fig. 2G has only one B value, which is different from the case where the B values in the statistical distribution chart 101 in fig. 2D1 are distributed on the specific coordinates of W and S and a plurality of B values are included in each of the (W, S) sections.
In the embodiment of the invention, after the fifth step is completed, the method further comprises the steps of carrying out OPC on various graph structures according to the obtained SSA table and judging whether the OPC result meets the requirement or not.
Then also comprises the following steps: and judging whether the OPC result meets the requirements or not according to the process manufacturing requirements and the initial offset value rule.
And judging whether the SSA table needs to be improved or not according to the OPC result, and if so, calculating only aiming at the final value of the B of the graph structure with poor result to realize the improvement of the SSA table.
And comparing OPC target values of various structures, and judging whether bug occurs during OPC operation according to the SSA table.
Different from the prior art, the embodiment of the invention does not need to obtain the actual graph after the OPC is operated to obtain the deviation between the actual graph and the target graph and obtain the SSA table, but obtains the simulation layer graph 3 by operating the OPC, replaces the actual graph with the simulation layer graph 3, analyzes and measures the simulation layer graph 3 and the original layer graph 2, namely the target graph required by design, comprises the distinguishing of one-dimensional edges and two-dimensional edges, measures the deviation value of the one-dimensional edges and obtains a group of corresponding (W, S, B) measured values, then performs the division of (W, S) intervals and performs the interval distribution calculation of the deviation value to obtain the SSA table, and the embodiment of the invention can be obtained by only giving the designed original layer graph 2 and the corresponding OPC model software automatic operation mode, therefore, an SSA table suitable for various structures can be obtained in a short time.
On the basis of obtaining the SSA table, the embodiment of the invention can carry out OPC on various graphic structures according to the SSA table and judge whether the OPC result meets the requirement, thereby judging whether bug occurs during OPC operation according to the SSA table; whether the SSA table needs to be improved or not can be judged according to the OPC result, if the OPC result is not good, for example, after a corresponding graph structure is added in a new process, the SSA table needs to be improved, and the method provided by the embodiment of the invention is used for improving the SSA table only aiming at the newly added graph structure.
The present invention has been described in detail with reference to the specific embodiments, but these should not be construed as limitations of the present invention. Many variations and modifications may be made by one of ordinary skill in the art without departing from the principles of the present invention, which should also be considered as within the scope of the present invention.

Claims (15)

1. A method for forming SSA table of OPC is characterized by comprising the following steps:
firstly, carrying out OPC on an original layer graph to obtain an OPC simulated layer graph, wherein the simulated layer graph and the corresponding original layer graph are overlapped together;
step two, performing dimension division on each edge of the original layer graph, wherein the dimension division divides the corresponding edge into a one-dimensional edge and a two-dimensional edge; the dimension differentiation includes:
calculating position difference values of a plurality of positions of the edge of the original layer graph and the edge corresponding to the simulation layer graph;
the position difference value has a positive value and a negative value, the position difference value is a positive value when the edge of the simulation layer pattern is positioned outside the edge of the original layer pattern, and the position difference value is a negative value when the edge of the simulation layer pattern is positioned inside the edge of the original layer pattern;
distinguishing whether the corresponding edge is a one-dimensional edge or a two-dimensional edge according to the calculated position difference value, wherein an edge of which the difference value between the maximum position difference value and the minimum position difference value at each position is greater than a specified layer and the minimum position difference value is a negative value is defined as the two-dimensional edge, and an edge of which the difference value between the maximum position difference value and the minimum position difference value at each position is less than or equal to the specified layer is defined as the one-dimensional edge;
extracting the deviation value of each one-dimensional edge and obtaining a group of corresponding (W, S, B) measured values, wherein W represents the width of the original layer graph corresponding to the one-dimensional edge, S represents the distance between the original layer graphs corresponding to the one-dimensional edge, and B represents the deviation value of the one-dimensional edge; the deviation value of the one-dimensional edge is obtained from the position difference value of each position of the one-dimensional edge;
step four, carrying out (W, S) interval division, comprising:
taking W as an abscissa, wherein the numeric area of W is 0 to Wmax, and Wmax represents the maximum value of W of all the one-dimensional sides;
taking S as a vertical coordinate, wherein the value range of S is 0 to Smax, and Smax represents the maximum value in S of all the one-dimensional sides;
dividing the abscissa at equal intervals of a pitch Δ W and the ordinate at equal intervals of a pitch Δ S to form a plurality of the (W, S) sections;
step five, calculating the interval distribution of the deviation values, comprising the following steps:
placing B corresponding to the one-dimensional side in the corresponding (W, S) interval according to the (W, S) interval to which W and S belong in the (W, S, B) measured value of each one-dimensional side;
after all the B of the one-dimensional side are placed in the corresponding (W, S) intervals, each (W, S) interval comprises a plurality of corresponding B;
calculating a final value of one B according to a plurality of Bs in each (W, S) interval and forming a corresponding final value of (W, S, B);
SSA tables are formed from all of the (W, S, B) final values.
2. The method for forming an SSA table of an OPC of claim 1, wherein: in the dimension division in the second step, firstly, the edge of the original layer graph needs to be divided into a plurality of segmented edges, a position line is arranged in the center of each segmented edge, and the position line is vertically intersected with the edge of the original layer graph and is intersected with the edge of the simulation layer graph; then, placing the corresponding position lines at the positions with equal intervals except the central position of each segmented side;
reading the position difference corresponding to each position line;
and sequentially judging whether each segmented edge is a one-dimensional edge or a two-dimensional edge according to the position difference value.
3. The method of forming an SSA table of an OPC of claim 2, wherein: if all the segmented edges of the original layer graph are one-dimensional edges, the edges of the whole original layer graph are combined into one-dimensional edge;
if each of the segment sides of the edge of the original layer pattern includes a one-dimensional side and a two-dimensional side, the one-dimensional side corresponds to the segment side.
4. The method for forming an SSA table of an OPC of claim 1, wherein: in step three, in the (W, S, B) measurement values: and taking the minimum value of the W when the original layer graph corresponding to the one-dimensional edge has different W at each position.
5. The method for forming an SSA table of an OPC in claim 1 or 4, wherein: in step three, in the (W, S, B) measurement values:
the deviation value of the one-dimensional edge is taken as the average value of the position difference values at each position of the one-dimensional edge;
or, the deviation value of the one-dimensional edge is taken as the position difference value at the midpoint of the one-dimensional edge.
6. The method for forming an SSA table of an OPC of claim 1, wherein: in the first step, the original layer pattern includes a plurality of original layer patterns.
7. The method of forming an SSA table for OPC of claim 6, wherein: the original layer patterns comprise all patterns required in an OPC process.
8. The method of forming an SSA table for OPC of claim 7, wherein: step three, forming a statistical distribution map consisting of the (W, S, B) measured values corresponding to all the original layer patterns;
and W is used as an abscissa, S is used as an ordinate and B is used as a value in the statistical distribution map.
9. The method for forming an SSA table of an OPC of claim 1, wherein: in the fourth step, Δ W and Δ S vary with the variation of the process node, and the smaller the process node is, the smaller the corresponding Δ W and Δ S are.
10. The method for forming an SSA table of an OPC of claim 1, wherein: in step four, as W and S increase, the corresponding Δ W and Δ S increase.
11. The method for forming an SSA table of an OPC of claim 1, wherein: in step five, the method for calculating the final value of B in each of the (W, S) intervals is:
when the B values are in centralized distribution in all the B values in the (W, S) interval, taking the average value of the B values in the centralized distribution or the single B value in the centralized distribution as the final value of B;
in all B values in the (W, S) interval, when the B values are uniformly distributed in a proportion, taking the average value of all B values in the (W, S) interval as the final value of B;
and in all the B values in the (W, S) interval, when the B values are uniformly distributed in proportion and simultaneously have a centrally distributed maximum B value or a centrally distributed minimum B value, the maximum B value or the minimum B value of the centrally distributed is removed, and then the average value of all the B values is taken as the final value of B.
12. The method for forming an SSA table of an OPC of claim 1, wherein: and after the fifth step is finished, performing OPC on various graph structures according to the obtained SSA table and judging whether the OPC result meets the requirement.
13. The method of forming an SSA table for OPC of claim 12, wherein: and judging whether the OPC result meets the requirements or not according to the process manufacturing requirements and the initial offset value rule.
14. The method of forming an SSA table for OPC of claim 12, wherein: and judging whether the SSA table needs to be improved or not according to the OPC result.
15. The method of forming an SSA table for OPC of claim 12, wherein: and comparing OPC target values of various structures, and judging whether bug occurs during OPC operation according to the SSA table.
CN202010563173.XA 2020-06-19 2020-06-19 Method for forming SSA table of OPC Active CN111708255B (en)

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