DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Specification
The disclosure is objected to because of the following informalities:
(Text within parentheses is either a missing or a corrected information to character(s) in bold.)
[00116] … The edge pixels r1 andr2 also have orientation angles φ1 and φ2, respectively, which as discussed above may be obtained by taken (taking) the normal line or normal vector at the edge segment in the pixel.
[00117] … The kernel G2 describes the near field correction at the pixel r due to the interaction between the two edge pixels 1 and r2.
Appropriate correction is required.
Claim Objections
(Text within parentheses is either a missing or a corrected information to character(s) in bold.)
Claim 17 objected to because of the following informalities:
The method of claim 16, wherein: a margin of interaction is applied to each of the sub-tiles; the edge interaction kernel for each of the sub-tiles includes a contribution made by the margin of interaction; and the near filed (field) correction is determined by removing the contribution made by the margin of interaction.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 18 and 20 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Igor Ivonin et. al. (US 20070209029 A1), hereinafter Ivonin.
Regarding claim 18
Ivonin discloses
A method, comprising
(Ivonin, p. 15, [0191] “Yet another variation on the embodiments above is a method of efficiently applying optical proximity correction for printing highly accurate patterns on a workpiece in microlithography.”)
accessing a portion of a mask layout
(Ivonin, p. 15, [0192] “Any of these embodiments extend to producing features of a semi-conductor device. In one method, a feature is patterned, developed and produced on a mask, which is, in turn, used to pattern and produce a feature on a semiconductor substrate.”)
wherein the portion of the mask layout includes a plurality of patterns
(Ivonin, p. 1, [0003] “The present disclosure teaches a method to project an optical image of an original (typically a pattern on a photomask or a spatial light modulator (SLM)) onto a workpiece with extremely high resolution and fidelity given the constraints of the optics.”)
(Ivonin, p. 13-14, [0179] “…The disclosed technology has most benefit where general patterns need to be printed with equally good fidelity for all features, small and large, 1D and 2D. The typical application is masks.”)
a plurality of edge pixels located on edges of the patterns, and a plurality of influenced pixels;
(Ivonin, p. 5, [0082] “One chooses a merit function for optimization. The number of possible patterns in the neighborhood within, say, 500 nm around an edge is immense and to optimize all of them would be difficult.”)
(Ivonin, p. 5, [0085] “The merit function is set up to fulfill some or all of the following objectives. The first one is to make all lines larger than a specified limit print with no CD errors, i.e., to make the CD linearity plot flat above the limit. If all feature classes satisfy this, there is no influence between edges at a distance larger than the limit. This is a large benefit, since it limits the range of the OPC adjustments needed to make a pattern print accurately. During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range.”)
(Ivonin, p. 11, [0149] “… For a neighborhood range of four pixels in each direction (a 9.times.9 grid), the 4-D nature of model yields 130,000 terms for an interaction length of 600 nm.”)
(Ivonin, p. 13, [0173] “… This means that we need to apply correction only to pixels at the edge or adjacent to it and they can be recognized by their grayness in an analog bitmap.”)
determining, for each of the influenced pixels, an influence exerted by the plurality of edge pixels;
(Ivonin, p. 5, [0085] “The merit function is set up to fulfill some or all of the following objectives. The first one is to make all lines larger than a specified limit print with no CD errors, i.e., to make the CD linearity plot flat above the limit. If all feature classes satisfy this, there is no influence between edges at a distance larger than the limit. This is a large benefit, since it limits the range of the OPC adjustments needed to make a pattern print accurately. During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range.”)
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
(Ivonin, p. 11, [0149] “… For a neighborhood range of four pixels in each direction (a 9.times.9 grid), the 4-D nature of model yields 130,000 terms for an interaction length of 600 nm.”)
(Ivonin, p. 13, [0173] “… This means that we need to apply correction only to pixels at the edge or adjacent to it and they can be recognized by their grayness in an analog bitmap.”)
generating, based at least in part on the influence exerted to each of the influenced pixels by the plurality of edge pixels, a plurality of edge interaction kernels corresponding to the plurality of influenced pixels, respectively.
(Ivonin, p. 5, [0085] “The merit function is set up to fulfill some or all of the following objectives. The first one is to make all lines larger than a specified limit print with no CD errors, i.e., to make the CD linearity plot flat above the limit. If all feature classes satisfy this, there is no influence between edges at a distance larger than the limit. This is a large benefit, since it limits the range of the OPC adjustments needed to make a pattern print accurately. During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range.”)
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
(Ivonin, p. 11, [0149] “… In the general case the kernel K is four-dimensional and (4) contains
(
2
*
n
r
a
n
g
e
+
1
)
4
terms, a large number even for small interaction ranges
n
r
a
n
g
e
. For a neighborhood range of four pixels in each direction (a
9
×
9
grid), the 4-D nature of model yields 130,000 terms for an interaction length of 600 nm. We have to simplify (4) immensely in order to compute it for every gray pixel in a reticle--which could in the worst case be every pixel in a reticle,
10
13
of them.”)
(Ivonin, p. 13, [0173] “… This means that we need to apply correction only to pixels at the edge or adjacent to it and they can be recognized by their grayness in an analog bitmap.”)
(Ivonin, p. 14, [0188] “… This method may include correcting in a pixel domain a rasterized image of at least part of a feature, wherein the correction applies a four dimensional kernel with selected non-zero values to compensate for perturbations caused by optical proximity effects, whereby the number of additions and multiplications required to calculate the correction in the pixel domain, after selection of the non-zero values to include in the four dimensional kernel, are a substantially less than would be required for a full kernel.”)
(Ivonin, p. 15, [0191] “Yet another variation on the embodiments above is a method of efficiently applying optical proximity correction for printing highly accurate patterns on a workpiece in microlithography. This method may include rasterizing at least part of a feature including assigning pixel gray values and correcting edge perturbations caused by additional features within a range of interaction from the part of the feature by applying a correction kernel to the pixel gray values.”)
and determining, based at least in part on the plurality of edge interaction kernels, a near field correction associated with the portion of the mask layout.
(Ivonin, p. 5, [0085] “The merit function is set up to fulfill some or all of the following objectives. The first one is to make all lines larger than a specified limit print with no CD errors, i.e., to make the CD linearity plot flat above the limit. If all feature classes satisfy this, there is no influence between edges at a distance larger than the limit. This is a large benefit, since it limits the range of the OPC adjustments needed to make a pattern print accurately. During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range.”)
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
(Ivonin, p. 11, [0149] “… In the general case the kernel K is four-dimensional and (4) contains
(
2
*
n
r
a
n
g
e
+
1
)
4
terms, a large number even for small interaction ranges
n
r
a
n
g
e
. For a neighborhood range of four pixels in each direction (a
9
×
9
grid), the 4-D nature of model yields 130,000 terms for an interaction length of 600 nm. We have to simplify (4) immensely in order to compute it for every gray pixel in a reticle--which could in the worst case be every pixel in a reticle,
10
13
of them.”)
(Ivonin, p. 13, [0173] “… This means that we need to apply correction only to pixels at the edge or adjacent to it and they can be recognized by their grayness in an analog bitmap.”)
(Ivonin, p. 14, [0188] “… This method may include correcting in a pixel domain a rasterized image of at least part of a feature, wherein the correction applies a four dimensional kernel with selected non-zero values to compensate for perturbations caused by optical proximity effects, whereby the number of additions and multiplications required to calculate the correction in the pixel domain, after selection of the non-zero values to include in the four dimensional kernel, are a substantially less than would be required for a full kernel.”)
(Ivonin, p. 15, [0191] “Yet another variation on the embodiments above is a method of efficiently applying optical proximity correction for printing highly accurate patterns on a workpiece in microlithography. This method may include rasterizing at least part of a feature including assigning pixel gray values and correcting edge perturbations caused by additional features within a range of interaction from the part of the feature by applying a correction kernel to the pixel gray values.”)
(Ivonin, p. 1, [0002] “There are two aspects to the technology disclosed: First, a method and device with a reduced field of interaction, which simplifies and reduces the need for optical proximity correction (OPC).”)
(Ivonin, p. 9, [0124] “Going back to the bitmap processing for a mask writer or direct-writer, the corrections are rather small and have a simple relation to the features inside the limit of no interaction. A suitable method to do the correction is by convolution of the bitmap by a kernel that corrects for the residual errors.”)
A pattern can effectively be considered as part of the mask layout.
Regarding claim 20
Ivonin teaches all aspects of claim 18, as disclosed above and further discloses
The method of claim 18, wherein each of the edge interaction kernels is generated as a function of a plurality of vectors.
(Ivonin, p. 5, [0085] “The merit function is set up to fulfill some or all of the following objectives. The first one is to make all lines larger than a specified limit print with no CD errors, i.e., to make the CD linearity plot flat above the limit. If all feature classes satisfy this, there is no influence between edges at a distance larger than the limit. This is a large benefit, since it limits the range of the OPC adjustments needed to make a pattern print accurately. During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range.”)
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
(Ivonin, p. 11, [0149] “… In the general case the kernel K is four-dimensional and (4) contains
(
2
*
n
r
a
n
g
e
+
1
)
4
terms, a large number even for small interaction ranges
n
r
a
n
g
e
. For a neighborhood range of four pixels in each direction (a
9
×
9
grid), the 4-D nature of model yields 130,000 terms for an interaction length of 600 nm. We have to simplify (4) immensely in order to compute it for every gray pixel in a reticle--which could in the worst case be every pixel in a reticle,
10
13
of them.”)
(Ivonin, p. 13, [0173] “… This means that we need to apply correction only to pixels at the edge or adjacent to it and they can be recognized by their grayness in an analog bitmap.”)
(Ivonin, p. 14, [0188] “… This method may include correcting in a pixel domain a rasterized image of at least part of a feature, wherein the correction applies a four dimensional kernel with selected non-zero values to compensate for perturbations caused by optical proximity effects, whereby the number of additions and multiplications required to calculate the correction in the pixel domain, after selection of the non-zero values to include in the four dimensional kernel, are a substantially less than would be required for a full kernel.”)
(Ivonin, p. 15, [0191] “Yet another variation on the embodiments above is a method of efficiently applying optical proximity correction for printing highly accurate patterns on a workpiece in microlithography. This method may include rasterizing at least part of a feature including assigning pixel gray values and correcting edge perturbations caused by additional features within a range of interaction from the part of the feature by applying a correction kernel to the pixel gray values.”)
(Ivonin, p. 14, [0190] “… This method may include correcting in a pixel domain a rasterized image of at least part of a feature, wherein the correction applies a four dimensional kernel with selected non-zero values to compensate for perturbations caused by optical proximity effects, whereby the number of additions and multiplications required to calculate the correction in the pixel domain, after selection of the non-zero values to include in the four dimensional kernel, are a substantially less than would be required for a full kernel.”)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 1-2, 5-13, and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Igor Ivonin et. al. (US 20070209029 A1), hereinafter Ivonin in view of Joong-Won JEON et. al. (US 20150143312 A1) hereinafter JEON.
Regarding claim 1
Ivonin discloses parts b-d of the claim 1
A method, comprising accessing
a first pixel located on an edge of the first pattern, a second pixel located on an edge of the second pattern, and a third pixel that is outside of the first pattern and the second pattern;
(Ivonin, p. 1, [0005] “… Since microlithographic patterns are imaged onto a high-contrast resist and the resist is further raised by the etching process, the quality in the image is almost entirely related to the placement and quality of the feature edges.”)
(Ivonin, p. 5, [0082] “One chooses a merit function for optimization. The number of possible patterns in the neighborhood within, say, 500 nm around an edge is immense and to optimize all of them would be difficult. The inventors have found that analysis of a small set of pattern classes is sufficient for rotationally symmetric aperture functions.”)
(Ivonin, p. 5, [0085] “The merit function is set up to fulfill some or all of the following objectives. The first one is to make all lines larger than a specified limit print with no CD errors, i.e., to make the CD linearity plot flat above the limit. If all feature classes satisfy this, there is no influence between edges at a distance larger than the limit. This is a large benefit, since it limits the range of the OPC adjustments needed to make a pattern print accurately. During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range.”)
(Ivonin, p. 2, [0033] “FIG. 15: A single set of features that, if the pixels are smaller than the resolution of the optics, can be used to represent the range of possible patterns.”)
(Ivonin, p. 10, [0146] “In one embodiment, each computation only corrects one pixel, namely a gray pixel sitting on the edge.”)
(Ivonin, p. 10, [0147] “… For a long edge, one pass will have the edge close to the middle of the pixel.”)
(Ivonin, p. 13, [0173] “… This means that we need to apply correction only to pixels at the edge or adjacent to it and they can be recognized by their grayness in an analog bitmap. Furthermore, we need only correct for the pattern inside the range of optical interaction…”)
generating, based at least in part on the influence exerted to the third pixel by the first pixel and the second pixel, an edge interaction kernel for the third pixel;
(Ivonin, p. 2, [0010] “Previously disclosed methods and devices are extended in this application by two-dimensional analysis of optical proximity interactions and by fashioning a computationally efficient kernel for rapid calculation of adjustments to be made.”)
(Ivonin, p. 5, [0085] “The merit function is set up to fulfill some or all of the following objectives. The first one is to make all lines larger than a specified limit print with no CD errors, i.e., to make the CD linearity plot flat above the limit. If all feature classes satisfy this, there is no influence between edges at a distance larger than the limit. This is a large benefit, since it limits the range of the OPC adjustments needed to make a pattern print accurately. During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range.”)
(Ivonin, p. 13, [0173] “… This means that we need to apply correction only to pixels at the edge or adjacent to it and they can be recognized by their grayness in an analog bitmap. Furthermore, we need only correct for the pattern inside the range of optical interaction…”)
(Ivonin, p. 15, [0191] “Yet another variation on the embodiments above is a method of efficiently applying optical proximity correction for printing highly accurate patterns on a workpiece in microlithography. This method may include rasterizing at least part of a feature including assigning pixel gray values and correcting edge perturbations caused by additional features within a range of interaction from the part of the feature by applying a correction kernel to the pixel gray values.”)
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
(Ivonin, p. 14, [0188] “… This method may include correcting in a pixel domain a rasterized image of at least part of a feature, wherein the correction applies a four dimensional kernel with selected non-zero values to compensate for perturbations caused by optical proximity effects, whereby the number of additions and multiplications required to calculate the correction in the pixel domain, after selection of the non-zero values to include in the four dimensional kernel, are a substantially less than would be required for a full kernel.”)
and determining, based at least in part on the edge interaction kernel, a near field correction at the third pixel.
(Ivonin, p. 15, [0191] “Yet another variation on the embodiments above is a method of efficiently applying optical proximity correction for printing highly accurate patterns on a workpiece in microlithography. This method may include rasterizing at least part of a feature including assigning pixel gray values and correcting edge perturbations caused by additional features within a range of interaction from the part of the feature by applying a correction kernel to the pixel gray values.”)
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
(Ivonin, p. 14, [0188] “… This method may include correcting in a pixel domain a rasterized image of at least part of a feature, wherein the correction applies a four dimensional kernel with selected non-zero values to compensate for perturbations caused by optical proximity effects, whereby the number of additions and multiplications required to calculate the correction in the pixel domain, after selection of the non-zero values to include in the four dimensional kernel, are a substantially less than would be required for a full kernel.”)
(Ivonin, p. 1, [0002] “There are two aspects to the technology disclosed: First, a method and device with a reduced field of interaction, which simplifies and reduces the need for optical proximity correction (OPC).”)
(Ivonin, p. 9, [0124] “Going back to the bitmap processing for a mask writer or direct-writer, the corrections are rather small and have a simple relation to the features inside the limit of no interaction. A suitable method to do the correction is by convolution of the bitmap by a kernel that corrects for the residual errors.”)
A pattern can effectively be considered as part of the mask layout.
Ivonin does not teach part a of the claim 1
a tile that contains a first pattern, a second pattern
However, JEON discloses
a tile that contains a first pattern, a second pattern
(JEON, p. 1, [0011] “In accordance with an aspect of embodiments, a method of designing patterns of semiconductor devices includes forming a plurality of tiles having patterns on a wafer, measuring the patterns of the plurality of tiles, analyzing the measurements of the patterns and determining a tile having such a size that the measurements linearly vary according to a design size and pattern density, and modifying the pattern density of the determined tile.”)
Therefore, it would have been obvious before the effective priority date of the claim to a person having ordinary skill in the art to combine the teachings of Ivonin and JEON because the combination would cover all tiles in the mask for lithographical corrections, yielding the predictable results of more accurate printing and fabrication according to design intent and specifications, thereby increasing yield.
Regarding claim 2
Ivonin and JEON teach all aspects of claim 1, as disclosed above and further discloses
The method of claim 1, wherein the first pixel and the second pixel are located within a predefined proximity of one another.
(Ivonin, p. 1, [0002] “… This OPC may be calculated as an image is rasterized and loaded into an SLM, by adjusting adjacent pixels to more faithfully reproduce a design on a workpiece, with reduced need for serifs, assist lines and other OPC features.”)
(Ivonin, p. 11, [0149] “… For a neighborhood range of four pixels in each direction (a 9.times.9 grid), the 4-D nature of model yields 130,000 terms for an interaction length of 600 nm.”)
(Ivonin, p. 13, [0173] “… This means that we need to apply correction only to pixels at the edge or adjacent to it and they can be recognized by their grayness in an analog bitmap.”)
Regarding claim 5
Ivonin and JEON teach all aspects of claim 1, as disclosed above and further discloses
The method of claim 1, wherein the edge interaction kernel is generated to include a plurality of multi-dimensional vectors.
(Ivonin, p. 2, [0010] “Previously disclosed methods and devices are extended in this application by two-dimensional analysis of optical proximity interactions and by fashioning a computationally efficient kernel for rapid calculation of adjustments to be made.”)
(Ivonin, p. 3, [0050] “FIG. 31 is a visualization of selecting element from four-dimensional space for a 2D kernel.”)
(Ivonin, p. 15, [0191] “Yet another variation on the embodiments above is a method of efficiently applying optical proximity correction for printing highly accurate patterns on a workpiece in microlithography. This method may include rasterizing at least part of a feature including assigning pixel gray values and correcting edge perturbations caused by additional features within a range of interaction from the part of the feature by applying a correction kernel to the pixel gray values.”)
(Ivonin, p. 14, [0188] “… This method may include correcting in a pixel domain a rasterized image of at least part of a feature, wherein the correction applies a four dimensional kernel with selected non-zero values to compensate for perturbations caused by optical proximity effects, whereby the number of additions and multiplications required to calculate the correction in the pixel domain, after selection of the non-zero values to include in the four dimensional kernel, are a substantially less than would be required for a full kernel.”)
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
Regarding claim 6
Ivonin and JEON teach all aspects of claim 1, as disclosed above and further discloses
The method of claim 1, wherein:
the tile further contains a fourth pixel that is located on a further of the first pattern or the second pattern;
(Ivonin, p. 11, [0149] “… In the general case the kernel K is four-dimensional and (4) contains
(
2
*
n
r
a
n
g
e
+
1
)
4
terms, a large number even for small interaction ranges
n
r
a
n
g
e
. For a neighborhood range of four pixels in each direction (a
9
×
9
grid), the 4-D nature of model yields 130,000 terms for an interaction length of 600 nm. We have to simplify (4) immensely in order to compute it for every gray pixel in a reticle--which could in the worst case be every pixel in a reticle,
10
13
of them.”)
(Ivonin, p. 5, [0082] “One chooses a merit function for optimization. The number of possible patterns in the neighborhood within, say, 500 nm around an edge is immense and to optimize all of them would be difficult. The inventors have found that analysis of a small set of pattern classes is sufficient for rotationally symmetric aperture functions.”)
(Ivonin, p. 5, [0085] “The merit function is set up to fulfill some or all of the following objectives. The first one is to make all lines larger than a specified limit print with no CD errors, i.e., to make the CD linearity plot flat above the limit. If all feature classes satisfy this, there is no influence between edges at a distance larger than the limit. This is a large benefit, since it limits the range of the OPC adjustments needed to make a pattern print accurately. During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range.”)
the determining the influence comprises determining the influence exerted to the third pixel by the first pixel, the second pixel, and the fourth pixel;
(Ivonin, p. 5, [0085] “… If all feature classes satisfy this, there is no influence between edges at a distance larger than the limit. This is a large benefit, since it limits the range of the OPC adjustments needed to make a pattern print accurately. During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range. We will call it the limit of no interaction.”)
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
(Ivonin, p. 9, [0125] “… This can be implemented by a modified convolution, where the added adjustment of a pixel is a non-linear function of the values of the neighbors, possibly also of the value of the same pixels.”)
(Ivonin, p. 11, [0149] “…For a neighborhood range of four pixels in each direction (a
9
×
9
grid), the 4-D nature of model yields 130,000 terms for an interaction length of 600 nm. We have to simplify (4) immensely in order to compute it for every gray pixel in a reticle--which could in the worst case be every pixel in a reticle,
10
13
of them.”)
and the generating the edge interaction kernel is based on the influence exerted to the third pixel by the first pixel, the second pixel, and the fourth pixel.
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
(Ivonin, p. 11, [0149] “… In the general case the kernel K is four-dimensional and (4) contains
(
2
*
n
r
a
n
g
e
+
1
)
4
terms, a large number even for small interaction ranges
n
r
a
n
g
e
. For a neighborhood range of four pixels in each direction (a
9
×
9
grid), the 4-D nature of model yields 130,000 terms for an interaction length of 600 nm. We have to simplify (4) immensely in order to compute it for every gray pixel in a reticle--which could in the worst case be every pixel in a reticle, 10.sup.13 of them.”)
(Ivonin, p. 9, [0125] “… This can be implemented by a modified convolution, where the added adjustment of a pixel is a non-linear function of the values of the neighbors, possibly also of the value of the same pixels.”)
(Ivonin, p. 15, [0191] “Yet another variation on the embodiments above is a method of efficiently applying optical proximity correction for printing highly accurate patterns on a workpiece in microlithography. This method may include rasterizing at least part of a feature including assigning pixel gray values and correcting edge perturbations caused by additional features within a range of interaction from the part of the feature by applying a correction kernel to the pixel gray values.”)
(Ivonin, p. 14, [0188] “… This method may include correcting in a pixel domain a rasterized image of at least part of a feature, wherein the correction applies a four dimensional kernel with selected non-zero values to compensate for perturbations caused by optical proximity effects, whereby the number of additions and multiplications required to calculate the correction in the pixel domain, after selection of the non-zero values to include in the four dimensional kernel, are a substantially less than would be required for a full kernel.”)
(Ivonin, p. 5, [0085] “The merit function is set up to fulfill some or all of the following objectives. The first one is to make all lines larger than a specified limit print with no CD errors, i.e., to make the CD linearity plot flat above the limit. If all feature classes satisfy this, there is no influence between edges at a distance larger than the limit. This is a large benefit, since it limits the range of the OPC adjustments needed to make a pattern print accurately. During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range.”)
Regarding claim 7
Ivonin and JEON teach all aspects of claim 1, as disclosed above and further discloses
The method of claim 1, wherein:
the edge interaction kernel is a first edge interaction kernel;
(Ivonin, p. 15, [0191] “Yet another variation on the embodiments above is a method of efficiently applying optical proximity correction for printing highly accurate patterns on a workpiece in microlithography. This method may include rasterizing at least part of a feature including assigning pixel gray values and correcting edge perturbations caused by additional features within a range of interaction from the part of the feature by applying a correction kernel to the pixel gray values.”)
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
(Ivonin, p. 14, [0188] “… This method may include correcting in a pixel domain a rasterized image of at least part of a feature, wherein the correction applies a four dimensional kernel with selected non-zero values to compensate for perturbations caused by optical proximity effects, whereby the number of additions and multiplications required to calculate the correction in the pixel domain, after selection of the non-zero values to include in the four dimensional kernel, are a substantially less than would be required for a full kernel.”)
the tile further contains a plurality of additional first pixels that are respectively located on a plurality of additional edges of the first pattern, and a plurality of additional second pixels that are respectively located on a plurality of additional edges of the second pattern;
(Ivonin, p. 5, [0085] “The merit function is set up to fulfill some or all of the following objectives. The first one is to make all lines larger than a specified limit print with no CD errors, i.e., to make the CD linearity plot flat above the limit. If all feature classes satisfy this, there is no influence between edges at a distance larger than the limit. This is a large benefit, since it limits the range of the OPC adjustments needed to make a pattern print accurately. During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range.”)
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
(Ivonin, p. 11, [0149] “… For a neighborhood range of four pixels in each direction (a 9.times.9 grid), the 4-D nature of model yields 130,000 terms for an interaction length of 600 nm.”)
(Ivonin, p. 13, [0173] “… This means that we need to apply correction only to pixels at the edge or adjacent to it and they can be recognized by their grayness in an analog bitmap.”)
and the method further comprises:
based on the additional pixels and the additional second pixels, repeating the determining the influence and the generating the edge interaction kernel, thereby generating a plurality of additional edge interaction kernels for the third pixel.
(Ivonin, p. 5, [0085] “The merit function is set up to fulfill some or all of the following objectives. The first one is to make all lines larger than a specified limit print with no CD errors, i.e., to make the CD linearity plot flat above the limit. If all feature classes satisfy this, there is no influence between edges at a distance larger than the limit. This is a large benefit, since it limits the range of the OPC adjustments needed to make a pattern print accurately. During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range.”)
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
(Ivonin, p. 11, [0149] “… In the general case the kernel K is four-dimensional and (4) contains
(
2
*
n
r
a
n
g
e
+
1
)
4
terms, a large number even for small interaction ranges
n
r
a
n
g
e
. For a neighborhood range of four pixels in each direction (a
9
×
9
grid), the 4-D nature of model yields 130,000 terms for an interaction length of 600 nm. We have to simplify (4) immensely in order to compute it for every gray pixel in a reticle--which could in the worst case be every pixel in a reticle,
10
13
of them.”)
(Ivonin, p. 13, [0173] “… This means that we need to apply correction only to pixels at the edge or adjacent to it and they can be recognized by their grayness in an analog bitmap.”)
(Ivonin, p. 14, [0188] “… This method may include correcting in a pixel domain a rasterized image of at least part of a feature, wherein the correction applies a four dimensional kernel with selected non-zero values to compensate for perturbations caused by optical proximity effects, whereby the number of additions and multiplications required to calculate the correction in the pixel domain, after selection of the non-zero values to include in the four dimensional kernel, are a substantially less than would be required for a full kernel.”)
(Ivonin, p. 15, [0191] “Yet another variation on the embodiments above is a method of efficiently applying optical proximity correction for printing highly accurate patterns on a workpiece in microlithography. This method may include rasterizing at least part of a feature including assigning pixel gray values and correcting edge perturbations caused by additional features within a range of interaction from the part of the feature by applying a correction kernel to the pixel gray values.”)
(Ivonin, p. 2, [0010] “Previously disclosed methods and devices are extended in this application by two-dimensional analysis of optical proximity interactions and by fashioning a computationally efficient kernel for rapid calculation of adjustments to be made.”)
(Ivonin, p. 11, [0155] “The interaction is the same in elements i,j and j,i and we remove the redundant pixels (actually it is the symmetry in the AA* field in (3) that we remove, the kernel has even more symmetries but cannot be removed since AA* differ). Furthermore, we remove all elements that are small and end up with 12 elements. This kernel works for arbitrary one-dimensional patterns.”)
(Ivonin, p. 12, [0163] “As an alternative, which we call he pseudo-2D kernel, we have used the radial symmetry of the optics and rotated and added the 1D kernel in four angles (0, 45, 90, and 135 degrees, in FIG. 30) in the x,y plane, plus explicitly added the elements for the interaction between +/-1 in x with +/-1 in y. This kernel, which should give accurate results for lines and spaces in Manhattan and X geometries and reasonable results for corners and line ends, has 39 elements. “)
(Ivonin, p. 13, [0174] “… We, therefore, have everything we need to calculate the perturbation from Equation (2) due to the pattern within the range of interaction. If the interaction range is small, this is only a few pixels, e.g., 7 by 7 pixels, and the calculation can be done either in a high speed general purpose processor, a signal processor, an FPGA or custom logic. The operations are easy to compute in parallel and to pipeline, making an implementation with high capacity possible. When several passes are printed with an offset pixel grid, it is possible to apply the correction in all passes or only in those passes where the edge pixel is close to mid-gray. A compromise with more correction in those passes where the edge is off-grid (i.e., gray) is beneficial since it does not need to imply exposures outside of the dynamic range used elsewhere in the pattern.”)
Regarding claim 8
Ivonin and JEON teach all aspects of claim 7, as disclosed above and further discloses
The method of claim 7, wherein: determining the near field correction comprises performing an integral or a summation based on the first edge interaction kernel and the plurality of additional edge interaction kernels.
(Ivonin, p. 5, [0085] “The merit function is set up to fulfill some or all of the following objectives. The first one is to make all lines larger than a specified limit print with no CD errors, i.e., to make the CD linearity plot flat above the limit. If all feature classes satisfy this, there is no influence between edges at a distance larger than the limit. This is a large benefit, since it limits the range of the OPC adjustments needed to make a pattern print accurately. During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range.”)
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
(Ivonin, p. 11, [0149] “… In the general case the kernel K is four-dimensional and (4) contains
(
2
*
n
r
a
n
g
e
+
1
)
4
terms, a large number even for small interaction ranges
n
r
a
n
g
e
. For a neighborhood range of four pixels in each direction (a
9
×
9
grid), the 4-D nature of model yields 130,000 terms for an interaction length of 600 nm. We have to simplify (4) immensely in order to compute it for every gray pixel in a reticle--which could in the worst case be every pixel in a reticle,
10
13
of them.”)
(Ivonin, p. 13, [0173] “… This means that we need to apply correction only to pixels at the edge or adjacent to it and they can be recognized by their grayness in an analog bitmap.”)
(Ivonin, p. 14, [0188] “… This method may include correcting in a pixel domain a rasterized image of at least part of a feature, wherein the correction applies a four dimensional kernel with selected non-zero values to compensate for perturbations caused by optical proximity effects, whereby the number of additions and multiplications required to calculate the correction in the pixel domain, after selection of the non-zero values to include in the four dimensional kernel, are a substantially less than would be required for a full kernel.”)
(Ivonin, p. 15, [0191] “Yet another variation on the embodiments above is a method of efficiently applying optical proximity correction for printing highly accurate patterns on a workpiece in microlithography. This method may include rasterizing at least part of a feature including assigning pixel gray values and correcting edge perturbations caused by additional features within a range of interaction from the part of the feature by applying a correction kernel to the pixel gray values.”)
(Ivonin, p. 1, [0002] “There are two aspects to the technology disclosed: First, a method and device with a reduced field of interaction, which simplifies and reduces the need for optical proximity correction (OPC).”)
(Ivonin, p. 9, [0124] “Going back to the bitmap processing for a mask writer or direct-writer, the corrections are rather small and have a simple relation to the features inside the limit of no interaction. A suitable method to do the correction is by convolution of the bitmap by a kernel that corrects for the residual errors.”)
A pattern can effectively be considered as part of the mask layout.
Regarding claim 9
Ivonin and JEON teach all aspects of claim 1, as disclosed above and further discloses
The method of claim 1, wherein:
the tile further contains a plurality of additional pixels;
(Ivonin, p. 13, [0174] “… We, therefore, have everything we need to calculate the perturbation from Equation (2) due to the pattern within the range of interaction. If the interaction range is small, this is only a few pixels, e.g., 7 by 7 pixels, and the calculation can be done either in a high speed general purpose processor, a signal processor, an FPGA or custom logic. The operations are easy to compute in parallel and to pipeline, making an implementation with high capacity possible. When several passes are printed with an offset pixel grid, it is possible to apply the correction in all passes or only in those passes where the edge pixel is close to mid-gray. A compromise with more correction in those passes where the edge is off-grid (i.e., gray) is beneficial since it does not need to imply exposures outside of the dynamic range used elsewhere in the pattern.”)
(Ivonin, p. 11, [0149] “… In the general case the kernel K is four-dimensional and (4) contains
(
2
*
n
r
a
n
g
e
+
1
)
4
terms, a large number even for small interaction ranges
n
r
a
n
g
e
. For a neighborhood range of four pixels in each direction (a
9
×
9
grid), the 4-D nature of model yields 130,000 terms for an interaction length of 600 nm. We have to simplify (4) immensely in order to compute it for every gray pixel in a reticle--which could in the worst case be every pixel in a reticle,
10
13
of them.”)
(Ivonin, p. 13, [0173] “… This means that we need to apply correction only to pixels at the edge or adjacent to it and they can be recognized by their grayness in an analog bitmap.”)
the determining the influence comprises determining the influence exerted to each of the additional pixels by the first pixel and the second pixel;
(Ivonin, p. 5, [0085] “The merit function is set up to fulfill some or all of the following objectives. The first one is to make all lines larger than a specified limit print with no CD errors, i.e., to make the CD linearity plot flat above the limit. If all feature classes satisfy this, there is no influence between edges at a distance larger than the limit. This is a large benefit, since it limits the range of the OPC adjustments needed to make a pattern print accurately. During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range.”)
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
(Ivonin, p. 13, [0174] “… We, therefore, have everything we need to calculate the perturbation from Equation (2) due to the pattern within the range of interaction. If the interaction range is small, this is only a few pixels, e.g., 7 by 7 pixels, and the calculation can be done either in a high speed general purpose processor, a signal processor, an FPGA or custom logic. The operations are easy to compute in parallel and to pipeline, making an implementation with high capacity possible. When several passes are printed with an offset pixel grid, it is possible to apply the correction in all passes or only in those passes where the edge pixel is close to mid-gray. A compromise with more correction in those passes where the edge is off-grid (i.e., gray) is beneficial since it does not need to imply exposures outside of the dynamic range used elsewhere in the pattern.”)
the generating comprises generating, based on the influence exerted to each of the additional pixels by the first pixel and the second pixel, a plurality of additional edge interaction kernels corresponding to the additional pixels, respectively
(Ivonin, p. 5, [0085] “The merit function is set up to fulfill some or all of the following objectives. The first one is to make all lines larger than a specified limit print with no CD errors, i.e., to make the CD linearity plot flat above the limit. If all feature classes satisfy this, there is no influence between edges at a distance larger than the limit. This is a large benefit, since it limits the range of the OPC adjustments needed to make a pattern print accurately. During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range.”)
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
(Ivonin, p. 11, [0149] “… In the general case the kernel K is four-dimensional and (4) contains
(
2
*
n
r
a
n
g
e
+
1
)
4
terms, a large number even for small interaction ranges
n
r
a
n
g
e
. For a neighborhood range of four pixels in each direction (a
9
×
9
grid), the 4-D nature of model yields 130,000 terms for an interaction length of 600 nm. We have to simplify (4) immensely in order to compute it for every gray pixel in a reticle--which could in the worst case be every pixel in a reticle,
10
13
of them.”)
(Ivonin, p. 13, [0173] “… This means that we need to apply correction only to pixels at the edge or adjacent to it and they can be recognized by their grayness in an analog bitmap.”)
(Ivonin, p. 14, [0188] “… This method may include correcting in a pixel domain a rasterized image of at least part of a feature, wherein the correction applies a four dimensional kernel with selected non-zero values to compensate for perturbations caused by optical proximity effects, whereby the number of additions and multiplications required to calculate the correction in the pixel domain, after selection of the non-zero values to include in the four dimensional kernel, are a substantially less than would be required for a full kernel.”)
(Ivonin, p. 15, [0191] “Yet another variation on the embodiments above is a method of efficiently applying optical proximity correction for printing highly accurate patterns on a workpiece in microlithography. This method may include rasterizing at least part of a feature including assigning pixel gray values and correcting edge perturbations caused by additional features within a range of interaction from the part of the feature by applying a correction kernel to the pixel gray values.”)
(Ivonin, p. 13, [0174] “… We, therefore, have everything we need to calculate the perturbation from Equation (2) due to the pattern within the range of interaction. If the interaction range is small, this is only a few pixels, e.g., 7 by 7 pixels, and the calculation can be done either in a high speed general purpose processor, a signal processor, an FPGA or custom logic. The operations are easy to compute in parallel and to pipeline, making an implementation with high capacity possible. When several passes are printed with an offset pixel grid, it is possible to apply the correction in all passes or only in those passes where the edge pixel is close to mid-gray. A compromise with more correction in those passes where the edge is off-grid (i.e., gray) is beneficial since it does not need to imply exposures outside of the dynamic range used elsewhere in the pattern.”)
and the determining the near field correction comprises determining the near field correction for each of the additional pixels based on the additional edge interaction kernel corresponding to the respective additional pixel.
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
(Ivonin, p. 11, “In the general case the kernel K is four-dimensional and (4) contains
(
2
*
n
r
a
n
g
e
+
1
)
4
terms, a large number even for small interaction ranges
n
r
a
n
g
e
. For a neighborhood range of four pixels in each direction (a
9
×
9
grid), the 4-D nature of model yields 130,000 terms for an interaction length of 600 nm. We have to simplify (4) immensely in order to compute it for every gray pixel in a reticle--which could in the worst case be every pixel in a reticle, 10.sup.13 of them.”)
(Ivonin, p. 11, [0155] “The interaction is the same in elements i,j and j,i and we remove the redundant pixels (actually it is the symmetry in the AA* field in (3) that we remove, the kernel has even more symmetries but cannot be removed since AA* differ). Furthermore, we remove all elements that are small and end up with 12 elements. This kernel works for arbitrary one-dimensional patterns.”)
(Ivonin, p. 12, [0169] “In an example embodiment, the operations are performed in the pixel domain, focusing on the gray pixels in the bitmap, which in a typical pattern is only a fraction of the pixels. The processing unit could either be designed for the average processing needs in a pattern and process only the gray pixels based on a list, or it could scan through every pixel in the image an have capacity to process each one of them is necessary. The latter design appears far safer since there is no risk of constipation and he logic is simpler. The saving in capacity in the first case is offset by a more complex structure with queues and buffers. Is it possible to design a real-time OPC processing unit with enough processing power to apply calculate the correction in every pixel. Indeed, it is, using the algorithms described above and fast silicon.”)
(Ivonin, p. 13, [0173] “A real-time proximity correction scheme can be implemented as a perturbation correction to the already quite good data-to-image conversion provided by the data-path, SLM and optics. It need only correct the intensity (or E field) at the boundaries of the features. This means that we need to apply correction only to pixels at the edge or adjacent to it and they can be recognized by their grayness in an analog bitmap. Furthermore, we need only correct for the pattern inside the range of optical interaction, made small by the optimization of the optics.”)
(Ivonin, p. 13, [0174] “… We, therefore, have everything we need to calculate the perturbation from Equation (2) due to the pattern within the range of interaction. If the interaction range is small, this is only a few pixels, e.g., 7 by 7 pixels, and the calculation can be done either in a high speed general purpose processor, a signal processor, an FPGA or custom logic. The operations are easy to compute in parallel and to pipeline, making an implementation with high capacity possible. When several passes are printed with an offset pixel grid, it is possible to apply the correction in all passes or only in those passes where the edge pixel is close to mid-gray. A compromise with more correction in those passes where the edge is off-grid (i.e., gray) is beneficial since it does not need to imply exposures outside of the dynamic range used elsewhere in the pattern.”)
(Ivonin, p. 14, [0188] “… This method may include correcting in a pixel domain a rasterized image of at least part of a feature, wherein the correction applies a four dimensional kernel with selected non-zero values to compensate for perturbations caused by optical proximity effects, whereby the number of additions and multiplications required to calculate the correction in the pixel domain, after selection of the non-zero values to include in the four dimensional kernel, are a substantially less than would be required for a full kernel.”)
(Ivonin, p. 15, [0191] “Yet another variation on the embodiments above is a method of efficiently applying optical proximity correction for printing highly accurate patterns on a workpiece in microlithography. This method may include rasterizing at least part of a feature including assigning pixel gray values and correcting edge perturbations caused by additional features within a range of interaction from the part of the feature by applying a correction kernel to the pixel gray values.”)
(Ivonin, p. 1, [0002] “There are two aspects to the technology disclosed: First, a method and device with a reduced field of interaction, which simplifies and reduces the need for optical proximity correction (OPC).”)
(Ivonin, p. 9, [0124] “Going back to the bitmap processing for a mask writer or direct-writer, the corrections are rather small and have a simple relation to the features inside the limit of no interaction. A suitable method to do the correction is by convolution of the bitmap by a kernel that corrects for the residual errors.”)
A pattern can effectively be considered as part of the mask layout.
Regarding claim 10
Ivonin and JEON teach all aspects of claim 1, as disclosed above
Ivonin teaches
The method of claim 1, further comprising:
wherein the determining the influence, the generating the edge interaction kernel, and the determining the near field correction are performed based on the plurality of sub-tiles.
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
(Ivonin, p. 15, [0191] “Yet another variation on the embodiments above is a method of efficiently applying optical proximity correction for printing highly accurate patterns on a workpiece in microlithography. This method may include rasterizing at least part of a feature including assigning pixel gray values and correcting edge perturbations caused by additional features within a range of interaction from the part of the feature by applying a correction kernel to the pixel gray values. It further may include printing a microlithographic pattern on the workpiece. This variation can be combined with the concurrent operation or real time aspect applicable to the preceding embodiments.”)
(Ivonin, p. 11, “In the general case the kernel K is four-dimensional and (4) contains
(
2
*
n
r
a
n
g
e
+
1
)
4
terms, a large number even for small interaction ranges
n
r
a
n
g
e
. For a neighborhood range of four pixels in each direction (a
9
×
9
grid), the 4-D nature of model yields 130,000 terms for an interaction length of 600 nm. We have to simplify (4) immensely in order to compute it for every gray pixel in a reticle--which could in the worst case be every pixel in a reticle, 10.sup.13 of them.”)
(Ivonin, p. 14, [0188] “… This method may include correcting in a pixel domain a rasterized image of at least part of a feature, wherein the correction applies a four dimensional kernel with selected non-zero values to compensate for perturbations caused by optical proximity effects, whereby the number of additions and multiplications required to calculate the correction in the pixel domain, after selection of the non-zero values to include in the four dimensional kernel, are a substantially less than would be required for a full kernel.”)
(Ivonin, p. 1, [0002] “There are two aspects to the technology disclosed: First, a method and device with a reduced field of interaction, which simplifies and reduces the need for optical proximity correction (OPC).”)
(Ivonin, p. 9, [0124] “Going back to the bitmap processing for a mask writer or direct-writer, the corrections are rather small and have a simple relation to the features inside the limit of no interaction. A suitable method to do the correction is by convolution of the bitmap by a kernel that corrects for the residual errors.”)
A pattern can effectively be considered as part of the mask layout.
In essence, a pattern itself can be considered a sub-tile.
Ivonin does not explicitly teach
before the determining the influence, dividing the tile into a plurality of sub-tiles
However, JEON discloses
before the determining the influence, dividing the tile into a plurality of sub-tiles
(JEON, p. 1, [0011] “In accordance with an aspect of embodiments, a method of designing patterns of semiconductor devices includes forming a plurality of tiles having patterns on a wafer, measuring the patterns of the plurality of tiles, analyzing the measurements of the patterns and determining a tile having such a size that the measurements linearly vary according to a design size and pattern density, and modifying the pattern density of the determined tile.”)
Therefore, it would have been obvious before the effective priority date of the claim to a person having ordinary skill in the art to combine the teachings of Ivonin and JEON as edge interaction comes into play at small distances, which can be accounted for within a small tile, and also help in reducing computing demands.
Regarding claim 11
Ivonin and JEON teach all aspects of claim 10, as disclosed above and further discloses
The method of claim 10, further comprising: surrounding each of the sub-tiles with a respective margin of interaction.
(Ivonin, p. 5, [0085] “… During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range. We will call it the limit of no interaction.”)
(Ivonin, p. 5, [0087] “The third objective is to bring lines between the resolution limit and the limit of no interaction within acceptable bounds.”)
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
Regarding claim 12
Ivonin and JEON teach all aspects of claim 11, as disclosed above and further discloses
The method of claim 11, wherein the generating the edge interaction kernel comprises discarding contributions made by the margin of interaction to the edge interaction kernel.
(Ivonin, p. 5, [0085] “… During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range. We will call it the limit of no interaction.”)
(Ivonin, p. 5, [0087] “The third objective is to bring lines between the resolution limit and the limit of no interaction within acceptable bounds.”)
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
(Ivonin, p. 9, [0124] “Going back to the bitmap processing for a mask writer or direct-writer, the corrections are rather small and have a simple relation to the features inside the limit of no interaction.”)
Regarding claim 13
Ivonin discloses b-e parts claim 13
A method, comprising:
identifying edge pixels of the plurality of patterns;
(Ivonin, p. 5, [0085] “The merit function is set up to fulfill some or all of the following objectives. The first one is to make all lines larger than a specified limit print with no CD errors, i.e., to make the CD linearity plot flat above the limit. If all feature classes satisfy this, there is no influence between edges at a distance larger than the limit. This is a large benefit, since it limits the range of the OPC adjustments needed to make a pattern print accurately. During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range.”)
(Ivonin, p. 11, [0149] “… For a neighborhood range of four pixels in each direction (a 9.times.9 grid), the 4-D nature of model yields 130,000 terms for an interaction length of 600 nm.”)
(Ivonin, p. 13, [0173] “… This means that we need to apply correction only to pixels at the edge or adjacent to it and they can be recognized by their grayness in an analog bitmap.”)
(Ivonin, p. 1, [0003] “The present disclosure teaches a method to project an optical image of an original (typically a pattern on a photomask or a spatial light modulator (SLM)) onto a workpiece with extremely high resolution and fidelity given the constraints of the optics.”)
(Ivonin, p. 13-14, [0179] “…The disclosed technology has most benefit where general patterns need to be printed with equally good fidelity for all features, small and large, 1D and 2D. The typical application is masks.”)
determining an influence exerted to an influenced pixel by the plurality of edge pixels, wherein the influenced pixel is different from any of the edge pixels;
(Ivonin, p. 13, [0173] “… This means that we need to apply correction only to pixels at the edge or adjacent to it and they can be recognized by their grayness in an analog bitmap.”)
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
(Ivonin, p. 5, [0085] “The merit function is set up to fulfill some or all of the following objectives. The first one is to make all lines larger than a specified limit print with no CD errors, i.e., to make the CD linearity plot flat above the limit. If all feature classes satisfy this, there is no influence between edges at a distance larger than the limit. This is a large benefit, since it limits the range of the OPC adjustments needed to make a pattern print accurately. During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range.”)
(Ivonin, p. 11, [0149] “… For a neighborhood range of four pixels in each direction (a 9.times.9 grid), the 4-D nature of model yields 130,000 terms for an interaction length of 600 nm.”)
generating, based at least in part on the influence exerted to the influenced pixel by the plurality of edge pixels, an edge interaction kernel for the influenced pixel;
(Ivonin, p. 13, [0173] “… This means that we need to apply correction only to pixels at the edge or adjacent to it and they can be recognized by their grayness in an analog bitmap.”)
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
(Ivonin, p. 5, [0085] “The merit function is set up to fulfill some or all of the following objectives. The first one is to make all lines larger than a specified limit print with no CD errors, i.e., to make the CD linearity plot flat above the limit. If all feature classes satisfy this, there is no influence between edges at a distance larger than the limit. This is a large benefit, since it limits the range of the OPC adjustments needed to make a pattern print accurately. During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range.”)
(Ivonin, p. 11, [0149] “… For a neighborhood range of four pixels in each direction (a 9.times.9 grid), the 4-D nature of model yields 130,000 terms for an interaction length of 600 nm.”)
and determining, based at least in part on the edge interaction kernel, a near field correction at the influenced pixel.
(Ivonin, p. 15, [0191] “Yet another variation on the embodiments above is a method of efficiently applying optical proximity correction for printing highly accurate patterns on a workpiece in microlithography. This method may include rasterizing at least part of a feature including assigning pixel gray values and correcting edge perturbations caused by additional features within a range of interaction from the part of the feature by applying a correction kernel to the pixel gray values. It further may include printing a microlithographic pattern on the workpiece. This variation can be combined with the concurrent operation or real time aspect applicable to the preceding embodiments.”)
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
(Ivonin, p. 14, [0188] “… This method may include correcting in a pixel domain a rasterized image of at least part of a feature, wherein the correction applies a four dimensional kernel with selected non-zero values to compensate for perturbations caused by optical proximity effects, whereby the number of additions and multiplications required to calculate the correction in the pixel domain, after selection of the non-zero values to include in the four dimensional kernel, are a substantially less than would be required for a full kernel.”)
(Ivonin, p. 1, [0002] “There are two aspects to the technology disclosed: First, a method and device with a reduced field of interaction, which simplifies and reduces the need for optical proximity correction (OPC).”)
(Ivonin, p. 9, [0124] “Going back to the bitmap processing for a mask writer or direct-writer, the corrections are rather small and have a simple relation to the features inside the limit of no interaction. A suitable method to do the correction is by convolution of the bitmap by a kernel that corrects for the residual errors.”)
A pattern can effectively be considered as part of the mask layout.
(JEON, p. 1, [0011] “In accordance with an aspect of embodiments, a method of designing patterns of semiconductor devices includes forming a plurality of tiles having patterns on a wafer, measuring the patterns of the plurality of tiles, analyzing the measurements of the patterns and determining a tile having such a size that the measurements linearly vary according to a design size and pattern density, and modifying the pattern density of the determined tile.”)
Ivonin does not explicitly teaches
accessing a tile that contains a plurality of patterns;
However, JEON discloses
accessing a tile that contains a plurality of patterns;
(JEON, p. 1, [0011] “In accordance with an aspect of embodiments, a method of designing patterns of semiconductor devices includes forming a plurality of tiles having patterns on a wafer, measuring the patterns of the plurality of tiles, analyzing the measurements of the patterns and determining a tile having such a size that the measurements linearly vary according to a design size and pattern density, and modifying the pattern density of the determined tile.”)
Therefore, it would have been obvious before the effective priority date of the claim to a person having ordinary skill in the art to combine the teachings of Ivonin and JEON because the combination would cover all tiles in the mask for lithographical corrections, yielding the predictable results of more accurate printing and fabrication according to design intent and specifications, thereby increasing yield.
Regarding claim 15
Ivonin and JEON discloses all aspects of claim 13, as disclosed above and further discloses
The method of claim 13, wherein the tile contains a plurality of further influenced pixels, and wherein the determining the influence exerted and the generating the edge interaction kernel are repeated for each of the further influenced pixels, such that a plurality of further edge interaction kernels are generated, and wherein the near field correction is further based on the plurality of further edge interaction kernels.
(Ivonin, p. 5, [0085] “The merit function is set up to fulfill some or all of the following objectives. The first one is to make all lines larger than a specified limit print with no CD errors, i.e., to make the CD linearity plot flat above the limit. If all feature classes satisfy this, there is no influence between edges at a distance larger than the limit. This is a large benefit, since it limits the range of the OPC adjustments needed to make a pattern print accurately. During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range.”)
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
(Ivonin, p. 11, [0149] “… In the general case the kernel K is four-dimensional and (4) contains
(
2
*
n
r
a
n
g
e
+
1
)
4
terms, a large number even for small interaction ranges
n
r
a
n
g
e
. For a neighborhood range of four pixels in each direction (a
9
×
9
grid), the 4-D nature of model yields 130,000 terms for an interaction length of 600 nm. We have to simplify (4) immensely in order to compute it for every gray pixel in a reticle--which could in the worst case be every pixel in a reticle,
10
13
of them.”)
(Ivonin, p. 13, [0173] “… This means that we need to apply correction only to pixels at the edge or adjacent to it and they can be recognized by their grayness in an analog bitmap.”)
(Ivonin, p. 14, [0188] “… This method may include correcting in a pixel domain a rasterized image of at least part of a feature, wherein the correction applies a four dimensional kernel with selected non-zero values to compensate for perturbations caused by optical proximity effects, whereby the number of additions and multiplications required to calculate the correction in the pixel domain, after selection of the non-zero values to include in the four dimensional kernel, are a substantially less than would be required for a full kernel.”)
(Ivonin, p. 15, [0191] “Yet another variation on the embodiments above is a method of efficiently applying optical proximity correction for printing highly accurate patterns on a workpiece in microlithography. This method may include rasterizing at least part of a feature including assigning pixel gray values and correcting edge perturbations caused by additional features within a range of interaction from the part of the feature by applying a correction kernel to the pixel gray values.”)
(Ivonin, p. 1, [0002] “There are two aspects to the technology disclosed: First, a method and device with a reduced field of interaction, which simplifies and reduces the need for optical proximity correction (OPC).”)
(Ivonin, p. 9, [0124] “Going back to the bitmap processing for a mask writer or direct-writer, the corrections are rather small and have a simple relation to the features inside the limit of no interaction. A suitable method to do the correction is by convolution of the bitmap by a kernel that corrects for the residual errors.”)
A pattern can effectively be considered as part of the mask layout.
Regarding claim 16
Ivonin and JEON discloses all aspects of claim 13, as disclosed above and further discloses
The method of claim 13, further comprising: dividing the tile into a plurality of sub-tiles, wherein the determining the influence exerted and the generating the edge interaction kernel are performed for each of the sub-tiles.
(Ivonin, p. 5, [0087] “The third objective is to bring lines between the resolution limit and the limit of no interaction within acceptable bounds.”)
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
(Ivonin, p. 5, [0085] “The merit function is set up to fulfill some or all of the following objectives. The first one is to make all lines larger than a specified limit print with no CD errors, i.e., to make the CD linearity plot flat above the limit. If all feature classes satisfy this, there is no influence between edges at a distance larger than the limit. This is a large benefit, since it limits the range of the OPC adjustments needed to make a pattern print accurately. During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range.”)
Regarding claim 17
Ivonin and JEON discloses all aspects of claim 13, as disclosed above and further discloses
The method of claim 16, wherein:
a margin of interaction is applied to each of the sub-tiles;
(Ivonin, p. 5, [0085] “… During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range. We will call it the limit of no interaction.”)
(Ivonin, p. 5, [0087] “The third objective is to bring lines between the resolution limit and the limit of no interaction within acceptable bounds. Physics does not allow all lines to be printed perfectly, and the optimal solution is a trade-off. If the limit of no interaction is allowed to be higher and the resolution limit lower, the intermediate range can be made better. Depending on the application and the tolerances it can be brought within acceptable bounds, or it will need some adjustment in the data going to the SLM or to the mask writer in the case of a mask.”)
A pattern can be considered as a sub-tile.
the edge interaction kernel for each of the sub-tiles includes a contribution made by the margin of interaction;
(Ivonin, p. 5, [0085] “… During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range. We will call it the limit of no interaction.”)
(Ivonin, p. 5, [0087] “The third objective is to bring lines between the resolution limit and the limit of no interaction within acceptable bounds.”)
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
(Ivonin, p. 9, [0124] “Going back to the bitmap processing for a mask writer or direct-writer, the corrections are rather small and have a simple relation to the features inside the limit of no interaction. A suitable method to do the correction is by convolution of the bitmap by a kernel that corrects for the residual errors.”)
and the near filed correction is determined by removing the contribution made by the margin of interaction.
(Ivonin, p. 5, [0085] “… During the OPC processing of a pattern, the computational load depends strongly on the range of interactions that need to be analyzed, and the objective here is to limit that range. We will call it the limit of no interaction.”)
(Ivonin, p. 5, [0087] “The third objective is to bring lines between the resolution limit and the limit of no interaction within acceptable bounds.”)
(Ivonin, p. 8, [0121] “The linewidth range between the limit of no interaction and the resolution limit cannot be printed without errors depending on neighboring features and edges. (This is, in fact, the definition of the limit of no interaction.) However, this adjustment is much easier than full OPC and involves only closest-neighbor influences, perhaps just an edge bias depending on the distance to the next edge on each side.”)
(Ivonin, p. 9, [0124] “Going back to the bitmap processing for a mask writer or direct-writer, the corrections are rather small and have a simple relation to the features inside the limit of no interaction.”)
(Ivonin, p. 13, [0171] “… We use such a merit function and reduce the range of interaction in the pattern. With only short-range interaction, the needed OPC corrections will be much less demanding numerically.”)
(Ivonin, p. 15, [0191] “Yet another variation on the embodiments above is a method of efficiently applying optical proximity correction for printing highly accurate patterns on a workpiece in microlithography. This method may include rasterizing at least part of a feature including assigning pixel gray values and correcting edge perturbations caused by additional features within a range of interaction from the part of the feature by applying a correction kernel to the pixel gray values.”)
(Ivonin, p. 11, [0155] “The interaction is the same in elements i,j and j,i
and we remove the redundant pixels (actually it is the symmetry in the AA* field in (3) that we remove, the kernel has even more symmetries but cannot be removed since AA* differ). Furthermore, we remove all elements that are small and end up with 12 elements.”)
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Igor Ivonin et. al. (US 20070209029 A1) as applied to claim 18 above, and further in view of Kyohei Sakajiri et. al. (US 20130227500 A1), hereinafter Sakajiri.
Ivonin teach all aspects of claim 18, as disclosed above
Ivonin does not teach
The method of claim 18, further comprising: identifying the edge pixels at least in part by taking a gradient of each of the plurality of patterns.
However, Sakajiri discloses
The method of claim 18, further comprising: identifying the edge pixels at least in part by taking a gradient of each of the plurality of patterns.
(Sakajiri, p. 3, [0047] “… In one embodiment the optimized solution is found using a gradient descent. If the objective function is selected to have the form described by Equation 57, its gradient can be mathematically computed using convolution or cross-correlation, which is efficient to implement on a computer. The result of the optimization is a calculated transmission characteristic for each pixel in the mask data for the frame.”)
Therefore, it would have been obvious before the effective priority date of the claim to a person having ordinary skill in the art to combine the teachings of Ivonin and Sakajiri to utilize the gradient for optimization of the correction.
Claims 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Igor Ivonin et. al. (US 20070209029 A1) and Joong-Won JEON et. al. (US 20150143312 A1) as applied to claim 1 above, and further in view of Jaione Tirapu Azpiroz et. al. (US 20100175042 A1), hereinafter Azpiroz
Regarding claim 3
Ivonin and JEON teach all aspects of claim 1, as disclosed above
Ivonin and JEON do not teach
The method of claim 1, wherein at least the first pattern or the second pattern is a non-Manhattan pattern
However, Azpiroz discloses
The method of claim 1, wherein at least the first pattern or the second pattern is a non-Manhattan pattern
(Azpiroz, p. 2, [0011] “…For example, FIG. 1A illustrates a mask design 20, including a polygon shape 200 having Manhattan edges oriented along x- and y-directions. Non-Manhattan mask shapes may also be used.”)
(Azpiroz, p8, [0054] “According to another aspect of the invention, wherein said mask feature comprises non-Manhattan edges”)
Therefore, it would have been obvious before the effective priority date of the claim to a person having ordinary skill in the art to combine the teachings of Ivonin and Azpiroz as the combination would enable corrections to non-Manhattan patterns on curvilinear edges yielding predictable results of more accurate printing and fabrication according to design intent and specifications, thereby increasing yield.
Regarding claim 14
Ivonin and JEON teach all aspects of claim 13, as disclosed above
Ivonin and JEON do not teach
The method of claim 13, wherein at least some of the patterns are non-Manhattan patterns.
However, Azpiroz discloses
The method of claim 13, wherein at least some of the patterns are non-Manhattan patterns.
(Azpiroz, p. 2, [0011] “… For example, FIG. 1A illustrates a mask design 20, including a polygon shape 200 having Manhattan edges oriented along x- and y-directions. Non-Manhattan mask shapes may also be used.”)
(Azpiroz, p8, [0054] “According to another aspect of the invention, wherein said mask feature comprises non-Manhattan edges”)
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Ivonin and JEON as applied to claim 1 above, and further in view of Kyohei Sakajiri et. al. (US 20130227500 A1), hereinafter Sakajiri.
Ivonin and JEON teach all aspects of claim 1, as disclosed above
Ivonin and JEON does not teach
The method of claim 1, further comprising, before the accessing of the tile: identifying the first pixel at least in part by taking a gradient of the first pattern; or identifying the second pixel at least in part by taking a gradient of the second pattern.
However, Sakajiri discloses
The method of claim 1, further comprising, before the accessing of the tile: identifying the first pixel at least in part by taking a gradient of the first pattern; or identifying the second pixel at least in part by taking a gradient of the second pattern.
(Sakajiri, p. 3, [0047] “… In one embodiment the optimized solution is found using a gradient descent. If the objective function is selected to have the form described by Equation 57, its gradient can be mathematically computed using convolution or cross-correlation, which is efficient to implement on a computer. The result of the optimization is a calculated transmission characteristic for each pixel in the mask data for the frame.”)
Therefore, it would have been obvious before the effective priority date of the claim to a person having ordinary skill in the art to combine the teachings of Ivonin and Sakajiri as the combination would lead to efficient implementation of OPC correction through optimization yielding predictable results of more accurate printing and fabrication according to design intent and specifications, thereby increasing yield to utilize the gradient for optimization of the correction.
Conclusion
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/R.S./Examiner, Art Unit 2851
/JACK CHIANG/Supervisory Patent Examiner, Art Unit 2851
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