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 .
This Office Action is in response to claims filed on 10/04/2022
Claims 1-10 are pending.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1-6, 8-10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Haiqing Wei, US 8,165,854 Published: April 24, 2012, (hereafter Wei).
Regarding claim 1. Wei teaches a method for modeling a multi-stage series photoresist characterization system network (Col 22, Par 3, input signal to the step of photoresist exposure and development, formulated as a Volterra wiener nonlinear system)(Fig 11, model comprises a serial combination of a wiener operator, generate output ), comprising:
S1, receiving designation of one or a plurality of target photoresist processes (Fig 9, receive plurality of optical intensity distributions)(Fig 11, plurality of inputs);
S2, establishing a corresponding series model for each target photoresist process (Fig 11, series models for each process); and
S3, cascading each series model according to a process sequence to form the multi-stage series characterization system network (Fig 11, a multi stage series characterization system network is illustrated),
wherein step S2 comprises:
S21, receiving designation of the number of sub-cascading modules (Fig 11, a preset number, Wn,27);
S22, constructing each Wiener-Pade form sub-cascading module (Fig 10, convolve each developed resist distribution with each of a plurality of wiener); and
S23, sequentially connecting each Wiener-Pade form sub-cascading module in series to obtain a series model (Fig 11, wiener cascading module in series);
step S22 comprises:
S221, receiving designation of Wiener nonlinear orders, kernel function types, and quantities of a numerator and a denominator in a Pade approximation (Fig 10, receive developed resist distributions corresponding to distinct elevations)(Col 11, Par 3, wiener kernels to generate a plurality of convolution results);
S222, convolving, according to the kernel function types and quantities of the numerator and the denominator, an output result of a previous-stage Wiener-Pade form sub-cascading module with selected kernel functions of the numerator and the denominator in the Pade approximation, to obtain base function terms of the numerator and the denominator (Fig 10, convolve each Distribution with each of a plurality of wiener kernels)(Col 24, par 4, equation 14, having a base function of the numerator and a denominator);
S223, multiplying point by point, according to the Wiener nonlinear orders of the numerator and the denominator in the Pade approximation, base function term permutations and combinations of the numerator and the denominator to obtain base function terms of different orders in the numerator and the denominator (Fig 10, Cross multiply at least two of the convolution results);
S224, acquiring Wiener coefficients of the numerator and the denominator in the Pade approximation, and performing weighted summation on the base function terms of the different orders in the numerator and the denominator to obtain a numerator Wiener sum function term and a denominator Wiener sum function term (Fig 10, computing weighted summation of convolution results and cross products using wiener coefficients); and
S225, constructing the numerator Wiener sum function term and the denominator Wiener sum function term in a Pade approximation form to obtain a Wiener-Pade form sub-cascading module (Col 21, Par 2, cascaded blocks of signal processing)(Col 24, Par 3, equation 13, sum function, having wiener numerator and denominator).
Regarding claim 2. Wei teaches the method according to claim 1, wherein the Wiener-Pade form sub- cascading modules are specifically as follows:
M
W
P
n
J
n
-
1
(
x
,
y
)
=
W
S
m
(
x
,
y
)
W
S
d
(
x
,
y
)
,
W
S
d
(
x
,
y
)
≥
ε
(
x
,
y
)
>
0
PNG
media_image1.png
24
327
media_image1.png
Greyscale
or
M
W
P
n
J
n
-
1
x
,
y
=
W
S
m
x
,
y
E
+
W
S
d
x
,
y
,
E
+
W
S
d
x
,
y
≥
ε
x
,
y
>
0
(Col 24, Par 4, equation 14) wherein Mwpn represents a current Wiener-Pade form sub-cascading module,
J
n
-
1
x
,
y
represents an output result of a previous-stage Wiener-Pade form sub-cascading module,
W
S
m
x
,
y
represents the numerator Wiener sum function term,
W
S
d
x
,
y
represents the denominator Wiener sum function term,
ε
x
,
y
represents a set positive threshold matrix to avoid an ill- conditioned Pade approximation, E represents a matrix where all elements are 1, and an previous-stage input to the first-stage Wiener-Pade form cascading module is an original photoresist internal light intensity distribution (Fig 12 and 17, for the next iteration, k+1, is based on k)(Col 15, Par 2, error condition, sufficiently small, process is complete).
Regarding claim 3. Wei teaches the method according to claim 2, wherein outputs of the Wiener-Pade form sub-cascading modules are as follows:
J
n
x
,
y
=
β
0
M
W
P
n
J
n
-
1
x
,
y
+
β
1
I
(
x
,
y
)
⊗
k
(
x
,
y
)
(Fig 19, output J, based on I*H and Wn) wherein
J
n
x
,
y
and
J
n
-
1
x
,
y
represent outputs of the current and previous-stage sub- cascading modules respectively,
β
0
and
β
1
represent weighting coefficients between the output of the previous-stage sub-cascading module and an action of the current module,
I
(
x
,
y
)
represents the original photoresist internal light intensity distribution, and
k
x
,
y
represents a convolution kernel with the original photoresist internal light intensity distribution (Col 24 Par 2, equation 12, Wn for the next iteration is based on the previous iteration).
Regarding claim 4. Wei teaches a method for calibrating a multi-stage series photoresist characterization system network, the multi-stage series photoresist characterization system network is constructed using the method according to claim 1 (Rejected as claim 1)(Col 20, Par 2, model calibration of a Wiener system), the calibration method comprising:
T1, acquiring measured photoresist profile or critical dimension data (Col 23, Par 2, measured, calibrated using the measure data); and
T2, using a joint calibration method based on a constrained quadratic convex optimization algorithm,
cyclically comparing simulated photoresist profile or critical dimension data with the measured photoresist profile or critical dimension data, and sequentially calibrating a parameter of each sub-cascading module in the multi-stage series photoresist characterization system network (Fig 8, Simulation of optical exposure, compares iteratively the error metric profile, critical dimension, then for the next iteration a Wn is computed based on the previous iteration).
Regarding claim 5. Wei teaches the calibration method according to claim 4, wherein step T2 comprises:
T20, initializing a current process as the first target process (Fig 8, create test mask design);
T21, initializing a current module as the first Wiener-Pade sub-cascading module of the current process (Fig 8, Initialize wiener coefficients);
T22, determining a parameter to be calibrated for the current module, and randomly generating a set of non-zero parameters to be calibrated for the current process (Fig 8, setting values to Wiener coefficients Wn1, Wn2);
T23, determining whether the current process is the first target process, and if so, directly proceeding to T25; otherwise, proceeding to T24 (Fig 8, determining m=1..M);
T24, using the parameter obtained by calibration to fix the states of all sub-cascading modules preceding the current process, and using preset parameters to set a sub-cascading module following the current process to an identity equation or a simple linear operator (Fig 8, process optical intensity, using wiener coefficients, generate wiener output), and proceeding to T25;
T25, bringing the set of parameters to be calibrated into the current module to complete updating of the entire photoresist characterization system network (Fig 8, Extract critical dimension );
T26, inputting the original photoresist internal light intensity distribution into the updated characterization system network, acquiring an output result of the last-stage sub-cascading module, and in conjunction with a photoresist threshold, acquiring the simulated photoresist profile or critical dimension data (Fig 8, create physical test mask)(Fig 8, developed resist to measure critical dimensions);
T27, comparing the photoresist profile or critical dimension data obtained by simulation with the corresponding measured data; if a current process accuracy convergence condition is not met, updating the calibrated parameter set and returning to step T25; otherwise, determining whether the current module is the last-stage sub-cascading module of the current process, and if so, proceeding to T28; otherwise, updating the current module to a next sub-cascading module of the current process and proceeding to step T22 (Fig 8, Evaluate error metric between CDRV and CDRR, not sufficiently small, update Wn, yes and no); and
T28, determining whether the current process is a final target process, and if so, indicating that the system network calibration is concluded; otherwise, updating the current process to the next target process and proceeding to step T21 (Fig 8, error sufficiently small, done)(Fig 8, compute Wn based on Wn from the previous iteration).
Regarding claim 6. Wei teaches the calibration method according to claim 4, wherein the using preset parameters to set a sub-cascading module following the current process to an identity equation or a simple linear operator in step T24 is any of the following (Fig 8, process optical intensity, using wiener coefficients, generate wiener output):
setting each Wiener coefficient in a Pade approximation numerator of the sub- cascading module to 0 or setting the first term of a weighting coefficient between an output of a previous sub-cascading module and an action of the current module to 0, such that the module is equivalent to an operator that only scales an input signal in an equal proportion (Col 8, Par 2, filled with zeros for the coordinate components that are out of domain)(Fig 5, R(x,ym,zn), initializes at zero)(Fig 20, coefficients are initialized at 0);
directly treating the sub-cascading module as an equivalent unit operator, that is, outputting an input signal as it is (Fig 19, wnj=polyn,j(var1,var2), as input of the sum);
treating the sub-cascading module as an equivalent bias operator, that is, performing addition or subtraction with respect to an input signal as a whole by the same constant (Fig 19, wnj=polyn,j(var1,var2), adding with respect to the input by the same constant coefficient).
Regarding claim 8. Wei teaches the calibration method according to claim 4, wherein for comparison and evaluation between the simulated photoresist profile and the measured photoresist profile, a constrained quadratic convex optimization algorithm is used to obtain by comparison the difference between a light intensity distribution corresponding to an actual profile point in the output result of the last stage sub-cascading module and a threshold:
W
S
m
C
x
,
y
-
T
∙
E
+
W
S
d
C
(
x
,
y
)
1
2
/
∞
≤
δ
C
∙
E
+
W
S
d
C
(
x
,
y
)
E
+
W
S
d
x
,
y
≥
ε
x
,
y
>
0
(Fig 8, evaluate error metric, measure critical dimension and extract critical dimension)
for comparison and evaluation between the simulated photoresist critical dimension and the measured photoresist critical dimension (Col 24, Par 6, solutions on a convex set C, projection onto the admissible set C), a constrained quadratic convex optimization algorithm is used to compare and measure the differences between measured light intensity distributions at two ends C and D and the threshold:
M
W
P
n
C
D
(
P
1
)
-
T
M
W
P
n
'
C
D
(
P
1
)
-
M
W
P
n
C
D
P
2
-
T
M
W
P
n
'
C
D
P
2
+
P
2
-
P
1
-
C
D
m
1
2
/
∞
≤
δ
C
D
wherein
W
S
m
x
,
y
represents a numerator Wiener sum function term,
W
S
d
x
,
y
represents a denominator Wiener sum function term (Col 24, Par 3, equation 13),
C
x
,
y
represents the simulated profile obtained by performing edge extraction on the simulation binary image(Fig 5, R(x,y,zn))(col 9, par 3, binary contour plot), T represents a photoresist reaction threshold (Fig 4, T, R(x,y,zn)), E represents a matrix where all elements are 1,
δ
C
D
represents a convergence threshold between the simulated profile and the measured profile (Fig 20, error sufficiently small between third and first information);
1
2
/
∞
represents a 1 norm, a 2 norm, or an infinite norm(Col 24, equation 13, norm2) (Col 19, Par 4, L norm of the input signal, L2 norm);
M
W
P
n
represents the current Wiener-Pade form sub-cascading module,
M
W
P
n
'
represents the derivative of the output result of the last-stage sub-cascading module in the CD direction (Col 23, par 2, spatial derivatives, slopes SLs), CD( ) represents the coordinates at the critical end point (Col 24, Par 1, x,y);
P
1
and
P
2
represent the two endpoints of the measured critical dimension, respectively (Col 24, Par 1,point inside the simulated CD subtracting the I, value of a point outside, with both points being in proximity to the interested end point).
Regarding claim 9. Wei teaches a method for efficient online simulation of a photoresist profile, comprising:
R1, acquiring photoresist profile or critical dimension data under discrete distributions of different process parameters in different variation intervals (Fig 8, extract critical dimensions, measure critical dimensions);
R2, using measured data in a variation interval of the same process parameter as an input, using the calibration method according to claim 4 to repeatedly correct a photoresist characterization system network, so as to obtain a coefficient of a Wiener-Pade form sub-cascading module at each stage in the photoresist characterization system network and a photoresist internal light intensity distribution under discrete variation of the process parameter (Rejected as claim 4)(Fig 8, measured dimensions as input to evaluate);
R3, performing, according to the variation regularity of coefficients of different sub- cascading modules, low-order multivariate polynomial equivalence on the discretely varying module coefficients, and establishing a coefficient library of sub-cascading modules under continuous variation of the process parameter (Fig 18B, polynomial functions of process variation factors);
R4, acquiring a light intensity distribution under any process parameter condition in the discrete variation interval of the process parameter by using an interpolation method, and establishing a photoresist internal light intensity distribution library under continuous variation of the process parameter (Fig 14, compute target intensity patten by summing the target partial intensity)(Fig 16, I(x,y,z), based on PL);
R5, repeating steps R1 to R4 to establish a module coefficient library and a photoresist internal light intensity distribution library corresponding to continuous variations of target process parameter combinations (Fig 20, 2010, 2012, 2014,2016); and
R6, at a simulation stage, using a process parameter combination set for simulation as an index, using a library matching method to extract a corresponding system parameter and photoresist internal light intensity distribution under the process condition, and bringing the system parameter and internal light intensity distribution into the photoresist characterization system network, to perform efficient online simulation prediction and evaluation of a photoresist profile (Col 3, Par 2, simulation results, in a short period of time, faster computation times, and sufficiently fast simulations).
Regarding claim 10. Wei teaches a system for efficient online simulation of a photoresist profile (Col 3, Par 2, simulation results, in a short period of time, faster computation times, and sufficiently fast simulations), comprising a processor and a memory (Col 4, Par 2, computer simulation, thus having a processor and memory);
the memory being configured to store a computer program or instructions (Col 3, Par 2, computer program product simulating, firmware and software);
the processor being configured to execute the computer program or instructions in the memory such that the method according to claim 9 is performed (Rejected as claim 9).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 7 are rejected under 35 U.S.C. 103 as being unpatentable over Haiqing Wei, US 8,165,854 Published: April 24, 2012, (hereafter Wei), in views of Emmy S. Wei, NPL, “Aliasing-Free Convolutional Nonlinear Networks Using Implicitly Defined Functions”, Published 21 March 2022 (hereafter Emmy).
Regarding claim 7. Wei teaches the calibration method according to claim 4, wherein the method for data comparison in step T27 is specifically as follows:
T271,;
T272, using a photoresist reaction threshold T to truncate the final output result into a simulated binary image
I
2
s
(
x
,
y
)
; extracting from the output result a light intensity distribution curve
L
(
x
,
y
)
on a ruler, extracting a critical dimension endpoint
P
i
(
x
,
y
)
by using
P
i
x
,
y
;
L
P
i
-
T
*
L
P
i
+
1
-
T
<
0
, (Fig 4, Tn, R(x,y,zn))(Fig 5, R(x,ym,zn)) (col 9, par 3, predetermined threshold old value T) and calculating the distance between two endpoints as the simulated critical dimension data CDs (Col 24, Par 1, region near points x,y), wherein
L
P
i
represents a light intensity value at a critical dimension endpoint on the light intensity distribution curve (Col 9, par 5, CDRm, critical dimension)(Col 10, Par 2, critical dimension each for a predetermined region of interest);
T273, converting the measured profile to a binary image
I
2
m
(
x
,
y
)
with inner 1 and outer 0 (fig 5, R(x,y,zn), 0, 1), and performing an XOR Boolean operation on
I
2
m
(
x
,
y
)
and
I
2
s
(
x
,
y
)
to obtain a profile difference map
I
2
o
r
(
x
,
y
)
(Fig 11, R(x,y,z) binary input, H1, Wn, outputs J(x,y,zn) differentiates the input), and evaluating a simulated profile extraction result by using the following formula:
∆
E
P
E
=
N
u
m
I
2
o
r
x
,
y
=
1
N
u
m
I
2
o
r
x
,
y
d
p
i
x
e
l
wherein Num represents a pixel count function, the numerator in the above formula is the number of counted pixels with a value of 1, the denominator in the above formula is the total number of counted pixels in the binary image, and
d
p
i
x
e
l
represents the length of each pixel (Col 9, Par 3, binary contour plot R(x,y,zn))(Col 23, Par 2, generate a binary valued image approximating the developed resist patterns)(Col 24, equation 13);
evaluating a simulated critical dimension data extraction result by using the following formula:
∆
E
P
E
=
∑
1
N
(
C
D
s
-
C
D
m
)
2
N
wherein
C
D
s
and
C
D
m
represent the simulated and measured critical dimensions respectively, and N is the total number of
C
D
m
(Col 24, Par 3, equation 13, e(k), CDs-CDM)(Col 22, Par 3, the threshold may be chosen, arithmetic mean, least square mean, minimize the least square error).
Wei does not teach upsampling an output result of the last Wiener-Pade form sub-cascading module.
Emmy teaches upsampling an output result of the last Wiener-Pade form sub-cascading module (Page 6, sec 3, input signal upsampled with a USF)(Page 7, sec 3, test image is upsampled).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Wei to incorporate the teachings of Emmy to upsample an image array because having sufficient bandwidth to accommodate newly generated high frequency signal components by the polynomial function, without any spectrum overlapping or aliasing (Emmy, abstract, Page 6, sec 3).
Conclusion
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/A.C./Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189
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