Evaluation method, semiconductor element manufacturing method, and evaluation system

Abstract

The embodiment relates to an evaluation method, a method for manufacturing a semiconductor element, and an evaluation system. According to one embodiment, an evaluation method includes performing a first conversion process on first two-dimensional data indicating a defect distribution of a first substrate on which a pattern is formed by imprinting a photosensitizer on the first substrate by an original plate, and second two-dimensional data indicating a predicted defect distribution of a second substrate, respectively, to generate a first spectrum and a second spectrum. The evaluation method includes performing filtering processing on each of the generated first spectrum and the generated second spectrum. The evaluation method includes performing a second conversion process on each of the processed first spectrum and the processed second spectrum to decode the first two-dimensional data and the second two-dimensional data. The evaluation method includes performing threshold processing on the decoded first two-dimensional data and the decoded second two-dimensional data. The evaluation method includes applying a comparison function to the threshold-processed first two-dimensional data and the threshold-processed second two-dimensional data to calculate a degree of coincidence.

Description

[0001] [Related applications]

[0002] This application claims the priority of Japanese Patent Application No. 2023-136914 (filing date: August 25, 2023) as a basic application. The present application incorporates the entire contents of the basic application by reference. Technical Field

[0003] The present embodiment relates to an evaluation method, a method for manufacturing a semiconductor element, and an evaluation system. Background Art

[0004] In NIL (Nano Inprint Lithography), an original plate is prepared, a photosensitizer is coated on a substrate, and the original plate is imprinted onto the photosensitizer on the substrate. The photosensitizer transfers the shape of the original plate and is exposed and hardened. The original plate is demolded from the substrate, and the substrate is processed using the pattern formed on the photosensitizer as a mask to form a pattern on the substrate. The industry expects that the conditions related to original plate imprinting in NIL are appropriate. Summary of the invention

[0005] According to this embodiment, an evaluation method is provided. The evaluation method includes performing a first transformation process on a first two-dimensional data representing a defect distribution of a first substrate and a second two-dimensional data representing a predicted defect distribution of a second substrate, respectively, to generate a first spectrum and a second spectrum, wherein the first substrate is formed with a pattern due to the photosensitizer thereon being imprinted by an original plate. The evaluation method includes performing a filtering process on the generated first spectrum and the generated second spectrum, respectively. The evaluation method includes performing a second transformation process on the processed first spectrum and the processed second spectrum, respectively, to decode the first two-dimensional data and the second two-dimensional data. The evaluation method includes performing a threshold process on the decoded first two-dimensional data and the decoded second two-dimensional data. The evaluation method includes applying a comparison function to the threshold-processed first two-dimensional data and the threshold-processed second two-dimensional data to calculate the consistency.

[0006] According to one embodiment, conditions related to master plate imprinting can be optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1A and Figure 1B It is a plan view showing the structure of an original plate and a substrate in an embodiment.

[0008] Figure 2A and Figure 2B It is a cross-sectional view showing the imprint of the original plate in the embodiment.

[0009] Figure 3A to Figure 3G This is a cross-sectional view showing how a pattern is formed on a substrate by imprinting of a master plate in an embodiment.

[0010] Figure 4 It is a diagram showing the functional configuration of an evaluation system that executes the evaluation method according to the embodiment.

[0011] Figure 5 It is a diagram showing the hardware configuration of an evaluation system that executes the evaluation method according to the embodiment.

[0012] Figure 6 This is a flowchart showing a method for manufacturing a semiconductor device including an evaluation method according to an embodiment.

[0013] Figure 7 : is a flowchart showing the calibration process in the embodiment.

[0014] Figure 8 This is a data flow diagram showing the operation of the evaluation system in the embodiment.

[0015] Fig. 9 This is a flowchart showing the flow of the evaluation method in the embodiment.

[0016] Fig.10 This is a flowchart showing the flow of data extraction processing in the embodiment.

[0017] Fig.11A and Fig. 11B It is a diagram showing two-dimensional data in the embodiment.

[0018] Fig.12 Detailed description of the invention is given in the following figure: FIG.

[0019] Fig.13 This is a data flow diagram showing the operation of the evaluation system in the embodiment.

[0020] Fig.14 It is a flowchart showing the condition optimization processing in the implementation mode. DETAILED DESCRIPTION

[0021] Hereinafter, an embodiment of the present invention will be described with reference to the accompanying drawings. This embodiment does not limit the present invention. The accompanying drawings are schematic diagrams or conceptual diagrams, and the ratios of the various parts may not be the same as the actual situation. In the specification and the accompanying drawings, the same symbols are marked for the same elements as those described above with respect to the accompanying drawings that have appeared, and detailed descriptions are omitted as appropriate.

[0022] (Implementation Method)

[0023] The evaluation method in the embodiment is to evaluate NIL (Nano Inprint Lithography). In NIL, an original plate is prepared, a photosensitizer is coated on a substrate, and the original plate is pressed onto the substrate. The photosensitizer is exposed and hardened, the original plate is demolded from the substrate, and the substrate is processed using the pattern of the photosensitizer as a mask to form a pattern on the substrate. In the evaluation method, defects such as poor pattern of the substrate caused by NIL are evaluated.

[0024] For example, in NIL, prepare Figure 1A Hereinafter, the direction perpendicular to the back surface OGb of the original plate OG is referred to as the Z direction, and two directions perpendicular to each other in a plane perpendicular to the Z direction are referred to as the X direction and the Y direction. Figure 1A and Figure 1B It is an XY plan view showing the structure of the original plate OG and the substrate SB. Figure 1A This is a picture of the original OG observed from the front OGa.

[0025] The original plate OG is a plate-like component and may have a roughly rectangular shape in the XY plane view. The original plate OG is also called a casting mold, a mold or a template. The original plate OG can be formed of a light-transmitting material, such as glass, quartz, transparent plastic, etc. The original plate OG has a base portion MS near the center in the XY direction. The base portion MS includes a pattern area PT and a peripheral area PH. The pattern area PT extends in the XY direction and has a pattern including a concave portion PTa and a convex portion PTb. The peripheral area PH is arranged on the XY direction outer side of the pattern area PT and surrounds the pattern area PT.

[0026] The original OG can also be stamped to Figure 1B The substrate SB is shown. Figure 1B This is a diagram showing the substrate SB as viewed from the front surface SBa side.

[0027] The substrate SB is a plate-like member and may have a circular shape in an XY plane view. The substrate SB is also called a wafer. The substrate SB may be formed of a material having a semiconductor (eg, silicon) as a main component.

[0028] Original OG Figure 2A and Figure 2B The substrate SB is imprinted as shown in FIG. 3 , and a pattern is formed on the substrate SB as shown in FIG. 3 . Figure 2A and Figure 2B They are YZ cross-sectional views showing the embossing of the original plate OG. Figure 2A , Figure 2B Each step of imprinting the original plate OG is schematically shown. Fig. 3 is a YZ cross-sectional view showing a pattern formed on the substrate SB by imprinting the original plate OG. Figure 3A to Figure 3F Each step of printing the original OG is shown in detail.

[0029] Figure 2A , Figure 2B , a cross section of the original plate OG when cut at line AA in FIG. 1 is shown. Figure 2A As shown, in the original plate OG, the base portion MS is a portion that rises in a base shape toward the -Z side more than the front surface OGa in the YZ cross-sectional view.

[0030] Figure 2A , Figure 3A In the step shown, a photosensitizer RG is applied on the substrate SB. The base region BS and the film FM of the substrate SB are stacked in sequence in the Z direction. The photosensitizer RG is formed of a photosensitive resin, for example, a resin having photocurability. The photosensitizer RG is also called a resist. The photosensitizer RG can be applied on the substrate SB by a spin coating method or by an inkjet method. Figure 3A In the above, the case of coating by an inkjet method is exemplified.

[0031] Figure 2A , Figure 3B In the steps shown, the XY positions of the original plate OG and the substrate SB are adjusted so that the original plate OG and the substrate SB are arranged to face each other in the Z direction.

[0032] Figure 2B , Figure 3C In the steps shown, the original plate OG and the substrate SB are relatively close in the Z direction until the base portion MS contacts the photosensitizer RG. The original plate OG is pressed onto the substrate SB. The base portion MS contacts the photosensitizer RG, and when a predetermined time has passed and the concave portion PTa of the original plate OG is filled with the photosensitizer RG, light such as ultraviolet rays is irradiated to the photosensitizer RG. The photosensitizer RG is exposed and hardened, forming a pattern RP of the photosensitizer.

[0033] Figure 3D In the steps shown, the original plate OG and the substrate SB are relatively separated in the Z direction. The original plate OG is demolded from the substrate SB. The pattern RP of the photosensitizer includes the concave portion RPa corresponding to the convex portion PTb.

[0034] Figure 3E In the step shown, the substrate SB is processed using the pattern RP of the photosensitizer as a mask. The pattern RP of the photosensitizer is transferred to the film FM, and a recess FMa corresponding to the recess RPa is formed in the film FM.

[0035] Figure 3F In the step shown, the pattern RP of the photosensitizer is removed from the substrate SB, and the substrate SB in which the film FM is formed with the pattern of the recessed portions FMa is obtained.

[0036] Then, the original OG is shifted in XY position relative to the substrate SB, and the Figure 2A , Figure 3B The steps after the steps shown. Figure 1B As shown, a plurality of exposure shot areas SH-1 to SH-n (n is any integer greater than 2) are formed on the substrate SB. The plurality of exposure shot areas SH-1 to SH-n can be arranged along the XY direction on the front side SBa of the substrate SB. Each exposure shot area SH includes a device area DV and a cutout area. The device area DV corresponds to the pattern area PT of the original plate OG. The device area DV is an area for transferring the pattern of the pattern area PT. The cutout area KF corresponds to the peripheral area PH of the original plate OG. The cutout area KF is arranged outside the device area DV in the XY direction and surrounds the device area DV in the XY plane view. The plurality of exposure shot areas SH-1 to SH-n are divided by a peripheral area DL. The peripheral area DL is arranged outside the exposure shot areas SH in the XY direction. The peripheral area DL is also called a cutting line.

[0037] In NIL, defects such as poor patterning of the substrate SB may occur. The following are the possible causes of defects:

[0038] (1) Impurities or abnormalities in the original pattern (e.g., collapse of the convex parts of the original OG),

[0039] (2) abnormality of the pattern RP of the photosensitizer RG (for example, collapse or peeling of the pattern RP),

[0040] (3) Anomalies related to imprinting of the original plate OG onto the substrate SB (for example, insufficient filling of the photosensitizer RG into the recessed portions of the original plate OG or bleeding of the photosensitizer RG outside the exposure irradiation area SH).

[0041] Among them, (3) is a phenomenon unique to pattern direct transfer methods such as NIL, and may occur depending on the conditions related to the imprinting of the original plate OG. The conditions related to the imprinting of the original plate OG include, for example, the coating conditions of the photosensitizer PG and the imprinting conditions of the original plate OG.

[0042] The coating conditions of the photosensitizer RG may include the material of the photosensitizer RG. In the case of coating the photosensitizer RG by a spin coating method, the coating conditions of the photosensitizer RG may include the rotation speed of the substrate SB during coating, the supply speed of the photosensitizer RG, etc. In the case of coating the photosensitizer RG by an inkjet method, the coating conditions of the photosensitizer RG may include the size of the photosensitizer RG droplets, the supply speed of the photosensitizer RG droplets, the supply density of the photosensitizer RG droplets, etc.

[0043] The imprinting conditions of the original plate OG may include the magnitude of the force applied to the original plate OG during imprinting, the specified time for filling the photosensitizer RG into the recessed portion PTa of the original plate OG, the magnitude of the force for peeling the original plate OG off the substrate SB during demolding, etc.

[0044] For example, the defect distribution state of (3) is determined by the complex interaction of various NIL parameters related to the imprint of the original plate OG. Various NIL parameters include the type of photosensitizer RG, the base layer, various physical properties of the ambient gas, the mechanical structure of the original plate OG, various conditions when the original plate OG is pressed, the density distribution and size of the imprinted pattern, and the initial state of the photosensitizer RG.

[0045] Although it is not easy for the photosensitizer RG to seep out of the exposure irradiation area SH and directly become a defect, with some exceptions, insufficient filling of the photosensitizer RG into the concave portion PTa of the original plate OG can directly become a defect. The so-called partial exceptions include the case where the location where the defect will occur is known in advance and the location is a pattern that allows the existence of defects, or the location is a location where the influence of the defect can be eliminated.

[0046] In the early stages of adjusting the conditions associated with the imprinting of the original OG, defects with various distributions are generated. This defect distribution is believed to reflect various NIL parameters, and therefore provides a hint for obtaining the optimized values ​​of the NIL parameters. In other words, if the NIL parameters can be adjusted and the defect distribution is judged to be "better" than the previous time by some comparison method, it can be optimized.

[0047] However, in reality, there are many NIL parameters that need to be adjusted, and it is difficult to adjust all NIL parameters through experiments. In addition, when adjusting NIL parameters by increasing or decreasing the number of defects, it is likely that only a local appropriate solution (pseudo-solution) will be obtained.

[0048] Therefore, by high-precision simulation (equipped with complex physical models) and incorporating the distribution of defects during adjustment, a NIL parameter set that is closer to the true appropriate solution can be effectively obtained.

[0049] The evaluation method is to quantitatively evaluate the two-dimensional defect distribution in the substrate pattern caused by NIL. Regarding the two-dimensional defect distribution, the measured defect distribution and the simulated defect distribution are prepared, and their probability density distribution is respectively distributed, and the similarity between the two is numerically evaluated using a comparison function. Through this quantitative evaluation, the simulation accuracy can be verified and the NIL parameters can be adjusted.

[0050] The evaluation method can be Figure 4 The evaluation system 1 shown is implemented. Figure 4 It is a diagram showing the functional configuration of an evaluation system 1 that executes the evaluation method.

[0051] When receiving the start request, the evaluation system 1 can start the evaluation program 81 and execute the evaluation method according to the evaluation program 81. The details of the evaluation method will be described below.

[0052] The evaluation system 1 functionally includes a generation unit 2, a conversion unit 3, a filter unit 4, a decoding unit 5, a threshold processing unit 6, a calculation unit 7, a storage unit 8, and a display control unit 9. The storage unit 8 stores an evaluation program 81.

[0053] The generating section 2 generates two-dimensional data TD1. The two-dimensional data TD1 represents defect distribution of a substrate SB1 in which a pattern is formed by imprinting a master plate OG1 onto a photosensitizer RG2 on the substrate SB1.

[0054] For example, Figure 2A , Figure 2B , Figure 3A to Figure 3F As shown in FIG. 1 , the original plate OG1 is imprinted onto the photosensitizer RG on the substrate SB1 to form a pattern on the substrate SB1. The substrate SB1 is carried into the defect inspection device 200 (see FIG. 1 ). Figure 8 ).like Figure 3G As shown, the defect inspection device 200 obtains an image of the front side of the substrate SB1 (front image) optically. The defect inspection device 200 analyzes the front image and generates measurement data 83 based on the analysis result. The measurement data 83 includes the defect level and two-dimensional position of one or more defects. The two-dimensional position may include coordinates with a reference position in the exposure irradiation area SH (for example, the center of the exposure irradiation area SH) as the origin. The defect inspection device 200 supplies the measurement data 83 to the evaluation system 1. The evaluation system 1 receives the measurement data 83 from the defect inspection device 200 and stores it in the memory unit 8. The generation unit 2 reads the measurement data 83 from the memory unit 8, and generates two-dimensional data TD1 based on the measurement data 83, and the two-dimensional data TD1 maps the level of one or more defects in the exposure irradiation area SH (refer to Fig.11A ).

[0055] The generating section 2 generates two-dimensional data TD2. The two-dimensional data TD2 represents the predicted defect distribution of the substrate SB2.

[0056] For example, the defect occurrence simulation device 300 (see Figure 8 ) Use with Figure 2A , Figure 2B , Figure 3A to Figure 3FThe NIL parameters are predicted using a physical model equivalent to the process shown. The defect occurrence simulation device 300 uses the predicted NIL parameters to calculate the defect distribution that occurs when a pattern is formed on the substrate SB2 due to the NIL. The defect occurrence simulation device 300 generates simulation data 84 as a calculation result. The simulation data 84 includes defect levels and two-dimensional positions of one or more defects. The two-dimensional position may include coordinates with a reference position in the exposure irradiation area SH (for example, the center of the exposure irradiation area SH) as the origin. The evaluation system 1 receives the simulation data 84 from the defect occurrence simulation device 300 and stores it in the memory unit 8. The evaluation system 1 may receive the simulation data 84 in advance for a plurality of different NIL parameter sets. The evaluation system 1 may also construct the simulation data 84 as a library in correspondence with the information for identifying the NIL parameter set and store it in the memory unit 8. The generation unit 2 generates two-dimensional data TD2 based on the simulation data 84, and the two-dimensional data TD2 maps the level and two-dimensional position of one or more defects in the exposure irradiation area SH (refer to Fig. 11B ).

[0057] The conversion unit 3 obtains the two-dimensional data TD1 from the generation unit 2. The conversion unit 3 performs a first conversion process on the two-dimensional data TD1 to generate a spectrum SP1.

[0058] The first transform process may use any transform that can realize an inverse transform or any series expansion that can realize an inverse series expansion. The first transform process may be a transform corresponding to a series expansion using an orthogonal basis function. The first transform process may be a discrete cosine transform corresponding to a Fourier series expansion, a series expansion of Legendre polynomials (hereinafter referred to as Legendre series expansion), or a series expansion of Chebyshev polynomials (hereinafter referred to as Chebyshev series expansion).

[0059] The first transformation process may use a transformation that can realize an inverse transformation and is suitable for defect distribution or a series expansion that can realize an inverse series expansion and is suitable for defect distribution. For example, when the defect is located along the cut region KF (refer to Figure 1B ) distribution trend, the first transformation processing can be a discrete cosine transform.

[0060] The defect is located along the peripheral area DL (refer to Figure 1B ) distribution trend, the first transformation processing can be a Legendre series expansion.

[0061] The defect is located along the cut area KF (ref. Figure 1B ) and along the peripheral area DL (refer to Figure 1B ) distribution trend, the first transformation processing can be a discrete cosine transform.

[0062] The conversion unit 3 obtains the two-dimensional data TD2 from the generation unit 2. The conversion unit 3 performs a first conversion process on the two-dimensional data TD2 to generate a spectrum SP2.

[0063] The filter unit 4 obtains the spectrum SP1 from the conversion unit 3. The filter unit 4 performs a filter process on the spectrum SP1.

[0064] The filtering process uses any process suitable for converting the defect distribution indicated by the two-dimensional data TD1 before the transformation into a probability density distribution. The filtering process may also use a process for smoothing the defect distribution indicated by the two-dimensional data TD1 before the transformation. The filtering process may be a low-pass filtering process or a process for retaining components below a specified order among the components of each order of the series expansion.

[0065] The filter unit 4 obtains the spectrum SP2 from the conversion unit 3. The filter unit 4 performs filtering processing on the spectrum SP2.

[0066] The decoding unit 5 obtains the spectrum SP1 after filtering from the filter unit 4. The decoding unit 5 performs a second transformation process on the spectrum SP1 after filtering to decode the two-dimensional data TD1a. The two-dimensional data TD1a is data obtained by converting the original two-dimensional data TD1 into a probability density distribution. The two-dimensional data TD1a can be regarded as data that two-dimensionally maps the probability density level of defects in the exposure irradiation area SH.

[0067] The second transform process is an inverse transform process of the first transform process. When the first transform process is a discrete cosine transform, the second transform process may be an inverse discrete cosine transform. When the first transform process is a Legendre series expansion, the second transform process may be an inverse Legendre series expansion. When the first transform process is a Chebyshev series expansion, the second transform process may be an inverse Chebyshev series expansion. Here, the so-called inverse series expansion refers to a method of decoding the original distribution using the spectrum obtained by the series expansion and the basis functions used for the expansion.

[0068] The decoding unit 5 obtains the spectrum SP2 after filtering from the filter unit 4. The decoding unit 5 performs the second transformation process on the spectrum SP2 after filtering to decode the two-dimensional data TD2a. The two-dimensional data TD2a is the data of the probability density distribution of the original two-dimensional data TD2. The two-dimensional data TD2a can be regarded as data that two-dimensionally maps the probability density level of defects in the exposure irradiation area SH.

[0069] The threshold processing unit 6 obtains the decoded two-dimensional data TD1a from the decoding unit 5. The threshold processing unit 6 performs threshold processing on the decoded two-dimensional data TD1a. The threshold processing unit 6 replaces the level of data lower than the threshold level among the plurality of data included in the decoded two-dimensional data TD1a with the threshold level.

[0070] Threshold processing is a process of replacing the level of data below the threshold level among multiple data with the threshold level to increase the lowest level. By increasing the lowest level, the influence of data equivalent to small defects or errors can be alleviated, thereby improving the accuracy of the comparison processing that should be performed after the threshold processing.

[0071] The threshold level may be determined in advance by experimentation. The threshold level may be determined in advance by experimentation based on the maximum level of data when the defect is 1. The threshold level may also be determined by multiplying the maximum level of data when the defect is 1 by a coefficient greater than 0 and less than 1. The coefficient may be a number greater than 0 and less than 1 (e.g., 0.5) determined in advance by experimentation.

[0072] Alternatively, the threshold level may be determined experimentally in advance based on the maximum level of data when the defect diameter is about the thickness of the photosensitizer RG. The threshold level may also be determined by multiplying the maximum level of data when the defect diameter is about the thickness of the photosensitizer RG by a coefficient greater than 0 and less than 1. The coefficient may be a number greater than 0 and less than 1 (e.g., 0.5) determined experimentally in advance.

[0073] Alternatively, the threshold level may be determined experimentally in advance based on the maximum level of data when the area of ​​the defect is approximately the square of the thickness of the photosensitizer RG. The threshold level may also be determined by multiplying the maximum level of data when the area of ​​the defect is approximately the square of the thickness of the photosensitizer RG by a coefficient greater than 0 and less than 1. The coefficient may be a number greater than 0 and less than 1 (e.g., 0.5) determined experimentally in advance.

[0074] For example, when the level of data included in the two-dimensional data TD1 a is set to D1 and the threshold level is set to Δ, the threshold processing unit 6 performs the processing represented by the following Expression 1.

[0075] D1←max(D1,Δ)…Equation 1

[0076] In Formula 1, max() is a max function that returns the maximum value of the argument in the parentheses. Thus, the threshold processing unit 6 can obtain two-dimensional data TD1b after threshold processing.

[0077] The threshold processing unit 6 obtains the decoded two-dimensional data TD2a from the decoding unit 5. The threshold processing unit 6 performs threshold processing on the decoded two-dimensional data TD2a. The threshold processing unit 6 replaces the level of data lower than the threshold level among the plurality of data included in the decoded two-dimensional data TD2a with the threshold level.

[0078] For example, when the level of data included in the two-dimensional data TD2 a is set to D2 and the threshold level is set to Δ, the threshold processing unit 6 performs the processing shown in the following Expression 2.

[0079] D2←max(D2,Δ)…Equation 2

[0080] In Formula 2, max() is a max function that returns the maximum value of the argument in the parentheses. Thus, the threshold processing unit 6 can obtain two-dimensional data TD2b after threshold processing.

[0081] The calculation unit 7 obtains the threshold-processed two-dimensional data TD1b and the threshold-processed two-dimensional data TD2b from the threshold processing unit 6. The calculation unit 7 applies a comparison function to the threshold-processed two-dimensional data TD1b and the threshold-processed two-dimensional data TD2b to calculate the degree of consistency. The degree of consistency is a numerical value between 0 and 1, and the more similar the distribution shapes are, the closer it is to 1.

[0082] The calculation unit 7 applies the first comparison function to the processed first two-dimensional data and the processed second two-dimensional data to calculate the first consistency. The first comparison function can be a function suitable for evaluating the similarity (trend consistency) of distribution shapes. The first comparison function can be, for example, a comparison function based on mode coupling theory. The first consistency is a numerical value between 0 and 1, and the more similar the distribution shapes are, the closer it is to 1.

[0083] For example, when the level of data included in the two-dimensional data TD1b is set to D1 and the level of data included in the two-dimensional data TD2b is set to D2, the calculation unit 7 calculates the degree of match ξ represented by the following equation 3.

[0084]

[0085] In Formula 3, the denominator on the right represents the product of the convolution integral of the square of the data level D1 and the convolution integral of the square of the data level D2. The numerator on the right represents the square of the convolution integral of the product of the data level D1 and the data level D2. The consistency ξ is a value between 0 and 1, and the more similar the distribution of the two-dimensional data TD1b is to the distribution of the two-dimensional data TD2b, the closer it is to 1.

[0086] The calculation unit 7 may switch the comparison function to be used from the first comparison function to the second comparison function when the specified condition is satisfied. After the switching, the calculation unit 7 applies the second comparison function to the processed first two-dimensional data and the processed second two-dimensional data to calculate the second degree of consistency.

[0087] The second comparison function may be a function suitable for evaluating the absolute consistency. For example, the second comparison function may be a comparison function using the Jaccard index. The second consistency is a numerical value between 0 and 1, and is closer to 1 as the distribution shapes are more similar.

[0088] For example, when the level of data included in the two-dimensional data TD1b is set to D1 and the level of data included in the two-dimensional data TD2b is set to D2, the calculation unit 7 calculates the degree of match Σ represented by the following equation 4.

[0089]

[0090] In formula 4, max() is the max function, which returns the maximum value of the argument in the parentheses. min() is the min function, which returns the minimum value of the argument in the parentheses. The denominator on the right represents the convolution integral of the data level D1 and the data level D2, whichever is larger. The numerator on the right represents the convolution integral of the data level D1 and the data level D2, whichever is smaller. The consistency Σ takes a value between 0 and 1, and the closer the distribution of the two-dimensional data TD1b is to the distribution of the two-dimensional data TD2b, the closer it is to 1.

[0091] The specified conditions include that the first degree of coincidence exceeds a specified value (eg, 0.5) and the first degree of coincidence becomes a specified ratio (eg, 10% or more) of the past maximum value a specified number of times (eg, 10 times).

[0092] Alternatively, the calculation unit 7 may calculate the second degree of consistency by applying the second comparison function to the processed first two-dimensional data and the processed second two-dimensional data from the beginning.

[0093] Alternatively, the calculation unit 7 may calculate the first consistency using the first comparison function and the second consistency using the second comparison function in parallel. The calculation unit 7 may calculate scores corresponding to the first consistency and the second consistency. The score may be obtained by multiplying the first consistency by the second consistency and taking the square root.

[0094] For example, when the degree of match calculated using the first comparison function is ξ and the degree of match calculated using the second comparison function is Σ, the calculation unit 7 can calculate the score SC as shown in the following equation 5.

[0095]

[0096] In addition, the calculation unit 7 may switch the object of the calculation result from the score to the second consistency according to the number of times that the score SC becomes a specified ratio or more (for example, more than 10%) of the previous maximum value for a specified number of times (for example, 10 times). After the switch, the calculation unit 7 may calculate the second consistency using the second comparison function instead of calculating the first consistency using the first comparison function.

[0097] After calculating the degree of consistency, the calculation unit 7 may compare the degree of consistency with the degree of consistency calculated last time, and obtain the correlation between the change direction of the NIL parameter set and the change direction of the degree of consistency.

[0098] For example, the calculation unit 7 compares the last consistency with the current consistency. The calculation unit 7 compares the last NIL parameter with the current NIL parameter to identify the direction of change of the NIL parameter. In the case where the current consistency is higher than the previous consistency, the calculation unit 7 identifies that the direction of change of the NIL parameter is a direction that makes the consistency higher. The calculation unit 7 can store information related to the direction of change of the NIL parameter that makes the consistency higher as associated information 82 in the memory unit 8.

[0099] Alternatively, the calculation unit 7 compares the last consistency with the current consistency and specifies the direction of change (e.g., the direction of change that makes the consistency higher, or the direction of change that makes the consistency lower). The calculation unit 7 compares the last NIL parameter with the current NIL parameter and specifies the direction of change of the NIL parameter. The calculation unit 7 may store the information that establishes a correspondence between the direction of change of the NIL parameter and the direction of change of the consistency as association information 82 in the memory unit 8.

[0100] Thus, the consistency between the measured defect distribution and the simulated defect distribution can be quantitatively evaluated, so that the consistency can be used as a criterion for improving the simulation accuracy. For example, as long as the consistency is above a specified value, it can be determined that the simulation meets the required accuracy. In addition, by using the associated information 82 to perform Bayesian inference, etc., the consistency can be effectively improved, and the total processing time for improving the simulation accuracy can be shortened.

[0101] The storage unit 8 stores an evaluation program 81 , related information 82 , measurement data 83 , and simulation data 84 .

[0102] The display control unit 9 displays the designated information on the display screen. The display control unit 9 can display the two-dimensional data TD1 and TD2 on the display screen. The display control unit 9 can also display the decoded two-dimensional data TD1a and TD2a on the display screen. The display control unit 9 can also display the two-dimensional data TD1b and TD2b after threshold processing on the display screen. The display control unit 9 can also display the calculation result of the consistency degree on the display screen. In this way, the user can be informed of the information related to the evaluation of the evaluation system 1.

[0103] The evaluation system 1 can be composed of Figure 5 The hardware structure shown. Figure 5 It is a diagram showing the hardware configuration of the evaluation system 1 .

[0104] The evaluation system 1 includes a CPU (Central Processing Unit) 101, a nonvolatile memory unit 102, a volatile memory unit 103, a display unit 104, an input unit 105, and a bus 106. The CPU 101, the nonvolatile memory unit 102, the volatile memory unit 103, the display unit 104, and the input unit 105 are connected via the bus 106 so as to be able to communicate with each other. The nonvolatile memory unit 102 is a ROM (Read Only Memory), a HDD (Hard Disk Drive), a SSD (Solid State Drive), etc. The volatile memory unit 103 is a RAM (Random Access Memory), etc. The display unit 104 is a liquid crystal display, etc. The input unit 105 is a keyboard, a mouse, a touch panel, etc.

[0105] Generator 2, converter 3, filter 4, decoder 5, threshold processor 6, calculator 7, display controller 9 (see Figure 4 ) can each be installed in the CPU 101 in hardware through circuits, etc. The generating unit 2, the transforming unit 3, the filtering unit 4, the decoding unit 5, the threshold processing unit 6, the calculating unit 7, and the display control unit 9 can each be installed in software as a functional module expanded in the buffer area of ​​the volatile memory unit 103 once when the program is compiled, or successively as the processing based on the program proceeds. Alternatively, the generating unit 2, the transforming unit 3, the filtering unit 4, the decoding unit 5, the threshold processing unit 6, the calculating unit 7, and the display control unit 9 can be partially installed in hardware, and the remaining parts can be installed in software. The memory unit 8 (refer to Figure 4 ) can be assembled by hardware such as the non-volatile memory unit 102 or the volatile memory unit 103.

[0106] The evaluation program 81 may be stored in the nonvolatile memory unit 102 .

[0107] Alternatively, the evaluation program 81 may be provided in the form of an installable or executable file recorded on a recording medium such as a CD-ROM, a floppy disk (FD), a CD-R, or a DVD (Digital Versatile Disk) that can be read by the evaluation system 1 .

[0108] Alternatively, the evaluation program 81 may be stored in a computer connected to a network such as the Internet and provided to the evaluation system 1 by downloading via the network. In addition, the program to be executed by the evaluation system 1 of this embodiment may be provided or distributed via a network such as the Internet.

[0109] Next, use Figure 6A method for manufacturing a semiconductor device including an evaluation method will be described. Figure 6 This is a flowchart showing a method for manufacturing a semiconductor device including an evaluation method. Figure 6 In this paper, the focus is on the manufacturing of the second component. Consider a situation where a batch containing the first component and a batch containing the second component are processed in sequence in a semiconductor component production line. In this situation, in order to manufacture the second component, the first component that was processed previously is used.

[0110] The evaluation system 1 performs a calibration process using the first element (S1). The evaluation system 1 adjusts the NIL parameters of the defect occurrence simulation device 300 to the target (characteristics of the actual element) so that the consistency is within the allowable range. Thus, the evaluation system 1 makes the defect distribution of the actual element (the first element) consistent with the defect distribution of the simulator to a certain extent, and at the same time specifies the correlation between the change direction of the NIL parameters of the defect occurrence simulation device 300 and the change direction of the defect distribution, and generates correlation information 82.

[0111] The evaluation system 1 performs layout optimization processing on the second element (S2). The evaluation system 1 optimizes the layout of the NIL for the actual element (the second element) while adjusting the NIL parameters of the defect occurrence simulation device 300 using the association information 82 generated in S1.

[0112] The evaluation system 1 performs condition optimization processing for the second element (S3). The evaluation system 1 uses the association information 82 generated in S1 to adjust the NIL parameters of the defect occurrence simulation device 300, while optimizing the conditions related to the imprinting of the original plate OG for the actual element (the second element). Then, the optimized conditions are used to form a pattern on the substrate SB as shown in Figures 2 and 3, thereby manufacturing the actual element (the second element).

[0113] Next, use Figure 7 The details of the calibration process ( S1 ) of the first element will be described. Figure 7 : is a flowchart showing the calibration process (S1).

[0114] The circuit design device performs circuit design for the first element based on designated design information and / or instructions from a user ( S11 ), generates schematic data, and supplies the schematic data to the layout design device.

[0115] The layout design device performs layout design based on the schematic data and / or instructions from the user ( S12 ) to generate layout data.

[0116] The layout design device performs layout design of dummy patterns (S13). The layout design device generates a plurality of layout data and supplies them to the defect occurrence simulation device 300. The plurality of layout data is obtained by changing the presence or absence and the number of dummy patterns added to the layout data of S12. When the layout design of the dummy patterns is completed, the layout design device notifies the evaluation system 1 of the completion.

[0117] In response to the notification from the layout design device, the evaluation system 1 repeatedly performs simulations in the defect occurrence simulation device 300 based on the design in S13 to optimize the pattern design including the presence or absence of dummy patterns ( S14 ).

[0118] The evaluation system 1 supplies the proposal of the NIL parameter to the defect occurrence simulation device 300. The defect occurrence simulation device 300 calculates the defect distribution that occurs when a pattern is formed on the substrate SB by the NIL based on the layout data, while changing the NIL parameter based on the proposal from the evaluation system 1 for each of the plurality of layout data generated in S13. The defect occurrence simulation device 300 supplies the calculation result as simulation data 84 to the evaluation system 1.

[0119] The evaluation system 1 selects the layout data satisfying the first criterion from among the plurality of layout data as appropriate layout data based on the simulation data 84. The first criterion may be, for example, the minimum average number of defects with respect to the plurality of NIL parameters or the minimum number of defects with respect to the plurality of NIL parameters. The evaluation system 1 supplies the optimized layout data to the defect occurrence simulation device 300.

[0120] The evaluation system 1 repeatedly performs simulations in the defect occurrence simulation device 300 based on the design in S14 to optimize various processing conditions during NIL imprinting ( S15 ).

[0121] The evaluation system 1 supplies the proposal of the NIL parameter to the defect occurrence simulation device 300. The defect occurrence simulation device 300 changes the NIL parameter between a plurality of NIL parameters for the layout data selected in S14 according to the proposal from the evaluation system 1, and calculates the defect distribution that occurs when a pattern is formed on the substrate SB by the NIL according to the layout data. The defect occurrence simulation device 300 supplies the calculation result to the evaluation system 1 as simulation data 84.

[0122] The evaluation system 1 selects a NIL parameter that satisfies a second criterion among the plurality of NIL parameters as an appropriate NIL parameter based on the simulation data 84. The second criterion may be, for example, the minimum number of defects. The evaluation system 1 supplies the optimized NIL parameter to the defect occurrence simulation device 300.

[0123] The evaluation system 1 performs final adjustment by NIL experiment using the actual original OG (S16). The evaluation system 1 starts the evaluation program 81 and executes the evaluation method. The details of S16 are described below.

[0124] The pattern and processing conditions optimized in S11 to S16 are processed including NIL processing to manufacture the first element (S17).

[0125] For example, information of the pattern of the original plate OG is generated based on the pattern optimized in S14 and finally adjusted in S16. Based on the information of the pattern of the original plate OG, the original plate OG is manufactured. A photosensitizer is applied to the substrate SB. The original plate OG is imprinted onto the substrate SB under the conditions optimized in S15 and finally adjusted in S16. The photosensitizer is exposed and hardened, the original plate OG is demolded from the substrate SB, and the substrate SB is processed using the pattern of the photosensitizer as a mask to form a pattern on the substrate SB. Thus, the first element is manufactured.

[0126] Next, the details of S16 will be described.

[0127] In S16, the evaluation system 1 is as follows Figure 8 The action shown is performed Fig. 9 Evaluation method shown. Figure 8 It is a data flow diagram showing the operation of the evaluation system 1 . Fig. 9 is a flow chart showing the evaluation method.

[0128] Figure 8 In order to simplify the description, in the evaluation system 1, the generating unit 2, the transforming unit 3, the filtering unit 4, the decoding unit 5, the threshold processing unit 6, and the calculating unit 7 (see Figure 4 ) The corresponding structure is used as the operation device 1a, a part of the memory unit 8 is used as the memory device 1b, and the other part is used as the memory device 1c.

[0129] The storage device 1b is disposed between the defect inspection device 200 and the calculation device 1a. The storage device 1b can store the measurement data 83 supplied from the defect inspection device 200. The calculation device 1a can read the measurement data 83 from the storage device 1b.

[0130] The storage device 1c is disposed between the defect occurrence simulation device 300 and the calculation device 1a. The storage device 1c can store the simulation data 84 supplied from the defect occurrence simulation device 300. The calculation device 1a can read the simulation data 84 from the storage device 1c.

[0131] In S16 , the evaluation system 1 performs the following processing.

[0132] The evaluation system 1 obtains the measurement data 83 as target data from the defect inspection device 200 ( S21 ), and stores the obtained measurement data in the storage device 1 a .

[0133] The evaluation system 1 determines the NIL parameters optimized in S15 as initial parameters ( S22 ).

[0134] The evaluation system 1 performs simulation in the defect occurrence simulation device 300 (S23). The evaluation system 1 supplies the proposal of the NIL parameter to the defect occurrence simulation device 300 (see Figure 8 ). If it is the first time, the evaluation system 1 supplies the proposal of the NIL parameter determined in S22 to the defect occurrence simulation device 300. If it is the second time or later, the evaluation system 1 supplies the proposal of the NIL parameter different from the proposed NIL parameter to the defect occurrence simulation device 300.

[0135] The defect occurrence simulation device 300 performs simulation using NIL parameters according to the proposal of the evaluation system 1, and calculates the defect distribution that occurs when the pattern is formed on the substrate SB due to the NIL. The defect occurrence simulation device 300 generates simulation data 84 as a calculation result. The simulation data 84 includes the defect level and two-dimensional position of one or more defects. The two-dimensional position may include coordinates with a reference position (for example, the center of the exposure irradiation area SH) in the exposure irradiation area SH as the origin. The evaluation system 1 receives the simulation data 84 of the defect occurrence simulation device 300 and stores it in the memory device 1c.

[0136] The evaluation system 1 performs data extraction processing (S24). In the data extraction processing, if Fig.10 As shown, relatively appropriate data are extracted from the measurement data 83 obtained in S21 and the simulation data 84 obtained in S23. Fig.10 This is a flowchart showing the flow of the data extraction process (S24).

[0137] In the data extraction process (S24), the processes of S31 to S33 and the processes of S34 to S36 are executed in parallel.

[0138] In S31 , the evaluation system 1 generates two-dimensional data TD1 based on the measurement data 83 obtained in S21 .

[0139] The evaluation system 1 generates the following information based on the defect level and two-dimensional position information contained in the measurement data 83: Fig.11A The two-dimensional data TD1 is shown. Fig.11A and Fig. 11B 2 is a diagram showing two-dimensional data TD1 and TD2. The two-dimensional data TD1 is data in which the level of one or more defects is mapped in the exposure shot area SH. Fig.11AIn the example, the defect level is represented by the color shade, and the closer the color is to white, the greater the defect level. In the two-dimensional data TD1, the boundary between the whiter area and the darker area is relatively clear. In other words, the defect level of the two-dimensional data TD1 varies greatly between the defective part and the non-defective part, and the ability to express the position difference of the defective part is weak, which is not suitable for comparison.

[0140] Therefore, in S32 , the evaluation system 1 converts the two-dimensional data TD1 into a probability density distribution.

[0141] The evaluation system 1 performs a first conversion process on the two-dimensional data TD1 to generate a spectrum SP1.

[0142] The first transform process may be any transform that can achieve an inverse transform. The first transform process may be a transform corresponding to a series expansion using an orthogonal basis function. The first transform process may also be a discrete cosine transform corresponding to a Fourier series expansion, a series expansion of Legendre polynomials, or a series expansion of Chebyshev polynomials.

[0143] The first transformation process may be a transformation that can achieve an inverse transformation and is suitable for the defect distribution. For example, when the defect is located along the cut region KF (refer to Figure 1B ) distribution trend, the first transformation processing can be a discrete cosine transform.

[0144] The defect is located along the peripheral area DL (refer to Figure 1B ) distribution trend, the first transformation processing can be a Legendre series expansion.

[0145] The defect is located along the cut area KF (ref. Figure 1B ) and along the peripheral area DL (refer to Figure 1B ) distribution trend, the first transformation processing can be a discrete cosine transform.

[0146] The evaluation system 1 performs filtering processing on the spectrum SP1.

[0147] The filtering process may adopt any process suitable for distributing the probability density of the defect distribution shown in the two-dimensional data TD1 before the transformation. The filtering process may adopt a process that smoothes the defect distribution shown in the two-dimensional data TD1 before the transformation. The filtering process may be a low-pass filtering process or a process that retains the components below the specified order in the components of each order of the series expansion.

[0148] The evaluation system 1 performs a second conversion process on the filtered spectrum SP1 to decode the two-dimensional data TD1a. The two-dimensional data TD1a is the data obtained by converting the original two-dimensional data TD1 into a probability density distribution. The two-dimensional data TD1a can be regarded as data that two-dimensionally maps the probability density level of defects in the exposure area SH.

[0149] The second transform process is an inverse transform of the first transform process. When the first transform process is a discrete cosine transform, the second transform process may be an inverse discrete cosine transform. When the first transform process is a Legendre series expansion, the second transform process may be an inverse Legendre series expansion. When the first transform process is a Chebyshev series expansion, the second transform process may be an inverse Chebyshev series expansion.

[0150] The obtained two-dimensional data TD1a contains minute defects or noise levels, but the minute defects or noise levels may hinder when comparing overall trends.

[0151] Therefore, in S33, the evaluation system 1 performs threshold processing on the decoded two-dimensional data TD1a. The evaluation system 1 replaces the level of data lower than the threshold level among the plurality of data included in the decoded two-dimensional data TD1a with the threshold level.

[0152] Threshold processing is a process of replacing the level of data below the threshold level among multiple data with the threshold level to increase the lowest level. By increasing the lowest level, the influence of micro defects or noise levels can be alleviated, thereby improving the accuracy of the comparison processing that should be performed after the threshold processing.

[0153] The threshold level can be determined in advance by experiments. The threshold level can be determined in advance by experiments based on the maximum level of the data when the defect is 1. The threshold level can be determined by multiplying the maximum level of the data when the defect is 1 by a coefficient greater than 0 and less than 1. The coefficient can be a number greater than 0 and less than 1 (for example, 0.5) determined in advance by experiments. The threshold level can be determined in advance by experiments based on the maximum level of the data when the defect diameter is about the thickness of the photosensitizer RG. The threshold level can also be determined by multiplying the maximum level of the data when the defect diameter is about the thickness of the photosensitizer RG by a coefficient greater than 0 and less than 1. The coefficient can be a number greater than 0 and less than 1 (for example, 0.5) determined in advance by experiments.

[0154] For example, when the level of data included in the two-dimensional data TD1 a is set to D1 and the threshold level is set to Δ, the evaluation system 1 performs the processing shown in Expression 1.

[0155] In Formula 1, max() is a max function that returns the maximum value of the argument in the parentheses. Thus, the evaluation system 1 can obtain two-dimensional data TD1b after threshold processing.

[0156] On the other hand, in S34 , the evaluation system 1 generates two-dimensional data TD2 based on the simulation data 84 obtained in S23 .

[0157] The evaluation system 1 generates the following information based on the defect level and two-dimensional position information contained in the simulation data 84: Fig. 11B The two-dimensional data TD2 is shown. The two-dimensional data TD2 is data that maps the level of one or more defects in the exposure shot area SH. Fig. 11B In the example, the defect level is represented by the color shade, and the closer the color is to white, the greater the defect level. In the two-dimensional data TD2, the boundary between the whiter area and the darker area is relatively clear. In other words, the defect level of the two-dimensional data TD2 varies greatly between the defective part and the non-defective part, and the ability to express the position difference of the defective part is weak, which is not suitable for comparison.

[0158] Therefore, in S35 , the evaluation system 1 converts the two-dimensional data TD2 into a probability density distribution.

[0159] The evaluation system 1 performs a first transformation process on the two-dimensional data TD2 to generate a spectrum SP2. The evaluation system 1 performs a filtering process on the spectrum SP2. The evaluation system 1 performs a second transformation process on the spectrum SP2 after the filtering process to decode the two-dimensional data TD2a. The two-dimensional data TD2a is data obtained by converting the original two-dimensional data TD2 into a probability density distribution. The two-dimensional data TD2a can be regarded as data that two-dimensionally maps the probability density level of defects in the exposure irradiation area SH.

[0160] The obtained two-dimensional data TD2a contains minute defects or noise levels, but the minute defects or noise levels may hinder when comparing overall trends.

[0161] Therefore, in S36, the evaluation system 1 performs threshold processing on the decoded two-dimensional data TD2a. The evaluation system 1 replaces the level of data lower than the threshold level among the plurality of data included in the decoded two-dimensional data TD2a with the threshold level.

[0162] Threshold processing is a process of replacing the level of data below the threshold level among multiple data with the threshold level to increase the lowest level. By increasing the lowest level, the influence of micro defects or noise levels can be alleviated, thereby improving the accuracy of the comparison processing that should be performed after the threshold processing.

[0163] For example, when the level of data included in the two-dimensional data TD2 a is set to D2 and the threshold level is set to Δ, the evaluation system 1 performs the processing shown in Expression 2.

[0164] In Formula 2, max() is a max function that returns the maximum value of the argument in the parentheses. Thus, the evaluation system 1 can obtain two-dimensional data TD2b after threshold processing.

[0165] When both the two-dimensional data TD1b of S33 and the two-dimensional data TD2b of S36 are obtained, the evaluation system 1 is as follows Fig. 9 and Fig.10 As shown, a quantitative comparison process (S25) is performed.

[0166] Specifically, the evaluation system 1 applies a comparison function to the threshold-processed two-dimensional data TD1b and the threshold-processed two-dimensional data TD2b to calculate the degree of consistency. The degree of consistency can be a value between 0 and 1, and is closer to 1 when the distribution shapes are more similar.

[0167] The evaluation system 1 can apply the first comparison function to the processed first two-dimensional data and the processed second two-dimensional data to calculate the first consistency. The first comparison function can be a function suitable for evaluating the similarity (trend consistency) of distribution shapes. The first comparison function can be, for example, a comparison function based on mode coupling theory. The first consistency is a numerical value between 0 and 1, and the more similar the distribution shapes are, the closer it is to 1.

[0168] For example, when the level of data included in the two-dimensional data TD1 b is set to D1 and the level of data included in the two-dimensional data TD2 b is set to D2 , the calculation unit 7 calculates the degree of match ξ shown in Expression 3.

[0169] In Formula 3, the denominator on the right represents the product of the convolution integral of the square of the data level D1 and the convolution integral of the square of the data level D2. The numerator on the right represents the square of the convolution integral of the product of the data level D1 and the data level D2. The consistency ξ is a value between 0 and 1, and the more similar the distribution of the two-dimensional data TD1b is to the distribution of the two-dimensional data TD2b, the closer it is to 1.

[0170] According to satisfying the specified conditions, the evaluation system 1 can switch the comparison function to be used from the first comparison function to the second comparison function. After the switch, the calculation unit 7 applies the second comparison function to the processed first two-dimensional data and the processed second two-dimensional data to calculate the second consistency. The second comparison function can be a function suitable for evaluating absolute consistency. The second comparison function can be, for example, a comparison function applying the Jaccard index. The second consistency is a numerical value between 0 and 1, and the more similar the distribution shapes are, the closer it is to 1.

[0171] For example, when the rank of data included in the two-dimensional data TD1 b is set to D1 and the rank of data included in the two-dimensional data TD2 b is set to D2 , the evaluation system 1 calculates the degree of match Σ shown in Expression 4.

[0172] In formula 4, max() is the max function, which returns the maximum value of the independent variable in the parentheses. min() is the min function, which returns the minimum value of the independent variable in the parentheses. The denominator on the right represents the convolution integral of the data level D1 and the data level D2, whichever is larger. The numerator on the right represents the convolution integral of the data level D1 and the data level D2, whichever is smaller. The consistency Σ is a value between 0 and 1, and the closer the distribution of the two-dimensional data TD1b is to the distribution of the two-dimensional data TD2b, the closer it is to 1.

[0173] Alternatively, the evaluation system 1 may calculate the second degree of consistency by applying the second comparison function to the processed first two-dimensional data and the processed second two-dimensional data from the beginning.

[0174] Alternatively, the evaluation system 1 may calculate the first degree of consistency using the first comparison function and the second degree of consistency using the second comparison function in parallel. The calculation unit 7 may calculate scores corresponding to the first degree of consistency and the second degree of consistency. The score may be obtained by multiplying the first degree of consistency by the second degree of consistency and taking the square root.

[0175] For example, when the degree of consistency calculated using the first comparison function is ξ and the degree of consistency calculated using the second comparison function is Σ, the evaluation system 1 can calculate the score SC as shown in Formula 5.

[0176] In addition, the evaluation system 1 may switch the object of the calculation result from the score to the second consistency according to the number of times the score SC becomes a specified ratio or more (for example, 10% or more) of the previous maximum value for a specified number of times (for example, 10 times). After the switch, the calculation unit 7 may calculate the second consistency using the second comparison function instead of calculating the first consistency using the first comparison function.

[0177] The evaluation system 1 determines whether the result of S25 satisfies the standard ( S26 ).

[0178] For example, when the first consistency is calculated in S25, the evaluation system 1 may determine that the result of S25 does not meet the benchmark when the first consistency is below the first reference value, and determine that the result of S25 meets the benchmark when the first consistency exceeds the first reference value. The first reference value may be determined in advance by experimentation.

[0179] Alternatively, when the second consistency is calculated in S25, the evaluation system 1 may determine that the result of S25 does not meet the benchmark when the second consistency is below the second reference value, and determine that the result of S25 meets the benchmark when the second consistency exceeds the second reference value. The second reference value may be determined in advance by experimentation.

[0180] Alternatively, when the consistency calculated in S25 is switched from the first consistency to the second consistency midway, the evaluation system 1 may determine that the result of S25 does not meet the benchmark before the switch. After the switch, the evaluation system 1 may determine that the result of S25 does not meet the benchmark when the second consistency is below the second benchmark value. The evaluation system 1 may determine that the result of S25 meets the benchmark when the second consistency exceeds the second benchmark value. The second benchmark value may be determined in advance by experimentation.

[0181] Alternatively, when the score is calculated in S25, the evaluation system 1 may determine that the result of S25 does not meet the benchmark when the score is below the third benchmark value, and determine that the result of S25 meets the benchmark when the score exceeds the third benchmark value. The third benchmark value may be determined in advance by experimentation.

[0182] After calculating the degree of consistency, the evaluation system 1 compares the degree of consistency with the degree of consistency calculated last time, and obtains the correlation between the change direction of the NIL parameter set and the change direction of the degree of consistency.

[0183] For example, the evaluation system 1 compares the last consistency with the current consistency. The evaluation system 1 compares the last NIL parameter with the current NIL parameter and identifies the direction of change of the NIL parameter. When the current consistency is higher than the previous consistency, the evaluation system 1 identifies that the direction of change of the NIL parameter is a direction that makes the consistency higher. The evaluation system 1 can store information related to the direction of change of the NIL parameter that makes the consistency higher as associated information 82 in the memory device 1c.

[0184] Alternatively, the evaluation system 1 compares the last consistency with the current consistency and identifies the direction of change (e.g., the direction of change that makes the consistency higher, or the direction of change that makes the consistency lower). The evaluation system 1 compares the last NIL parameter with the current NIL parameter and identifies the direction of change of the NIL parameter. The evaluation system 1 can store the information that establishes a correspondence between the direction of change of the NIL parameter and the direction of change of the consistency as association information 82 in the memory device 1c.

[0185] When the result of S25 does not satisfy the benchmark (No in S26), the evaluation system 1 changes the NIL parameter (S27). The evaluation system 1 performs Bayesian inference while referring to the associated information 82, and identifies the change direction of the NIL parameter such as the increase in the degree of consistency, and determines the changed NIL parameter based on the identified change direction. Thereafter, the evaluation system 1 performs the processing after S23.

[0186] That is, the evaluation system 1 repeats the repetition instruction processing of S23 to S27 until the result of S25 satisfies the benchmark (No in S26), and when the result of S25 satisfies the benchmark (Yes in S26), the processing is terminated. That is, the change of the NIL parameter is repeatedly performed until the result of S25 satisfies the benchmark, so that the NIL parameter can be adjusted to match the target data obtained in S21 to some extent. In addition, the association with the change direction of the NIL parameter set and the change direction of the consistency can be obtained as the association information 82. As a result, the accuracy of the simulation can be easily improved.

[0187] Next, use Fig.12 The details of the layout optimization process ( S2 ) will be described. Fig.12 It is a flowchart which shows the layout optimization process (S2).

[0188] The circuit design device performs circuit design for the second element based on designated design information and / or instructions from the user ( S51 ), generates schematic data, and supplies the schematic data to the layout design device.

[0189] The layout design device performs layout design based on the schematic data and / or instructions from the user ( S52 ) to generate layout data.

[0190] The layout design device performs layout design of the dummy pattern (S53). The layout design device generates a plurality of layout data and supplies them to the defect occurrence simulation device 300. The plurality of layout data is obtained by changing the addition of dummy patterns and the number of additions to the layout data of S52. When the layout design of the dummy pattern is completed, the layout design device notifies the evaluation system 1 of the completion.

[0191] The evaluation system 1 repeats the simulation in the defect occurrence simulation device 300 based on the design in S53 according to the notification from the layout design device, thereby optimizing the pattern design including the presence or absence of the dummy pattern ( S54 ).

[0192] In S54, the evaluation system 1 may Fig.13 Action shown. Fig.13 It is a data flow diagram showing the operation of the evaluation system 1 .

[0193] exist Fig.13In the evaluation system 1 shown, the memory device 1b can store the simulation data 84 supplied from the defect occurrence simulation device 300. The memory device 1c stores the related information 82 and the two-dimensional data 85. The two-dimensional data 85 is data that maps the defect distribution two-dimensionally in the exposure irradiation area SH based on the measurement data obtained from the defect inspection device 200 in advance. The two-dimensional data 85 may be data generated based on the measurement data of the first element obtained in S16, or may be data generated based on the measurement data of other elements. Figure 8 The evaluation system 1 shown is the same.

[0194] In S54, the evaluation system 1 supplies the proposal of the NIL parameter to the defect occurrence simulation device 300. The evaluation system 1 may change the NIL parameter while referring to the associated information 82, and supply the proposal of the changed NIL parameter to the defect occurrence simulation device 300. The defect occurrence simulation device 300 calculates the defect distribution that occurs when the pattern is formed on the substrate SB by the NIL according to the layout data while changing the NIL parameter according to the proposal from the evaluation system 1 for each of the plurality of layout data generated in S53. The defect occurrence simulation device 300 supplies the calculation result as the simulation data 84 to the evaluation system 1.

[0195] The evaluation system 1 can select layout data satisfying a first criterion as appropriate layout data from among the plurality of layout data based on the simulation data 84. The first criterion can be, for example, the minimum average defect count relative to the plurality of NIL parameters or the minimum defect count relative to the plurality of NIL parameters.

[0196] Alternatively, the evaluation system 1 can also use the simulation data 84 and the two-dimensional data 85 to perform the same operation on a plurality of layout data. Fig. 9 In this case, the two-dimensional data 85 is equivalent to the two-dimensional data TD1 corresponding to the target data, and the acquisition of the target data (S21) is omitted in the evaluation method. The other points are the same as those performed in S16. Fig. 9 The evaluation method is the same as that of . The evaluation system 1 can select layout data satisfying the fourth criterion from among the plurality of layout data as appropriate layout data. The fourth criterion can be, for example, the maximum degree of coincidence.

[0197] The evaluation system 1 supplies the optimized layout data to the defect occurrence simulation device 300 .

[0198] Next, use Fig.14 The details of the condition optimization process (S3) will be described. Fig.14 It is a flowchart which shows the condition optimization process (S3).

[0199] In the condition optimization process (S3), the process of S61 and the process of S62 are executed in parallel.

[0200] In S61 , the evaluation system 1 repeats the simulation in the defect occurrence simulation device 300 based on the design in S54 , and optimizes various processing conditions during NIL imprinting.

[0201] In S61, the evaluation system 1 may Fig.13 Action shown.

[0202] The evaluation system 1 supplies the proposal of the NIL parameter to the defect occurrence simulation device 300. The defect occurrence simulation device 300 changes the NIL parameter between a plurality of NIL parameters for the layout data selected in S54 according to the proposal from the evaluation system 1, and calculates the defect distribution that occurs when a pattern is formed on the substrate SB by the NIL according to the layout data. The defect occurrence simulation device 300 supplies the calculation result as simulation data 84 to the evaluation system 1.

[0203] The evaluation system 1 selects, as appropriate NIL parameters, NIL parameters that satisfy the second criterion among the plurality of NIL parameters based on the simulation data 84. The second criterion may be, for example, the minimum number of defects.

[0204] Alternatively, the evaluation system 1 can also use the simulation data 84 and the two-dimensional data 85 for a plurality of layout data to perform the same Fig. 9 In this case, the two-dimensional data 85 is equivalent to the two-dimensional data TD1 corresponding to the target data, and the acquisition of the target data (S21) is omitted in the evaluation method. The other points are the same as those performed in S16. Fig. 9 The evaluation method is the same as that of . The evaluation system 1 can select layout data satisfying the fourth criterion from among the plurality of layout data as appropriate layout data. The fourth criterion can be, for example, the maximum degree of coincidence.

[0205] The evaluation system 1 supplies the optimized NIL parameters to the defect occurrence simulation device 300 .

[0206] On the other hand, in S62, an original plate OG for the second element is manufactured (S62). Information on the pattern of the original plate OG is generated based on the pattern optimized in S54. The original plate OG is manufactured based on the information on the pattern of the original plate OG.

[0207] When the processing of S61 and S62 is completed, the evaluation system 1 performs final adjustment by NIL experiment using the actual original OG (S63). The evaluation system 1 starts the evaluation program 81 and executes the evaluation method. In S63, the evaluation system 1 performs the following steps: Figure 8 The action shown is performed Fig. 9The details of the evaluation method are the same as those of S16 except that the first element is replaced by the second element, and the description thereof is omitted.

[0208] The pattern and processing conditions optimized in S51 to S63 are processed including NIL processing to manufacture the second element (S64).

[0209] For example, a photosensitizer is applied to the substrate SB. The original plate OG is imprinted onto the substrate SB under the conditions optimized in S61 and finally adjusted in S63. The photosensitizer is exposed and hardened, the original plate OG is demolded from the substrate SB, and the substrate SB is processed using the pattern of the photosensitizer as a mask to form a pattern on the substrate SB. Thus, the second element is manufactured.

[0210] As described above, in this embodiment, the evaluation system 1 converts the two-dimensional defect distribution obtained by actual measurement and the two-dimensional defect distribution obtained by simulation into probability density distributions, and uses a comparison function to numerically evaluate the similarity between the two. The evaluation system 1 calculates the consistency indicating the similarity between the two. Thus, the two-dimensional defect distribution in the substrate pattern formed by NIL can be quantitatively evaluated, so that the simulation accuracy can be verified and the NIL parameters can be adjusted. As a result, the simulation accuracy can be easily improved.

[0211] Several embodiments of the present invention have been described, but these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be implemented in many other ways, and can be omitted, replaced, and changed in various ways without departing from the scope of the invention. These embodiments and their variations are included in the scope and spirit of the invention, and are also included in the invention described in the claims and their equivalents.

[0212] [Explanation of symbols]

[0213] 1: Evaluation system

[0214] 2: Production Department

[0215] 3: Transformation

[0216] 4: Filter section

[0217] 5: Decoding unit

[0218] 6: Threshold processing unit

[0219] 7: Computing Department

[0220] 8: Memory Department

[0221] 9: Display control unit.

Claims

1. An evaluation method comprising: Performing a first transformation process on first two-dimensional data representing defect distribution of a first substrate and second two-dimensional data representing predicted defect distribution of a second substrate, respectively, to generate a first spectrum and a second spectrum, wherein the first substrate has a pattern formed by a photosensitizer thereon being imprinted by a master plate; performing filtering processing on the generated first spectrum and the generated second spectrum respectively; performing a second transformation process on the processed first spectrum and the processed second spectrum respectively, decoding the first two-dimensional data and the second two-dimensional data; performing threshold processing on the decoded first two-dimensional data and the decoded second two-dimensional data; as well as A comparison function is applied to the first two-dimensional data after the threshold processing and the second two-dimensional data after the threshold processing to calculate the degree of consistency.

2. The evaluation method according to claim 1, wherein Implementing the threshold processing includes: replacing the level of data lower than a threshold level among a plurality of data included in the decoded first two-dimensional data with the threshold level; as well as The level of data lower than the threshold level among a plurality of data included in the decoded second two-dimensional data is replaced with the threshold level.

3. The evaluation method according to claim 1, wherein The second transformation process is opposite to the first transformation process.

4. The evaluation method according to claim 1, wherein The first transformation processing includes a transformation corresponding to a series expansion using an orthogonal basis function, The second transformation process is an inverse transformation of the first transformation process.

5. The evaluation method according to claim 2, wherein The threshold level is determined in advance based on the maximum level of data when the defect is 1.

6. The evaluation method according to claim 5, wherein The threshold level is determined in advance by multiplying the maximum level of data when the defect is 1 by a coefficient.

7. The evaluation method according to claim 6, wherein The coefficient is a number greater than 0 and less than 1.

8. The evaluation method according to claim 1, wherein The degree of consistency calculated using the comparison function is a numerical value between 0 and 1, and is closer to 1 as the distribution shapes are more similar.

9. The evaluation method according to claim 1, wherein The comparison function includes a function suitable for evaluating the similarity of distribution shapes or a function suitable for evaluating the absolute consistency.

10. The evaluation method according to claim 1, wherein The application of the comparison function includes: Applying a first comparison function to the first two-dimensional data after the threshold processing and the second two-dimensional data after the threshold processing to calculate a first degree of consistency; as well as Based on the first degree of consistency satisfying a specified condition, a second degree of consistency is calculated by applying a second comparison function to the first two-dimensional data after the threshold processing and the second two-dimensional data after the threshold processing.

11. The evaluation method according to claim 10, wherein The first comparison function includes a function suitable for evaluating the similarity of distribution shapes. The second comparison function includes a function suitable for evaluating the absolute degree of coincidence.

12. The evaluation method according to claim 10, wherein The specified conditions include: The first degree of coincidence exceeds a specified value, and the number of times the first degree of coincidence is equal to or greater than a specified ratio of a past maximum value reaches a specified number of times.

13. The evaluation method according to claim 10, wherein The application of the comparison function also includes: Scores corresponding to the first degree of match and the second degree of match are calculated.

14. The evaluation method according to claim 1, wherein The application of the comparison function comprises performing the following operations in parallel: A first comparison function is applied to the first two-dimensional data after the threshold processing and the second two-dimensional data after the threshold processing to calculate a first consistency; and a second comparison function is applied to the first two-dimensional data after the threshold processing and the second two-dimensional data after the threshold processing to calculate a second consistency.

15. The evaluation method according to claim 14, wherein The first comparison function includes a function suitable for evaluating the similarity of distribution shapes, and The second comparison function includes a function suitable for evaluating the absolute degree of coincidence.

16. The evaluation method according to claim 14, wherein The application of the comparison function also includes: Scores corresponding to the first degree of match and the second degree of match are calculated.

17. A method for manufacturing a semiconductor element, comprising: Applying the following evaluation method, while changing the parameter, calculating the consistency between the first two-dimensional data and the second two-dimensional data, so as to generate correlation information indicating the correlation between the change direction of the parameter and the change direction of the consistency; Using the correlation information, while changing the parameters, the defect distribution of the third substrate is predicted, and a pattern to be formed on the third substrate is optimized; Using the correlation information, the defect distribution of the third substrate is predicted while changing the parameters, thereby optimizing the conditions related to the imprinting of the original plate; as well as Using the optimized pattern and optimized conditions, imprinting the original plate onto the third substrate to form a semiconductor device; The evaluation method includes: performing a first transformation process on the first two-dimensional data representing the defect distribution of the first substrate and the second two-dimensional data representing the predicted defect distribution of the second substrate, respectively, to generate a first spectrum and a second spectrum, wherein the first substrate has a pattern formed thereon due to the photosensitizer thereon being imprinted by an original plate, and the defect distribution of the second substrate is predicted based on the parameters representing the relevant conditions of the original plate imprint; performing a filtering process on the generated first spectrum and the generated second spectrum, respectively; performing a second transformation process on the processed first spectrum and the processed second spectrum, respectively, to decode the first two-dimensional data and the second two-dimensional data; performing a threshold process on the decoded first two-dimensional data and the decoded second two-dimensional data; and applying a comparison function to the threshold-processed first two-dimensional data and the threshold-processed second two-dimensional data to calculate the consistency.

18. The method for manufacturing a semiconductor element according to claim 17, wherein Generating the associated information includes: Calculating the consistency between the first two-dimensional data and the second two-dimensional data; Comparing the calculated consistency with the consistency calculated last time, and finding the correlation between the change direction of the parameter and the change direction of the consistency; as well as Based on the obtained correlation, the correlation information related to the change direction of the parameter that increases the degree of consistency is generated.

19. An evaluation system comprising: A transformation unit performs a first transformation process on first two-dimensional data representing defect distribution of a first substrate and second two-dimensional data representing predicted defect distribution of a second substrate, respectively, to generate a first spectrum and a second spectrum, wherein the first substrate has a pattern formed by a photosensitizer thereon being imprinted by a master plate; A filter unit performs filtering processing on the generated first spectrum and the generated second spectrum respectively; a decoding unit that performs a second transformation process opposite to the first transformation process on the processed first spectrum and the processed second spectrum, respectively, to decode the first two-dimensional data and the second two-dimensional data; a processing unit that performs threshold processing on the decoded first two-dimensional data and the decoded second two-dimensional data; as well as The calculation unit calculates the degree of coincidence by applying a comparison function to the first two-dimensional data after the threshold processing and the second two-dimensional data after the threshold processing.

20. The evaluation system according to claim 19, wherein The processing unit replaces the level of data below the threshold level among multiple data included in the decoded first two-dimensional data with the threshold level, and replaces the level of data below the threshold level among multiple data included in the decoded second two-dimensional data with the threshold level.

Images