Abstract:
Structures and methods for forming the same. The method includes providing design information of a design layer. The design layer includes M original design features and N original dummy features. The method further includes (i) creating a cluster of P representative dummy features, P being a positive integer less than N, (ii) performing OPC for the cluster of the P representative dummy features but not for the N original dummy features, resulting in P OPC-applied representative dummy features, and (iii) forming the mask including N mask dummy features. The N mask dummy features are identical. Each mask dummy feature of the N mask dummy features of the mask has an area which is a function of at least an area of an OPC-applied representative dummy feature of the P OPC-applied representative dummy features. The N mask dummy features have the same relative positions as the N original dummy features.

Description:
FIELD OF THE INVENTION 
       [0001]    The present invention relates to formation of masks/reticles, and more specifically, to formation of the masks/reticles having dummy features. 
       BACKGROUND OF THE INVENTION 
       [0002]    In a conventional process of forming a mask for a design layer having M original design features and N original dummy features, an OPC (Optical Proximity Correction) program is typically run on characteristic data sets of the M original design features and the N original dummy features resulting in OPC-applied characteristic data sets of M OPC-applied design features and N OPC-applied dummy features, respectively. Next, the mask is formed from the OPC-applied characteristic data sets of the M OPC-applied design features, and the N OPC-applied dummy features. The numbers M and N are usually very large, and therefore running OPC program consumes a lot of time. Therefore, there is a need for a method for forming the mask that takes less time than in the prior. 
       SUMMARY OF THE INVENTION 
       [0003]    The present invention provides a method of forming a mask, comprising providing design information of a design layer, wherein the design layer includes M original design features and N original dummy features, M and N being positive integers; creating a design cluster of P representative dummy features, P being a positive integer and less than N; performing OPC (Optical Proximity Correction) for the design cluster of the P representative dummy features but not for the N original dummy features, resulting in P OPC-applied representative dummy features such that if the P OPC-applied representative dummy features were printed on the mask and if a lithographic process was performed on a photoresist layer through the printed mask, then the P representative dummy features would be created in the photoresist layer; forming the mask including N mask dummy features, wherein the N mask dummy features are identical, wherein each mask dummy feature of the N mask dummy features of the mask has an area which is a function of at least an area of an OPC-applied representative dummy feature of the P OPC-applied representative dummy features, and wherein the N mask dummy features have the same relative positions as the N original dummy features. 
         [0004]    The present invention provides a computer system of claim  26 , wherein said creating the design cluster of the P representative dummy features comprises superimposing a square of a predetermined size on an area of the design layer such that only P original dummy features of the N original dummy features are inside the square; and using the area limited by the square as the design cluster of the P representative dummy features. 
         [0005]    The present invention provides a method for forming the mask that takes less time than in the prior. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0006]      FIG. 1  shows a flowchart of a method, in accordance with embodiments of the present invention. 
           [0007]      FIG. 2  shows a flowchart of another method, in accordance with embodiments of the present invention. 
           [0008]      FIGS. 3A and 3B  illustrate a method of choosing a central OPC-applied representative dummy feature, in accordance with embodiments of the present invention. 
           [0009]      FIG. 4  shows a flowchart of yet another method, in accordance with embodiments of the present invention. 
           [0010]      FIGS. 5A and 5B  illustrate a method of forming an effective dummy feature, in accordance with embodiments of the present invention. 
           [0011]      FIG. 6  illustrates a computer system used for creating a mask for lithography including design features and dummy features, in accordance with embodiments of the present invention. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0012]      FIG. 1  shows a flowchart of a method  100 , in accordance with embodiments of the present invention. The method  100  is for forming a photo mask/reticle (hereinafter, referred to as a mask) (not shown) for a design layer  100   a  ( FIG. 1A , top down view). The design layer  100   a  ( FIG. 1A ) includes original design features  140   a ,  140   b , and  140   c , and original dummy features  150 . It should be noted that the design layer  100   a  may have large numbers of the original design features and the original dummy features but only the original design features  140   a ,  140   b , and  140   c , and the original dummy features  150  are shown for simplicity. As an example, assume that the design layer  100   a  has fifty thousand (50,000) features including ten thousand (10,000) original design features (similar to the original design features  140   a ,  140   b , and  140   c  of  FIG. 1A ) and forty thousand (40,000) original dummy features (similar to the original dummy features  150  of  FIG. 1A ). This example will be referred to further below. 
         [0013]    The original design features  140   a ,  140   b , and  140   c  are features that are necessary for the operation of the design, whereas the original dummy features  150  are features that are not necessary for the operation of the design but are included in the design to make the pattern density of the mask more uniform across the design layer  100   a  during the fabrication process. In one embodiment, the original design features  140   a ,  140   b , and  140   c  can be design trenches (not shown) in an interlevel dielectric layer, which are to be filled to form electrically conductive lines (not shown), whereas the original dummy features  150  can be dummy trenches, which are to be filled to form dummy lines (not shown). The detailed method  100  is as follows. 
         [0014]    In one embodiment, the method  100  starts with a step  110  ( FIG. 1 ) in which a design input file of the design layer  100   a  ( FIG. 1A ) is provided. The design input file contains a characteristic data set for each feature of the original design features  140   a ,  140   b , and  140   c , and the original dummy features  150 . The characteristic data set contains such information as shape, size, position, etc of the feature. All of the original dummy features  150  have the same shape and size. The original dummy features  150  have square shapes. 
         [0015]    Next, in one embodiment, steps  120   a  and  120   b  ( FIG. 1 ) are performed. It should be noted that the steps  120   a  and  120   b  can be performed in any order. In the step  120   a , an optical proximity correction (OPC) program is run on the characteristic data sets of the original design features  140   a ,  140   b , and  140   c  ( FIG. 1A ) resulting in OPC-applied characteristic data sets of OPC-applied design features  160   a ,  160   b , and  160   c  ( FIG. 1B ), respectively. It should be noted that the original design features  140   a ,  140   b , and  140   c  are also shown in  FIG. 1B  as dotted lines for comparison. The OPC program is run on one characteristic data set of an original design feature after another. OPC is a photolithography enhancement technique commonly used to compensate for design errors due to diffraction or process effects. The OPC program is a software run by a computer system (not shown). In the example above, the OPC program is run on the 10,000 characteristic data sets of the 10,000 original design features (similar to the original design features  140   a ,  140   b , and  140   c  of  FIG. 1A ) resulting in the 10,000 OPC-applied characteristic data sets of 10,000 OPC-applied design features (similar to the OPC-applied design features  160   a ,  160   b , and  160   c  of  FIG. 1B ), respectively. 
         [0016]    In the step  120   b  ( FIG. 1 ), in one embodiment, the OPC program is run on the characteristic data sets of the original dummy features  150  ( FIG. 1A ) resulting in OPC-applied characteristic data sets of OPC-applied dummy features  170  ( FIG. 1B ), respectively. It should be noted that the original dummy features  150  are also shown in  FIG. 1B  as dotted lines for comparison. The OPC program is run on one characteristic data set of an original dummy feature  150  after another. In the example above, the OPC program is run on the 40,000 characteristic data sets of the 40,000 original dummy features (similar to the original dummy features  150  of  FIG. 1A ) resulting in the 40,000 OPC-applied dummy features of 40,000 OPC-applied dummy features (similar to the OPC-applied dummy features  170  of  FIG. 1B ), respectively. 
         [0017]    Next, in step  130  ( FIG. 1 ), in one embodiment, the mask is built from the OPC-applied characteristic data sets of (i) the OPC-applied design features  160   a ,  160   b , and  160   c  (the result of step  120   a ), and (ii) the OPC-applied dummy features  170  (the result of step  120   b ). In the example above, the mask is built having the 10,000 OPC-applied design features and the 40,000 OPC-applied dummy features. 
         [0018]      FIG. 2  shows a flowchart of a method  200 , in accordance with embodiments of the present invention. The method  200  is for forming a photo mask/reticle (hereinafter, referred to as a mask) (not shown) for the design layer  100   a  ( FIG. 1A , top down view). The detailed method  200  is as follows. 
         [0019]    In one embodiment, the method  200  starts with a step  210  ( FIG. 2 ). The step  210  of the method  200  is similar to the step  110  of the method  100  ( FIG. 1 ). 
         [0020]    Next, in one embodiment, steps  220  and  230  ( FIG. 2 ) are performed. The step  220  of the method  200  is similar to the step  120   a  of the method  100  ( FIG. 1 ). In the example above, the OPC program is run on the 10,000 characteristic data sets of the 10,000 original design features (similar to the original design features  140   a ,  140   b , and  140   c  of  FIG. 1A ) resulting in 10,000 OPC-applied characteristic data sets of 10,000 OPC-applied design features (similar to the OPC-applied design features  160   a ,  160   b , and  160   c  of  FIG. 1B ), respectively. 
         [0021]    In the step  230  ( FIG. 2 ), in one embodiment, it is determined whether the OPC model is new. If no, then this means that steps  240  and  250  have previously been performed for this OPC model already. Therefore, a step  260  of the method  200  is performed. If the answer to the question in the step  230  is affirmative, then the step  240  of the method  200  is performed. 
         [0022]    In the step  240 , in one embodiment, a design cluster  300  of representative dummy features  310  ( FIG. 3A , top down view) is generated. More specifically, in one embodiment, the design cluster  300  is generated by superimposing an imaginary square  350  of a predetermined size on an area of the design layer  100   a  ( FIG. 1A ) such that only original dummy features  150  are inside the square  350 . Then, such area can be used as the design cluster  300 , and therefore, the original dummy features  150  inside the square  350  become the representative dummy features  310  of  FIG. 3A . The number of the representative dummy features  310  of the design cluster  300  can be less than two hundred. The circle on the right of  FIG. 3A  is an enlarged view of one representative dummy feature  310 . In the example above, the generated design cluster  300  comprises, illustratively, one hundred (100) representative dummy features  310  (as shown in  FIG. 3A ) that represent the 40,000 original dummy features  150  ( FIG. 1A ). 
         [0023]    Next, in the step  250  ( FIG. 2 ), in one embodiment, the OPC program is run on the characteristic data sets of the representative dummy features  310  ( FIG. 3A ) resulting in OPC-applied characteristic data sets of OPC-applied representative dummy features  320  ( FIG. 3B ), respectively. It should be noted that the representative dummy features  310  are also shown in  FIG. 3B  as dotted lines for comparison. In the example above, the OPC program is run on the 100 characteristic data sets of the 100 representative dummy features  310  resulting in the 100 OPC-applied characteristic data sets of the 100 OPC-applied representative dummy features  320 , respectively. 
         [0024]    Next, in one embodiment, a central OPC-applied representative dummy feature  320 ′ is chosen as follows. Firstly, an intersection point  353  of two diagonals  351  and  352  of the imaginary square  350  is determined (as shown in  FIG. 3B ). Next, if the intersection point  353  is within an OPC-applied representative dummy feature, that OPC-applied representative dummy feature is chosen as the central OPC-applied representative dummy feature  320 ′. If the intersection point  353  is not within any the OPC-applied representative dummy features  320 , the OPC-applied representative dummy feature nearest the intersection point  353  is chosen as the central OPC-applied representative dummy feature  320 ′. In  FIG. 3B , the central OPC-applied representative dummy feature  320 ′ is circled and the large circle on the right of  FIG. 3B  is an enlarged view of the central OPC-applied representative dummy feature  320 ′. 
         [0025]    Next, in step  260  ( FIG. 2 ), in one embodiment, the mask is built from the OPC-applied characteristic data sets of (i) the OPC-applied design features (similar to the OPC-applied design features  160   a ,  160   b , and  160   c  of  FIG. 1B ), and (ii) the central OPC-applied representative dummy feature  320 ′. In the example above, the mask is built having the 10,000 OPC-applied design features (the result of step  220 ) and 40,000 mask dummy features identical in terms of size and shape to the central OPC-applied representative dummy feature  320 ′. The 40,000 mask dummy features are located at the same locations as the 40,000 original dummy features. 
         [0026]    In summary, instead of running the OPC program on the 40,000 characteristic data sets of the 40,000 original dummy features as in method  100 , the OPC program is run on only the 100 characteristic data sets of the 100 representative dummy features  310  as in method  200 . Then, one of the resulting  100  OPC-applied representative dummy features  320  (i.e. the central OPC-applied representative dummy feature  320 ′) is used for forming all 40,000 mask dummy features of the mask. As a result, the 40,000 mask dummy features are identical in terms of size and shape to the central OPC-applied representative dummy feature  320 ′. 
         [0027]      FIG. 4  shows a flowchart of a method  400 , in accordance with embodiments of the present invention. The method  400  is for forming a photo mask/reticle (hereinafter, referred to as a mask) (not shown) for the design layer  100   a  ( FIG. 1A , top down view). The detailed method  400  is as follows. 
         [0028]    In one embodiment, the method  400  starts with a step  410  ( FIG. 4 ). The step  410  of the method  400  is similar to the step  110  of the method  100  ( FIG. 1 ). 
         [0029]    Next, in one embodiment, steps  420  and  430  ( FIG. 2 ) are performed. The step  420  of the method  400  is similar to the step  120   a  of the method  100  ( FIG. 1 ). In the example above, the OPC program is run on the 10,000 characteristic data sets of the 10,000 original design features (similar to the original design features  140   a ,  140   b , and  140   c  of  FIG. 1A ) resulting in 10,000 OPC-applied characteristic data sets of 10,000 OPC-applied design features (similar to the OPC-applied design features  160   a ,  160   b , and  160   c  of  FIG. 1B ), respectively. 
         [0030]    In the step  430  ( FIG. 4 ), in one embodiment, it is determined whether the OPC model is new. If no, then this means that steps  440 ,  450 , and  452  have previously been performed for this OPC model already. Therefore, a step  460  of the method  400  is performed. If the answer to the question in the step  430  is affirmative, then the step  440  of the method  400  is performed. 
         [0031]    In the step  440 , in one embodiment, a design cluster  500  (similar to the design cluster  300  of  FIG. 3A ) of representative dummy features  510  (as shown in  FIG. 5A  as dotted lines for comparison) is generated. The step  440  of the method  400  is similar to the step  240  of the method  200 . In the example above, the design cluster  500  comprises, illustratively, 100 representative dummy features  510  ( FIG. 5A ) that represent the 40,000 original dummy features  150  ( FIG. 1A ). 
         [0032]    Next, in the step  450  ( FIG. 4 ), in one embodiment, the OPC program is run on the characteristic data sets of the representative dummy features  510  ( FIG. 5A ) resulting in OPC-applied characteristic data sets of OPC-applied representative dummy features  520  (as shown in  FIG. 5A ), respectively. Next, a central OPC-applied representative dummy feature  520 ′ is chosen. The step  450  of the method  400  is similar to the step  250  of the method  200 . In the example above, the OPC program is run on the 100 characteristic data sets of the 100 representative dummy features  510  resulting in the 100 OPC-applied characteristic data sets of the 100 OPC-applied representative dummy features  520 , respectively. In  FIG. 5A , the central OPC-applied representative dummy feature  520 ′ is circled and the large circle on the right of  FIG. 5A  is an enlarged view of the central OPC-applied representative dummy feature  520 ′. 
         [0033]    Next, in the step  452  ( FIG. 4 ), in one embodiment, the area of the central OPC-applied representative dummy feature  520 ′ is measured. Next, an effective dummy feature  530  (as shown in  FIG. 5B ) is created whose area is equal to the area of the central OPC-applied representative dummy feature  520 ′ (as shown in  FIG. 5B  as dotted line for comparison). In an alternative embodiment, the area of the effective dummy feature  530  is equal to the average area of the 100 OPC-applied representative dummy features  520  (in the example above). The effective dummy feature  530  has a square shape. 
         [0034]    Next, in step  460  ( FIG. 4 ), in one embodiment, the mask is built from the OPC-applied characteristic data sets of (i) the OPC-applied design features (similar to the OPC-applied design features  160   a ,  160   b , and  160   c  of  FIG. 1B ), and (ii) the effective dummy feature  530  of the  FIG. 5B . In the example above, the mask is built having the 10,000 OPC-applied design features (the result of step  420 ) and the 40,000 mask dummy features identical in terms of size and shape to the effective dummy feature  530 . The 40,000 mask dummy features are located at the same locations as the 40,000 original dummy features. 
         [0035]    In summary, instead of running the OPC program on the 40,000 characteristic data sets of the 40,000 original dummy features as in method  100 , the OPC program is run on only the 100 characteristic data sets of the 100 representative dummy features  510  as in method  400 . Then, the effective dummy feature  530  is created and used for forming all 40,000 mask dummy features of the mask. As a result, the 40,000 mask dummy features are identical in terms of size and shape to the effective dummy feature  530 . In the embodiments described above, the design information of the design layer  100   a  ( FIG. 1A ) can be stored in a design file which in turn can be stored in a memory device (not shown) such as a computer hard disk. The design clusters  300  and  500  ( FIGS. 3A and 5A , respectively) can be stored in a separate file which can be stored in the same hard disk as the design file or in a different memory device. The OPC-applied features (design or dummy) can also be stored in files which can be stored in the same memory device as the design file or in a different memory devices. 
         [0036]      FIG. 6  illustrates a computer system  90  used for creating a mask for lithography including design features and dummy features, in accordance with embodiments of the present invention. The computer system  90  comprises a processor  91 , an input device  92  coupled to the processor  91 , an output device  93  coupled to the processor  91 , and memory devices  94  and  95  each coupled to the processor  91 . The input device  92  may be, inter alia, a keyboard, a mouse, etc. The output device  93  may be, inter alia, a printer, a plotter, a computer screen, a magnetic tape, a removable hard disk, a floppy disk, etc. The memory devices  94  and  95  may be, inter alia, a hard disk, a floppy disk, a magnetic tape, an optical storage such as a compact disc (CD) or a digital video disc (DVD), a dynamic random access memory (DRAM), a read-only memory (ROM), etc. The memory device  95  includes a computer code  97 . The computer code  97  includes an algorithm for creating a mask for lithography including design features and dummy features. The processor  91  executes the computer code  97 . The memory device  94  includes input data  96 . The input data  96  includes input required by the computer code  97 . The output device  93  displays output from the computer code  97 . Either or both memory devices  94  and  95  (or one or more additional memory devices not shown in  FIG. 9 ) may be used as a computer usable medium (or a computer readable medium or a program storage device) having a computer readable program code embodied therein and/or having other data stored therein, wherein the computer readable program code comprises the computer code  97 . Generally, a computer program product (or, alternatively, an article of manufacture) of the computer system  90  may comprise said computer usable medium (or said program storage device). 
         [0037]    While  FIG. 6  shows the computer system  90  as a particular configuration of hardware and software, any configuration of hardware and software, as would be known to a person of ordinary skill in the art, may be utilized for the purposes stated supra in conjunction with the particular computer system  90  of  FIG. 6 . For example, the memory devices  94  and  95  may be portions of a single memory device rather than separate memory devices. 
         [0038]    While particular embodiments of the present invention have been described herein for purposes of illustration, many modifications and changes will become apparent to those skilled in the art. Accordingly, the appended claims are intended to encompass all such modifications and changes as fall within the true spirit and scope of this invention.