Abstract:
Integrated circuit mask layouts are modified for the purpose of migration to abide a new set of design rules, or for the purpose of optimization for timing, power, signal integrity and manufacturability, among other purposes. The modified layout is required to satisfy a set of constraints generated from design rules, electrical specifications, user specifications among other requirements. The present invention provides a system and a method of representing constraint sets, each of which consists of two or more sets of constraints that are mutually exclusive to each other. In the preferred embodiment, one method of formulation is presented, and a method of solving the layout modification problem under the constraint sets is presented.

Description:
CLAIM OF BENEFIT TO PROVISIONAL APPLICATION 
   This patent application claims the benefit of the earlier-filed U.S. Provisional Patent Application entitled “System and method of modification of integrated circuit mask layout”, having Ser. No. 60/818,782, and filed Jul. 5, 2006. 

   TECHNICAL FIELD OF APPLICATION 
   This invention relates generally to the design and manufacture of integrated circuitry and more particularly to a method and a system of modifying integrated circuit layout. 
   BACKGROUND OF THE INVENTION 
   It is desirable to modify an integrated circuit layout under certain circumstances. One such circumstance is process migration, in which an integrated circuit_layout developed for one process technology is modified to abide a new set of design rules required by another process technology, normally from another foundry or another process node. Another example is layout optimization, in which an integrated circuit_layout is modified to improve the integrated circuit on certain metrics, such as timing, power consumption, signal integrity or manufacturability. The process of integrated circuit layout modification is performed either manually by layout designers using layout editing tools such as Cadence Virtuoso; or by a design automation computer program run on a computer system. 
   The integrated circuit layout modification is performed either in two-dimensional manner, in which both horizontal and vertical orientations are modified simultaneously; or by alternating between horizontal and vertical orientations, modifying layout in one orientation while keeping integrated circuit layout unchanged in the other orientation. Two-dimensional approach is considered superior for a plurality of reasons. Among others, first, some spatial constraints between layout shapes are intrinsically two-dimensional, such as, but not limited to, constraints between two geometric corners. Handling constraints of these types in one orientation at a time inevitably forces decisions to be made on the other orientation in advance, which may cause sub-optimal results or infeasibilities. Infeasibilities happen when there is no feasible solution that satisfies all constraints simultaneously. Second, modern process technologies are very complex, the design rules provided by foundries usually contain a plurality of conditional rules, most of which are two-dimensional. One example is width-dependent spacing rule, where the spacing between two shapes in one orientation depends on the overlapping length of the projections of these two shapes in another orientation. Handling constraints of these types in one orientation at a time inevitably forces decisions to be made on the other orientation in advance, which may cause sub-optimal results. Third, the quality of results of layout modification performed in one orientation at a time depends on which orientation to start with, therefore the results may not be optimal. 
   One existing approach of performing automated integrated circuit_layout modification is based on integrated circuit_layout compaction. Using this approach, the existing layout is examined to generate a collection of sets of edges. The edges in each set are relevant to each other. Then spatial constraints are generated between each set of edges from design rules and other specifications. By allocating variables for locations of edges and points, the constraints are translated to a collection of inequalities and equations that form the constraints of a Linear Programming (“LP”) problem. The objective function of the LP is constructed to reflect the desirable qualities of an integrated circuit. For example, smaller die size is desirable to achieve lower cost and higher running clock frequency of an integrated circuit. In turn achieving smaller die size is translated to minimization of layout area. After an optimal or close to optimal solution of the LP is found, the existing integrated circuit layout is modified according to the solution. If the design structure of an integrated circuit layout is flat, and the formulation of constraints is such that each spatial constraint constructed between two edges contains two linear terms each containing the two variables representing the position of the two edges, the layout modification problem may be represented by a constraint graph model, which may be solved more efficiently. 
   Another approach of performing automated layout modification is based on minimum perturbation of an integrated circuit layout. It enforces design rules and other specifications while maintaining similarity to an existing integrated circuit layout. An LP is formulated using constraints generated from design rules and other specifications. The objective function of the LP is constructed to measure location perturbation and separation perturbation of objects in layouts. The solving of the LP minimizes the perturbation to both location and separation while enforcing constraints. 
   Some prior art formulate the optimization problem by including all the active constraints. In the case when mutually exclusive spatial constraints or groups of spatial constraints exist, decision has to be made in advance which spatial constraints or which groups of spatial constraints should be active, while the other spatial constraints or groups of spatial constraints should be deactivated. The decision of activating which spatial constraints or groups of spatial constraints depends mostly on the original layout. This practice limits the flexibility and capability for integrated circuit layout modification process to obtain optimal or close to optimal solution. 
   Some prior art deal with conditional spatial constraints or two-dimensional spatial constraints by using a branch and bound approach. By pruning the decision tree branches that generate worse results then that already recorded, it is possible to achieve close to optimal compaction result. However, the approaches were presented in the cases that can be modeled by constraint graphs, and where the solution search space is always feasible. When constraints of equality types are presented, for example, when device size is fixed, or when one dimension of the design is of fixed value, the order of variables handled by the branch and bound algorithm may have huge impact on integrated circuit layout modification run time to make the approaches practically not useable. 
   SUMMARY OF THE INVENTION 
   The present invention provides a system and a method to formulate integrated circuit layout modification problem involving conditional spatial constraints and two-dimensional spatial constraints, and a system and a method of solving the integrated circuit layout modification problem efficiently are also described. 
   These and other objects, features and advantages in accordance with the present invention are provided by a system and a method of modification of an existing integrated circuit layout. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  illustrates a computing environment used in some embodiments of the present invention. 
       FIG. 2  illustrates a flow of automated modification of integrated circuit layout. 
       FIG. 3  illustrates an exemplary implementation of representing the location of an edge with variables. 
       FIG. 4  illustrates an example of spatial constraints generated from design rules. 
       FIG. 5(   a )-( d ) illustrate an example of corner-to-corner spatial constraints. 
       FIG. 6(   a )-( c ) illustrate an example of end-of-line enclosure spatial constraints. 
       FIG. 7  illustrates an example of forbidden zone spacing spatial constraints. 
       FIG. 8  illustrates an exemplary flow of solving mixed integer programming problem in integrated circuit layout modification. 
   

   DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
   This invention relates to an integrated circuit layout modification system and an integrated circuit layout modification method, and more to a methodology for generating constraint sets and solving of these constraint sets generated from complex design rules and design requirements. The present invention describes a system and a method of representing mutually exclusive collections of spatial constraints or groups of spatial constraints, each of which consists of two or more sets of spatial constraints that are mutually exclusive. This includes, but not limited to, the cases of conditional spatial constraints and two-dimensional spatial constraints. In the preferred embodiment, a system and a method of formulation and a system and a method of solving the integrated circuit layout modification problem efficiently under the presented formulation are presented. 
   In the following description, numerous details are set forth for purpose of explanation. However, one of skill in the art will realize that the invention may be practiced with the variations of these specific details. In other instances, well-known structures or operations are not shown in detail to avoid obscuring the invention. 
     FIG. 1  illustrates a block diagram of the computing environment that one embodiment of the present invention is implemented. Even though the computer system is described with specific components and architecture for illustration, it should be understood that the present invention might be implemented in several other types of embodiments. For example, the invention can be implemented on single computer with a processor chip containing 2 or more processor cores with each core containing additional hardware to maintain state of two or more threads of execution. In addition, each component can be implemented as a combination of one or more of hardware, software and firmware, even though many features of the present invention are described herein as being implemented in software. 
   The computing environment  100  may contain one or more components such as a communication fabric  110 , random access memory (RAM)  120 , central processing unit (CPU)  130 , read only memory  140  (ROM), secondary memory (Storage)  150 , output devices  160 , input Devices  170 , network interface  180 . All the components may communicate with each other over communication fabric  110 . The communication fabric  110  collectively represents all systems, peripherals, chipset buses and all other communication pathways that can connect the components of the computing environment  100 . The components of  FIG. 1  are described below in further detail. 
   CPU  130  retrieves the instructions and data to process in order to execute the processes of this invention from the various storage components of computing environment  100 . The ROM  140  stores the static instruction and data not modified during normal operation and are needed by CPU  130  and any other component of the computing environment  100 . Read-write memory (RAM)  120  is a volatile storage that requires power to be supplied to store the instructions and data. Storage  150  is nonvolatile storage that doesn&#39;t need power to store instructions and data. In some embodiments, storage  150  use fixed mass-storage devices such as disk drives. Other embodiments use removal mass-storage devices such as removable disk drives. The RAM  120  stores some of the instructions and data that the CPU  130  needs. In some embodiments, the invention&#39;s processes are stored in the CPU  130 , RAM  120 , ROM  140 , and/or storage  150 . 
   The input device  170  enables the user to issue commands to the computing environment. Examples of an input device  170  include but are not limited to, keyboards, mouse, and/or tablet and stylus. The output device  160  is used to display images generate by the computing environment such as but not limited to modified integrated circuit layout. 
   Network interface  180  may be implemented using protocols such as TCP/IP, ATM and/or Ethernet. In this manner, the computer can be a part of a network of computers (such as a local area network (“LAN”), a wide area network (“WAN”), or an Intranet) or a network of networks (such as the Internet). Any, some or all of the components of computing environment  100  may be used in conjunction with the invention. However, one of ordinary skill in the art would appreciate that any other system configuration may also be used in conjunction with the present invention. 
   As noted above, CPU  130  may retrieve the software instructions, and execute the instructions to provide various features of the present invention. The features of the present invention are described below in further detail. 
     FIG. 2  illustrates the process  200  used by some embodiments of the current invention to automate modification of an integrated circuit layout. 
   The existing layout  202  is examined so that a plurality of spatial constraints is generated in  204 . A spatial constraint reflects required relationship between a set of edges such as, but not limited to, a pair of edges or a set of edges; or reflects a required position of an edge or a point. The spatial constraints are generated according to design rules  210 , electrical specifications  212 , or user specifications  214 , among other requirements. The constraints are generated between edges that are identified as relevant to each other according to the design rules, electrical specifications or user specifications, among other requirements. For example, generation of minimum spacing constraints between two edges that are invisible to each other due to blockage of other shapes in layout needs not to be considered. 
   Variables are allocated to represent positions of edges of shapes that are to be modified. Therefore spatial constraints are represented in the form of inequalities and equations containing the variables representing coordinates of edges of shapes in layout. These constraints are either linear or may be relaxed into linear constraints. They form the constraints of an LP problem. The objective function of the LP is a weighted combination of desirable qualities of an integrated circuit, including, but not limited to, die size, electrical specifications, and user specifications, among other requirements. For example, minimization of manufacturing cost may be translated to minimization of die area. The construction of the objective function reflects users&#39; priorities. The objective function contains a subset of same variables as in the constraints. 
   The LP is then solved by a solver in  206 . If a optimal or close to optimal feasible solution is found, the variables representing coordinates of integrated circuit layout shapes are updated, and therefore the integrated circuit layout is modified according to the updated values of these variables. The modified integrated circuit layout is saved to a data repository on one or more processor readable storage devices. 
     FIG. 3  illustrates an exemplary implementation of canonically representing the position of an edge with variables. It is recognized that in an integrated circuit layout, the angles of all edges are multiples of 45 degrees. Each edge is represented by an angle, and a position variable. In cases when the edge is horizontal as edge  302 , the angle is 0 or 180 degrees depending on the selection of the starting end point, the position variable is the intersection of the edge or its extension and Y-axis; in cases when the edge is vertical as edge  304 , the angle is 90 or 270 degrees depending on the selection of the starting end point, the position variable is the intersection of the edge or its extension and X-axis; in cases when the angle is 45 or 225 degrees as edge  306  depending on the selection of the starting end point, the position variable is the intersection of the edge or its extension and Y-axis; in cases when the angle is 135 or 315 degrees as edge  308  depending on the selection of the starting end point, the position variable is the intersection of the edge or its extension and Y-axis. A corner in a layout is recognized as an artificial product of the two edges that intersects at the corner. It is represented by the variables defining the two edges. The formulation depends on the orientation of the two edges. A shape in a integrated circuit layout database is represented by the position of the vertices defining the shapes. 
   The spatial constraints between two edges, points or shapes are therefore converted to inequalities and equations containing the variables representing these geometrical entities.  FIG. 4  illustrates an example  400  of spatial constraints generated from design rules. Design rules require the minimum width of a shape  402  on METAL1 layer to be d. The distance between edge  404  and edge  406  should be greater than or equal to d. The position of vertical edge  404  is represented by the x-coordinate of all the points on this edge, variable x 1 . In the same manner, the position of vertical edge  406  is represented by variable x 2 . During the integrated circuit layout modification process, the edges  404  and  406  maintain the same orientation, and the same relative position. The spatial constraint reflecting that requirement direction is
 
 x 2− x 1≧ d   (1)
 
   It is recognized that there are spatial constraints or groups of spatial constraints generated from a integrated circuit layout based on design rules and other specifications are mutually exclusive. The definition of “mutually exclusive” refers to the inclusion of certain constraints into the LP to be solved. Being “mutually exclusive” does not necessarily mean that when one constraint or group of constraint is satisfied, the others are violated; it means that only one constraint of group of constraint needs to be satisfied and therefore is active in the LP, while the others are deactivated in the LP. 
     FIG. 5(   a )-( d ) illustrate an example of corner-to-corner spatial constraints as an example of mutually exclusive spatial constraints. In  FIG. 5(   a ), the two facing corners of shapes  502  and  504  should be spaced apart by a minimum distance d, according to a design rule. One of three possible spatial constraints  506 ,  508  and  510  needs to be enforced. In  FIG. 5(   b ), constraint  310  is enforced, the relative placement of the two facing corners of  502  and  504  is maintained, i.e., the corner of  502  is kept to be right to and above the corner of  504 , and the distance between these corners needs to be at least d. In  FIG. 5(   c ), constraint  506  is enforced, shape  502  is allowed to slide down, but the distance between the two facing edges of  502  and  504  needs to be at least d. In  FIG. 5(   d ), constraint  508  is enforced, shape  502  is allowed to slide to the left, but the distance between the two facing edges of  502  and  504  needs to be at least d. 
     FIG. 6(   a )-( c ) illustrate an example of end-of-line contact/via enclosure spatial constraints as another example of mutually exclusive groups of spatial constraints. The enclosure of shape  602  over  604  is at least d 1  in one orientation and d 2  in another orientation. Therefore, either  606  and  612  are active, or  608  and  610  are active. Without loss of generality, assume d 1 &gt;d 2 . In  FIG. 6(   b ), constraints  606  and  612  are enforced. In  FIG. 6(   c ), constraints  608  and  610  are enforced. 
     FIG. 7  illustrates an example of forbidden zone spacing spatial constraints as an example of mutually exclusive spatial constraints. The design governing the spacing between shape  702  and  704  has a minimum value of d 2  and a “forbidden zone” between d 3  and d 1 , assuming d 2 &lt;d 3 &lt;d 1 , i.e. the spacing d between  702  and  704  must satisfy either
 d≧d2 and d≦d3  (2) or d≧d1  (3) 
   Therefore either constraint  706  is enforced or the group of constraints including constraints  708  and  710  is enforced. 
   The efficient and flexible handling of the mutually exclusive spatial constraints or groups of spatial constraints is desirable in obtaining high quality of integrated circuit layout modification. The present invention uses an integer to formulate the mutual exclusiveness of spatial constraints or groups of spatial constraints. For example, in the case of constraints e 1  and e 2  are mutually exclusive, an integer variable v is used to represent this relationship:
         e 1  is active when v=0,   e 2  is active when v=1,
 
vεZ, v≧0 and v≦1  (4)
       

   Another example is the case of constraints e 1 , e 2  and e 3  are mutually exclusive, an integer variable v is used to represent this relationship:
         e 1  is active when v=0,   e 2  is active when v=1,   e 3  is active when v=2,
 
vεZ, v≧0 and v≦2  (5)
       

   Another example is the case of constraint groups g 1  and g 2  are mutually exclusive an integer variable v is used to represent this relationship:
         all constraints in g 1  are active when v=0,   all constraints in g 2  are active when v=1,
 
vεZ, v≧0 and v≦1  (6)
       

   There is a plurality of ways of incorporating these integer variables into an LP through transformation. An exemplary implementation, which should not be considered limiting to the attached claims, is to convert integer variable into one or more integer variables that may only take value of either 0 or 1 (“0-1 variables”). For example, an integer variable v, where vε[0, 2] may be represented by two 0-1 variables v 1  and v 2 , where
         v=0 is equivalent to v 1 =0 and v 2 =0,   v=1 is equivalent to v 1 =1 and v 2 =0,   v=2 is equivalent to v 1 =0 or 1 and v 2 =1,
 
v 1 εZ, v 1 ≧0 and v 1 ≦1
 
v 2 εZ, v 2 ≧0 and v 2 ≦1  (7)
       

   To incorporate a 0-1 variable in an LP, it is recognized that all the inequalities and equations may be converted to an inequality of minimum type, such as 
                     ∑   i     ⁢       a   i     ⁢     x   i         ≤   b           (   8   )               
and adding a number whose value is substantially larger than possible values of left hand side of the inequalities to the right hand side in practice deactivates the constraint. For example, in the case of constraints e 1  and e 2  are mutually exclusive, the constraints are transformed to:
 
   
     
       
         
           
             
               
                 
                   
                     
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   Therefore, the LP containing spatial constraints or groups of spatial constraints that are mutually exclusive to each other is transformed to a mixed integer programming problem, which contains 0-1 variables reflecting the relationship between the spatial constraints and the groups of spatial constraints, and other variables reflecting the positions of edges, points and other geometrical entities. It is to be recognized that the 0-1 variables in this mixed integer programming problem are usually not directly correlated, they are rather correlated through the constraints they reflect. 
     FIG. 8  illustrates an exemplary flow of solving this type of mixed integer programming problems. In process  802 , a feasible solution with all the 0-1 variables described above snapped to value 0 or 1 is found. Then in process  804 , an optimal or close to optimal solution is found by exploring the 0-1 variables by using Branch and Bound Optimization (BBO). Process  804  is terminated if an optimal solution is not found, but the limits on run time or other criteria are reached. 
   There is a plurality of possible approaches of implementing process  802 . An exemplary implementation is that: first, solving the LP by treating all 0-1 variables as regular variables in real domain. The result is that the value of the 0-1 variables in the solution may not be integers. The process then tries to snap the 0-1 variables in a random or a prioritized order one by one, by solving the LP with the variable set to 0 and 1 respectively. If both solutions are feasible, the one branch that results lower cost or is preferred is used, and the variable is set to the value corresponding to that branch; if only one branch is feasible, that branch is used; if both branches are infeasible, the process backtracks on that variable. When a variable previously visited are reached during backtracking, and both branches are feasible, the branch that was not chosen may be used. If the process is backtracked on a certain variable more than certain times, the variable is put in front of the variable queue, and the whole process is restarted. This process is guaranteed to find a feasible solution if such a solution exists. 
   It is recognized that if a 0-1 variable has a non-integer value, all the constraints of groups of constraints it controls are deactivated, such as in formulas (9) and (10). It may cause the initial LP solution too different from a feasible integer solution, and then causes the run time of process  802  longer than necessary. Numerous heuristics are helpful. For example, if there is a constraint that may be formulated as the common denominator of the two constraints, i.e. it is satisfied if either of the constraints is satisfied, it should be included in the LP. Another heuristics is to preset the values of 0-1 variables based on user preferences and initial layout configuration. 
   In process  804 , a branch of decision tree on a variable is pruned if it yields a higher cost than what was already recorded, or it yields an infeasibility, which means the existing constraints may not be satisfied at the same time. 
   Although the description above contains many specificities, these should be not be construed as limiting the scope of the invention but merely providing illustrations of some of the presently preferred embodiments of this invention. 
   Thus the scope of the invention should be determined by the appended claims and their equivalents, rather than by the examples given.