Sample plan creation for optical proximity correction with minimal number of clips

Methods, program products, and systems for improving optical proximity correction (OPC) calibration, and automatically determining a minimal number of clips, are disclosed. The method can include using a computing device to perform actions including: calculating a total relevancy score for a projected sample plan including a candidate clip, and wherein the relevancy score is derived from at least one relevancy criterion and a relevancy weight; calculating a relevancy score for the candidate clip, the relevancy score for the candidate clip being a contribution from the candidate clip to the total relevancy score; and adding the candidate clip to a sample plan for the IC layout and removing the candidate clip from the plurality of clips in response a difference in relevancy score between the projected sample plan and one or more previous sample plans substantially fitting a non-linear relevancy score function.

BACKGROUND

The present disclosure relates generally to improving the efficiency of a computer system for performing optical proximity correction (OPC). To improve the efficiency of a computer system for OPC, embodiments of the present disclosure can automatically create a minimal (i.e., lowest acceptable) size sample plan for optical proximity correction from an integrated circuit (IC) layout. The created sample plan may be composed of one or more portions of the IC layout known as “clips.” More specifically, the present disclosure relates to methods, program products, and systems which can create a sample plan based on whether a projected sample plan with a candidate clip would provide significant additional relevancy and coverage of the sampled IC layout.

As IC components have continued to decrease in size, improvements to scale have spawned design implementation issues for some types of features, e.g., in CMOS ICs with features sized less than approximately twenty-two nanometers (nm). As IC technology continues to shrink, the growing need for empirical data from a design may exacerbate the uncertainty of the manufacturing process, thereby increasing the risk of defects or impaired operability. Conventional approaches for traversing physical limits may apply manual or computer-implemented techniques for increasing the resolution of chips printed using optical lithography. One such technique is known as optical proximity correction (OPC). OPC is a computational method for correcting irregularities and distortions arising from diffraction effects by the transforming of mask geometries.

Conventional OPC approaches can use empirical approximation models, which must be calibrated by fitting the model to an existing group of portions within an IC layout, also known as clips. This group of clips can be known as a sample plan. Conventionally, the clips of the sample plan are chosen by application of several constraints, including aerial image-based constraints known as image parameters, which may define a minimum intensity, a maximum intensity, a slope, and a curvature, or a critical dimension (CD), such as the minimum space between printed shapes or width of a printable shape.

The quality of OPC modeling may depend on a user's success in selecting a sample plan from hundreds of thousands of clips, and compiling the sample plan as a test mask layout. Experts typically calibrate conventional OPC models. These experts can calibrate each model by choosing or adjusting the contents of the sample plan based on previous implementations and empirical data relevant to the present sample plan. However, this process greatly increases in complexity as advances in lithography demand smaller transistor sizes. In addition, pressure to deliver a product within time constraints may prohibit the manual building or adjusting of a sample plan. Existing automatic or semiautomatic approaches to select a sample plan are generally limited to selecting a small sample from a very large initial set of clips and/or manually defining at least a minimal size (i.e., number of clips) in a sample plan for a particular IC layout.

SUMMARY

A first aspect of the present disclosure provides a computer-implemented method for automatically creating a sample plan for optical proximity correction (OPC) calibration with a minimal number of clips, the method comprising using a computing device to perform actions including: defining a sample plan including a plurality of clips, each of the plurality of clips representing portions of an integrated circuit (IC) layout; calculating a total relevancy score of a projected sample plan for the IC layout, wherein the projected sample plan includes a candidate clip representing an additional portion of the IC layout, and wherein the relevancy score is derived from at least one relevancy criterion and a relevancy weight for the at least one relevancy criterion, the at least one relevancy criterion being one of a topology type of a clip, a printing difficulty of a clip, and a dimensional ratio between clips in the projected sample plan; calculating a relevancy score difference between the total relevancy score of the projected sample plan and a total relevancy score of the sample plan without the candidate clip; adding the candidate clip to the sample plan for the IC layout and removing the candidate clip from the plurality of clips in response to the relevancy score difference substantially fitting a non-linear relevancy score function; removing the candidate clip from the plurality of clips without adding the clip to the sample plan for the IC layout in response to the relevancy score difference substantially fitting a linear relevancy score function, wherein the candidate clip not being added to the sample plan indicates that the sample plan includes the minimal number of clips; and generating an OPC model using the sample plan with the minimal number of clips, wherein the sample plan with the minimal number of clips represents the target sample plan, and wherein the OPC model is used to manufacture at least one IC.

A second aspect of the present disclosure provides a program product stored on a computer readable storage medium, the program product operative to automatically create a sample plan for optical proximity correction (OPC) calibration with a minimal number of clips when executed, the computer readable storage medium comprising program code for: defining a sample plan including a plurality of clips, each of the plurality of clips representing portions of an integrated circuit (IC) layout; calculating a total relevancy score of a projected sample plan for the IC layout, wherein the projected sample plan includes a candidate clip representing an additional portion of the IC layout, and wherein the relevancy score is derived from at least one relevancy criterion and a relevancy weight for the at least one relevancy criterion, the at least one relevancy criterion being one of a topology type of a clip, a printing difficulty of a clip, and a dimensional ratio between clips in the projected sample plan; calculating a relevancy score difference between the total relevancy score of the projected sample plan and a total relevancy score of the sample plan without the candidate clip; adding the candidate clip to the sample plan for the IC layout and removing the candidate clip from the plurality of clips in response to the relevancy score difference substantially fitting a non-linear relevancy score function removing the candidate clip from the plurality of clips without adding the clip to the sample plan for the IC layout in response to the relevancy score difference substantially fitting a linear relevancy score function, wherein the candidate clip not being added to the sample plan indicates that the sample plan includes the minimal number of clips; and generating an OPC model using the sample plan with the minimal number of clips, wherein the sample plan with the minimal number of clips represents the target sample plan, and wherein the OPC model is used to manufacture at least one IC.

A third aspect of the present disclosure provides a system automatically creating a sample plan for optical proximity correction (OPC) calibration with a minimal number of clips, the system comprising: a computing device configured to perform actions including: defining a sample plan including a plurality of clips, each of the plurality of clips representing portions of an integrated circuit (IC) layout, calculating a total relevancy score of a projected sample plan for the IC layout, wherein the projected sample plan includes a candidate clip representing an additional portion of the IC layout, and wherein the relevancy score is derived from at least one relevancy criterion and a relevancy weight for the at least one relevancy criterion, the at least one relevancy criterion being one of a topology type of a clip, a printing difficulty of a clip, and a dimensional ratio between clips in the projected sample plan, adding the candidate clip to the sample plan for the IC layout and removing the candidate clip from the plurality of clips in response to the relevancy score difference substantially fitting a non-linear relevancy score function, and removing the candidate clip from the plurality of clips without adding the clip to the sample plan for the IC layout in response to the relevancy score difference substantially fitting a linear relevancy score function, wherein the candidate clip not being added to the sample plan indicates that the sample plan includes the minimal number of clips; and an OPC modeling device for generating an OPC model using the sample plan with the minimal number of clips, wherein the sample plan with the minimal number of clips represents the target sample plan, and wherein the OPC model is used to manufacture at least one IC.

DETAILED DESCRIPTION

INTRODUCTION AND GENERAL DEFINITIONS

Embodiments of the present invention are directed toward techniques for creating a sample plan for optical proximity correction (OPC) from an integrated circuit (IC) layout. Embodiments of the present disclosure provide for the automatic sizing of a sample plan and the automatic selection of portions of the IC layout, known as “clips,” to be included in the created sample plan. Embodiments of the present disclosure can base the inclusion or exclusion of particular clips in/from the sample plan on one or more relevancy criteria for the sample plan, expressed as a “relevancy score,” in addition to the amount by which each clip contributes to the relevancy score of the sample plan when included, relative to the contribution of other clips included within the sample plan. The embodiments discussed herein can provide processes for automatically selecting a minimal number of clips to include within the sample plan (i.e., creating a target sample plan) which retains adequate coverage while reducing the time needed to determine the size of a sample plan.

In an example embodiment, a method according to the present disclosure can include selecting a candidate clip from a group of clips not currently included within the sample plan. The candidate clip can be defined as one or more clips which are proposed to be added to the sample plan. The method can then include creating a projected sample plan which includes the candidate clip, and then calculating a total relevancy score for projected sample plan with the candidate clip included. The total relevancy score for the projected sample plan can be a weighted sum, average, or other representative quantity based on multiple characteristics known as “relevancy criteria” which reflect the total coverage of the sample plan relative to the complete IC layout. Example relevancy criteria can include topology classifications (i.e., the relationship between features in a clip and their ability to be processed by photolithography), printing difficulty of clips in the projected sample plan, and/or dimensional ratios between the clips in the projected sample plan. Each relevancy criterion can be multiplied by a relevancy weight (mathematically represented as a fraction of one), reflecting the criterion's relative importance to characterizing the clip. For example, in an IC layout where some clips are much more difficult to print than others, clips with a higher printing difficulty may be given a greater relevancy weight than in other implementations of the present disclosure.

After calculating a relevancy score for one or more clips in the group, the method includes calculating a difference between the total relevancy score of the projected sample plan (i.e., including the candidate clip) and the total relevancy score of the sample plan without the candidate clip. Embodiments of the present disclosure can compute this difference, e.g., by comparing the total relevancy score of the projected sample plan with a total relevancy score of a sample plan which does not include the candidate clip. The calculated difference can then be compared to linear and non-linear relevancy score functions for the sample plan. Where the calculated difference substantially fits a non-linear score function (i.e., contributions to the total relevancy score continue to diminish), the candidate clip can be removed from the group of clips and added to the sample plan. Where the calculated difference substantially fits a linear score function (i.e., contributions to the total relevancy score have stopped diminishing). As used herein, a “removed” clip(s) has been previously used as one candidate clip, and either added to the sample plan or not added to the sample plan. The process can repeat successively for the next clips selected until the contribution to the total relevancy score ceases to substantially fit a non-linear relevancy score function (i.e., no longer substantially fits a submodular set function). In other words, the relevancy score function becoming linear can indicate that a minimal number of clips have been added to the sample plan (i.e., the sample plan has become a target sample plan), and adding more clips to the sample plan would not increase the degree to which the sample plan represents an IC layout.

Conversion of IC Layouts into Sample Plans

FIG. 1provides a diagram illustrating an IC layout100for creating a sample plan110. Embodiments of the present disclosure can include methods, such as computer-implemented methods and/or program products, and/or systems configured to carry out the process steps described herein. As used herein, the term “system” can refer to a computer system, server, etc. composed wholly or partially of hardware and/or software components, one or more instances of a system embodied in software and accessible a local or remote user, all or part of one or more systems in a cloud computing environment, one or more physical and/or virtual machines accessed via the internet, other types of physical or virtual computing devices, and/or components thereof. The term “IC layout” can refer to a complete or partial IC chip which includes multiple circuit features (“features”)120. The various features120can be grouped together into portions of the IC layout, and each portion can be known as a clip130. Each clip130may include features120which are interrelated and/or designed to be manufactured together. A “sample plan” refers to a subset of clips130of IC layout100, which are used as a source of empirical data for use with a model during the process of optical proximity correction (“OPC”). InFIG. 1, each clip130is shown to be of a different size by way of example only. It is understood that each clip130in sample plan110may be of the same size and shape (i.e., each shape may be rectangular). The size and shape of clips130can also differ from one sample plan110to another, e.g., one sample plan110can have clips130with a uniform size, while another sample plan110can have clips130with a different uniform size. As shown inFIG. 1by example, sample plan110includes only a select group of clips130and features120, which may provide a group of empirical data for OPC. The effectiveness of sample plan110for OPC may depend wholly or partially by the degree to which clips130in sample plan110represent IC layout100. As each clip130may include one or more features120, clips130and features120are identified and referenced collectively as clips130.

Converting IC layout100into sample plan110can present trade-offs and related technical challenges. In practice, IC layout100may include, e.g., thousands of clips130which together may represent millions of distinct features120. Including every feature120and clip130of IC layout100in sample plan110may be prohibitively time consuming and expensive and may cause OPC to become excessively difficult for some IC layouts100. However, including too few features120and clips130in sample plan110may impair the accuracy and usefulness of sample plan110during the application of OPC. Inventive aspects of the present disclosure relate to the criteria by which some clips130and their corresponding features120are prioritized over others for inclusion within sample plan110. Specifically, aspects of the present disclosure can assign a generalized “relevancy score” to a projected sample plan based on one or more specific relevancy criteria. Each relevancy criterion can be correlated with a relevancy weight, based on the criterion's importance or contribution to sample plan110. Embodiments of the present disclosure can also calculate a difference in total relevancy score between a projected sample plan, including a specific candidate clip, and sample plan110without the candidate clip to determine whether the candidate clip can be added to sample plan110. In addition, aspects of the present disclosure can provide a sub-optimization technique, based on relevancy scores, to determine the smallest number of clips130added to sample plan110before successive clips cease to follow a non-linear relevancy score profile. The minimum relevancy score for sample plan110can be known as a target relevancy score, and can be the relevancy score of another sample plan110, which may be created from a different IC layout100.

To provide these advantages, embodiments of the present disclosure can apply a mathematical summing property known as diminishing returns. Diminishing returns, generally applicable to a type of mathematical model known as a submodular set functions, is a mathematical situation for summing operations where an additive series ranked from highest value to lowest value will approximately converge towards (i.e., become nearly equal to) the total sum of the series before all values in the series are included. Embodiments of the present disclosure characterize the total relevancy score for successive projected sample plans with increasing numbers of clips130as converging to the total relevancy score of a sample plan which includes every clip130of IC layout100. Steps according to the present disclosure determine a minimal number of clips130needed for the total relevancy score to approximate a relevancy score for a target sample plan. In contrast to conventional approaches, which may be limited to characterizing each clip130strictly by image parameters, relevancy scores for each projected sample plan can be based on one or more other types of criteria such as orders of diffraction (i.e., clip diversity), printing difficulty, and/or dimensional ratios, as alternatives or additions to image parameters.

FIG. 2provides a plot of relevancy score versus number of clips for an example IC layout100(FIG. 1) according to embodiments of the present disclosure. A reference sample plan110plotted inFIG. 2reaches a target relevancy score when approximately three hundred and seventy clips are included. The target relevancy score, illustrated as a hashed line inFIG. 2, can be derived from one or more existing sample plans110created from the same IC layout100or different IC layouts100. In addition or alternatively, the target relevancy score can be the total relevancy score of sample plan110with a set of critical clips included. Further explanation of the methods, systems, and program products discussed herein will be discussed relative to the example plot of FIG.2, but it is understood that embodiments of the present disclosure can be applicable to creating sample plans110(FIG. 1) from IC layouts100with different numbers of clips130(FIG. 1). The solid line inFIG. 2represents the total relevancy score of sample plans110which include a particular number of clips130(i.e., projected sample plans234discussed elsewhere herein), while the hashed line inFIG. 2represents the target relevancy score for created sample plan(s)110. InFIG. 2, each sample plan110may be assigned a relevancy score between zero and one based on relevancy criteria such as diversity, aerial-image quality, printing difficulty, and dimensional ratio. Each clip130added to sample plan110can increase the total relevancy score of sample plan110.

Two transition points appear in the plot ofFIG. 2. The term “transition point” can refer to a point on a plot of relevancy score versus number of clips where the marginal contribution to relevancy for each successive clip changes from one type of relevancy score function to another. InFIG. 2, first and second transition points occur at total relevancy scores of approximately 1.72 and 1.77, and the target relevancy score is approximately 1.80. In this example, sample plan110at the first transition point covers slightly above ninety-five percent of the target relevancy score by including approximately one-hundred and sixty five clips130in sample plan110. Between zero clips and the first transition point, the relevancy score function can be non-linear, e.g., by taking on an exponential curve. Further, sample plan110covers approximately ninety-eight percent of the target relevancy score by including approximately two-hundred and sixty clips out of approximately three-hundred and seventy clips, at the second transition point. Between the first and second transition points, the relevancy score function for sample plan110can also be exponential, and can exhibit a different degree of curvature. Both exponential curves ofFIG. 2are shown to provide diminishing contributions to relevancy score for each clip added to sample plan110. Embodiments of the present disclosure can identify transition points in of a plot of relevancy score versus number of clips by comparing the total relevancy score of sample plan110and the contribution from a candidate clip to non-linear and linear relevancy score function. As shown inFIG. 2, the relevancy score function for sample plan110can become linear after the second transition point. The identified transition points correspond to an approximate number of clips130which allow sample plan110to approximate the target score to a particular degree of accuracy.

Computer System and Example Components

Turning now toFIG. 3, an illustrative environment200for implementing the methods and/or systems described herein is shown. In particular, a computer system202is shown as including a computing device204. Computing device204can include a sample plan creation program206which creates sample plans for OPC of a particular size by performing any/all of the processes described herein and implementing any/all of the embodiments described herein.

Computer system202is shown including a processing unit208(e.g., one or more processors), an I/O component210, a memory212(e.g., a storage hierarchy), an external storage system214, an input/output (I/O) device216(e.g., one or more I/O interfaces and/or devices), and a communications pathway218. In general, processing unit208can execute program code, such as sample plan creation program206, which is at least partially fixed in memory212. While executing program code, processing unit208can process data, which can result in reading and/or writing transformed data from/to memory212and/or I/O device216. Pathway218provides a communications link between each of the components in environment200. I/O component210can comprise one or more human I/O devices, which enable a human user to interact with computer system202and/or one or more communications devices to enable a system user to communicate with the computer system202using any type of communications link. To this extent, sample plan creation program206can manage a set of interfaces (e.g., graphical user interface(s), application program interface(s), etc.) that enable system users to interact with sample plan creation program206. Further, sample plan creation program206can manage (e.g., store, retrieve, create, manipulate, organize, present, etc.) data, through several modules240contained within a relevancy scoring system220.

Further, sample plan creation program206can include a relevancy scoring system220. In this case, various modules of relevancy scoring system220can enable computer system202to perform a set of tasks used by sample plan creation program206, and can be separately developed and/or implemented apart from other portions of sample plan creation program206. Sample plan creation program206can also include sample plan110stored therewith. IC layout100can be divided into a plurality of clips230. One of the plurality of clips230of IC layout100can be selected as a candidate clip232. Candidate clip232can be added to a projected sample plan234, in addition to any other clips130previously selected for inclusion in sample plan110. Relevancy scoring system220can perform processes discussed herein to determine whether projected sample plan234can be created as a new sample plan110, including candidate clip232and any other clips130previously added to sample plan110. Process steps for assigning relevancy scores to candidate clip232and projected sample plan234, and creating sample plan(s)110based on these scores, are discussed in detail elsewhere herein.

Memory212can include various software modules240configured to perform different actions. Example modules can include, e.g., a comparator, a calculator, a clip sorter and/or extractor, etc. One or more modules240can use algorithm-based calculations, look up tables, software code, and/or similar tools stored in memory212for processing, analyzing, and operating on data to perform their respective functions. Each module discussed herein can obtain and/or operate on data from exterior components, units, systems, etc., or from memory212of computing device204. Relevancy scoring system220of sample plan creation program206can assist in creating sample plan110for OPC from IC layout100according to embodiments discussed herein. Plurality of clips230may represent distinct portions of IC layout100. One or more candidate clips232can be selected from plurality of clips230. As used herein, the term “candidate clip” can refer to any clip130from IC layout100which has not been previously considered for inclusion within sample plan110. Some attributes of candidate clip232can be converted into a data representation (e.g., a data matrix with several values corresponding to particular attributes) and stored electronically, e.g., within memory212of computing device204, storage system214, and/or any other type of data cache in communication with computing device204. As discussed elsewhere herein, each candidate clip232can be removed from plurality of clips230(e.g., by being flagged, marked-off, etc.) after being used in projected sample plan(s)234. Candidate clip232can additionally or alternatively be converted into data inputs or other inputs to sample plan creation program206with various scanning or extracting devices and/or manual entry of a user, e.g., by determining the dimensions of clip(s)130, measuring and/or determining topology measures (e.g., a polygon count, area density, and/or diffraction order coefficient), calculating a value of printing difficulty based on the attributes of clip(s)130, etc.

Computer system202can be operatively connected to or otherwise in communication with an OPC modeling device (“OPC modelor”)250. OPC modelor250can generate OPC models using, e.g., sample plan creation program206. OPC models generated with OPC modelor250, and which include the minimal number of clips, can be transmitted to a fabricating device or system to manufacture ICs. OPC modelor250can be one of several devices in a semiconductor manufacturing plant, or can be multiple devices each operatively connected to computer system202. Embodiments of the present disclosure can include creating sample plan110with a minimal number of clips130included, generating an OPC model using, e.g., modules240of sample plan creation program206, before using OPC modelor250to generate an OPC model.

Where computer system202comprises multiple computing devices, each computing device may have only a portion of sample plan creation program206and/or relevancy scoring system220fixed thereon (e.g., one or more modules). However, it is understood that computer system202and sample plan creation program206are only representative of various possible equivalent computer systems that may perform a process described herein. Computer system202can obtain or provide data, such as data stored in memory212or storage system214, using any solution. For example, computer system202can generate and/or be used to generate data from one or more data stores, receive data from another system, send data to another system, etc.

Operational Methodology

Referring toFIG. 4in conjunction withFIG. 3, a flow diagram of processes according to the present disclosure is shown. The process flow diagram ofFIG. 4provides an overview of various method steps and processes. The steps and processes can each be carried out with one or more modules240of relevancy scoring system220and described herein by example. Embodiments of the present disclosure, as illustrated by example inFIG. 4, can create sample plan110from plurality of clips230based on relevancy scores assigned to each clip130according to process steps discussed herein. Using the process steps discussed herein, relevancy scoring system220can create sample plans110which provide a minimum amount of coverage. In the example method steps discussed herein, sample plan110which provides the target score will be described as including a total of three-hundred and seventy clips of a particular IC layout100pursuant to the example plot ofFIG. 2. However, it is understood that IC layouts100for a referenced sample plan110and/or used to create new sample plans110can have any conceivable number of clips130with any conceivable dimensions, number of features, etc., and that other examples are discussed herein where appropriate.

In process P1, modules240of relevancy scoring system220can obtain a plurality of clips230for IC layout100. Each clip130obtained from IC layout100can include several IC elements and/or sub-structures, referred to generally as features120(FIG. 1). The obtaining of clips130can be provided, e.g., by a manual or automatic division of IC layout100into clips130. As discussed elsewhere herein, each clip130can be of uniform size and/or shape. IC layout100from which clips130are obtained in process P1can represent all of a single IC product and/or a distinct portion of a larger IC product. In any event, the obtaining of clips130from IC layout100can be automatic based on predetermined criteria defined by sample plan creation program206(e.g., receiving a map of clips130from an independent system, software program, etc.), can be dictated via user selection, and/or can be selected via rules generated by a system and/or programmed by a user. In any case, each of the plurality of clips230in process P1can be eligible for inclusion within sample plan110. In the example illustrated inFIG. 2, relevancy scoring system220divides IC layout100into hundreds of clips measured on x-axis.

The flow can optionally proceed to a process P2, in which modules240of relevancy scoring system220automatically adds one or more “critical clips” to sample plan110. One or more clips130obtained in process P1may represent features120and/or other portions of IC layout100which provide fundamental structures and/or functions. These critical clips can be predefined by a user, independent system or process, or sample plan creation program in an independent process. A user may desire for critical clips to be included in sample plan110, regardless of their contributions to the total relevancy score of sample plan110. At process P2, modules240of relevancy scoring system220can add all critical clips of IC layout100to sample plan110. Where IC layout100includes no critical clips, or where no critical clips are designated, the flow can bypass process P2as shown by the corresponding phantom process flow. In the example illustrated inFIG. 2, no critical clips are designated and process P2is bypassed.

The flow can proceed to process P3in which sample plan creation program206selects candidate clip232from plurality of clips230. Modules240of relevancy scoring system220can determine which clip130to select as candidate clip232based on, e.g., one clip130being the next clip130in a permuted list, making a random selection from plurality of clips230, selecting a clip with a highest predicted relevancy, combinations of these techniques, and/or any other currently known or later developed process for selecting one clip130from plurality230. In an alternative embodiment, multiple candidate clips232can be selected sequentially or simultaneously. Although candidate clip232is referenced in singular terms herein, it is understood that candidate clip232can be in the form of multiple candidate clips232. The flow can proceed to a process P4in which relevancy scoring system220can create projected sample plan234, which can include candidate clip232and other clips previously added to sample plan110, if any.

At process P5, relevancy scoring system220can calculate a total relevancy score for projected sample plan234. Where critical clips were previously designated in process P2, relevancy scoring system220can include these critical clips in the calculated total relevancy score for projected sample plan234. In alternative embodiments where processes discussed herein occur in a different order, the calculating of relevancy scores for projected sample plan234in process P3may occur before the adding of critical clips to sample plan110in process P2. The calculating of relevancy scores in process P5can include numerous sub-processes performed with different modules of relevancy scoring system220. An example group of sub-processes for process P5is shown inFIG. 5and discussed in further detail elsewhere herein. In any event, the calculated relevancy score for each clip130can depend on one or more relevancy criteria.

A first example relevancy criterion can include the “topology type” of candidate clip232and each clip130. Each topology type can represent a group of clips130which diffract light similarly to (e.g., within boundaries of light diffraction from an optical light source) other clips of the same topology type. The topology of each clip130in sample plan110can be measured based on a polygon count, area density, and/or diffraction order coefficient for clips130. Each clip130can be assigned to similarity and diffraction groups based on their polygon counts, area densities, and/or diffraction order coefficients. A polygon count can be calculated as a total number of shapes within each clip130. An area density can be calculated as a percentage of area (i.e., in two dimensions of space) of the projected sample plan occupied by polygons of each clip30. A diffraction order coefficient can calculated as a function of the layout mask for IC layout100and wavelengths produced by a corresponding light source. The first clip added to projected sample plan234can provide the greatest contribution to the total relevancy score for “topology type” for that particular group, with each successive clip130of one topology type contributing a lower relevancy score than the clip added to projected sample plan234from the same group.

A second example relevancy criterion can include the “printing difficulty” of candidate clip232and each clip130in projected sample plan234. The printing difficulty can be any representation, numerical, graphical, or otherwise, of the difficulty for printing clips130of IC layout100. Two example printing difficulty metrics can include: Lithographic Difficulty Estimators (LDE) and the critical dimension. An LDE as used herein, can refer to a multiplying coefficient calculated for a particular clip, which represents process-related factors which can increase the printing difficulty. The LDE can be directly proportional to the critical dimension of the clip. A “critical dimension,” as discussed herein, refers to the smallest distance between two features120of a particular clip130, below which features120cannot be reliably printed to a wafer or mask. Clips with higher value LDEs and/or smaller critical dimensions in a clip can increase the printing difficulty of the clip.

Similar to topology type, each clip130can be assigned to a group representing a particular range of printing difficulties (e.g., easiest difficulty clips, hardest difficulty clips, etc.). The first clip added to projected sample plan234can provide the largest contribution to total relevancy score for a particular printing difficulty group, with each successive clip130of one printing difficulty contributing a lower relevancy score than the previous clips added to projected sample plan234from the same group. Clips130with higher printing difficulties may be more relevant to the eventual sample plan110than clips130of a lower printing difficulty, and modules240can optionally apply scaling factors (i.e., multipliers) to emphasize the contribution to relevancy score from clips130in higher printing difficulty groups. In an alternative embodiment, each clip130can be assigned a scaling factor based on the likelihood of each clip130being in a particular printing difficulty group. For example, over half of all clips130from IC layout100could part of a middle-difficulty group, with a minority of clips belonging to higher and lower printing difficulty groups. Here, a user may desire for over half of clips130in sample plan110to belong to the middle-difficulty group, and thus may assign higher weights to printing difficulty groups with more clips130.

A third example relevancy criterion can include the “dimensional ratio” between clips130of a particular category, including candidate clip232, in projected sample plan234. Alternatively, the dimensional ratio can be calculated as a ratio between clips of 130 of projected sample plan234and a different sample plan110. Design constraints may specify a desired ratio between the total number of clips of a particular type, e.g., the ratios between the total number of one-dimensional (“1D”) horizontal, 1D vertical, two-dimensional (“2D”) horizontal, and 2D vertical clips in projected sample plan234. A one dimensional clip generally refers to a clip with either a length or a width which is below a predetermined threshold value. The other clip dimension (length or width) can have any conceivable value. In an example implementation, 1D horizontal clips can have a length below the threshold value, and 1D vertical clips may have a width below the threshold value. Greater accuracy may correspond to one or more of these ratios, where a desired value is specified, being similar to the same ratios in sample plan110for IC layout100. Smaller differences between the dimensional ratios for sample plan110and their ideal values can increase the relevancy score for projected sample plan234with particular clips130being included.

One or more of types of relevancy criteria can contribute to the total relevancy score for projected sample plan234with candidate clip232included. At process P6, relevancy scoring system220can also calculate a difference in total relevancy score between projected sample plan234, including candidate clip232, and sample plan110without candidate clip232being included. Modules240of relevancy scoring system220can, for example, subtract the total relevancy score of sample plan110(without candidate clip232included) from the total relevancy score of projected sample plan234. The difference in total relevancy score can represent the amount by which candidate clip232increased the total relevancy score of projected sample plan234. Referring to the example ofFIG. 2, one projected sample plan234with forty-nine clips may have a total relevancy score of approximately 1.30 (i.e., at reference position R1), while another projected sample plan234with fifty clips can have a total relevancy score of approximately 1.31 (i.e., at reference position R2). Thus, approximately 0.1 of the total relevancy score of projected sample plan234between R1and R2be contributed from candidate clip232, and various fractions of this contribution can be provided from respective relevancy criteria.

At process P7, modules240with comparing functions can calculate one or more relevancy score functions for sample plan110. More specifically, modules240can model the relationship between the number of clips130in sample plan110and the total relevancy score of sample plan110. This relationship can approximately follow, e.g., an exponential distribution based on empirical data, such as clips130included in sample plan110and the relevancy score functions found in other sample plans110. A difference between the relevancy score function(s) fit to sample plan110and the total relevancy score of sample plan110for each respective number of clips130can be expressed as a “fitting error.” The fitting error can be numerically expressed as a percentage difference between expected total relevancy scores and actual total relevancy scores for sample plan110at a particular number of clips130. In process P7, modules240can calculate multiple relevancy score functions for a particular number of clips130in sample plan110, some of which may be exponential (i.e., exhibit diminishing returns), while others may be linear (i.e., each successive clip130would contribute substantially the same amount to the total relevancy score of sample plan110. In the example ofFIG. 2, a contribution of 0.1 from candidate clip232as the fiftieth clip may be less than the contribution to relevancy score of approximately 0.12 from the forty-ninth clip. Thus, the fiftieth selected clip would continue to follow a non-linear relevancy score function. In contrast, each clip130added to sample plan110after the second transition point may contribute approximately 0.01 to the total relevancy score of sample plan110, and thus would substantially fit a linear relevancy score function.

At process P8, modules240with comparing functions can compare the relevancy score function for projected sample plan234and previous sample plans110with fewer clips130included with linear and non-linear relevancy score functions. More specifically, modules240can determine whether projected sample plan234substantially fits (i.e., is within the fitting error) of a non-linear relevancy score function or a linear relevancy score function. The fitting error used in the comparison at process P8can vary depending on user constraints and proposed implementations, and in an example embodiment can be an error of approximately 95% (i.e., the measured value is no more than approximately five percent different from the expected value). The coverage fitting error between the calculated relevancy score function and the relevancy scores of previous sample plans110and projected sample plans234in this comparison can be defined by a user and/or independent process. Referring to the example ofFIG. 2, a maximum fitting error for the total relevancy score to “substantially fit” a particular relevancy score function could be approximately ninety-five percent or approximately ninety-eight percent of the target relevancy score. In process P8, modules240of relevancy scoring system220can compare the total relevancy scores of previous s ample plans and110projected sample plan234with linear and non-linear relevancy score functions, e.g., by way of mathematical comparisons. Where the relevancy score plot for projected sample plan234and preceding sample plans110ceases to substantially fit a non-linear relevancy score function (i.e., “no” at process P8), the flow can proceed to process P10where modules240of relevancy scoring system220can remove candidate clip232from the plurality of clips230without candidate clip232being added to sample plan110. Where the total relevancy score of projected sample plan234, when added to the plot of relevancy scores for sample plans110, continues to substantially fit a non-linear relevancy score function (i.e., “yes” at process P8), the flow can proceed to process P9, where relevancy scoring system220adds candidate clip232to sample plan110. Where candidate clip232is added to sample plan110in process P9, modules240of relevancy scoring system220can then remove candidate clip232from plurality of clips230in process P10. In the example ofFIG. 2, a linear relevancy score function may occur at approximately the second transition point, where the quotient of total relevancy score at the second transition point over target relevancy score is approximately equal to 1.77 divided by 1.80, or approximately ninety eight percent

In an embodiment, the process flow can conclude (“done”) where only one candidate clip232or group of candidate clips232is considered for addition to sample plan110. In alternative embodiments, the process flow can return to process P4(i.e., along the phantom process flow from process P10to process P4) where relevancy scoring system220selects a new candidate clip232. The process flow discussed herein can be repeated successively until, e.g., all clips130are removed from plurality of clips230, until the contribution from one or more candidate clips contribute to the total relevancy score of projected sample plan234substantially fits a linear relevancy score function (i.e., the contribution to total relevancy score from successive clips ceases to diminish). In the example ofFIG. 2, the process flow from processes P3through P10repeats approximately two-hundred and fifty-five times until the relevancy score function for sample plan110substantially fits a linear relevancy score function at the second transition point.

Detailed Examples of Relevancy Criteria

Referring toFIGS. 3 and 5together, embodiments of the present disclosure can calculate the total relevancy score of projected sample plan234based on one or more of at least four relevancy criteria applied to clips130and candidate clip232. The sub-processes described herein can occur at process P5where relevancy scoring system220can calculate relevancy scores for projected sample plan234. As is discussed herein, the relevancy score for candidate clip232and projected sample plan234can be based in part on topology type, printing difficulty, and dimensional ratios. The sub-processes of process P5can calculate the contributions from one or more relevancy criteria in the alternative, simultaneously, or sequentially.

First Example

A first example relevancy criterion applied in process P5can include the topology type. The topology types for clips130in IC layout100can span a set of known topologies for CMOS technology. Furthermore, the first clip130added to sample plan110for a particular topology type can have the highest contribution to its relevancy score for this relevancy criteria. Each successive clip130from the same topology type can have a lower contribution to relevancy score from topology type. Thus, dividing plurality of clips230into different topology types can provide a relevancy scoring approach for creating sample plans110which reflect the diversity of clips130in IC layout100and/or sample plan110which provides the target relevancy score.

At process P5-1, modules240of relevancy scoring system220can calculate one or more topology measurements for clips130in projected sample plan234. For example, modules240can calculate a polygon count for IC layout100. The polygon count can be expressed as a total number of shapes for each clip130. A “polygon” or “rectilinear polygon” refers to any polygonal shape where all edges of the polygon meet another edge at a right angle (i.e., at approximately ninety degrees relative to each other in a given plane). Modules240can also calculate an area density for each IC layout100. The area density can represent a percentage of area in projected sample plan232taken up by polygons in each clip130. Modules240can also calculate several diffraction order coefficients representing the effect on groups of clips130from an optical light source. Modules240of relevancy scoring system220can begin to compute a diffraction order coefficient for each of plurality of clips230, e.g., by first representing each clip130as a plurality of rectilinear polygons. To calculate the value of the diffraction order coefficient, the aerial image of IC layout100can be approximated by performing a Fourier Transform upon the rectilinear polygon representation of IC layout100and multiplying the transformed representation of IC layout100with an optical light source wavelength function (i.e., a mathematical function for determining the wavelength(s) from a light source during processing) for IC layout100. The various topology measures calculated in process P5-1may not have significantly greater or lesser relevance to accurately representing IC layout100in sample plan110, but it may be desirable for a particular number of clips130from each diffraction order to be included in the created sample plan110.

In process P5-2, modules240can group or cluster each clip130in projected sample plan232into similarity groups and/or diffraction clusters. Each group or cluster can represent a range of attributes for some clips130. For example, each “similarity group” can correspond to a predetermined range of polygon counts and area densities, which may be calculated according to need or set manually by a user. Each “diffraction cluster” can correspond to a predetermined range of diffraction order coefficients for each clip130. In any event, modules240can define the ranges for each diffraction cluster and/or similarity group in process P5-2after computing the approximated aerial representation of IC layout100, e.g., by applying a “nearest neighbor” sorting algorithm to the group of approximated clips130. The determination in process P5-2can be based on identifying the number of polygons and the feature density (i.e., number of features120(FIG. 1) in clip130divided by a cross-sectional area) of a transformed clip130and associating the polygon number and/or feature density of clips130or candidate clip232with the characteristics of a particular clip topology.

Based on the number of clips130in each cluster and/or group, modules240in process P5-3can then assign topology score(s) to each clip130in a particular cluster and/or group. In an example embodiment, the scores assigned to each diffraction order can be relative to the number of clips from each diffraction order included in sample plan110and/or projected sample plan234, i.e., the first clip added from a given diffraction cluster may receive a score of 0.05, while the next clip added from the same diffraction cluster may receive a score of 0.025, etc. In the example provided inFIG. 2, the changing relevancy score functions for sample plans110after the first and second transition points may represent a situation where clips130included in sample plan110cover most or all of the similarity groups and/or diffraction clusters. In this manner, the relevancy of clip130and/or candidate clip234can be quantified based on whether other clips130in projected sample plan234are obtained from different topology types. Relevancy scoring system220can use topology type as a sole relevancy criterion for each candidate clip232and projected sample plan234, or can use topology type in conjunction with other relevancy criteria discussed herein.

Referring briefly toFIG. 6, a schematic diagram for calculating relevancy score based on topology type is shown. In the example ofFIG. 6, multiple clips130are divided into three topology types (i.e., similarity groups, diffraction clusters, and/or pairs of similarity and diffraction clusters) based on, e.g., the topology measures for each clip130discussed herein. Although each topology type can include any conceivable number of clips130, the example ofFIG. 6shows one clip130in topology types one and three, while three clips130are included in topology type two. Clips130from topology types one and three can contribute topology relevancy scores T1and T3, respectively. Each clip130from topology type two can contribute topology relevancy scores T2a, T2b, T2c. Where the first clip added to projected sample plan234(FIG. 3) from topology type two contributes topology relevancy score T2a, successive topology relevancy score T2bcan be less than topology relevancy score T2a. Further, the next successive topology relevancy score T2ccan be less than both T2aand T2b.

Second Example

Returning toFIGS. 3 and 5, a second example relevancy criterion can include the printing difficulty of each clip130in projected sample plan234. As a relevancy criterion, the printing difficulty can numerically illustrate the relative difficulty of printing each clip130in IC layout100relative to other clips. Two factors, layout dependent effects and critical dimension, can contribute to the printing difficulty of each clip130. The critical dimension and layout dependent effects for candidate clip232, relative to other clips130, can be represented numerically as the printing difficulty for candidate clip232. In process P5-4modules240can calculate the printing difficulty of each clip130of plurality of clips230. The printing difficulty can be calculated, e.g., by subtracting the average value, maximum value, or minimum value of a critical dimension or area of a layout dependent effect from the critical dimension or area of a layout dependent effect for a particular clip. Modules240of relevancy scoring system220can then calculate the square root of the difference to disregard whether a particular clip is above or below the average value, maximum value, etc. In embodiments, modules240can combine multiple printing difficulty values, relative to respective quantities (average critical dimension, maximum critical dimension, minimum critical dimension etc.), into a single representative printing difficulty.

A user may prefer for sample plan110to include, in accurate proportion, clips130of IC layout100which illustrate the range of printing difficulties throughout IC layout100. In process P5-5, modules240of relevancy scoring system220can define printing difficulty groups, e.g., by determining the critical dimension and layout dependent effects of each clip130, ranking each clip130from highest difficulty to lowest difficulty, and dividing the ranked clips into sub-groups based on the number of desired groups, threshold difficulty values, etc. Based on the number of printing difficulty groups, a calculator of modules240in process P5-6can assign difficulty score(s) to each group of printing difficulties and/or clips130therein. In an example embodiment, the scores assigned to each group of printing difficulties can be relative to the number of clips from each printing difficulty included in sample plan110and/or projected sample plan234, i.e., the first clip added from a given difficulty order may receive a score of 0.07, while the next clip added from a given diffraction order may receive a score of 0.02, etc. In some situations, a user may desire for sample plan110to include more clips130of higher difficulty groups than clips130of lower difficulty groups. Modules240of relevancy scoring system in process P5-7can apply one or more “scaling factors” to each clip130in a group of clips with higher printing difficulties relative to the other groups. The scaling factors applied in process P5-7can in the form of multipliers to the difficulty scores assigned in process P5-6. For example, the scaling factors can increase the relevancy of clips130in higher difficulty groups by multiplying the relevant scores from higher difficulty groups by greater factors than clips from lower difficulty groups. Referring to the example ofFIG. 2, the diminishing contribution to relevancy score from each successive candidate clip234can illustrate clips from printing difficulty groups with higher scaling factors added to sample plan110before clips from printing difficulty groups with lower scaling factors being added to sample plan110in later iterations of the process steps discussed herein.

In some implementations, it may be desirable to apply scaling factors to each clip130in plurality of clips230according to the distribution of printing difficulties in IC layout100. For example, using a permuted list of most difficulty printing difficulties may reduce the contribution to relevancy of sample plan110from clips130which correspond to more frequent printing difficulty groups. For the distribution of clips130in sample plan110to more closely resemble the distribution of printing difficulties in IC layout100, modules240of relevancy scoring system220can assign each clip130of plurality of clips230a scaling factor based on the distribution of printing difficulties throughout IC layout100in process P5-7. The probability weights can be assigned to each clip130based on a distribution of printing difficulties for the IC layout100from which clips130originate, or the distribution of printing difficulties in other IC layouts100and/or sample plans110.

In the example provided inFIG. 2, the changing relevancy score functions for sample plans110after the first and second transition points may represent a situation where clips130included in sample plan110cover most or all of the difficulty groups defined in process P5-6. In this manner, the relevancy of clip130and/or candidate clip234can be quantified based on whether other clips130in projected sample plan234are obtained from a diverse group of printing difficulties and/or higher difficulty groups. Relevancy scoring system220can use printing difficulty as the sole relevancy criterion for each candidate clip232and projected sample plan234, or can use printing difficulties in conjunction with other relevancy criteria discussed herein.

Referring briefly toFIG. 7, a schematic diagram for calculating relevancy score(s) based on printing difficulty is shown. In the example ofFIG. 7, multiple clips130are divided into three printing difficulty groups based on, e.g., the critical dimension(s) and LDE(s) of each clip130. Although each printing difficulty group can include any conceivable number of clips130, the example ofFIG. 7shows one clip130in printing difficulty group one, while two clips130are included printing difficulty groups two and three. Each printing difficulty group can be assigned to a corresponding relevancy weight, w1, w2, w3. For example, where printing difficulty group three includes the most difficult clips130for printing, w3can be greater than w1and w2. Clip130from printing difficulty group1one and three can contribute difficulty relevancy score D1. Each clip130from difficulty groups two and three can contribute difficulty relevancy scores D2a, D2b, D3a, D3b. Where the first clip added to projected sample plan234(FIG. 3) from printing difficulty groups two and three contribute difficulty relevancy scores D2a, D3a, successive difficulty relevancy scores D2b, D3b, can be less than difficulty relevancy scores D2a, D3a. Each additional difficulty relevancy score from another clip130in the same difficulty relevancy group can be less than the difficulty relevancy score for previous clips added to projected sample plan234.

Referring now toFIG. 8, a schematic diagram for applying scaling factors to the relevancy score for projected sample plan234(FIG. 3) based on a distribution of printing difficulties is shown. In the example ofFIG. 8, each printing difficulty group of sample plan110(FIGS. 1, 3) can be assigned a probability score between zero and one. Modules240(FIG. 3) of relevancy scoring system220can then multiply the scaling factor for each printing difficulty group by the contribution to relevancy score by each clip130in projected sample plan234(w(D1), w(D2), . . . w(Dn) to calculate a relevancy score D based on the printing difficulty of each clip and the distribution of printing difficulty groups. Although a normal distribution (i.e., bell curve) is shown inFIG. 8, it is understood that each printing difficulty group can be distributed according to any conceivable probability distribution.

Third Example

Returning toFIGS. 3 and 5together, a third type of relevancy criterion can include the dimensional ratio between candidate clip232and/or other clips130in projected sample plan234, and/or the dimensional ratio between clips130in projected sample plan234and clips130in another sample plan110. The dimensional ratio, as a relevancy criterion, can be based on the contents of sample plan110and/or projected sample plan234. Design requirements may specify a desired ratio of dimensions between clips130within IC layout100, e.g., ratios between one-dimensional (“1D”) horizontal, 1D vertical, two-dimensional (“2D”) horizontal, and 2D vertical clips. At process P5-8, modules240of relevancy scoring system220can obtain the ideal (i.e., referenced) dimensional ratio for sample plan110. The ideal dimensional ratio for sample plan110, in some instances, may be particular dimensional ratios between clips130of different IC layouts100. In other instances, the ideal dimensional ratio may be obtained from empirical data, such as dimensional ratios present in past implementations. In any event, modules240of relevancy scoring system220can calculate the dimensional ratio for projected sample plan234in process P5-9. Modules240of relevancy scoring system220can calculate one or more dimensional ratios for projected sample plan234by dividing the number of clips in one category (i.e., 1D horizontal, 1D vertical, 2D horizontal, etc.) by the number of clips in another category.

At process P5-10, modules240of relevancy scoring system220can calculate a contribution to the relevancy score of projected sample plan234from its dimensional ratio(s). In an embodiment, modules240of relevancy scoring system220can compare the calculated dimensional ratios for projected sample plan234with the ideal dimensional ratio(s) for sample plan110obtained in process P5-8. The amount of difference between the calculated dimensional ratio(s) for projected sample plan234and the ideal dimensional ratio(s) for sample plan110can correspond to particular relevancy scores. The addition to relevancy score from dimensional ratios can be inversely proportional to the ideal dimensional ratio(s) obtained in process P5-8and the dimensional ratios for projected sample plan234calculated in process P5-9. To determine the difference between the ideal dimensional ratio(s) and the calculated dimensional ratio(s) in absolute terms, modules240of relevancy scoring system220can perform a scaling operation on calculated difference(s), such as applying a root mean square operation. Relevancy scoring system220can use dimensional ratios as the sole relevancy criterion for each candidate clip232and projected sample plan234, or can use dimensional ratios in conjunction with other relevancy criteria discussed herein. In the example ofFIG. 2, the changing relevancy score functions for sample plans110after the first and second transition points represent a situation where successive projected sample plans234have dimensional ratios which become closer to ideal values. In this situation, adding successive clips130to projected sample plan234may have the same effect on the difference between the dimensional ratio(s) of projected sample plan234relative to a referenced sample plan110.

Referring briefly toFIG. 9, a schematic diagram for calculating the relevancy score for projected sample plan234(FIG. 3) based on dimensional ratio(s) is shown. Each clip130included in projected sample plan234and/or other sample plans110can be divided into 1D horizontal, 1D vertical, 2D vertical, and 2D horizontal clips130based on their dimensions (e.g., lengths, widths), and other attributes such as whether clip130has a greater horizontal dimension than its vertical dimension or vice versa. Relevancy scoring system220(FIG. 3) can count the number of clips in each group and calculate dimensional ratios by dividing the number of clips130in one group by the number of clips130in another group. Modules240(FIG. 3) of relevancy scoring system220can then compare the calculated dimensional ratios with ideal dimensional ratios to determine a relevancy score for projected sample plan234based on the difference between the calculated and ideal values.

Conversion from Relevancy Criteria to Relevancy Score (Processes P5-11Through P5-14)

Returning toFIGS. 3 and 5together, processes for converting the scores for each relevancy criteria discussed herein to a total relevancy score are discussed. Regardless of which relevancy criterion or criteria relevancy scoring system220uses to calculate relevancy scores, the various contributed criteria can be further processed and scaled before relevancy scoring system220determines whether to add candidate clip232to sample plan110. In process P5-11, modules240of relevancy scoring system220can multiply the relevancy score from one or more applicable relevancy criteria by a corresponding relevancy weight. For example, where the contribution to relevancy scores from topology type (i.e., from process P5-3) and dimensional ratios (i.e., from process P5-10) carry different orders of magnitude, one of these relevancy criteria can be multiplied by a relevancy weight which brings each contribution to relevancy score to the same order of magnitude (e.g., a multiplier of one hundred, one thousand, one million, etc.). In other situations, the topology type of each clip130in projected sample plan234may be of greater relevance than other criteria, such as printing difficulty. Here, the relevancy weight for topology type in this situation can be arbitrarily set to a multiplying factor which emphasizes the contribution to relevancy score from topology type over other attributes. Where no relevancy weights are desired, the contribution to relevancy score from each criterion can simply be multiplied by a relevancy weight of one, or relevancy scoring system220can bypass process P5-11altogether. It is also understood that a user may define and/or modify the relevancy weights applied in process P5-11to suit particular applications.

In process P5-12, modules240of relevancy scoring system220can compute the total relevancy score for projected sample plan234based on one or more of the criteria discussed herein. More specifically, modules240can compute the arithmetic sum for the contribution of each relevancy criterion, with each contribution being the product of a score for the relevancy criterion (e.g., calculated or assigned in processes P5-3, P5-6, P5-10, and/or P5-12) and their corresponding relevancy weights. The calculated total relevancy score can illustrate the relevancy score of projected sample plan234with candidate clip232therein, in addition to any other clips130previously added to sample plan110and represented in projected sample plan234.

In process P5-13, modules240of relevancy scoring system220can multiply the total relevancy score calculated in process P-12by a scaling factor. The scaling factor can be a further multiplier for, e.g., bringing the value of the total relevancy score closer to the value or order of magnitude of a total relevancy score for a different projected sample plan234, sample plans110for other IC layouts, etc.

It is understood that the method illustrated inFIG. 4and further detailed inFIG. 5can each correspond to several successive implementations, e.g., in which successive projected sample plans234are created with successive candidate clips232therein. Thus, the total relevancy score calculated in process P5can be one of many total relevancy scores each corresponding to an earlier created sample plan110for the same IC layout100. To mitigate the effects of bias on the calculated total relevancy scores, a calculator of modules240of relevancy scoring system220can calculate set relevancy score of the first projected sample plan, with candidate clips232thereof providing the greatest contribution to total relevancy score, as being equal to one. Modules240can then scale (e.g., through multiplication by fractions of one) the value of the total relevancy score calculated in process P5-12to normalize the resulting relevancy score(s) in process P-14. These processes can generally be described as mathematical normalization, and among other benefits can allow the relevancy score for candidate clip232to be calculated in terms of a normalized value, and for the relevancy score functions calculated in process P7to also be expressed as a mathematically normalized value. In the example ofFIG. 2, projected sample plan234with only one clip thereon can have a total relevancy score of one, with each successive projected sample plan contributing a relevancy score of less than one, in successively lesser amounts.

Methods according to the present disclosure can calibrate OPC for one or more IC layouts100by creating sample plan110with a minimal number of clips130therein. Modules240, following the various processes described herein, can generate an OPC model which includes sample plan110with the minimal number of clips. Where applicable, fabricator250can fabricate one or more ICs which include IC layout100using the OPC model with sample plan110therein. Thus, embodiments of the present disclosure can fabricate one or more ICs after automatically creating sample plan110with a minimal number of clips and generating an OPC model with sample plan110included.

Technical effects of the present disclosure can include, without limitation, the ability to automatically select a minimal number of clips130to include in sample plan110which adequately represent the attributes of IC layout100. In addition, technical effects of the present disclosure include automatically generating sample plans110which include fewer than all clips130of IC layout100, and thus provide a set of empirical data which increases the speed of OPC processes performed for particular IC layouts100. Embodiments of the present disclosure can thus reduce the need for a review of sample plan by human users, thereby increasing the pace at which any irregularities and distortions within an IC layout can be corrected automatically. Furthermore, embodiments of the present disclosure automatically select a minimal number of clips130to include in sample plan110by identifying a transition point where the a relevancy score function for successive clips130added to sample plan110changes from a non-linear (e.g., exponential with diminishing returns) to a linear score function (e.g., each successive clip contributes the same amount to total relevancy score).

Alternative Embodiments and Implementations

As used herein, the term “configured,” “configured to” and/or “configured for” can refer to specific-purpose features of the component so described. For example, a system or device configured to perform a function can include a computer system or computing device programmed or otherwise modified to perform that specific function. In other cases, program code stored on a computer-readable medium (e.g., storage medium), can be configured to cause at least one computing device to perform functions when that program code is executed on that computing device. In these cases, the arrangement of the program code triggers specific functions in the computing device upon execution. In other examples, a device configured to interact with and/or act upon other components can be specifically shaped and/or designed to effectively interact with and/or act upon those components. In some such circumstances, the device is configured to interact with another component because at least a portion of its shape complements at least a portion of the shape of that other component. In some circumstances, at least a portion of the device is sized to interact with at least a portion of that other component. The physical relationship (e.g., complementary, size-coincident, etc.) between the device and the other component can aid in performing a function, for example, displacement of one or more of the device or other component, engagement of one or more of the device or other component, etc.