Abstract: One method implementation disclosed includes detecting matching leaf cells that have functionally identical designs (optionally, similar designs) and assigning matching names for the matching leaf cells to replace original, non-matching names. Optionally, digests can be calculated for the leaf cells and used to detect similarities and/or differences. The matching names are propagated to at least some higher-level cells in the hierarchical design, in place of the original names. The method can further include calculating digests for at least some of the higher level cells after the propagating of the matching names into the higher level cells. Various design matching technologies can be used in combination with cell renaming and new name propagation, not limited to use of digests. Dependency chains can be calculated to improve propagation of names through the hierarchy.

Abstract: One method implementation disclosed includes detecting matching leaf cells that have functionally identical designs (optionally, similar designs) and assigning matching names for the matching leaf cells to replace original, non-matching names. Optionally, digests can be calculated for the leaf cells and used to detect similarities and/or differences. The matching names are propagated to at least some higher-level cells in the hierarchical design, in place of the original names. The method can further include calculating digests for at least some of the higher level cells after the propagating of the matching names into the higher level cells. Various design matching technologies can be used in combination with cell renaming and new name propagation, not limited to use of digests. Dependency chains can be calculated to improve propagation of names through the hierarchy.

Abstract: One method implementation disclosed includes detecting matching leaf cells that are functionally identical (optionally, functionally similar) and assigning matching names for the matching leaf cells to replace original, non-matching names. Optionally, digests can be calculated for the leaf cells and used to detect similarities and/or differences. The matching names are propagated to at least some higher-level cells in the hierarchical design, in place of the original names. The method can further include calculating digests for at least some of the higher level cells after the propagating of the matching names into the higher level cells. Various design matching technologies can be used in combination with cell renaming and new name propagation, not limited to use of digests. Dependency chains can be calculated to improve propagation of names through the hierarchy.

Abstract: One method implementation disclosed includes detecting matching leaf cells that are functionally identical (optionally, functionally similar) and assigning matching names for the matching leaf cells to replace original, non-matching names. Optionally, digests can be calculated for the leaf cells and used to detect similarities and/or differences. The matching names are propagated to at least some higher-level cells in the hierarchical design, in place of the original names. The method can further include calculating digests for at least some of the higher level cells after the propagating of the matching names into the higher level cells. Various design matching technologies can be used in combination with cell renaming and new name propagation, not limited to use of digests. Dependency chains can be calculated to improve propagation of names through the hierarchy.

Abstract: The technology disclosed relates to granular analysis of design data used to prepare chip designs for manufacturing and to identification of similarities and differences among parts of design data files. In particular, it relates to parsing data and organizing into canonical forms, digesting the canonical forms, and comparing digests of design data from different sources, such as designs and libraries of design templates. Organizing the design data into canonical forms generally reduces the sensitivity of data analysis to variations in data that have no functional impact on the design. The details of the granular analysis vary among design languages used to represent aspects of a design. For various design languages, granular analysis includes partitioning design files by header/cell portions, by separate handling of comments, by functionally significant/non-significant data, by whitespace/non-whitespace, and by layer within a unit of design data.

Abstract: The technology disclosed relates to granular analysis of design data used to prepare chip designs for manufacturing and to identification of similarities and differences among parts of design data files. In particular, it relates to parsing data and organizing into canonical forms, digesting the canonical forms, and comparing digests of design data from different sources, such as designs and libraries of design templates. Organizing the design data into canonical forms generally reduces the sensitivity of data analysis to variations in data that have no functional impact on the design. The details of the granular analysis vary among design languages used to represent aspects of a design. For various design languages, granular analysis includes partitioning design files by header/cell portions, by separate handling of comments, by functionally significant/non-significant data, by whitespace/non-whitespace, and by layer within a unit of design data.

Abstract: The technology disclosed relates to granular analysis of design data used to prepare chip designs for manufacturing and to identification of similarities and differences among parts of design data files. In particular, it relates to parsing data and organizing into canonical forms, digesting the canonical forms, and comparing digests of design data from different sources, such as designs and libraries of design templates. Organizing the design data into canonical forms generally reduces the sensitivity of data analysis to variations in data that have no functional impact on the design. The details of the granular analysis vary among design languages used to represent aspects of a design. For various design languages, granular analysis includes partitioning design files by header/cell portions, by separate handling of comments, by functionally significant/non-significant data, by whitespace/non-whitespace, and by layer within a unit of design data.

Abstract: The technology disclosed relates to granular analysis of design data used to prepare chip designs for manufacturing and to identification of similarities and differences among parts of design data files. In particular, it relates to parsing data and organizing into canonical forms, digesting the canonical forms, and comparing digests of design data from different sources, such as designs and libraries of design templates. Organizing the design data into canonical forms generally reduces the sensitivity of data analysis to variations in data that have no functional impact on the design. The details of the granular analysis vary among design languages used to represent aspects of a design. For various design languages, granular analysis includes partitioning design files by header/cell portions, by separate handling of comments, by functionally significant/non-significant data, by whitespace/non-whitespace, and by layer within a unit of design data.