Abstract: Techniques relating to synthesizing masks for use in manufacturing a semiconductor device are disclosed. A plurality of training masks, for a machine learning (ML) model, are generated by synthesizing one or more polygons, relating to a design pattern for the semiconductor device, using Inverse Lithography Technology (ILT) (106). The ML model is trained using both the plurality of training masks generated using ILT, and the design pattern for the semiconductor device, as inputs (108). The trained ML model is configured to synthesize one or more masks, for use in manufacturing the semiconductor device, based on the design pattern (110).

Abstract: A system performs mask rule checks (MRC) for curvilinear shapes. The width of a curvilinear shape is different along different parts of the shape. A medial axis for a curvilinear shape is determined. The medial axis is trimmed to exclude portions that are within a threshold distance from corners or too far from edges. The trimmed medial axis is used to perform width checks for mask rules. The system generates medial axis between geometric shapes and uses it to determine whether two geometric shapes are at least a threshold distance apart. The system performs acute angle checks for sharp corners. The system determines angles using lines drawn from vertices to end points on the boundary of the shape that are at a threshold distance. These angles are used for checking acute angle mask rule violations.

Abstract: A system performs distributed or parallel pattern extraction and clustering for pattern classification of large layouts of electronic circuits. The system identifies circuit patterns with a layout representation. The system encodes the circuit patterns using a neural network based autoencoder to generate encoded circuit patterns that can be stored efficiently. The system clusters the encoded circuit patterns into an arbitrary number of clusters based upon a high degree of similarity. The clusters of circuit patterns may be used for training and evaluation of machine learning based models.

Abstract: A system performs mask rule checks (MRC) for curvilinear shapes. The width of a curvilinear shape is different along different parts of the shape. A medial axis for a curvilinear shape is determined. The medial axis is trimmed to exclude portions that are within a threshold distance from corners or too far from edges. The trimmed medial axis is used to perform width checks for mask rules. The system generates medial axis between geometric shapes and uses it to determine whether two geometric shapes are at least a threshold distance apart. The system performs acute angle checks for sharp corners. The system determines angles using lines drawn from vertices to end points on the boundary of the shape that are at a threshold distance. These angles are used for checking acute angle mask rule violations.

Abstract: A system performs mask rule checks (MRC) for curvilinear shapes. The width of a curvilinear shape is different along different parts of the shape. A medial axis for a curvilinear shape is determined. The medial axis is trimmed to exclude portions that are within a threshold distance from corners or too far from edges. The trimmed medial axis is used to perform width checks for mask rules. The system generates medial axis between geometric shapes and uses it to determine whether two geometric shapes are at least a threshold distance apart. The system performs acute angle checks for sharp corners. The system determines angles using lines drawn from vertices to end points on the boundary of the shape that are at a threshold distance. These angles are used for checking acute angle mask rule violations.

Abstract: A system performs mask rule checks (MRC) for curvilinear shapes. The width of a curvilinear shape is different along different parts of the shape. A medial axis for a curvilinear shape is determined. The medial axis is trimmed to exclude portions that are within a threshold distance from corners or too far from edges. The trimmed medial axis is used to perform width checks for mask rules. The system generates medial axis between geometric shapes and uses it to determine whether two geometric shapes are at least a threshold distance apart. The system performs acute angle checks for sharp corners. The system determines angles using lines drawn from vertices to end points on the boundary of the shape that are at a threshold distance. These angles are used for checking acute angle mask rule violations.

Abstract: A system generates a mask for a circuit design while enforcing symmetry and consistency across random areas of the mask. The system builds a mask solutions database mapping circuit patterns to mask patterns. The system uses the mask solutions database to replace circuit patterns of the circuit design with mask patterns. The system identifies properties in circuit patterns of the circuit design and enforces the same property in the corresponding mask patterns. Examples of properties enforced include symmetry within circuit patterns and similarity across circuit patterns. The system combines mask patterns in different regions of the circuit and resolves conflicts that occur when there are multiple masks within a region.

Abstract: A system generates a mask for a circuit design while enforcing symmetry and consistency across random areas of the mask. The system builds a mask solutions database mapping circuit patterns to mask patterns. The system uses the mask solutions database to replace circuit patterns of the circuit design with mask patterns. The system identifies properties in circuit patterns of the circuit design and enforces the same property in the corresponding mask patterns. Examples of properties enforced include symmetry within circuit patterns and similarity across circuit patterns. The system combines mask patterns in different regions of the circuit and resolves conflicts that occur when there are multiple masks within a region.

Abstract: A system performs mask rule checks (MRC) for curvilinear shapes. The width of a curvilinear shape is different along different parts of the shape. A medial axis for a curvilinear shape is determined. The medial axis is trimmed to exclude portions that are within a threshold distance from corners or too far from edges. The trimmed medial axis is used to perform width checks for mask rules. The system generates medial axis between geometric shapes and uses it to determine whether two geometric shapes are at least a threshold distance apart. The system performs acute angle checks for sharp corners. The system determines angles using lines drawn from vertices to end points on the boundary of the shape that are at a threshold distance. These angles are used for checking acute angle mask rule violations.

Abstract: Techniques relating to synthesizing masks for use in manufacturing a semiconductor device are disclosed. A plurality of training masks, for a machine learning (ML) model, are generated by synthesizing one or more polygons, relating to a design pattern for the semiconductor device, using Inverse Lithography Technology (ILT) (106). The ML model is trained using both the plurality of training masks generated using ILT, and the design pattern for the semiconductor device, as inputs (108). The trained ML model is configured to synthesize one or more masks, for use in manufacturing the semiconductor device, based on the design pattern (110).

Abstract: A system uses machine learning models, such as neural networks for generating mask design from a circuit design. The machine learning models have inputs and outputs which are localized to a small region of the circuit design. The machine learning model takes as input features describing the circuit design in the neighborhood of a location and generates an offset distance as output. The system uses the offset distance to generate features of the mask design, for example, main features or assist features corresponding to a circuit design polygon. The system may use the offset distance for target optimization by modifying the circuit design polygon to obtain a circuit design polygon that has improved manufacturability.

Abstract: A system generates a mask for a circuit design while enforcing symmetry and consistency across random areas of the mask. The system builds a mask solutions database mapping circuit patterns to mask patterns. The system uses the mask solutions database to replace circuit patterns of the circuit design with mask patterns. The system identifies properties in circuit patterns of the circuit design and enforces the same property in the corresponding mask patterns. Examples of properties enforced include symmetry within circuit patterns and similarity across circuit patterns. The system combines mask patterns in different regions of the circuit and resolves conflicts that occur when there are multiple masks within a region.

Abstract: A system performs mask rule checks (MRC) for curvilinear shapes. The width of a curvilinear shape is different along different parts of the shape. A medial axis for a curvilinear shape is determined. The medial axis is trimmed to exclude portions that are within a threshold distance from corners or too far from edges. The trimmed medial axis is used to perform width checks for mask rules. The system generates medial axis between geometric shapes and uses it to determine whether two geometric shapes are at least a threshold distance apart. The system performs acute angle checks for sharp corners. The system determines angles using lines drawn from vertices to end points on the boundary of the shape that are at a threshold distance. These angles are used for checking acute angle mask rule violations.

Abstract: A system performs distributed or parallel pattern extraction and clustering for pattern classification of large layouts of electronic circuits. The system identifies circuit patterns with a layout representation. The system encodes the circuit patterns using a neural network based autoencoder to generate encoded circuit patterns that can be stored efficiently. The system clusters the encoded circuit patterns into an arbitrary number of clusters based upon a high degree of similarity. The clusters of circuit patterns may be used for training and evaluation of machine learning based models.

Abstract: A matrix is produced for a semiconductor design. Interactions between mask edges in forming semiconductor shapes are determined and a graph created that shows those interactions. The graph is then partitioned into groups using a coloring algorithm, with each group representing one or more non-interacting mask edges. A lithography simulation is performed for each group, with the edges of that group perturbed, but the edges of other groups unmoved. The partial derivatives are calculated for the edges of a group based on the simulation with those edges perturbed, and used to populate locations in a Jacobian matrix. The Jacobian matrix is then used to solve an Optical Proximity Correction (OPC) problem by finding a mask edge correction vector for a given wafer targeting error vector.

Abstract: A design layout for a semiconductor chip includes information on shapes desired to be fabricated. Clusters of photolithographic exposure “shots” are generated and subject to a measure of shot density to approximate a mask shape that generates the desired fabricated shapes when exposed during wafer fabrication. A simulation is run on the clusters of shots to estimate the resulting fabrication shapes that the clusters of shots create. The clusters of shots are modified to align the estimated fabrication shapes more closely with desired fabrication shapes. The process of simulating and modifying the shots is iterative, repeating until the estimated fabrication shapes are within a desired error difference of the planned fabrication shape.

Abstract: A design layout for a semiconductor chip includes information on shapes desired to be fabricated. Clusters of photolithographic exposure “shots” are generated and subject to a measure of shot density to approximate a mask shape that generates the desired fabricated shapes when exposed during wafer fabrication. A simulation is run on the clusters of shots to estimate the resulting fabrication shapes that the clusters of shots create. The clusters of shots are modified to align the estimated fabrication shapes more closely with desired fabrication shapes. The process of simulating and modifying the shots is iterative, repeating until the estimated fabrication shapes are within a desired error difference of the planned fabrication shape.

Abstract: A design layout for a semiconductor chip includes information on shapes desired to be fabricated. Clusters of photolithographic exposure “shots” are generated and subject to a measure of shot density to approximate a mask shape that generates the desired fabricated shapes when exposed during wafer fabrication. A simulation is run on the clusters of shots to estimate the resulting fabrication shapes that the clusters of shots create. The clusters of shots are modified to align the estimated fabrication shapes more closely with desired fabrication shapes. The process of simulating and modifying the shots is iterative, repeating until the estimated fabrication shapes are within a desired error difference of the planned fabrication shape.

Abstract: A matrix is produced for a semiconductor design. Interactions between mask edges in forming semiconductor shapes are determined and a graph created that shows those interactions. The graph is then partitioned into groups using a coloring algorithm, with each group representing one or more non-interacting mask edges. A lithography simulation is performed for each group, with the edges of that group perturbed, but the edges of other groups unmoved. The partial derivatives are calculated for the edges of a group based on the simulation with those edges perturbed, and used to populate locations in a Jacobian matrix. The Jacobian matrix is then used to solve an Optical Proximity Correction (OPC) problem by finding a mask edge correction vector for a given wafer targeting error vector.

Abstract: A design layout for a semiconductor chip includes information on shapes desired to be fabricated. Clusters of photolithographic exposure “shots” are generated and subject to a measure of shot density to approximate a mask shape that generates the desired fabricated shapes when exposed during wafer fabrication. A simulation is run on the clusters of shots to estimate the resulting fabrication shapes that the clusters of shots create. The clusters of shots are modified to align the estimated fabrication shapes more closely with desired fabrication shapes. The process of simulating and modifying the shots is iterative, repeating until the estimated fabrication shapes are within a desired error difference of the planned fabrication shape.