Abstract: Some embodiments use a machine-trained network during routing to provide the router with sufficient information to improve the quality of routes generated by a router. This machine-trained network in some embodiments is referred to as the “digital twin” of a lengthy design and/or manufacturing process that produces the design of an IC layout and/or manufactures an IC based on a designed IC layout. The digital twin in some embodiments provides information regarding parasitics, regarding redundant vias for insertion or regarding complexity of subsequent manufacturing processes used to manufacture an IC based on the IC design layout.

Abstract: Some embodiments use a machine-trained network during routing to provide the router with sufficient information to improve the quality of routes generated by a router. This machine-trained network in some embodiments is referred to as the “digital twin” of a lengthy design and/or manufacturing process that produces the design of an IC layout and/or manufactures an IC based on a designed IC layout. The digital twin in some embodiments provides information regarding parasitics, regarding redundant vias for insertion or regarding complexity of subsequent manufacturing processes used to manufacture an IC based on the IC design layout.

Abstract: Some embodiments use a machine-trained network during routing to provide the router with sufficient information to improve the quality of routes generated by a router. This machine-trained network in some embodiments is referred to as the “digital twin” of a lengthy design and/or manufacturing process that produces the design of an IC layout and/or manufactures an IC based on a designed IC layout. The digital twin in some embodiments provides information regarding parasitics, regarding redundant vias for insertion or regarding complexity of subsequent manufacturing processes used to manufacture an IC based on the IC design layout.

Abstract: Some embodiments use a machine-trained network during routing to provide the router with sufficient information to improve the quality of routes generated by a router. This machine-trained network in some embodiments is referred to as the “digital twin” of a lengthy design and/or manufacturing process that produces the design of an IC layout and/or manufactures an IC based on a designed IC layout. The digital twin in some embodiments provides information regarding parasitics, regarding redundant vias for insertion or regarding complexity of subsequent manufacturing processes used to manufacture an IC based on the IC design layout.

Abstract: Some embodiments use a machine-trained network during routing to provide the router with sufficient information to improve the quality of routes generated by a router. This machine-trained network in some embodiments is referred to as the “digital twin” of a lengthy design and/or manufacturing process that produces the design of an IC layout and/or manufactures an IC based on a designed IC layout. The digital twin in some embodiments provides information regarding parasitics, regarding redundant vias for insertion or regarding complexity of subsequent manufacturing processes used to manufacture an IC based on the IC design layout.

Abstract: Some embodiments use a machine-trained network during routing to provide the router with sufficient information to improve the quality of routes generated by a router. This machine-trained network in some embodiments is referred to as the “digital twin” of a lengthy design and/or manufacturing process that produces the design of an IC layout and/or manufactures an IC based on a designed IC layout. The digital twin in some embodiments provides information regarding parasitics, regarding redundant vias for insertion or regarding complexity of subsequent manufacturing processes used to manufacture an IC based on the IC design layout.

Abstract: Some embodiments provide a method for computing and displaying of minimum overlap for semiconductor layer interfaces, such as metal-via and metal-contact. The method leverages a machine-trained network (e.g., a trained neural network) to quickly, but accurately, infer the contours for the manufactured shapes across a range of process variations. The method also models the semiconductor process manufacturing layer-to-layer misalignment. The combined set of information (from the machine-trained network and from the modeling) is used by the method to compute the minimum overlap shapes at multiple layer interfaces. The method in some embodiments then uses the minimum overlap shapes to obtain an accurate calculation of the via or contact resistance.

Abstract: Some embodiments provide a method for computing and displaying of minimum overlap for semiconductor layer interfaces, such as metal-via and metal-contact. The method leverages a machine-trained network (e.g., a trained neural network) to quickly, but accurately, infer the contours for the manufactured shapes across a range of process variations. The method also models the semiconductor process manufacturing layer-to-layer misalignment. The combined set of information (from the machine-trained network and from the modeling) is used by the method to compute the minimum overlap shapes at multiple layer interfaces. The method in some embodiments then uses the minimum overlap shapes to obtain an accurate calculation of the via or contact resistance.

Abstract: Some embodiments provide a method for computing and displaying of minimum overlap for semiconductor layer interfaces, such as metal-via and metal-contact. The method leverages a machine-trained network (e.g., a trained neural network) to quickly, but accurately, infer the contours for the manufactured shapes across a range of process variations. The method also models the semiconductor process manufacturing layer-to-layer misalignment. The combined set of information (from the machine-trained network and from the modeling) is used by the method to compute the minimum overlap shapes at multiple layer interfaces. The method in some embodiments then uses the minimum overlap shapes to obtain an accurate calculation of the via or contact resistance.

Abstract: Some embodiments provide a method for computing and displaying of minimum overlap for semiconductor layer interfaces, such as metal-via and metal-contact. The method leverages a machine-trained network (e.g., a trained neural network) to quickly, but accurately, infer the contours for the manufactured shapes across a range of process variations. The method also models the semiconductor process manufacturing layer-to-layer misalignment. The combined set of information (from the machine-trained network and from the modeling) is used by the method to compute the minimum overlap shapes at multiple layer interfaces. The method in some embodiments then uses the minimum overlap shapes to obtain an accurate calculation of the via or contact resistance.

Abstract: A method for manufacturing-aware editing of circuit layouts driven by predictions regarding predicted manufactured wafer contours generated by a machine-trained network. The method allows for fast edit loops in interactive editing timeframes, in which the predicted manufactured wafer contours corresponding to design edits are presented within seconds of the edits themselves. In some embodiments, the wafer contours take mask OPC/ILT and lithography effects into account, as determined by the machine trained network.

Abstract: A method for manufacturing-aware editing of circuit layouts driven by predictions regarding predicted manufactured wafer contours generated by a machine-trained network. The method allows for fast edit loops in interactive editing timeframes, in which the predicted manufactured wafer contours corresponding to design edits are presented within seconds of the edits themselves. In some embodiments, the wafer contours take mask OPC/ILT and lithography effects into account, as determined by the machine trained network.

Abstract: A method for manufacturing-aware editing of circuit layouts driven by predictions regarding predicted manufactured wafer contours generated by a machine-trained network. The method allows for fast edit loops in interactive editing timeframes, in which the predicted manufactured wafer contours corresponding to design edits are presented within seconds of the edits themselves. In some embodiments, the wafer contours take mask OPC/ILT and lithography effects into account, as determined by the machine trained network.

Abstract: A method for manufacturing-aware editing of circuit layouts driven by predictions regarding predicted manufactured wafer contours generated by a machine-trained network. The method allows for fast edit loops in interactive editing timeframes, in which the predicted manufactured wafer contours corresponding to design edits are presented within seconds of the edits themselves. In some embodiments, the wafer contours take mask OPC/ILT and lithography effects into account, as determined by the machine trained network.

Abstract: On-chip data transport network architectural units are assigned preferred placement locations based on architecture-level constraints. The preferred placement locations are used to generate placement constraints for a place and route tool. The placement constraints are applied to cells that are synthesized from each architectural unit. Constraints are blockages, fences, regions, and guides. Preferred placement locations are mapped to grid elements. Each grid elements defines a cell placement constraint.

Abstract: On-chip data transport network architectural units are assigned preferred placement locations based on architecture-level constraints. The preferred placement locations are used to generate placement constraints for a place and route tool. The placement constraints are applied to cells that are synthesized from each architectural unit. Constraints are blockages, fences, regions, and guides. Preferred placement locations are mapped to grid elements. Each grid elements defines a cell placement constraint.