Abstract: Some embodiments of the invention provide an integrated circuit (IC) that has a novel non-preferred direction (NPD) wiring architecture. In some embodiments, the IC includes a substrate and multiple wiring layers, which include a first set of one or more wiring layers with no preferred wiring directions, and a second set of one or more wiring layers with preferred wiring directions. In some embodiments, the first set of wiring layers includes the third and fourth wiring layers, while the second set of wiring layers includes the fifth and higher metal layers with successive neighboring layers having different (e.g., alternating) preferred wiring directions. The first set of wiring layers in other embodiments includes the third wiring layer but not the fourth wiring layer, which in these embodiments has a preferred wiring direction.

Abstract: Some embodiments of the invention provide an integrated circuit (IC) that has a novel non-preferred direction (NPD) wiring architecture. In some embodiments, the IC includes a substrate and multiple wiring layers, which include a first set of one or more wiring layers with no preferred wiring directions, and a second set of one or more wiring layers with preferred wiring directions. In some embodiments, the first set of wiring layers includes the third and fourth wiring layers, while the second set of wiring layers includes the fifth and higher metal layers with successive neighboring layers having different (e.g., alternating) preferred wiring directions. The first set of wiring layers in other embodiments includes the third wiring layer but not the fourth wiring layer, which in these embodiments has a preferred wiring direction.

Abstract: Some embodiments of the invention provide an integrated circuit (IC) that has a novel non-preferred direction (NPD) wiring architecture. In some embodiments, the IC includes a substrate and multiple wiring layers, which include a first set of one or more wiring layers with no preferred wiring directions, and a second set of one or more wiring layers with preferred wiring directions. In some embodiments, the first set of wiring layers includes the third and fourth wiring layers, while the second set of wiring layers includes the fifth and higher metal layers with successive neighboring layers having different (e.g., alternating) preferred wiring directions. The first set of wiring layers in other embodiments includes the third wiring layer but not the fourth wiring layer, which in these embodiments has a preferred wiring direction.

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 of the invention provide an integrated circuit (IC) that has a novel non-preferred direction (NPD) wiring architecture. In some embodiments, the IC includes a substrate and multiple wiring layers, which include a first set of one or more wiring layers with no preferred wiring directions, and a second set of one or more wiring layers with preferred wiring directions. In some embodiments, the first set of wiring layers includes the third and fourth wiring layers, while the second set of wiring layers includes the fifth and higher metal layers with successive neighboring layers having different (e.g., alternating) preferred wiring directions. The first set of wiring layers in other embodiments includes the third wiring layer but not the fourth wiring layer, which in these embodiments has a preferred wiring direction.

Abstract: Some embodiments provide a method for calculating parasitic parameters for a pattern to be manufactured on an integrated circuit (IC) substrate. The method receives a definition of a wire structure as input. The method rasterizes the wire structure (e.g., produces pixel-based definition of the wire structure) to produce several images. Before rasterizing the wire structure, the method in some embodiments decomposes the wire structure into several components (e.g., several wires, wire segments or wire structure portions), which it then individually rasterizes. The method then uses the images as inputs to a neural network, which then calculates parasitic parameters associated with the wire structure. In some embodiments, the parasitic parameters include unwanted parasitic capacitance effects exerted on the wire structure. Conjunctively, or alternatively, these parameters include unwanted parasitic resistance and/or inductance effects on the wire structure.

Abstract: Methods and systems involve a plurality of patterns, each pattern in the plurality of patterns comprising a plurality of edges. Methods and systems also involve determining a neighborhood open area density for the plurality of patterns; determining a geometric loading effect correction, wherein the geometric loading effect correction comprises a calculated offset from an edge of a pattern in the plurality of patterns, and wherein the calculated offset is determined using the neighborhood open area density; and adjusting the edge of the pattern in the plurality of patterns using the geometric loading effect correction.

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: 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 of the invention provide an integrated circuit (IC) that has a novel non-preferred direction (NPD) wiring architecture. In some embodiments, the IC includes a substrate and multiple wiring layers, which include a first set of one or more wiring layers with no preferred wiring directions, and a second set of one or more wiring layers with preferred wiring directions. In some embodiments, the first set of wiring layers includes the third and fourth wiring layers, while the second set of wiring layers includes the fifth and higher metal layers with successive neighboring layers having different (e.g., alternating) preferred wiring directions. The first set of wiring layers in other embodiments includes the third wiring layer but not the fourth wiring layer, which in these embodiments has a preferred wiring direction.

Abstract: Some embodiments provide a method for calculating parasitic parameters for a pattern to be manufactured on an integrated circuit (IC) substrate. The method receives a definition of a wire structure as input. The method rasterizes the wire structure (e.g., produces pixel-based definition of the wire structure) to produce several images. Before rasterizing the wire structure, the method in some embodiments decomposes the wire structure into several components (e.g., several wires, wire segments or wire structure portions), which it then individually rasterizes. The method then uses the images as inputs to a neural network, which then calculates parasitic parameters associated with the wire structure. In some embodiments, the parasitic parameters include unwanted parasitic capacitance effects exerted on the wire structure. Conjunctively, or alternatively, these parameters include unwanted parasitic resistance and/or inductance effects on the wire structure.

Abstract: Some embodiments provide a method for calculating parasitic parameters for a pattern to be manufactured on an integrated circuit (IC) substrate. The method receives a definition of a wire structure as input. The method rasterizes the wire structure (e.g., produces pixel-based definition of the wire structure) to produce several images. Before rasterizing the wire structure, the method in some embodiments decomposes the wire structure into several components (e.g., several wires, wire segments or wire structure portions), which it then individually rasterizes. The method then uses the images as inputs to a neural network, which then calculates parasitic parameters associated with the wire structure. In some embodiments, the parasitic parameters include unwanted parasitic capacitance effects exerted on the wire structure. Conjunctively, or alternatively, these parameters include unwanted parasitic resistance and/or inductance effects on the wire structure.

Abstract: A method for manufacturing a semiconductor chip involves generating exposure instructions from a Quantized Tone Mask (QTM) using charged particle beam technology, wherein the QTM is a 2-tone mask translated from a Continuous Tone Mask (CTM) using a cost function for mask value regularization.

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: Systems for determining a scanner aerial image from a mask inspection image include a computer processor configured to receive the mask inspection image, wherein the mask inspection image has been generated by a mask inspection machine; and a computer processor configured to generate the scanner aerial image from the mask inspection image using a neural network. Systems include a computer processor configured to train a neural network with a set of images, such as with a simulated scanner aerial image and another image selected from a simulated mask inspection image, a simulated Critical Dimension Scanning Electron Microscope (CD-SEM) image, a simulated scanner emulator image and a simulated actinic mask inspection image.

Abstract: Methods for reticle enhancement technology (RET) for use with variable shaped beam (VSB) lithography include inputting a substrate design, wherein a low-pass filter is applied to the substrate design to form a target wafer pattern. An initial mask pattern is determined from the target wafer pattern. An initial set of VSB shots is determined based on the initial mask pattern. A substrate pattern is calculated from a simulated mask pattern calculated with the initial set of VSB shots. The target wafer pattern is compared with the substrate pattern, and the initial set of VSB shots is adjusted until the substrate pattern and the target wafer pattern are within a predetermined tolerance. The adjusting of the initial set of VSB shots creates an adjusted set of VSB shots.

Abstract: Methods for reticle enhancement technology include inputting a target wafer pattern, the target wafer pattern spanning an entire design area, and iterating a proposed mask for the entire design area until the proposed mask meets criteria towards producing the target wafer pattern. Each iteration includes calculating a predicted wafer pattern from the proposed mask. The calculating comprises calculating a cost and derivative data, the cost and the derivative data being based on comparing the predicted wafer pattern to the target wafer pattern. The cost further comprises specifications for mask manufacturability.

Abstract: Some embodiments provide a method for calculating parasitic parameters for a pattern to be manufactured on an integrated circuit (IC) substrate. The method receives a definition of a wire structure as input. The method rasterizes the wire structure (e.g., produces pixel-based definition of the wire structure) to produce several images. Before rasterizing the wire structure, the method in some embodiments decomposes the wire structure into several components (e.g., several wires, wire segments or wire structure portions), which it then individually rasterizes. The method then uses the images as inputs to a neural network, which then calculates parasitic parameters associated with the wire structure. In some embodiments, the parasitic parameters include unwanted parasitic capacitance effects exerted on the wire structure. Conjunctively, or alternatively, these parameters include unwanted parasitic resistance and/or inductance effects on the wire structure.

Abstract: Methods for calculating a pattern to be manufactured on a substrate include inputting a physical design pattern, determining a plurality of possible neighborhoods for the physical design pattern, generating a plurality of possible mask designs for the physical design pattern, calculating a plurality of possible patterns on the substrate, calculating a variation band from the plurality of possible patterns, and modifying the physical design pattern to reduce the variation band. Embodiments also include inputting a set of parameters for a neural network to calculate a pattern to be manufactured on a substrate, calculating a plurality of patterns to be manufactured on the substrate for the physical design in each possible neighborhood of the plurality of possible neighborhoods, training the neural network with the calculated plurality of patterns, and adjusting the set of parameters to reduce the manufacturing variation for the calculated plurality of patterns to be manufactured on a substrate.

Abstract: Methods and systems incorporate variable side wall angle (VSA) into calculated patterns, using a mask 3D (M3D) effect. Aspects include determining the M3D effect, which may be performed using a neural network such as a multi-head UNet model. Determining the M3D effect may include determining the VSA. Aspects may include determining a VSA; calculating a calculated pattern on a substrate using the mask 3D effect; and modifying a mask exposure information based on the calculated pattern on the substrate.