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 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.

Abstract: Methods include generating a scanner aerial image using a neural network, where the scanner aerial image is generated using a mask inspection image that has been generated by a mask inspection machine. Embodiments also include training the 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 and systems for generation of shape data for a set of electronic designs include inputting a set of shape data, where the set of shape data represents a set of shapes for a device fabrication process. A convolutional neural network is used on the set of shape data to determine a set of generated shape data, where the convolutional neural network comprises a generator trained with a set of pre-determined discriminators. The set of generated shape data comprises a scanning electron microscope (SEM) image.

Abstract: Methods incorporate variable side wall angle (VSA) into calculated patterns, using a mask 3D (M3D) effect. Embodiments include inputting a mask exposure information and determining the M3D effect. Determining the M3D effect may include determining the VSA. Embodiments may include calculating a VSA; and calculating a pattern on a substrate using the calculated VSA, wherein calculating the pattern on the substrate includes a mask 3D effect.

Abstract: Systems for training a convolutional neural network to register images for masks or wafers in semiconductor manufacturing include a computer processor configured to receive a first pair of images aligned in a first modality and a second pair of images aligned in a second modality. Images in the first pair of images and the second pair of images are a computer aided design (CAD) image pre-aligned with a scanning electron microscope (SEM) image. An affine transformation is generated with a convolutional neural network, using one image from the first pair of images and one image from the second pair of images. The one image from the first pair of images is in the first modality and the one image from the second pair of images is in the second modality. Systems for registering images for masks or wafers in semiconductor manufacturing use the trained convolutional neural network.

Abstract: Methods for training a convolutional neural network to register images for electronic designs include inputting a first pair of images aligned in a first modality and a second pair of images aligned in a second modality. An affine transformation is generated with a convolutional neural network, using one image from the first pair of images and one image from the second pair of images. The one image from the first pair of images is in the first modality and the one image from the second pair of images is in the second modality. Methods for registering images for electronic designs include inputting a pair of images, wherein the pair of images comprises a computer aided design (CAD) image and a scanning electron microscope (SEM) image. The CAD image is registered to the SEM image, using a trained convolutional neural network. The trained convolutional neural network further comprises an affine transformation.

Abstract: Methods incorporate variable side wall angle (VSA) into calculated patterns, using a mask 3D (M3D) effect. Embodiments include inputting a mask exposure information and determining the M3D effect. Determining the M3D effect may include determining the VSA. Embodiments may include calculating a VSA; and calculating a pattern on a substrate using the calculated VSA, wherein calculating the pattern on the substrate includes a mask 3D effect.

Abstract: Methods and systems for reticle enhancement technology (RET) include inputting a target wafer pattern, where the target wafer pattern spans an entire design area. The entire design area is divided into a plurality of tiles, each tile having a halo region surrounding the tile. An optimized mask is calculated, wherein the optimized mask is generated by a first trained neural network using the target wafer patter. The calculating is performed for each tile in the plurality of tiles including its halo region.

Abstract: Methods incorporate variable side wall angle (VSA) into calculated patterns, using a mask 3D (M3D) effect. Embodiments include inputting a mask exposure information, calculating a mask 2D (M2D) effect from the mask exposure information, and determining the M3D effect from the M2D effect. Determining the M3D effect may include determining the VSA, such as by using a neural network. Embodiments may include determining a dose margin from mask exposure information; calculating a VSA using the dose margin; and calculating a pattern on a substrate using the calculated VSA, wherein calculating the pattern on the substrate includes a mask 3D effect.

Abstract: Methods and systems for generation of shape data for a set of electronic designs include inputting a set of shape data, where the set of shape data represents a set of shapes for a device fabrication process. A convolutional neural network is used on the set of shape data to determine a set of generated shape data, where the convolutional neural network comprises a generator trained with a set of pre-determined discriminators. The set of generated shape data comprises a scanning electron microscope (SEM) image.

Abstract: Methods and systems for compressing shape data for a set of electronic designs include inputting a set of shape data, where the shape data comprises mask designs. A convolutional autoencoder encodes the set of shape data, where the encoding compresses the set of shape data to produce a set of encoded shape data. The convolutional autoencoder is tuned for increased accuracy of the set of encoded shape data based on design rules for the set of electronic designs. The convolutional autoencoder comprises a set of parameters comprising weights, and the convolutional autoencoder has been trained to retain important information needed, based on the design rules for the set of electronic designs.

Abstract: Methods for generation of shape data for a set of electronic designs include inputting a set of shape data, where the set of shape data represents a set of shapes for a device fabrication process. A convolutional neural network is used on the set of shape data to determine a set of generated shape data, where the convolutional neural network comprises a generator trained with a pre-determined set of discriminators. The set of generated shape data comprises a scanning electron microscope (SEM) image.

Abstract: Methods for compressing shape data for a set of electronic designs include inputting a set of shape data, where the shape data comprises mask designs. A convolutional autoencoder encodes the set of shape data, where the encoding compresses the set of shape data to produce a set of encoded shape data. The convolutional autoencoder is tuned for increased accuracy of the set of encoded shape data based on design rules for the set of shape data. The convolutional autoencoder comprises a set of parameters comprising weights, and the convolutional autoencoder has been trained to determine what information to keep based on the weights.

Abstract: Methods incorporate variable side wall angle (VSA) into calculated patterns, using a mask 3D (M3D) effect. Embodiments include inputting a mask exposure information, calculating a mask 2D (M2D) effect from the mask exposure information, and determining the M3D effect from the M2D effect. Determining the M3D effect may include determining the VSA, such as by using a neural network. Embodiments may include determining a dose margin from mask exposure information; calculating a VSA using the dose margin; and calculating a pattern on a substrate using the calculated VSA, wherein calculating the pattern on the substrate includes a mask 3D effect.

Abstract: Methods include generating a scanner aerial image using a neural network, where the scanner aerial image is generated using a mask inspection image that has been generated by a mask inspection machine. Embodiments also include training the 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 generation of shape data for a set of electronic designs include inputting a set of shape data, where the set of shape data represents a set of shapes for a device fabrication process. A convolutional neural network is used on the set of shape data to determine a set of generated shape data, where the convolutional neural network comprises a generator trained with a pre-determined set of discriminators. The set of generated shape data comprises a scanning electron microscope (SEM) image.

Abstract: Methods for training a convolutional neural network to register images for electronic designs include inputting a first pair of images aligned in a first modality and a second pair of images aligned in a second modality. An affine transformation is generated with a convolutional neural network, using one image from the first pair of images and one image from the second pair of images. The one image from the first pair of images is in the first modality and the one image from the second pair of images is in the second modality. Methods for registering images for electronic designs include inputting a pair of images, wherein the pair of images comprises a computer aided design (CAD) image and a scanning electron microscope (SEM) image. The CAD image is registered to the SEM image, using a trained convolutional neural network. The trained convolutional neural network further comprises an affine transformation.

Abstract: Methods for fracturing or mask data preparation are disclosed in which a set of single-beam charged particle beam shots is input; a calculated image is calculated using a neural network, from the set of single-beam charged particle beam shots; and a set of multi-beam shots is generated based on the calculated image, to convert the set of single-beam charged particle beam shots to the set of multi-beam shots which will produce a surface image on the surface. Methods for training a neural network include inputting a set of single-beam charged particle beam shots; calculating a set of calculated images using the set of single-beam charged particle beam shots; and training the neural network with the set of calculated images.

Abstract: Methods for compressing shape data for a set of electronic designs include inputting a set of shape data, where the shape data comprises mask designs. A convolutional autoencoder encodes the set of shape data, where the encoding compresses the set of shape data to produce a set of encoded shape data. The convolutional autoencoder is tuned for increased accuracy of the set of encoded shape data based on design rules for the set of shape data. The convolutional autoencoder comprises a set of parameters comprising weights, and the convolutional autoencoder has been trained to determine what information to keep based on the weights.