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 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 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 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 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 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 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.