Abstract: An embodiment of the invention may include a method for ensuring semiconductor design integrity. The method may include analyzing a photomask design for a semiconductor circuit. The photomask may include a primary electrical design necessary for the operation of the semiconductor circuit, and white space, which has no primary electrical design. The method may include inserting a secondary electrical design into the white space of the photomask design for the semiconductor circuit. The secondary electrical design may have known electrical properties for validating the semiconductor circuit design.

Abstract: A computer-implemented method, computer program product, a computer processing system are provided for generating synthetic via layout patterns by a Recurrent Neural Network (RNN). The method includes generating, by a processor, a training data set of coordinate arrays that specify coordinates of vias in a set of physical design layouts. The method further includes training, by the processor, the RNN with the training data set of coordinate arrays. The method also includes generating, by the processor, using the RNN, new synthetic via patterns.

Abstract: An embodiment of the invention may include a semiconductor structure for ensuring semiconductor design integrity. The semiconductor structure may include an electrical circuit necessary for the operation of the semiconductor circuit and white space having no electrical circuit. The semiconductor structure may include an optical pattern used for validating the semiconductor circuit design formed in the white space of the electrical circuit. In an embodiment of the invention, the optical pattern may include one or more deposition layers. In an embodiment of the invention, the optical pattern may include covershapes. In an embodiment of the invention, the optical pattern may be physically isolated from the electrical circuit. The optical pattern may include a Moiré pattern.

Abstract: Techniques for modifying a mask fabrication process based the identification of an abnormality in a pattern of a fabricated lithography mask are disclosed including comparing a fabricated lithography mask to a lithography mask design where the fabricated lithography mask is fabricated based at least in part on the lithography mask design using a mask fabrication process. An abnormality in a pattern of the fabricated lithography mask relative to a corresponding one of the plurality of patterns in the lithography mask design is identified based at least in part on the comparison of the fabricated lithography mask to the lithography mask design. A calibrated mask model is generated based at least in part on the identified abnormality in the pattern of the fabricated lithography mask and the mask fabrication process is modified based at least in part on the calibrated mask model.

Abstract: A method for generating physical design layout patterns includes selecting as training data one or more physical design layout patterns of integrated multi-layers for features in at least two layers of a given patterned structure. The method also includes converting the physical design layout patterns into three-dimensional arrays, a given three-dimensional array comprising a set of two-dimensional arrays each representing features of one layer of the layers in a given one of the physical design layout patterns. The method further includes training, utilizing the three-dimensional arrays, a generative adversarial network (GAN) comprising a discriminator neural network and a generator neural network. The method further includes generating synthetic three-dimensional arrays utilizing the generator neural network of the trained GAN, a given synthetic three-dimensional array comprising a set of two-dimensional arrays each representing features for a new layer of a new physical design layout pattern.

Abstract: A method for generating physical design layout patterns includes selecting as training data a set of physical design layout patterns of features in a given layer of a given patterned structure and converting the physical design layout patterns into two-dimensional (2D) arrays comprising entries for different locations in the given layer of the given patterned structure with values representing presence of the features at the different locations. The method also includes training, utilizing the 2D arrays, a generative adversarial network (GAN) comprising a discriminator neural network and a generator neural network. The method further includes generating one or more synthetic 2D arrays utilizing the trained generator neural network of the GAN, a given synthetic 2D array comprising entries for different locations in the given layer of a new physical design layout pattern with values representing presence of the features at the different locations of the new physical design layout pattern.

Abstract: A computer-implemented method, computer program product, a computer processing system are provided for generating synthetic via layout patterns by a Recurrent Neural Network (RNN). The method includes generating, by a processor, a training data set of coordinate arrays that specify coordinates of vias in a set of physical design layouts. The method further includes training, by the processor, the RNN with the training data set of coordinate arrays. The method also includes generating, by the processor, using the RNN, new synthetic via patterns.

Abstract: A method of modeling distributions of post-lithography critical dimensions includes the following steps. A plurality of aerial images of respective portions of a physical design layout of a semiconductor wafer are generated, and the plurality of aerial images are employed as training data. In the method, first and second portions of a neural network architecture are generated. The first portion includes a neural network which is shared by a plurality of output channels, and the second portion includes a plurality of neural networks, wherein each of the plurality of neural networks respectively correspond to one of the plurality of output channels. The method further includes training the first and second portions of the neural network architecture with the training data, and outputting the distributions of the post-lithography critical dimensions based on the plurality of output channels.

Abstract: Techniques for modifying a mask fabrication process based the identification of an abnormality in a pattern of a fabricated lithography mask are disclosed including comparing a fabricated lithography mask to a lithography mask design where the fabricated lithography mask is fabricated based at least in part on the lithography mask design using a mask fabrication process. An abnormality in a pattern of the fabricated lithography mask relative to a corresponding one of the plurality of patterns in the lithography mask design is identified based at least in part on the comparison of the fabricated lithography mask to the lithography mask design. A calibrated mask model is generated based at least in part on the identified abnormality in the pattern of the fabricated lithography mask and the mask fabrication process is modified based at least in part on the calibrated mask model.

Abstract: An embodiment of the invention may include a method for ensuring semiconductor design integrity. The method may include analyzing a photomask design for a semiconductor circuit. The photomask may include a primary electrical design necessary for the operation of the semiconductor circuit, and white space, which has no primary electrical design. The method may include inserting a secondary electrical design into the white space of the photomask design for the semiconductor circuit. The secondary electrical design may have known electrical properties for validating the semiconductor circuit design.

Abstract: An embodiment of the invention may include a method for ensuring semiconductor design integrity. The method may include analyzing a photomask design for a semiconductor circuit. The photomask may include a primary electrical design necessary for the operation of the semiconductor circuit, and white space, which has no primary electrical design. The method may include inserting a secondary electrical design into the white space of the photomask design for the semiconductor circuit. The secondary electrical design may have known electrical properties for validating the semiconductor circuit design.

Abstract: A method is presented for generating a plurality of physical design layout patterns. The method includes selecting one or more physical design layouts for neural network training, converting the plurality of physical design layout patterns into coordinate arrays, a coordinate array of the coordinate arrays including via center coordinates of vias in a physical design layout pattern of the plurality of physical design layout patterns, training, by employing the coordinate arrays, a variational autoencoder (VAE), and generating one or more new synthetic coordinate arrays by employing the trained VAE, a synthetic coordinate array of the one or more new synthetic coordinate arrays including via center coordinates of vias for a new physical design layout pattern.

Abstract: A method for generating physical design layout patterns includes the step of selecting one or more physical design layouts, a given physical design layout comprising a set of physical design layout patterns for features in at least one layer of a given patterned structure. The method also includes the step of converting the physical design layout patterns into coordinate arrays, a given coordinate array comprising feature center coordinates for the features in a given one of the physical design layout patterns. The method further includes the step of training, utilizing the coordinate arrays, a generative adversarial network (GAN) comprising discriminator and generator neural networks. The method further includes the step of generating one or more synthetic coordinate arrays utilizing the trained generator neural network of the GAN, a given one of the synthetic coordinate arrays comprising feature center coordinates of features for a new physical design layout pattern.

Abstract: A computer-implemented method, computer program product, a computer processing system are provided for generating synthetic via layout patterns by a Recurrent Neural Network (RNN). The method includes generating, by a processor, a training data set of coordinate arrays that specify coordinates of vias in a set of physical design layouts for a set of integrated circuit elements. The method further includes training, by the processor, the RNN with the training data set of coordinate arrays. The method also includes generating, by the processor, using the RNN, new synthetic via patterns.

Abstract: A computer-implemented method, computer program product, and computer processing system are provided for generating synthetic layout patterns. The method includes receiving, by a processor, a set of physical design layouts that include a variety of layout patterns for neural network training. The method further includes generating, by the processor, a set of training layout pattern images for the neural network training by performing automatic image capturing on the set of physical design layouts with scripts. The method also includes training, by the processor, a feedforward neural network (FFNN)-based Variational Autoencoder (VAE) with the set of training layout pattern images. The method additionally includes generating, by the processor using the FFNN-based VAE, new synthetic layout images.

Abstract: A method is presented for constructing a deep neural network based model to concurrently simulate post-lithography critical dimensions (CDs) and post-etch critical dimensions (CDs) and to improve the modeling accuracy of each process respectively. The method includes generating lithographic aerial images of physical design layout patterns, constructing a multi-task neural network including two output channels, training the multi-task neural network with the training data of the lithographic aerial images, and outputting simulated critical dimension values pertaining to lithography and etch processes.

Abstract: A method of modeling distributions of post-lithography critical dimensions includes the following steps. A plurality of aerial images of respective portions of a physical design layout of a semiconductor wafer are generated, and the plurality of aerial images are employed as training data. In the method, first and second portions of a neural network architecture are generated. The first portion includes a neural network which is shared by a plurality of output channels, and the second portion includes a plurality of neural networks, wherein each of the plurality of neural networks respectively correspond to one of the plurality of output channels. The method further includes training the first and second portions of the neural network architecture with the training data, and outputting the distributions of the post-lithography critical dimensions based on the plurality of output channels.

Abstract: A method is presented for constructing a deep neural network based model to concurrently simulate post-lithography critical dimensions (CDs) and post-etch critical dimensions (CDs) and to improve the modeling accuracy of each process respectively. The method includes generating lithographic aerial images of physical design layout patterns, constructing a multi-task neural network including two output channels, training the multi-task neural network with the training data of the lithographic aerial images, and outputting simulated critical dimension values pertaining to lithography and etch processes.

Abstract: A method is presented for generating a plurality of physical design layout patterns. The method includes selecting one or more physical design layouts for neural network training, converting the plurality of physical design layout patterns into coordinate arrays, a coordinate array of the coordinate arrays including via center coordinates of vias in a physical design layout pattern of the plurality of physical design layout patterns, training, by employing the coordinate arrays, a variational autoencoder (VAE), and generating one or more new synthetic coordinate arrays by employing the trained VAE, a synthetic coordinate array of the one or more new synthetic coordinate arrays including via center coordinates of vias for a new physical design layout pattern.

Abstract: A method for generating physical design layout patterns includes selecting as training data a set of physical design layout patterns of features in a given layer of a given patterned structure and converting the physical design layout patterns into two-dimensional (2D) arrays comprising entries for different locations in the given layer of the given patterned structure with values representing presence of the features at the different locations. The method also includes training, utilizing the 2D arrays, a generative adversarial network (GAN) comprising a discriminator neural network and a generator neural network. The method further includes generating one or more synthetic 2D arrays utilizing the trained generator neural network of the GAN, a given synthetic 2D array comprising entries for different locations in the given layer of a new physical design layout pattern with values representing presence of the features at the different locations of the new physical design layout pattern.