Abstract: Systems and methods generate synthetic sensor data, such as synthetic radar, lidar, and/or sonar data from three dimensional (3D) scene data that may be custom designed. Reflectivity coefficients in the radar, lidar, and/or sonar spectrums may be determined for objects included in the 3D scene data. The reflectivity coefficients may be utilized by a game engine for computing the synthetic sensor data. The synthetic sensor data may be used in the creation, evaluation, and/or verification of a design for a controller or other system that utilizes such data.

Abstract: One or more optical images of a portion of a semiconductor wafer are obtained. The one or more optical images show a first structure in a first process layer and a second structure in a second process layer. The one or more optical images are provided to a machine-learning model trained to estimate an overlay offset between the first structure and the second structure. An estimated overlay offset between the first structure and the second structure is obtained from the machine-learning model.

Abstract: Methods and systems for generating optimized geometric models of semiconductor structures parameterized by a set of variables in a latent mathematical space are presented herein. Reference shape profiles characterize the shape of a semiconductor structure of interest over a process space. A set of observable geometric variables describing the reference shape profiles is transformed to a set of latent variables. The number of latent variables is smaller than the number of observable geometric variables, thus the dimension of the parameter space employed to characterize the structure of interest is reduced. This dramatically reduces the mathematical dimension of the measurement problem to be solved. As a result, measurement model solutions involving regression are more robust, and training of machine learning based measurement models is simplified.

Abstract: Methods and systems for measurements of semiconductor structures based on a trained parameter conditioned measurement model are described herein. The shape of a measured structure is characterized by a geometric model parameterized by one or more conditioning parameters and one or more non-conditioning parameters. A trained parameter conditioned measurement model predicts a set of values of each non-conditioning parameter based on measurement data and a corresponding set of predetermined values for each conditioning parameter. In this manner, the trained parameter conditioned measurement model predicts the shape of a measured structure. Although a parameter conditioned measurement model is trained at discrete geometric points of a structure, the trained model predicts values of non-conditioning parameters for any corresponding conditioning parameter value.

Abstract: Methods and systems for training and implementing metrology recipes while dynamically controlling the convergence trajectories of multiple performance objectives are described herein. Performance metrics are employed to regularize the optimization process employed during measurement model training, model-based regression, or both. Weighting values associated with each of the performance objectives in the loss function of the model optimization are dynamically controlled during model training. In this manner, convergence of each performance objective and the tradeoff between multiple performance objectives of the loss function is controlled to arrive at a trained measurement model in a stable, balanced manner. A trained measurement model is employed to estimate values of parameters of interest based on measurements of structures having unknown values of one or more parameters of interest.

Abstract: Systems and methods generate synthetic sensor data, such as synthetic radar, lidar, and/or sonar data from three dimensional (3D) scene data that may be custom designed. Reflectivity coefficients in the radar, lidar, and/or sonar spectrums may be determined for objects included in the 3D scene data. The reflectivity coefficients may be utilized by a game engine for computing the synthetic sensor data. The synthetic sensor data may be used in the creation, evaluation, and/or verification of a design for a controller or other system that utilizes such data.

Abstract: Systems and methods generate synthetic sensor data, such as synthetic radar, lidar, and/or sonar data from three dimensional (3D) scene data that may be custom designed. Reflectivity coefficients in the radar, lidar, and/or sonar spectrums may be determined for objects included in the 3D scene data. The reflectivity coefficients may be utilized by a game engine for computing the synthetic sensor data. The synthetic sensor data may be used in the creation, evaluation, and/or verification of a design for a controller or other system that utilizes such data.

Abstract: Systems and methods generate synthetic sensor data, such as synthetic radar, lidar, and/or sonar data from three dimensional (3D) scene data that may be custom designed. Reflectivity coefficients in the radar, lidar, and/or sonar spectrums may be determined for objects included in the 3D scene data. The reflectivity coefficients may be utilized by a game engine for computing the synthetic sensor data. The synthetic sensor data may be used in the creation, evaluation, and/or verification of a design for a controller or other system that utilizes such data.