Abstract: Methods, systems, and computer program products for machine learning model decision boundary enhancement are provided. Aspects include determining a decision boundary associated with a first machine learning model, wherein the first machine learning model is trained with an initial training set of features and associated classifications from a verification model, obtaining a plurality of new feature sets, analyzing, by the first machine learning model, the plurality of new feature sets to determine that a subset of feature sets have classification predictions within a threshold range of the decision boundary associated with the first machine learning model, inputting the subset of feature sets into the verification model to determine enhanced training data based on outputs of the verification model, and creating an enhanced machine learning model by further training the first machine learning model with the initial training data and the enhanced training data.

Abstract: Methods, systems, and computer program products for machine learning model decision boundary enhancement are provided. Aspects include determining a decision boundary associated with a first machine learning model, wherein the first machine learning model is trained with an initial training set of features and associated classifications from a verification model, obtaining a plurality of new feature sets, analyzing, by the first machine learning model, the plurality of new feature sets to determine that a subset of feature sets have classification predictions within a threshold range of the decision boundary associated with the first machine learning model, inputting the subset of feature sets into the verification model to determine enhanced training data based on outputs of the verification model, and creating an enhanced machine learning model by further training the first machine learning model with the initial training data and the enhanced training data.

Abstract: Systems and methods of developing an integrated circuit implement selecting a desired yield for a circuit used in the integrated circuit. The desired yield corresponds to a desired failure probability of the circuit. The method includes determining a parameter threshold value that corresponds with the desired yield. The circuit passes if a parameter associated with the circuit is below the parameter threshold value and the desired yield indicates a percentage of instances of the circuit that pass according to the parameter threshold value. The method also includes using the parameter threshold value that corresponds with the desired yield during testing and improvement of a design of the integrated circuit, and providing the design of the integrated circuit for fabrication.

Abstract: Systems and methods of developing an integrated circuit implement selecting a desired yield for a circuit used in the integrated circuit. The desired yield corresponds to a desired failure probability of the circuit. The method includes determining a parameter threshold value that corresponds with the desired yield. The circuit passes if a parameter associated with the circuit is below the parameter threshold value and the desired yield indicates a percentage of instances of the circuit that pass according to the parameter threshold value. The method also includes using the parameter threshold value that corresponds with the desired yield during testing and improvement of a design of the integrated circuit, and providing the design of the integrated circuit for fabrication.

Abstract: Methods, systems, can computer program products for optimizing statistical method of computing output yields are provided. Aspects include determining a target criteria of a system for statistical analysis, based on the target criteria, determining a statistical analysis algorithm for the simulation, determining a block size for a plurality of statistical samples of the system for a parallelization of the statistical analysis algorithm, generating the plurality of statistical samples of the system, simulating the plurality of statistical samples of the system to determine one or more output yields, calculating a confidence interval for each of the one or more output yields, wherein the confidence interval comprises a lower bound, comparing the lower bound to a threshold standard deviation of a probability density function, and adjusting the block size for the plurality of statistical samples based on determining that the lower bound is less than the threshold standard deviation.