Predicting no-defect-found physical failure analysis results using Bayesian inference and generalized linear models

A method for generating a Physical Fault Analysis (PFA) outcome prediction for a selected integrated circuit device, said method being executed by a processor of a computer and comprising:

• generating a Bayesian Generalized Linear Model using a training data library including a plurality of first training data entries, where each first training data entry includes corresponding first evaluation data that respectively operably characterizes one or more measured operating characteristics of a corresponding first integrated circuit device, and corresponding first physical failure analysis result data that operably characterizes results of a corresponding physical failure analysis of said corresponding first integrated circuit device;
• and
• utilizing the Bayesian Generalized Linear Model to generate said PFA outcome prediction for a second evaluation data that operably characterizes said one or more measured operating characteristics of said selected integrated circuit device,
• wherein the PFA outcome prediction includes at least one of a posterior distribution and a credible interval, said posterior distribution being a portion of said Bayesian Generalized Linear Model corresponding to said second evaluation data, and said credible interval being a partial certainty region defined by the posterior distribution.

A non-transitory computer readable medium comprising stored instructions, which when executed by a processor, cause the processor to:

• generate a Bayesian Generalized Linear Model using a training data library including a plurality of first training data entries, where each first training data entry includes corresponding first evaluation data that respectively operably characterizes one or more measured operating characteristics of a corresponding first integrated circuit chip, and corresponding first physical failure analysis result data that operably characterizes results of a corresponding physical failure analysis of said corresponding first integrated circuit chip;
• and
• utilize the Bayesian Generalized Linear Model to generate a physical fault analysis (PFA) outcome prediction for a second evaluation data that operably characterizes said one or more measured operating characteristics of a corresponding second integrated circuit chip,
• wherein the PFA outcome prediction includes at least one of a posterior distribution and a credible interval, said posterior distribution being a portion of said Bayesian Generalized Linear Model corresponding to said second evaluation data, and said credible interval being a partial certainty region defined by the posterior distribution.