Abstract: Systems and methods are provided for improving a high-volume manufacturing (HVM) line by assessing robustness and performance of an early fault detection machine learning (EFD ML) models. Learning curve(s) may be constructed from a received amount of data from the electronics' production line, the learning curve representing a relation between a performance of the EFD ML model and a sample size of the data on which the EFD ML model is based. Learning curve(s) may be used to derive estimation(s) of model robustness by (i) fitting the learning curve to a power law function and (ii) estimating a tightness of the fitting and/or by (iii) applying a machine learning algorithm that is trained on a given plurality of learning curves and related normalized performance values.

Abstract: Systems and methods are provided for improving a high-volume manufacturing (HVM) line that has a test pass ratio of at least 90%, by constructing a genetic neural architecture search (GNAS) network that detects anomalies in the HVM line at a detection rate of at least 85%. Disclosed systems and methods combine data balancing of the highly skewed raw data with a network construction that is based on building blocks that reflect technical knowledge related to the HVM line. The GNAS network construction is made thereby both simpler and manageable and provides meaningful insights for improving the production process.