This technology can deal with a huge amount of profile data collected from the device sensors in equipment with quality measurement, through ensemble learning and machine learning algorithms, to perform automatic feature extraction and definition from a data-driven perspective, providing engineers the ranking of features for process control, shorten the progress of yield ramping, and effectively accelerate the schedule for the production stage.In the past, when using equipment sensing data for fault detection and classification, it is necessary to rely on expert domain knowledge to define appropriate key intervals and statistics in advance for analysis and monitoring. Now, when the quality measurement is feedbacked, this technology precisely defines the key intervals and feature statistics, and provides decision indicators as the confidence level of the model, reducing the engineer’s time expenditure and closing the experience gap, and accelerating the mass production of products.This technology can be applied to process control, especially for fault detection and classification. Since the technology does not rely on domain knowledge, it can be quickly applied to various industries with various types of analytical needs, such as the semiconductor industry, electronics industry, or the aviation manufacturing industry.