摘要：AbstractFor decades, manufacturers have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. With the vast amount of information and hidden knowledge in all of these data, the challenge for these manufacturers to monitor their equipment units, is the extraction of an appropriate health indicator from these data that illustrates the actual state of their equipment units. In this paper, we are interested in extracting the health indicator of semiconductor equipment where manufacturing is performed by batch. For that, a novel automatic approach named Significant Points combined to the Least Absolute Shrinkage and Selection Operator (SP-LASSO) is proposed. This approach is mainly based on LASSO regression model. Its accuracy is illustrated by numerical application on simulated data.
