Data-Based Approach for Final Product Quality Inspection: Application to a Semiconductor Industry

The early information about the health state of the final product quality plays a vital role in the intact management of production. In semiconductor manufacturing, quality control of a too-small number of wafers is routinely carried on specific metrology stations, and the obtained quality measurements are generalized over the entire lot. The unavailability of sufficient product quality information results in a lack of that for a high proportion of products. The latter leads to some overlooked quality problems that might cause a malfunction in the final product. This malfunction is usually conducive to yield loss, resource consumption through its remaining production line steps and also needs a considerable amount of time to be source-identified. This paper proposes a final quality classification data-driven approach using machine learning techniques and alarm events data collected during the production operations. We use the k-mean clustering algorithm to group production lots into clusters based on their passages over equipment. Each cluster has its decision tree classification model elaborated after various information extraction techniques and manipulation applied to alarm event texts. The obtained results show a satisfactory performance demonstrated on a real-world dataset collected over the whole semiconductor fabrication facility.