Machine learning for fab automated diagnostics

Process optimization depends largely on field engineer’s knowledge and expertise. However, this practice turns out to be less sustainable due to the fab complexity which is continuously increasing in order to support the extreme miniaturization of Integrated Circuits. On the one hand, process optimization and root cause analysis of tools is necessary for a smooth fab operation. On the other hand, the growth in number of wafer processing steps is adding a considerable new source of noise which may have a significant impact at the nanometer scale. This paper explores the ability of historical process data and Machine Learning to support field engineers in production analysis and monitoring. We implement an automated workflow in order to analyze a large volume of information, and build a predictive model of overlay variation. The proposed workflow addresses significant problems that are typical in fab production, like missing measurements, small number of samples, confounding effects due to heterogeneity of data, and subpopulation effects. We evaluate the proposed workflow on a real usecase and we show that it is able to predict overlay excursions observed in Integrated Circuits manufacturing. The chosen design focuses on linear and interpretable models of the wafer history, which highlight the process steps that are causing defective products. This is a fundamental feature for diagnostics, as it supports process engineers in the continuous improvement of the production line.