Machine learning robustness in overlay metrology

Abstract

Tool-induced shift (TIS) is a common method used to quantify measurement error or accuracy of an overlay (OVL) metrology tool and is often attributed to tool asymmetry issues. In a previous paper, we introduced Modeled-TIS (mTIS), a machine learning (ML) based algorithm to predict per-site TIS correction on Image- Based Overlay (IBO) measurements. Recently, we adapted mTIS to support the requirements of high-volume manufacturing (HVM) in 3D NAND production. During our work in an HVM environment, we observed that the mTIS model is sensitive to complex process variations and many tool states. Such variations can change the measurement distribution and deteriorate the mTIS accuracy and reliability over time. In this paper, we present an automatic retrain triggering mechanism (“Trigger”) to monitor and adaptively update mTIS. The proposed methodology incorporates statistical and unsupervised ML algorithms which automatically detect shifts in measurement distribution, and initiate data collection to continuously adjust the model to process variations and tool states. The proposed mechanism does not require periodic retraining but rather initiates data collection and model updates only when required. Trigger chooses the wafers which possess novel information reducing the number of wafers required for the training and time to the model, critical requirements for our customers. We demonstrate the mTIS results of Trigger on various 3D NAND layers running in production. Finally, our approach can also be applied to future ML-based solutions.