Artificial intelligence measurement in imaging-based overlay metrology for performance boost

Advanced semiconductor devices target sub-2nm on-product overlay (OPO) and manufacturers utilize dense overlay (OVL) sampling and non-zero offset (NZO) control to enable such strict performance. Accurate optical OVL metrology systems with fast move-and-measurement (MAM) utilized at the after-develop inspection (ADI) step are required to support this OPO trend. This work presents an innovative Artificial Intelligence (AI) based, ultra-high-speed, overlay target focusing and centering approach on imaging-based overlay (IBO) measurements in the ADI step. The algorithm uses pre-trained image features and a deep learning model. The algorithm allows the measurement of every site across the wafer in its best centering and contrast focus position and thus overcomes intra-wafer process variations and enhanced measurement accuracy. The data will include results from multi-lot advanced DRAM process with basic performance analysis such as total measurement uncertainty (TMU), tool-to-tool matching (TTTM) and additional key performance indicators (KPIs).