Many state-of-the-art fabs are operating with increasingly diversified product mixes. For example, at Cypress
Semiconductor, it is not unusual to be concurrently running multiple technologies and many devices within each
technology. This diverse product mix significantly increases the difficulty of manually controlling overlay process
corrections. As a result, automated run-to-run feedforward-feedback control has become a necessary and vital
component of manufacturing.
However, traditional run-to-run controllers rely on highly correlated historical events to forecast process corrections.
For example, the historical process events typically are constrained to match the current event for exposure tool, device,
process level and reticle ID. This narrowly defined process stream can result in insufficient data when applied to lowvolume
or new-release devices.
The run-to-run controller implemented at Cypress utilizes a multi-level query (Level-N) correlation algorithm, where
each subsequent level widens the search criteria for available historical data. The paper discusses how best to widen the
search criteria and how to determine and apply a known bias to account for tool-to-tool and device-to-device differences.
Specific applications include offloading lots from one tool to another when the first tool is down for preventive
maintenance, utilizing related devices to determine a default feedback vector for new-release devices, and applying bias
values to account for known reticle-to-reticle differences. In this study, we will show how historical data can be
leveraged from related devices or tools to overcome the limitations of narrow process streams. In particular, this paper
discusses how effectively handling narrow process streams allows Cypress to offload lots from a baseline tool to an