Application development of virtual metrology in semiconductor industry

TLDR

Daily wafer fabrication in semiconductor foundries relies on extensive metrology operations that require many tools, increase investment and cycle time, yet add no value beyond quality assurance. The article introduces virtual metrology (VM) to use sensor data from 300 mm fab tools to forecast wafer and tool quality. The VM approach builds a neural‑network control model and deploys it following SEMI EDA equipment‑data‑acquisition standards.

Abstract

Daily wafer fabrication in semiconductor foundry depends on considerable metrology operations for tool-quality and process-quality assurance. The metrology operations required a lot of metrology tools, which increase FAB's investment. Also, these metrology operations will increase cycle time of wafer process. Metrology operations do not bring any value added to wafer but only quality assurance. This article provides a new method denoted virtual metrology (VM) to utilize sensor data collected from 300 mm FAB's tools to forecast quality data of wafers and tools. This proposed method designs key steps to establish a VM control model based on neural networks and to develop and deploy applications following SEMI EDA (equipment data acquisition) standards.