Maintenance of virtual metrology models

Virtual metrology (VM) predicts on-wafer properties, such as thickness and uniformity, using equipment data (e.g., sensors, constants) and potentially on-wafer properties or predictions from previous steps. While much literature is devoted to developing VM models, maintaining them (e.g., so they can be used for months in production) presents a different set of challenges. Behavior of equipment can change over time, causing degradation of VM models. Maintenance events can abruptly reset sensors and process conditions, invalidating VM models. In addition, sensor noise, sensor imperfection and aging parts can disturb performance of VM models. While some of these issues can be addressed during modeling by incorporating certain variables that capture the dynamics associated with these issues, others are harder to foresee. Resolving these issues may require tuning the models as they age or manually rebuilding the models after they become ineffective. To mitigate these problems, techniques including monitoring residual error and tool degradation, compensating for sensor reset and residual error, and auto-retraining models can significantly help VM models maintain their performance during deployment.