Abstract:
An apparatus for providing processing gases to a process chamber with improved uniformity is disclosed. One embodiment provides a gas delivery assembly. The gas delivery assembly includes a hub, a nozzle, and one or more gas diffusers disposed in the nozzle. The nozzle has a cylindrical body with a side wall and a top surface. A plurality of injection passages are formed inside the nozzle to deliver processing gases into the process chamber via a plurality of outlets disposed in the side wall. The injection passages are configured to direct process gases out of each outlet disposed in the side wall in a direction which is not radially aligned with a centerline of the hub.
Abstract:
Embodiments of the present invention relate to an apparatus for providing processing gases to a process chamber with improved uniformity. One embodiment of the present invention provides a gas delivery assembly. The gas delivery assembly includes a hub, a nozzle, and one or more gas diffusers disposed in the nozzle. The nozzle has a cylindrical body with a side wall and a top surface. A plurality of injection passages are formed inside the nozzle to deliver processing gases into the process chamber via a plurality of outlets disposed in the side wall. The injection passages are configured to direct process gases out of each outlet disposed in the side wall in a direction which is not radially aligned with a centerline of the hub.
Abstract:
This disclosure describes methods and systems for building a spatial model to predict performance of processing chamber, and using the spatial model to converge faster to a desired process during the process development phase. Specifically, a machine-learning engine obtains an empirical process model for a given process for a given processing chamber. The empirical process model is calibrated by using the in-line metrology data as reference. A predictive model is built by refining the empirical process model by a machine-learning engine that receives customized metrology data and outputs one or more spatial maps of the wafer for one or more dimensions of interest across the wafer without physically processing any further wafers, i.e. by performing spatial digital design of experiment (Spatial DoE).