Semiconductor processing tools with improved performance by use of hybrid learning models

A method of baselining a chamber executing a semiconductor manufacturing process, comprising:

• running a limited design of experiment (DoE) of wafers with external metrology to baseline chamber performance;
• adding wafer outcomes and metrology data from the limited DoE to a hybrid model as a calibration data set, wherein the hybrid model comprises:
• a statistical model;
• and
• a physical model;
• adjusting the model's predictions to account for specific chamber conditions and/or wafer conditions identified by the limited DoE;
• predicting optimized process parameters to achieve a desired wafer outcome for wafers processed in the chamber and to provide optimized process parameters;
• and
• applying the optimized process parameters to a wafer.

A method for processing wafers in a processing tool, comprising:

• providing a hybrid model of a processing tool, wherein the hybrid model comprises:
• a statistical model;
• and
• a physical model;
• executing a recipe in the processing tool to process a first wafer;
• obtaining wafer data from the first wafer after execution of the recipe;
• obtaining process data from the processing tool relating to the execution of the recipe;
• providing the wafer data and the process data to the hybrid model to generate an updated hybrid model;
• using the updated hybrid model to generate a modified recipe to account for chamber drift in the processing tool;
• and
• applying the modified recipe to a second wafer in the processing tool.