Run-to-Run Control of Chemical Mechanical Polishing Head With Adjustable Partition Pressure Based on Deep Reinforcement Learning

Abstract

The polishing head is one of the most important components in the chemical mechanical polishing (CMP) process, used to achieve an ultra-smooth surface on the wafer. Due to the effect of the retaining ring and the replacement of the polishing pad, the polishing process is subjected to drift and shift interference. The CMP head with partition pressure is designed to exert pressure separately according to the situation of the wafer edge being affected, and compensate for the disturbance caused by slight changes in different areas inside the wafer. A run-to-run (R2R) control method based on deep reinforcement learning (DRL) is proposed to adjust the process parameters between runs. A dual experience replay mechanism is adopted to effectively utilize the interactive information between the agent and the CMP environment designed. Simulation experiments are conducted under the conditions of pad drift and pad replacement. Results demonstrate that the proposed method achieves superior performance in MRR optimization. The WIWNU is optimized to a minimum of 0.3%, and the maximum difference of the polished wafer thickness under the two experimental conditions is below 25 nm. Thus, the proposed method can effectively compensate for the drift caused by equipment aging, and can sense environmental changes and adjust process parameters.