As the digitization of the manufacturing process is accelerating, various data-driven approaches using machine learning are being developed in chemical mechanical polishing (CMP). For a more accurate prediction in contact-based CMP, it is necessary to consider the real-time changing pad surface roughness during polishing. Changes in pad surface roughness result in non-uniformity of the real contact pressure and friction applied to the wafer, which are the main causes of material removal rate variation. In this paper, we predicted the material removal rate based on pressure and surface roughness using a deep neural network (DNN). Reduced peak height (Rpk) and real contact area (RCA) were chosen as the key parameters indicative of the surface roughness of the pad, and 220 data were collected along with the process pressure. The collected data were normalized and separated in a 3 : 1 : 1 ratio to improve the predictive performance of the DNN model. The hyperparameters of the DNN model were optimized through random search techniques and 5 cross-validations. The optimized DNN model predicted the material removal rate with high accuracy in ex-situ CMP. This study is expected to be utilized in data-driven machine learning decision making for cyber-physical CMP systems in the future.

Chemical mechanical polishing achieves surface planarity through combined mechanical and chemical means. The role of the chemical reaction is very important in a metal CMP like aluminum. The slurry used in aluminum CMP typically consists of oxidizers, a chelating agent, corrosion inhibitors, and abrasives. This study investigates the effect of oxalic acid as a chelating agent for aluminum CMP with H2O2. To study the chemical effect of the chelating agent, the two methods of a polishing experiment and an electrochemical analysis were used. Lastly, it was confirmed that the optimum concentration of oxalic acid significantly improved the removal rate and surface roughness of aluminum.