Higher density nano-devices and more metallization layers in microelectronic chips are unceasing goals to the present semiconductor industry. However, topological imperfections (higher non-uniformity) on the wafer surfaces and lower material removal rates (MRR) seriously hamper these pursuing motivations. Since'90, industry has been using chemical mechanical planarization/polishing (CMP) to overcome these obstacles for fabricating integrated circuits (IC) with interconnect geometries of < 0.18 &#956;m. Obviously, the much needed understanding of this new technique is derived basically on the ancient lapping process. Modeling and simulation are critical to transfer CMP from an engineering 'art' to an engineering 'science'. Many efforts in CMP modeling have been made in the last decade, but the available analytical MRR and surface uniformity models cannot precisely describe this highly complicated process, involving simultaneous chemical reactions (and etching), and mechanical abrasion. In this investigation, neural networks (NN), adaptive-based-network fuzzy inference system (ANFIS), and evolutionary algorithms (EA) techniques were applied to successfully overcome the aforementioned modeling and simulation problems. In addition, fine-tuning techniques for re-modifying ANFIS models for sparse-data case using are developed. Furthermore, multi-objective evolutionary algorithms (MOEA) are firstly applied to search for the optimal input settings for CMP process to trade-off the higher MRR and lower non-Uniformity by using the previously constructed models. The results also show the simulation of MOEA optimization can certainly provide accurate guidance to search the optimal input settings for CMP process to produce lower non-uniform wafer surfaces under higher MRR.Mechanical & Aerospace Engineerin
