Nonlinear Sequential Bayesian Analysis-Based Decision Making for End-Point Detection of Chemical Mechanical Planarization (CMP) Processes


IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, vol.24, no.4, pp.523-532, 2011 (SCI-Expanded) identifier identifier


Chemical mechanical planarization (CMP) process has been widely used in the semiconductor manufacturing industry for realizing highly polished (surface roughness Ra similar to 1 nm) and planar [WIWNU similar to 1%, thickness variation standard deviation similar to 3 nm] surfaces of an in-process wafer. In CMP, accurate and timely decisions for end-point detection (EPD) are extremely important to enable the process to effectively respond to demand variations and disruptions. In this paper, we apply nonlinear sequential Bayesian analysis and decision theory to establish a quantitative relationship that connects the features (inputs) extracted from on-line wireless vibration sensor signals with the process performance measures, such as material removal (outputs) for EPD in copper CMP process. A case study with actual CMP data is provided to demonstrate the effectiveness of the present approach. Note to practitioners. The semiconductor industry widely uses CMP process for realizing highly polished planar surfaces on inter-level dielectrics and metallic interconnects in the fabrication of integrated circuits. Accurate and timely detection of the end-point (EPD) of the CMP process is critical to prevent over-polishing or under-polishing of wafer surfaces, and thus meet the wafer yield requirements under growing demands on wafer density and performance. An EPD system uses information from in-process sensors and/or inspection instruments to facilitate decisions on when to stop the polishing process, and adjust process settings for optimal performance. However, the issue of developing cost-effective sensors, and addressing the uncertainty in the sensor information remains a challenge. We have developed an EPD system based on deriving and sequentially updating a cost-function using the uncertain information from wireless MEMS vibration sensors mounted on a CMP apparatus. Decisions on EPD are made based on optimizing the updated cost function at every time-step. Our experimental investigations suggest that the sensor information can be effectively used for implementing EPD, and it can minimize the costs of over-polishing and under-polishing of wafers during CMP process. As part of future work, we are investigating the robustness of the EPD system to different forms of uncertainty in the sensor information, and much wider configurations of sensors and CMP setups.