Improvement of Photolithography Process by Second Generation Data Mining

The advanced process control (APC) system has been developed. The APC system has already been introduced regarding critical dimension (CD) and overlay controls in a photolithography process. It has improved the productivity and device performance. However, the current APC is based on the inspection data where process deviation is mingled with machine fluctuation and which has a very small quantity to be analyzed, then it has the limit in the effect. We have collected and stored the CD and overlay inspection data as well as the log data of the exposure tool in a relational database. So, we have investigated the method to compensate and solve the above-mentioned problem. First, we have extracted relationships between inspection data and many equipment parameters, especially correlation coefficients, in huge tool log data. Next, we have investigated the issues with significant relationships and have consequently extracted useful information not extracted by the conventional method. The purpose of this paper is to show that we have developed a second generation data mining system in cooperation with APC to prove the effect of stabilizing machine fluctuation.