Huiyuan Cheng1, Melanie Po-Leen Ooi1, Ye Chow Kuang1, Serge Demidenko2, Bryan Cheah3
1Monash University, 2RMIT International University, 3Freescale Semiconductor
Systematic defect clusters are a common observation in the semiconductor manufacturing process. It is well known that most of the defect clusters found on the fabricated semiconductor wafers have an assignable cause, which if rectified quickly can improve product quality and lower the production cost. Unfortunately direct investigation of the root cause is often too expensive to be frequently performed. This paper proposes a statistical correlation method that extends an existing so-called Automatic Defect Cluster Analysis System (ADCAS), which can be implemented in real-time such that the manufacturing cost will not be negatively affected. The system proposed in this paper will generate a list of equipments that has a high likelihood of causing the systematic failure. This technique is fast and easy to implement, and provides early detection and prevention of problematic equipment/process during manufacturing.