Development of an Alarm Pattern Detection Scheme for Managing Alarm Floods in Bumping Process

The pursuit of high yield in semiconductor packaging manufacturing is hindered by the increasing complexity of monitoring and alarm systems, leading to alarm floods which not only interfere with operations but also mask critical issues affecting yield. Therefore, this paper proposes an Alarm Pattern Detection (APD) Scheme to address the problem of alarm floods in semiconductor packaging manufacturing. The APD scheme intelligently identifies critical production machines using the random forest algorithm and applies PrefixSpan to find alarm patterns from these machines. Minwise Hashing and Locality Sensitive Hashing techniques are then adopted to retain significant alarm patterns. The effectiveness of the APD Scheme is first demonstrated through a simulation study, where it achieves superior precision and scalability compared to traditional methods such as FP-Growth and PrefixSpan. Further, it is validated with actual manufacturing process data that the APD Scheme can 1) identify critical alarm patterns affecting yield, and 2) reduce the burden of alarm floods on operators so as to achieve the goal of improving manufacturing yield through the monitoring and management of these patterns.