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Competition in the semiconductor industry is forcing manufacturers to continuously improve the capability of their equipment. The analysis of real-time sensor data from semiconductor manufacturing equipment presents the opportunity to reduce the cost of ownership of the equipment. Previous work by the authors showed that time series filtering in combination with multivariate analysis techniques can be utilized to perform statistical process control, and thereby generate real-time alarms in the case of equipment malfunction. A more robust version of this fault detection algorithm is presented. The algorithm is implemented through RTSPC, a software utility which collects real-time sensor data from the equipment and generates realtime alarms. Examples of alarm generation using RTSPC on a plasma etcher are presented.

This study is aimed at determining the worker training time and proficiency threshold for each activity in precast component production based on the learning curve theory. Training data for precast component production for the past 5 years were collected in Taiwan, including 317,832 datasets for 14 production activities involving a total of 4,352 worker participations and 492 completion times. A learning curve model for workers to master the manufacture of precast component was developed, yielding the major finding that training time for workers to learn precast component production has a learning curve slope = -0.75.The training time required to reach proficiency varies from 3.87 to 26.15 days for noncomplex activities. The findings also show that 4 out of 14 activities can be identified as complex with a learning curve slope of -0.75. Practitioners should mainly focus on worker training for those complex activities as the critical path to improving precast component productivity. The findings also provide thresholds (in days) for all activities which helps to quantify how much time is needed to efficiently train workers for precast component production.

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