Data mining solves tough semiconductor manufacturing problems

Quickly solving product yield and quality problems in a complex manufacturing process is becoming increasingly more difficult. The “low hanging fruit” has been plucked using process control, statistical analysis, and design of experiments which have established a solid base for a well tuned manufacturing process. However, the dynamic “higher-tier” problems coupled with quicker time to market expectations is making finding and resolving problems quickly an overwhelming task. These dynamic “higher tier” problems include: multi-factor & nonlinear interactions; intermittent problems; dynamically changing processes; installing new processes; multiple products; and, of course, the increasing volumes of data. Data mining technology can increase product yield and quality to the next higher level by quickly finding and solving these tougher problems. Case studies of semiconductor wafer manufacturing problems are presented. A combination of self-organizing neural networks and rule induction is used to identify the critical poor yield factors from normally collected wafer manufacturing data. Subsequent controlled experiments and process changes confirmed the solutions. Wafer yield problems were solved 10x faster than standard approaches; yield increases ranged from 3% to 15%; endangered customer product deliveries were saved. This approach is flexible and can be appropriate for a number of complex manufacturing processes