Data mining solves tough semiconductor manufacturing problems.pdf

Data Mining Solves Tough Semiconductor Manufacturing Problems Mike Gardner Motorola Laboratories 2100 East Elliot Road, MD EL508 Tempe, Arizona 85284, USA 480-413-5187 axsm10@ Jack Bieker Motorola Laboratories 2100 East Elliot Road, MD EL508 Tempe, Arizona 85284, USA 480-413-4671 Jack.Bieker@ ABSTRACT 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 Keywords Semiconductor yield enhancement, manufacturing optimization, machine learning, data mining, neural networks, self organizing maps, rule induction, pattern recognition. 1 THE PUZZLE: DEBUGGING COMPLEX MANUFACTURING PROCESSES If the world were well