Data Warehousing for Manufacturing Yield Improvement

This paper will describe the data warehouse/decision support/data mining system that we have developed to improve disk drive manufacturing yields in the IBM Storage Technology Division. The data warehouse combines operational data from our disk, wafer and head stack processing plants. Data is extracted weekly, cleansed and correlated with the test results for the disk drives assembled from the corresponding components. The decision support system, based on SAS/IntrNet®, provides OLAP capability for yield analysis. This system enables the user to compare yield loss rates, by failure code, component source, EC level, date of manufacture, etc. The system also shows the sensitivity of drive yield to each continuous in-line measurement, and predicts the yield improvement that would result from component process improvements. The yield sensitivity studies are also used to identify critical parameters for input to the data mining algorithms of SAS/Enterprise Miner®. We use decision trees to identify combinations of parameters with unusually high or low yield, and neural networks to create multi-dimensional models of yield as a function of in-line measurements. This system allows us to quickly identify the factors causing yield loss, evaluate the cost/benefit of proposed changes, and operate our plants with optimal processes and specifications.