Simultaneous control and identification for multiple product and process environments in semiconductor manufacturing

It is typical in semiconductor manufacturing to see many different products being made by using a variety of process conditions on the same pieces of equipment. Because of the nature of the process conditions and the high cost of materials, it is very difficult and often impossible to obtain measurements of key process variables while the process is operating. Product wafers are run in batches on processing tools using recipes, which specify the parameters necessary to run the tool such as pressure, temperature, and processing time. Measurements are made after processing steps are completed in order to determine if batches meet their specifications. Run-to-run control methods use the measurement data available at the end of each run to determine better recipe settings for subsequent batches. This task is made more difficult by the fact that measurements are often confounded by several different possible sources of variation.; This research investigates a Kalman filter-based state estimation scheme that views a process area with all the tools, products, and processes it contains as a single interrelated system. This formulation maximizes the amount of information that is shared across different batches by capturing their common characteristics in common parameters. The estimation scheme performs state updates correctly even when measurement data is missing or delayed. A set of simulations are used to demonstrate the performance of the algorithm under different operating conditions.; The trace of the state error covariance matrix from the Kalman filter is used as a metric for determining the apparent value of a particular data set to the run-to-run control algorithm. Processing decisions such as batch scheduling, tool allocation, and sampling plans are shown to have an effect on controller performance. Algorithms using the state error covariance matrix are developed that can recommend ways to optimize the scheduling aspects of the factory in order to provide run-to-run control algorithms with the best possible information. Simulation results demonstrate that measurable improvements in state estimation and control output performance can be achieved by using information from the process controller to help make better scheduling and sampling decisions.