Improved state estimation for high-mix semiconductor manufacturing

High-mix manufacturing in semiconductor industry has driven the development of several non-threaded state estimation methods, which share information among different manufacturing context and avoid data segregation that threaded methods require. However, existing non-threaded methods consider either white noise disturbance or integrated white noise disturbance. In this work, we derive the state-space representation of the non-threaded state estimation problem which not only considers the integrated moving average disturbance, but also considers the fact that if a context item is not involved in a process run then its state does not change. In addition we develop an improved non-threaded state estimation method based on the Kalman filter. Simulation examples are given to demonstrate the effectiveness of the proposed method. The performance of the proposed approach is also compared with the existing Kalman filter approach that considers the integrated white noise only.