Modeling and Characterization of Plasma Processes Using Modular Neural Network

摘要

In semiconductor manufacturing, complex and nonlinear fabrication processes are ubiquitous. Plasma processing such as plasma enhanced chemical vapor deposition (PECVD) and reactive ion etching (RIE) are workhorses in semiconductor fabrication, but also play as yield limiters due the nature of complexity of plasma process. In this paper, modular neural network (MNN) is applied for the purpose of plasma process modeling and characterization in the area of semiconductor manufacturing. MNN consists of a number of local expert networks (LENs) and one gating network. LENs compete using supervised learning to learn different regions of the data space under the supervision of gating network. Once proper MNNs for various responses of interest are established, response surfaces are generated to visually assist the characterization of the processes. As either an alternative or an augmentation to existing methods, this can provide more reliable and flexible flat form of process modeling and characterization in semiconductor manufacturing environment.