The sensor signals (i.e., data streams of process parameters) of semiconductor processes exhibit nonlinear, multimodal trajectories with some common structural features. In this paper, we propose a process fault-detection approach based on the structural features of the sensor signals, such as the geometric shape, length, and height. The approach aims at constructing a shared univariate model and a multivariate model. The shared univariate model is set up for individual process parameters and clusters the process recipes of similar products. The result is a tree where the leaf nodes and intermediate nodes correspond to individual recipes and feature-based fault-detection criteria, respectively. The recipes with the same parent nodes share the criteria specified in the nodes. On the other hand, the multivariate model is constructed for a process recipe. It builds a Hotelling's T2 that considers the correlations between the signal structures of the process parameters. We demonstrated that the test results of the two models using the data collected from a work-site etch process were encouraging.
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Industrial and Manufacturing Engineering
- Electrical and Electronic Engineering