Plasma Etching Endpoint Detection in the Presence of Chamber Variations Through Nonlinear Manifold Learning and Density-Based Clustering

The consistent decrease in the open ratio of wafers has spurred a demand for advanced endpoint detection (EPD) techniques to ensure accurate plasma etching in nonlinear optical emission spectroscopy (OES) data characterized by a low signal-to-noise ratio. Additionally, precise detection of endpoint is hindered by variations between plasma chambers arising from diverse issues. To address these issues, this study proposes a nonlinear manifold learning-based EPD model and a chamber condition identification framework. The EPD model demonstrates the capability to extract endpoint-related latent variables from complex nonlinear OES data. Moreover, the model exhibits the ability to generalize to larger datasets through density-based time series clustering. The chamber condition identification framework not only classifies plasma conditions but also automates the determination of the conditions …