This article proposes a new approach for etch endpoint detection of small open area wafers. The traditional endpoint detection technique uses a few manually selected wavelengths, which are adequate for large open areas. As the integrated circuit devices continue to shrink in geometry and increase in device density, detecting the endpoint for small open areas presents a serious challenge to process engineers. In this work, a high-resolution optical emission spectroscopy (OES) system is used to provide the necessary sensitivity for detecting subtle endpoint signals. Principal component analysis is used to analyze the OES data and extract key components that capture the endpoint signal. Data analysis from many wafers shows that the endpoint pattern in the principal components is repeatable. Two methods are used to select wavelengths so as to improve reliability and reduce susceptibility to noise. The first method is to remove those spectrum windows that contain no endpoint information. The second method is to use a “sphere” criterion to select the most relevant wavelengths. The final endpoint algorithm using a much-reduced number of wavelengths shows more distinguishable and reliable endpoint features.

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and Conclusions TMSDEA gives consistently higher contact angles than HMDS on substrates typically encountered in IC processing. The effect of water drop contact angle on resist adhesion is demonstrated. A solution of 0.5% vol TMSDEA in HMDS, used at a 50C hotplate temperature, was found to be optimum for a Tokyo Electron Laboratory Mk-V track vapor prime process. Observations made with three reticle levels on bare silicon do not show any exposure dose or resist profile differences between TMSDEA and HMDS vapor primed wafers.

The use of wafer randomization and positional analysis in manufacturing is ubiquitous and well established. Wafer electrical and yield data can be traced back to specific operations in the manufacturing process with the help of wafer sequencing records. Tight process windows or complex process technologies may however require that the statistical parameter versus sequence signal be combined with other variables including parameter spatial distribution on wafer, batch loading sequence, and an intimate knowledge of the process tool itself. In this paper case studies are presented to demonstrate how the above were combined to optimize the robustness of an especially critical gate oxidation process module. Finally possible enhancements to analysis techniques are discussed.