In semiconductor wafer fabrication, etching is one of the most critical processes, by which a material layer is selectively removed. Because of difficulty to correct a mistake caused by over etching, it is critical that etch should be performed correctly. This paper 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) sensor is used to provide the necessary sensitivity for detecting subtle endpoint signal. Partial Least Squares (PLS) method is used to analyze the OES data which reduces dimension of the data and increases gap between classes. Support Vector Machine (SVM) is employed to detect endpoint using the data after PLS. SVM classifies normal etching state and after endpoint state. Two data sets from OES are used in training PLS and SVM. The other data sets are used to test the performance of the model.The results show that the trained PLS and SVM hybrid algorithm model detects endpoint accurately.
In current semiconductor manufacturing, as the feature size of integrated circuit (IC) devices continuously shrinks, detecting endpoint in low open area plasma etch process is more difficult than before. For endpoint detection, various kinds of sensors are installed in many semiconductor manufacturing equipment, and sensor data sampled with predefined sampling rate. To solve this problem, a combination of Signal to Noise Ratio (SNR), Principal Component Analysis (PCA) and Expanded Hidden Markov model (eHMM) technique is applied to optical emission spectroscopy (OES) signals.
Bosch process is developed for advanced microstructure devices and etch endpoint detection (EPD) is demanded for 'notching' (as feature profile degradation) or reducing thickness of the underlying stop. One method commonly used to detect plasma process endpoint is utilizing optical emission spectrometry (OES) sensor. OES analyzes the light emitted from plasma source to draw inferences about the chemical and physical state of the plasma process. In this paper, an endpoint detection algorithm conjunction with Principal Component Analysis (PCA) and extended Hidden Markov Model (eHMM) using OES signal in Bosch process is proposed. PCA is used to reduce dimension of data without information loss and eHMM is applied to correctly detect endpoint. In 120 um TSVs, this work shows excellent performance.
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