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Supervised Multivariate Kernel Density Estimation for Enhanced Plasma Etching Endpoint Detection
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DOI：10.1109/ACCESS.2022.3155513.png)
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Abstract
En 中文
The advancement of semiconductor technology nodes requires precise control of their manufacturing process, including plasma etching, which is highly important in terms of the yield, cost, and device performance. Endpoint detection (EPD) is an imperative technique for controlling this process. Here, we propose a novel EPD scheme based on multivariate kernel density estimation (MKDE). The proposed approach is developed by extending the conventional unsupervised learning MKDE method to supervised learning. The performance of the proposed scheme is validated on randomly selected optical emission spectroscopy data collected from an industrial semiconductor manufacturing process. Because the proposed approach uses target values (labeling) of data, it demonstrates enhanced EPD performance compared to the conventional MKDE method, even without threshold presetting.
Keywords:
Etching
Plasmas
Probability density function
Kernel
Stimulated emission
Semiconductor device manufacture
Optical sensors
Multivariate kernel density estimation
plasma etching
endpoint detection
anomaly detection
supervised learning
semiconductor manufacturing
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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