Supervised Multivariate Kernel Density Estimation for Enhanced Plasma Etching Endpoint Detection

doi:10.1109/ACCESS.2022.3155513

2022-01-01

DOI：10.1109/ACCESS.2022.3155513

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摘要

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.

Keyword:

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

期刊

IEEE Access

IF:

3.6

论文数:

9.8W

被引数:

29.4W

机构

Soongsil University

学者数:

3.4K

论文数: 3.5K

被引数: 3.2K

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