Unsupervised novelty pattern classification of shmoo plots for visualizing the test results of integrated circuits
作者：
Highlights：
• Shmoo plots in semiconductor manufacturing are used for indicating device state.
• We address high-dimensional and multiclass imbalance challenges for shmoo plots.
• We introduce a feature extraction process for presenting clear shmoo plot patterns.
• We propose a two-stage clustering process to solve multiclass imbalance situations.
• To demonstrate the applicability of the proposed model, real field data is used.
摘要
•Shmoo plots in semiconductor manufacturing are used for indicating device state.•We address high-dimensional and multiclass imbalance challenges for shmoo plots.•We introduce a feature extraction process for presenting clear shmoo plot patterns.•We propose a two-stage clustering process to solve multiclass imbalance situations.•To demonstrate the applicability of the proposed model, real field data is used.
论文关键词：Shmoo plot,Class imbalance,High dimensionality,Unlabeled data,Feature extraction,Clustering
论文评审过程：Received 23 March 2021, Revised 14 March 2022, Accepted 25 April 2022, Available online 28 April 2022, Version of Record 2 May 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117341
