Stacked LSTM Based Wafer Classification

被引：1

作者：

Shinde, Neeta ^{[1
]}

Chandana, S. ^{[1
]}

Patil, Shashank Anand ^{[1
]}

Chandana, K. Siri ^{[1
]}

Pendari, Neha Tarannum ^{[1
]}

Hiremath, P. G. Sunitha ^{[1
]}

Gangisetty, Shankar ^{[1
]}

机构：

[1] KLE Technol Univ, Hubballi, India

来源：

2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2021年

关键词：

Soft Sensing; Multivariate Time Series Classification; LSTM; Regularization; Wafer Manufacturing;

D O I：

10.1109/BigData52589.2021.9671835

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The sensors are used to analyze the quality of wafers in wafer manufacturing industries. The data from sensors is very helpful in framing solutions to predict the pass or fail status of the wafer by classifying high dimensional sensor data using machine learning techniques. In this work, we propose a stacked long short term memory (LSTM) approach i.e., a seq2seq architecture suitable for time-series data. We perform an exhaustive empirical analysis of the proposed model on the Seagate soft sensing dataset. The evaluation metric used is ROC-AUC score. The proposed stacked LSTM approach gave a best ROC-AUC score of 0.7445 on validation data and 0.729 on test data, which is significantly better than the baseline models.

引用

页码：5786 / 5790

页数：5

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