In the era of technological advances and Industry 4.0, massive data collection and analysis
is a common approach followed by many industries and companies worldwide. One of the …

Unit-to-unit variation among the production chambers is a long-lasting challenge for Fault
Detection and Classification (FDC) development in the semiconductor industry. Currently …

Nowadays, profile data mining techniques facilitate effective process monitoring, quality
control, fault diagnosis, etc., with considerable benefits to manufacturing industry. However …

This paper presents a systematic literature review of applied methods for data-driven
decision-making (DDD) in shop floor quality management (QM). The goal is to give an …

In today's semiconductor manufacturing industry, fault detection (FD) and classification
(FDC) techniques are usually leveraged for equipment health monitoring (EHM) and …

As semiconductor manufacturing processes become more complex and sophisticated,
manufacturers demand high quality, efficient process monitoring and fault detection (FD) …

In the era of Industry 4.0, Machine Learning (ML) technologies are viewed as the main
potential tools for analysing the huge amount of data collected by industries and predict …

Abstract Prognostics and Health Management (PHM) system is facing new challenges in
reproducing consistent results in different industrial scenarios in the current industry …

In the era of Industry 4.0, Machine Learning (ML) technologies are viewed as the main
potential tools for analysing the huge amount of data collected by industries and predict …