A review of data mining applications in semiconductor manufacturing
For decades, industrial companies have been collecting and storing high amounts of data
with the aim of better controlling and managing their processes. However, this vast amount …
with the aim of better controlling and managing their processes. However, this vast amount …
Response surface methodology using observational data: a systematic literature review
In the response surface methodology (RSM), the designed experiment helps create
interfactor orthogonality and interpretable response models for the purpose of process and …
interfactor orthogonality and interpretable response models for the purpose of process and …
A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products
Cleaner production (CP) is considered as one of the most important means for
manufacturing enterprises to achieve sustainable production and improve their sustainable …
manufacturing enterprises to achieve sustainable production and improve their sustainable …
[HTML][HTML] Predictive model-based quality inspection using Machine Learning and Edge Cloud Computing
J Schmitt, J Bönig, T Borggräfe, G Beitinger… - Advanced engineering …, 2020 - Elsevier
The supply of defect-free, high-quality products is an important success factor for the long-
term competitiveness of manufacturing companies. Despite the increasing challenges of …
term competitiveness of manufacturing companies. Despite the increasing challenges of …
Data mining in battery production chains towards multi-criterial quality prediction
Battery production has become an increasingly important issue for industry eg due to the
advent of electric cars and the greening of grids. The battery production chain is very …
advent of electric cars and the greening of grids. The battery production chain is very …
Fault detection and diagnosis using self-attentive convolutional neural networks for variable-length sensor data in semiconductor manufacturing
Nowadays, more attention has been placed on cost reductions and yield enhancement in
the semiconductor industry. During the manufacturing process, a considerable amount of …
the semiconductor industry. During the manufacturing process, a considerable amount of …
Bayesian inference for mining semiconductor manufacturing big data for yield enhancement and smart production to empower industry 4.0
Big data analytics have been employed to extract useful information and derive effective
manufacturing intelligence for yield management in semiconductor manufacturing that is …
manufacturing intelligence for yield management in semiconductor manufacturing that is …
Pitfalls and protocols of data science in manufacturing practice
Driven by ongoing migration for Industry 4.0, the increasing adoption of artificial intelligence,
big data analytics, cloud computing, Internet of Things, and robotics have empowered smart …
big data analytics, cloud computing, Internet of Things, and robotics have empowered smart …
Decision-based virtual metrology for advanced process control to empower smart production and an empirical study for semiconductor manufacturing
Virtual metrology (VM) has been employed to improve the performance of advanced process
control for semiconductor manufacturing. A number of VM models have been proposed to …
control for semiconductor manufacturing. A number of VM models have been proposed to …
Wafer map defect pattern classification based on convolutional neural network features and error-correcting output codes
Defect clusters on the wafer map can provide important clue to identify the process failures
so that it is important to accurately classify the defect patterns into corresponding pattern …
so that it is important to accurately classify the defect patterns into corresponding pattern …