A Review of Data Mining Applications in Semiconductor Manufacturing

Processes

卷:9

EduardoM. G. Rodrigues¹  Radu Godina¹  Pedro Espadinha-Cruz² 

[1] Management and Production Technologies of Northern Aveiro—ESAN, Estrada do Cercal 449, Santiago de Riba-Ul, 3720-509 Oliveira de Azeméis, Portugal;

[2] UNIDEMI-Research and Development Unit in Mechanical and Industrial Engineering, Faculty of Science and Technology (FCT), Universidade NOVA de Lisboa, 2829-516 Almada, Portugal;

关键词: data mining;    semiconductor manufacturing;    quality control;    yield improvement;    fault detection;    process control;   

DOI  :  10.3390/pr9020305

来源: DOAJ

【 摘 要 】

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 of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are classified in function of the application area. The results are then analyzed and conclusions are drawn.

【 授权许可】

Unknown