Analysis method of metal contamination for the isopropyl alcohol (IPA)

Dobashi, Kazuya; Saito, Misako

doi:10.1109/issm.2016.7934542

Cited by 1 publication

(1 citation statement)

References 2 publications

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“…In manufacturing processes, impurities can affect the desired properties and the performance of the products. The metallic contaminants in semiconductor manufacturing will ruin the final products functionality [13]. The presence of contaminants inside metal during the manufacturing process has the potential to reduce the mechanical qualities of the products.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence in Particle and Impurity Detection and Removal: A Survey

Shuyuti,

Salami,

Dahari

et al. 2024

IEEE Access

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With the rapid development of artificial intelligence (AI), especially in machine learning and deep learning technologies, the particle and impurity detection and removal processes employed many industries have been improved. Particles and impurities at any size, shape and at any condition can be detected using the advanced technology in both areas. This paper presents a comprehensive overview of research papers that discuss the application of AI techniques for the detection and removal of particles and impurities. The publications featured in this review were mainly retrieved from the Web of Science (WoS) database, covering the timeframe from 2000 to 2023. This paper also covers the review on the impurity detection and removal specifically in edible bird's nest (EBN). The aim of this paper is to provide a valuable resource for the future development of AI applications in particle and impurity detection and removal technologies that have not been addressed in this study. Through the review and analysis of AI for particle and impurity detection and removal techniques in recent years, this paper includes the following parts: research trend in particle and impurity detection in general and AI in particle and impurity detection, applications of AI methods in particle and impurity detection in related industries including in EBN and AI application in particle and impurity removal. This review study will offer advantages to researchers engaged in the field of AI with regards to the detection and removal of particles and impurities.

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Section: Introductionmentioning

confidence: 99%