Citation:

Jyothi Swaroop Arlagadda Narasimharaju, 2023. Smart Semiconductor Wafer Inspection Systems: Integrating AI for Increased Efficiency ESP Journal of Engineering & Technology Advancements  3(2): 120-134.

Abstract:

The semiconductor industry has received the pressure of the need to develop techniques for higher efficiency and accuracy of wafer inspection processes. It has been a problem to inspect the complexity of the semiconductor wafers with traditional inspection systems and therefore sophisticated solution is required. This paper looks at the evaluation of Artificial Intelligent (AI) in semiconductor wafer inspection systems to improve the outcome. Applying the ML and Computer Vision approaches in AI allows automation of defect identification, sorting, and enhanced yield levels. From the points of methodology, the study offers a thorough analysis of the current research and development in the field of AI practices within wafer inspection and the effects that improvements have had on the manufacturing process. Some conclusions from experiment research and development show that the semiconductor organization’s distance in the speed of inspection time and the ratio of defect detection is notably enhanced, thus supporting the concept of AI convergence in the semiconductor organization.

References:

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Keywords:

Semiconductor, Wafer Inspection, Artificial Intelligence (AI), Machine Learning (ML), Computer Vision, Defect Detection, Yield Improvement, Deep Learning.