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Wafer yield management plays a crucial role in semiconductor manufacturing by addressing various factors that can influence the efficiency and quality of the wafer manufacturing process.
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The automotive industry is undergoing significant transformations in the realm of semiconductor technologies utilized in vehicles. With the increasing number of chips in cars and the growing levels of automation, traditional part average testing (PAT) methods are no longer sufficient to ensure the desired levels of quality and reliability.
While PAT has been a prevalent practice in the automotive sector for nearly three decades, relying on statistical control limits to enhance yield and end-of-the-line quality, the emergence of advanced AI systems and autonomous driving technologies necessitates the adoption of more sophisticated outlier detection techniques and enhanced inspection and test coverage.
Zero Defects in Semiconductor Manufacturing for Automotive Applications.pptx
The semiconductor industry plays a critical role in providing electronic components for automotive applications. With the increasing complexity and reliance on electronic systems in vehicles, the need for zero defects in semiconductor manufacturing has become paramount. Automotive customers demand high-quality components to ensure the reliability, safety, and performance of their vehicles. Therefore, semiconductor manufacturers are focusing on implementing stringent quality control measures to minimize defects, reduce field returns and warranty issues, and mitigate liability concerns.
Machine Learning and Data Analytics in Semiconductor Yield Management.pptx
In the semiconductor manufacturing industry, the need for continuous quality improvement has never been more pronounced. This demand is driven by an unprecedented influx of manufacturing data, with more than 1000 process parameters recorded for a single wafer, and tens of thousands of wafers being produced daily. Traditional statistical methods have proven insufficient to fully exploit these massive volumes of data.
As such, this paper explores the application of hybrid machine learning techniques, specifically Memory Based Reasoning (MBR) and Neural Network (NN) learning, as more powerful tools for managing this complexity and improving yield in semiconductor manufacturing.
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