Machine Learning for inspection and defect classification in semiconductor manufacturing

Moore’s Law, which predicts that the number of transistors on a microchip doubles every two years, has resulted in the miniaturization of semiconductor devices to nanometer scales. As the size of these devices decreases, the number of defects that can affect their performance and yield also increases.

Even the smallest nanometer-sized defects can cause significant performance degradation, impacting the overall yield of the semiconductor device. These defects can arise due to a variety of reasons, including process variations, material impurities, and equipment malfunctions. Detecting and mitigating these defects is crucial to ensure the high quality and reliability of semiconductor devices.

Machine learning has emerged as a powerful tool to detect and classify these nanometer-sized defects in semiconductor manufacturing. Machine learning algorithms can analyze large amounts of data from various sources, such as process sensors, metrology equipment, and inspection tools, to detect patterns and anomalies that indicate the presence of defects. By using machine learning, semiconductor manufacturers can identify defects that may have gone undetected by conventional inspection methods, improving the overall yield of their devices. Furthermore, machine learning can also help in identifying the root cause of these defects, enabling manufacturers to make the necessary process adjustments to prevent similar defects from occurring in the future.

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