=================================================================================

Feature engineering is a critical role in enhancing defect detection processes through ML, focusing on the identification of key features that can predict wafer defects with high accuracy. For instance, you are the quality assurance manager at a semiconductor manufacturing company. Recently, you noticed an increase in defects in the wafers used for microchip production that need to be filtered out to maintain product quality. You want to use machine learning to predict which wafers are defective and should be rejected. Then, some possible features in this machine learning use case to detect defective wafers can be:

• Surface scratch patterns and number of particles on wafer surface - This feature is critical as scratches and excessive particles can indicate handling issues or contamination during the manufacturing process, both of which can lead to defects.
• Patterns of electrical test results that differ from those found in non-defective wafers - Electrical testing can reveal functional defects in the wafers that might not be visible. Differences in these test patterns compared to those from non-defective wafers can be a strong indicator of issues.
• Wafer thickness and uniformity measurements - Non-uniform thickness can affect the wafer's ability to function correctly in electronic devices. Measuring and detecting variations in thickness can therefore help in identifying potentially defective wafers.
• Reflectivity and color uniformity of the wafer surface - Changes in the optical properties of the wafer surface, such as reflectivity and color, can indicate contamination or other issues that affect the wafer's quality.

===========================================