Why there is a growing interest in outlier detection
The automotive industry is experiencing rapid growth, and with this growth comes an increased demand for semiconductor components that are reliable and safe. As automotive semiconductor companies strive to produce high-quality products, there is a growing interest in outlier detection.
First, let’s start by explaining what outlier detection is. It is the process of identifying data points that deviate from the norm. In the context of the semiconductor industry, outliers may indicate defects or anomalies in the manufacturing process that could impact product performance and reliability.
The automotive industry has some of the highest safety standards of any industry, and semiconductor components used in automotive applications must be reliable and safe.
Any defect or anomaly in a semiconductor component could have serious consequences, including safety issues, system failure, and costly recalls.
Outlier detection helps automotive semiconductor companies identify and address potential issues early in the manufacturing process, reducing the risk of defects and improving product quality and reliability.
One of many reasons why there has been a growing interest in outlier detection is the increased complexity of semiconductor components. As semiconductor components used in automotive applications become more complex, the manufacturing process becomes more challenging.
There has also been a higher demand for autonomous vehicles. Autonomous vehicles require a complex array of sensors and processing components, and any defect or anomaly in these components could have serious implications. Outlier detection is critical in ensuring the safety and reliability of these components and helping automotive semiconductor companies meet the demand for high-quality products.
yieldHUB is a leading yield management platform that offers advanced outlier detection capabilities. Using an algorithm, outliers that deviate vastly from the norm can be found. In other words, you calculate what is average, then exclude what isn’t.
yieldHUB’s outlier detection module incorporates Part Average Testing (PAT) and GDBN and is a vital enabler for chip companies serving the automotive industry. A few key advantages that yieldHUB users get include:
Statistical Process Control (SPC)
yieldHUB's SPC capabilities allow users to monitor key process parameters and identify trends and patterns that may indicate outliers. By utilizing SPC, users can identify issues early on and take corrective action before they become significant problems.
Automated wafer mapping
yieldHUB's automated wafer mapping feature allows users to quickly and accurately identify and visualize the location of outliers on a wafer map. This enables users to quickly identify the root cause of outliers and take corrective action to address the issue.
Integrated data analysis
yieldHUB's software integrates data from multiple sources, including probe, test, and manufacturing data, to provide a comprehensive view of the manufacturing process. This integrated data analysis allows users to identify and address outliers across the entire manufacturing process, from wafer fabrication to final product testing.
yieldHUB's software allows users to set customizable alerts that trigger when outliers are detected. These alerts can be configured to notify users via email or other means, ensuring that potential issues are addressed promptly.
yieldHUB’s advanced outlier detection capabilities can help semiconductor companies identify and address potential issues early in the manufacturing process. yieldHUB's outlier detection capabilities were designed for high-volume wafer sort and the outlier detection algorithms will be easy to set up per test and per product.
By leveraging yieldHUB's outlier detection capabilities, semiconductor manufacturers can stay ahead of potential issues and reduce the risk of defects, improving their bottom line and strengthening relationships throughout the supply chain.
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