In semiconductor manufacturing, preventing process excursions is critical for optimizing yield and reducing costs. Yield management software and AI technology enhance the detection and analysis of process excursions through advanced methods such as statistical process control and fault detection. By empowering engineers with pattern recognition capabilities, manufacturers can proactively identify issues, improve process stability, and ensure high-quality products.

1. Optimizing Yield and Quality: The Role of AI
and Yield Management Software in
Preventing Process Excursions

2. In the fast-paced world of semiconductor manufacturing, preventing process excursions is crucial
for optimizing yield, reducing wafer scraps, and efficiently allocating engineering and
manufacturing resources. The prevention of process excursions is of utmost importance in the
semiconductor manufacturing industry as it directly impacts yield loss, wafer scraps, and the
efficient allocation of engineering and manufacturing resources.
Early detection and analysis of process excursions can have a significant positive impact on the
overall cost and product yield of the semiconductor manufacturing process. In this blog, we will
explore the importance of preventing process excursions and how yield management software
(YMS) and AI technology play a vital role in achieving this goal.

3. Detecting Process Excursions
In semiconductor manufacturing, process excursions can be detected through in-line inspections,
where deviations from the desired process parameters are identified. However, relying solely on in-line
inspections may not be sufficient to catch all excursions. In some cases, these excursions may go
unnoticed until later stages of production, such as during wafer testing in the probing area after the
completion of the manufacturing process. This delayed detection can result in substantial yield loss and
require significant engineering resources to rectify the issue.
The Role of Yield Management Software and AI Technology
To address the challenge of timely detection and prevention of process excursions, the semiconductor
industry has embraced the use of yield management software (YMS) and AI technology. These
advanced tools provide the means to detect, analyze, and prevent excursions efficiently and effectively.
Statistical Process Control (SPC)
One of the primary systems employed for preventing process excursions is statistical process control
semiconductor (SPC). SPC is a widely adopted method used for monitoring defect counts and in-line
wafer measurements, typically focusing on critical dimensions. However, with advancements in
technology, SPC has evolved to include the application of statistical analysis for electrical wafer test
data as well. This expansion enhances the capabilities of SPC, enabling manufacturers to detect
excursions with greater accuracy and timeliness.

4. Advanced Process Control (APC)
In addition to SPC, advanced process control (APC) tools are utilized to maintain optimal yield and quality
levels. These tools include fault detection and classification (FDC), virtual metrology (VM), and run-to-run
(R2R) control.
Fault Detection and Classification (FDC)
FDC employs statistical methods to continuously monitor equipment parameters such as temperature,
pressure, and other sensor data during wafer processing. By closely analyzing this data, FDC can swiftly
identify and eliminate undesired process conditions, leading to improved process stability and reduced
excursion occurrences.
Virtual Metrology (VM)
VM technology leverages sensor data from equipment to calculate and estimate key wafer properties, such
as deposited thickness layers. This enables real-time monitoring and prediction of wafer characteristics,
allowing for proactive adjustments to maintain process stability and prevent excursions.
Run-to-Run (R2R) Control
R2R control involves the continuous adjustment of process parameters based on measurements from
previously processed lots or wafers. By utilizing historical data and implementing feedback control
mechanisms, R2R control ensures that process variations are minimized, leading to enhanced yield and
reduced excursion risks.

5. Empowering Engineers with Advanced Pattern Recognition
A crucial component in preventing process excursions is the implementation of robust yield management software with advanced pattern recognition
capabilities. This software plays a pivotal role in efficient excursion detection and classification on wafers. By leveraging AI and machine learning algorithms,
the software can effectively detect and classify spatial patterns present on wafers, thereby enabling engineers to quickly identify and isolate affected wafers.
This capability not only saves valuable time for process and yield engineers but also facilitates in-depth analysis and comparison of patterns across different
wafers. The automated pattern classification further streamlines the process, ensuring accurate and swift identification of excursions.
Driving Process Optimization and Preventing Future Excursions
By determining the root cause of process excursions, semiconductor engineers can develop actionable plans to improve the manufacturing process and
prevent future excursions. This proactive approach enables targeted process optimizations and adjustments, leading to improved product wafer sort yield
and reduced manufacturing costs. With the insights gained from the analysis of excursions, manufacturers can make data-driven decisions to enhance
process stability, minimize variability, and ensure consistent high-quality semiconductor products.
Conclusion
In the competitive landscape of semiconductor manufacturing, preventing process excursions is essential for achieving high yield, reducing costs, and
optimizing resource utilization. Through the utilization of yield management software, AI technology, statistical process control, and advanced process
control, manufacturers can detect, analyze, and address process excursions promptly.
By implementing these advanced systems and approaches, the industry can enhance product quality, reduce yield loss, and improve overall manufacturing
efficiency. With the continuous advancements in AI and semiconductor manufacturing technologies, the future holds even more potential for preventing
excursions and driving innovation in the industry.
References:
1. Eustace, J. (2017). Statistical process control in semiconductor manufacturing. Semiconductor Manufacturing Handbook, Second Edition, 503-530.
2. Tao, T. (2020). Advanced process control for semiconductor manufacturing. CRC Press.
3. Rahman, A., Alghamdi, A., & El-Halwagi, M. M. (2021). Machine learning approaches for fault detection and classification in semiconductor
manufacturing processes: A comprehensive review. Journal of Manufacturing Systems, 60, 136-159.
4. Rathei, D. (2022). Yield Management Software and its Role in Process Excursion Prevention. Semiconductor Journal, 25(3), 45-52.