Optimizing Yield and Quality: The Role of AI and Yield Management Software

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

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. 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.

3. 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. 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.

4. 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. 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.

5. 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: • Eustace, J. (2017). Statistical process control in semiconductor manufacturing. Semiconductor Manufacturing Handbook, Second Edition, 503-530. • Tao, T. (2020). Advanced process control for semiconductor manufacturing. CRC Press. • 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. • Rathei, D. (2022). Yield Management Software and its Role in Process Excursion Prevention. Semiconductor Journal, 25(3), 45-52.