In the semiconductor manufacturing industry, the yield signifies the amount of product derived from a specific process. Yield can be evaluated in different dimensions such as die yield, wafer yield, and manufacturing yield. Enhancing yield is an intricate process involving rigorous data analysis and root cause identification to alleviate any bottlenecks in the manufacturing process.
The Role of Yield Management Systems in Semiconductor Manufacturing
Yield is scrutinized using Yield Management System (YMS) solutions in semiconductor manufacturing. The fusion of machine learning and data mining techniques into YMS can bring automation and support to the table, thereby increasing yield, reducing yield loss, and optimizing the overall production yield.
Stages of Yield Analysis in Semiconductor Manufacturing
The yield analysis in semiconductor manufacturing comprises three primary stages. The first stage focuses on monitoring failure map patterns of semiconductor wafers, identifying areas with high failure concentrations. The second stage involves identifying the root cause analysis in semiconductor of these failures, leveraging pattern mining techniques. The final stage is the tracking of failure recurrence, utilizing deep learning methodologies. Through each of these stages, data is thoroughly analyzed, patterns are recognized, and measures are implemented to prevent future occurrences, ultimately improving the overall yield.
Monitoring Failure Map Patterns
The first stage of yield analysis involves monitoring the failure map patterns of semiconductor wafers. A critical component in semiconductor manufacturing, any glitch in wafer production can significantly impact the overall yield. In the wafer fabrication stage, various tests like the Wafer Acceptance Test (WAT) are conducted. The data from these tests, when collated, generates a wafer failure map – an essential tool for yield analysis.
Identifying Causes of Failure
In the second stage of yield analysis, the causes of failure are identified. Pattern mining, a method of extracting valuable, recurrent patterns from voluminous datasets, can be utilized effectively to spot devices that could potentially cause failures. Notably, the FP-Growth algorithm proves efficient in finding complete sets of frequent patterns in large datasets, thus enabling swift and precise identification of the components causing a decline in manufacturing yield.
Monitoring the Recurrence of Failures
The third stage of yield analysis focuses on monitoring the recurrence of failures. A subset of machine learning, deep learning, is proposed as an effective tool for this stage. It involves training a neural network model on patterns, which can then autonomously classify new wafers and signify long-term failure occurrence trends.
The Application of Machine Learning in Yield Analysis
Machine learning plays a crucial role in yield analysis in the semiconductor manufacturing industry. It automates the detection of failure map patterns using algorithms such as K-Means, which group wafers exhibiting similar patterns, reducing manual work for engineers. In the process of pattern mining, algorithms like FP-Growth efficiently identify recurring failure patterns in large datasets, leading to a ranking of potential cause devices. Furthermore, deep learning, a subset of machine learning, aid in the long-term monitoring of failure occurrences by training neural network models to automatically classify new wafers and predict trends. The application of machine learning significantly enhances yield analysis, driving productivity and efficiency.
Cluster Detection Using K-Means Algorithm
The K-Means algorithm in machine learning can automate the grouping of wafers with similar failure map patterns. This automation of failure pattern recognition minimizes the manual work of yield enhancement engineers and test engineers.
Pattern Mining with the FP-Growth Algorithm
Pattern mining using the FP-Growth algorithm results in ranking potential cause devices. Such an approach allows for quick identification of problematic components, enabling yield enhancement engineers to promptly address these issues.
Long-term Failure Occurrence Trends through Deep Learning
For the implementation of this deep learning approach, several elements need to be considered: dataset requirements, network structure, learning rate settings, dropout technique, model averaging, and learning procedures. GPUs play a pivotal role in accelerating learning speed in these deep learning applications.
The Integration of Advanced Techniques into an Automated Monitoring System
The automated monitoring system designed with engineer-friendly interfaces can facilitate real-world semiconductor manufacturing settings and enable comprehensive and long-term monitoring automation. The integration of machine learning and data mining technologies is projected to reduce the labor of engineers, thus contributing to significant yield enhancement.
Impact of Machine Learning and Data Mining on Labor Reduction
By reducing the manual labor of engineers, product engineers, characterization engineers, and yield engineers, machine learning and data mining can increase efficiency and productivity while improving the production yield report.
The Significance of Enhanced Yield in Semiconductor Manufacturing
In summary, the application of machine learning and data mining to yield analysis heralds a groundbreaking approach to yield engineering in the semiconductor manufacturing industry. Integrating these advanced techniques into existing processes can significantly enhance manufacturing yield, reduce yield loss in manufacturing, and improve overall productivity and efficiency.
Embracing the digital era, these technologies promise a future of optimized yield, maximized productivity, and groundbreaking efficiency in the semiconductor manufacturing industry.
Conclusion
In conclusion, the integration of machine learning and data mining techniques into semiconductor manufacturing yield analysis marks a significant shift in the industry’s approach to yield engineering. Not only do these advanced methods help reduce labor and improve efficiency, but they also lead to substantial improvements in yield and productivity.
As the semiconductor industry continues to evolve and innovate, the application of these advanced techniques is set to become increasingly critical. They have the potential to revolutionize the way we approach yield analysis, making it more precise, efficient, and effective. The future of the semiconductor industry lies in the successful incorporation of these advanced techniques into existing manufacturing processes, promising unprecedented levels of yield enhancement and efficiency.
In the face of increasing demand and rapidly advancing technology, embracing these changes is not only beneficial but necessary. The semiconductor manufacturing industry must adapt and evolve, leveraging the power of machine learning and data mining to stay at the cutting edge of yield engineering. Only then can we fully realize the potential of these technologies and drive the industry forward into a future of higher yields, improved efficiency, and greater productivity.
References
- Zheng, N., Zhang, T., & Zhao, L. (2013). Yield enhancement in semiconductor manufacturing based on advanced data mining. Procedia CIRP, 7, 518-523.
- Khan, S. (2012). Semiconductor Manufacturing Technology: Materials, Processes, and Equipment. McGraw Hill Professional.
- Hsu, C. H., Yang, C. C., & Jiang, J. A. (2010). Semiconductor yield prediction using data mining. Expert Systems with Applications, 37(10), 7206-7216.
- Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD Record, 22(2), 207-216.
- Srivastava, J., Cooley, R., Deshpande, M., & Tan, P. N. (2000). Web usage mining: discovery and applications of usage patterns from Web data. SIGKDD Explorations, 1(2), 12-23.
- Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. In ACM sigmod record (Vol. 29, No. 2, pp. 1-12). ACM.
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
