Wafer Acceptance Testing (WAT) and Process Control Monitoring (PCM) are essential for optimizing semiconductor yield, providing systematic measurements of device parameters to enhance manufacturing consistency and quality. These processes deliver critical data to fabless companies, enabling detailed analysis for identifying yield loss and reliability issues, which is crucial for continuous improvement. With increasing chip complexity, the semiconductor industry must invest in advanced WAT/PCM systems and data management technologies to meet market demands.

1. Optimizing Semiconductor Yield with
Robust WAT and PCM Processes
https://yieldwerx.com/

2. Wafer Acceptance Testing (WAT) and Process Control Monitoring (PCM) are instrumental elements within the semiconductor manufacturing
industry. They are crucial tools utilized predominantly by fabless companies that seek to monitor and enhance their yield and defect rates.
WAT/PCM is the systematic measurement of various device parameters during different stages of wafer processing. It establishes control over the
manufacturing process, leading to better consistency and quality of the final product. This measurement process aims to build a comprehensive
database of process information, useful for a variety of process enhancement and control activities.
Understanding the Composition of WAT Data
On average, the wafer acceptance test (WAT) data consists of 40 to 100 tests per wafer. These tests help monitor the consistency of key
parameters across the wafer lot, such as sheet resistance, oxide thickness, junction depth, gate length, and others. The data are critical to Fab's
(foundry's) statistical process control system (spc), facilitating continuous improvement and helping to reduce variances in the manufacturing
process.
The Role of Data Delivery and Yield Management Systems
After the completion of the semiconductor manufacturing process, the data is delivered to the fabless customer by the Fab. Typically, the data is
delivered per lot, with one lot containing around 25 wafers. ASCII CSV or Excel is the preferred format for data delivery due to its wide
acceptability by data analysis and management software. This data is integral to modern yield management systems (YMS) or yield analysis
systems. Whether they are cloud-based or on-premise, these systems securely process and store this data, ensuring its integrity and accessibility
for subsequent analysis. The data is divided by wafer ID, and then further segmented into each site, i.e., the location of testing on the wafer,
which allows for a comprehensive and detailed analysis.
Advantages of Data Analysis and Visualization
Although the size of WAT data is relatively small, it offers enormous benefits in terms of data analysis and visualization. Despite its compact size, it
can provide comprehensive insights into the manufacturing process, potentially identifying variations or shifts in process parameters.
Visualization tools are extensively used to interpret WAT data. These tools help in identifying abnormalities or deviations in the data, which can
serve as early warnings for potential yield loss or reliability issues. Detecting such issues at the earliest stages can help companies prevent
substantial losses and continually improve their manufacturing processes.

3. The Importance of Correlating WAT Data
Correlating WAT data against wafer sort and final test data can provide meaningful insights into the causes of yield loss, reliability, and quality
issues. This analysis can identify problematic trends, forming a foundation for effective communication with Fab to understand and rectify the
root cause analysis in semiconductor. In essence, this correlation is a vital tool for decision-making and continuous improvement in the
semiconductor manufacturing process.
Automation of the correlation process, achieved through the deployment of suitable algorithms and tools, enables rapid identification of problem
areas. Once these areas have been identified, relevant teams can address the root cause, preventing recurrence and enhancing overall yield and
reliability.
The Imperative for Clean and Consolidated Data
A well-implemented system that effectively processes and analyzes WAT data requires clean and consolidated data, especially at the final test
stage. Given the vast number of parameters and the complexity of semiconductor devices, the risk of data contamination or inconsistency is high.
Implementing data cleansing methodologies is crucial to ensure the integrity and reliability of the WAT data.
Effectively processing and analyzing WAT data can result in substantial cost savings in the semiconductor manufacturing industry by improving
manufacturing processes, reducing yield loss, and alleviating reliability issues. Hence, investing in WAT/PCM monitoring and subsequent data
management and analysis is a worthy endeavor for any semiconductor manufacturing organization.

4. Conclusion
The growth of the semiconductor industry is directly proportional to the ability to efficiently manufacture high-quality and high-performance
semiconductor devices. This efficiency and quality heavily rely on robust monitoring and control of the manufacturing process, and here, WAT
and PCM play a pivotal role.
WAT and PCM have been an integral part of the semiconductor manufacturing process for several decades. Still, with increasing chip complexity
and decreasing geometries, their role is becoming even more critical. Moving forward, the semiconductor industry should continue to invest in
advanced WAT/PCM systems, automated data analysis, and correlation tools, and stringent data management protocols to keep up with the
ever-increasing demands of the market.
References
1. Dehan, M. (2010). Wafer acceptance test: What, why, and how? In 2010 IEEE International Test Conference.
2. Mathew, J., & Rao, M. (2003). Statistical analysis of wafer acceptance test (WAT) data. In 2003 IEEE International Conference on
Semiconductor Electronics (ICSE).
3. Semiconductor Manufacturing Technology, 2nd Edition by Michael Quirk and Julian Serda, ISBN-13: 978-0130815200
4. Yield Management Strategies for Semiconductor Manufacturing Industries: A Structural Approach. Sunil Kumar Nair, World Scientific,
2018, ISBN: 9789813235865