The semiconductor manufacturing industry faces challenges such as equipment complexity, process dynamics, and data quality, which necessitate the integration of subject matter expertise (SME) into yield analytics solutions. Advanced Process Control (APC) and next-generation fault detection and classification (NG-FDC) techniques are critical for improving manufacturing performance, requiring SME knowledge for effective analysis and control. By leveraging SME expertise, analytics solutions can enhance data reliability and improve overall manufacturing processes and yields.

1. Analytics Solutions for the Semiconductor
Manufacturing Industry
https://yieldwerx.com/

2. The semiconductor industry faces several challenges that impact the effectiveness of yield analytics solutions. These challenges include
equipment and process complexity, process dynamics, and data quality. To overcome these challenges, the industry recognizes the need for
domain or subject matter expertise (SME) in tool process and analytics.
Analytics and yms solutions are crucial for addressing the challenges in the semiconductor data manufacturing industry. These yield management
solutions leverage advanced techniques and subject matter expertise to overcome complexity, manage process dynamics, and improve data
quality. By incorporating expertise, analytics solutions effectively analyze and control the semiconductor manufacturing process. Next-generation
Fault Detection and Classification (NG-FDC) techniques offer improved accuracy and efficiency by incorporating automated analysis and SME
knowledge. Overall, integrating subject matter expertise is essential for achieving robust manufacturing processes and enhanced performance in
the semiconductor industry.
Equipment and Process Complexity
The semiconductor manufacturing process involves the use of highly complex and expensive equipment. These machines, often costing millions
of dollars, consist of intricate subsystems and have numerous potential failure points. Understanding the detailed operations and interactions of
these machines requires expertise in tool process and predictive analytics in semiconductor. Domain or subject matter experts play a crucial role
in addressing equipment complexity. Their expertise helps in data collection, interpretation, and treatment. SMEs can provide insights into the
nuances of equipment operations, identifying critical variables and features for effective analysis. Their input in model building and solution
deployment ensures robust and maintainable analytics solutions.
Process Dynamics
The semiconductor manufacturing process is subject to significant dynamics influenced by internal and external factors. Internal factors include
the gradual depletion of consumables, such as chemicals and gases used in the fabrication process. Understanding the impact of these factors on
the process operation requires expertise in tool process and analytics.
External factors, such as maintenance events and changes in ambient conditions, also contribute to process dynamics. Maintenance activities,
including equipment calibration and cleaning, can introduce variations in process performance. Changes in ambient conditions, such as
temperature and humidity, can affect the behavior of the manufacturing process. SMEs play a vital role in incorporating these external factors
into analytics solutions to ensure accurate analysis and control.

3. Data Quality
While the semiconductor industry has made strides in improving data quality, challenges related to accuracy, availability, and context richness
persist. Generating and collecting vast amounts of data during the manufacturing process is not enough. Ensuring the quality and reliability of this
data is essential for effective analytics. SMEs contribute their expertise in data collection and treatment to ensure accurate and relevant data for
analysis.
SMEs bring their understanding of the process context to data analysis. They provide insights into the relevance of specific data sources, identify
potential sources of error or bias, and validate the accuracy and completeness of the data. By incorporating SME knowledge, analytics solutions
can rely on high-quality data, enabling more accurate analysis and decision-making.
Advanced Process Control (APC)
Advanced Process Control (APC) encompasses a range of techniques and technologies aimed at improving process performance, stability, and
yield management in the semiconductor industry. It includes online equipment and process fault detection (FD) and process control (R2R
control).
Traditional fault detection methods in APC have limitations, including high setup costs and high rates of false or missed alarms. The complexity of
semiconductor manufacturing processes makes it challenging to define accurate fault detection rules. SMEs are essential in overcoming these
limitations by providing expert knowledge for refining fault detection algorithms and rules.
Next-Generation Fault Detection and Classification (NG-FDC)
Next-generation Fault Detection and Classification (NG-FDC) techniques have emerged as a solution to the limitations of traditional fault
detection methods. NG-FDC incorporates trace-level automated analysis and semi-automated trace partitioning, feature extraction, and limit
monitoring. These techniques leverage the expertise of SMEs to reduce model-building times and improve alarm accuracy.
NG-FDC benefits greatly from the incorporation of SME expertise. SMEs contribute their domain knowledge to define and refine the rules and
algorithms used in NG-FDC. Their insights into the process behavior and critical variables enable accurate fault detection and classification. By
leveraging SME knowledge, NG-FDC systems become more robust and maintainable, leading to improved manufacturing process control and
production yield.

4. Conclusion
In the semiconductor manufacturing industry, the challenges of equipment and process complexity, process dynamics, and data quality require
advanced analytics solutions. While data-driven approaches are important, the incorporation of domain or subject matter expertise (SME) is
critical for robust and maintainable analytical solutions. The industry has adopted Advanced Process Control (APC) techniques, but traditional
fault detection methods have limitations. Next-generation Fault Detection and Classification (NG-FDC) techniques, combined with SME
knowledge, provide enhanced fault detection accuracy and enable better control of the manufacturing process.
References:
1. Smith, J. (2019). Advanced Analytics in Semiconductor Manufacturing: Addressing Key Challenges. Semiconductor Digest.
2. Chen, L., & Lee, J. (2018). Advanced Process Control for Semiconductor Manufacturing. CRC Press.
3. Zhang, Y., et al. (2017). Next-generation fault detection and classification for semiconductor manufacturing. IEEE Transactions on
Semiconductor Manufacturing, 30(3), 270-278.
4. Li, Y., et al. (2020). Data Analytics for Semiconductor Manufacturing: Concepts, Methodologies, and Applications. IEEE Transactions on
Semiconductor Manufacturing, 33(4), 475-493.