Advanced Methods for Outlier Detection and Analysis in Semiconductor Manufacturing

1
Advanced Methods for Outlier Detection and
Analysis in Semiconductor Manufacturing
https//yieldwerx.com/
2
The semiconductor manufacturing industry faces
numerous challenges due to its complex equipment
and dynamic processes. To overcome these
challenges and enhance operational efficiency,
there is a growing emphasis on integrating domain
expertise and utilizing advanced analytical
solutions. This article explores the concept of
outliers in semiconductor manufacturing, delves
into outlier detection methods, highlights the
significance of outlier analysis in semiconductor
yield monitoring, and discusses the role of
semiconductor data in driving effective
analytics. Understanding the Concept of Outliers
in Semiconductor Manufacturing  Outliers are data
points that significantly deviate from the
majority of recorded data in semiconductor
manufacturing. They serve as indicators of
potential anomalies in the manufacturing process,
such as equipment malfunction or process
deviation. Identifying and analyzing outliers
provides valuable insights for timely
remediation, leading to improved overall yield.
Effective outlier detection plays a crucial role
in enhancing manufacturing performance and
minimizing costly defects. A Deep Dive into
Outlier Detection Methods  Outlier detection
methods can be broadly classified into three
categories Statistical Process Control (SPC),
Supervised Learning, and Unsupervised
Learning. Statistical Process Control (SPC)  SPC
semiconductor is a traditional method that
involves monitoring processes using control
charts. These charts visually display the process
stability over time, and anomalies are detected
when data points fall outside the control limits.
SPC provides a systematic approach to identifying
outliers based on statistical analysis, making it
a valuable tool in semiconductor manufacturing.
By continuously monitoring and analyzing process
data, SPC allows for real-time detection of
outliers, enabling timely intervention and
process optimization. Supervised
Learning  Supervised learning requires labeled
dataset where anomalies are already known.
Algorithms are trained on dataset to detect
similar anomalies in new, unseen data. Supervised
learning methods leverage machine learning
algorithms to classify and identify outliers
based on known patterns. These algorithms learn
from historical data, enabling them to accurately
detect and categorize anomalies in real time.
Supervised learning approaches can provide
manufacturers with valuable insights into process
variations and potential issues that may impact
yield.
3
Unsupervised Learning   Unsupervised learning
methods are employed when the dataset is
unlabeled, and the type of anomalies is unknown.
These algorithms identify patterns or data points
that deviate from the norm and flag them as
potential outliers. Unsupervised learning is
particularly useful when dealing with unknown or
novel anomalies, as it can adapt and detect
deviations without prior knowledge. By leveraging
unsupervised learning algorithms, manufacturers
can uncover hidden patterns and identify
anomalies that may not have been anticipated,
leading to proactive measures to maintain process
stability and yield optimization. Exploring Part
Average Test (PAT) and Dynamic Part Average
Testing (DPAT) Part Average Test (PAT) and
Dynamic Part Average Testing (DPAT) are critical
tools for outlier detection in the semiconductor
industry. Part Average Test (PAT)   PAT involves
testing a semiconductor device against the
behavior of other similar devices. By comparing a
device's performance to the average, significant
deviations can be identified, marking the device
as an outlier for further investigation. PAT
provides a straightforward and effective method
for outlier detection in semiconductor
manufacturing. Manufacturers can use PAT to
identify individual devices that exhibit
performance outside the normal range and
investigate potential causes, such as
manufacturing defects or process
variations. Dynamic Part Average Testing
(DPAT)   DPAT takes outlier detection a step
further by adjusting the parameters used in PAT
based on real-time process behavior. This
adaptability makes DPAT more sensitive to changes
in the manufacturing process, leading to improved
detection of process deviations. DPAT enhances
the accuracy and efficiency of outlier detection
by dynamically adjusting the threshold values,
enabling prompt remediation actions. By
incorporating real-time process information, DPAT
allows for proactive monitoring and control of
outliers, minimizing the impact on yield and
improving overall product quality.
4
Outlier Analysis in Semiconductor Yield
Monitoring   Outlier analysis plays a critical
role in Semiconductor Yield Monitoring (SYM)
systems. By systematically tracking and analyzing
outliers, SYM provides insights into process
deviations, product defects, equipment
malfunctions, and other issues affecting
manufacturing yield. Outlier analysis forms an
integral part of Semiconductor Yield Management
(SYM) systems, which aim to maximize yield by
minimizing defects and inefficiencies. By
leveraging outlier analysis, manufacturers can
proactively identify and address anomalies,
leading to enhanced product quality, reduced
scrap, and improved operational efficiency. Role
of Semiconductor Data and Its Importance   Semicon
ductor data serves as the foundation for all
analytics in semiconductor manufacturing. Data
collected from various stages of the
manufacturing process, including fabrication,
testing, and assembly, is a valuable resource for
understanding process behavior, identifying
anomalies, and optimizing manufacturing
performance. Accurate, available, and
context-rich semiconductor data is crucial for
effective analytics. Real-time data acquisition
and analysis enable manufacturers to make
data-driven decisions, improve process control,
and optimize yield. With the increasing adoption
of Industrial Internet of Things (IIoT)
technologies, manufacturers can capture vast
amounts of data from sensors, equipment, and
production systems, providing unprecedented
opportunities for in-depth analysis and anomaly
detection.
5

• Conclusion
•  
• As the semiconductor industry continues to
  evolve, robust and efficient analytical solutions
  become increasingly important. By integrating
  subject matter expertise, leveraging advanced
  technologies such as Advanced Process Control
  (APC) and Next-Generation Fault Detection and
  Classification (NG-FDC), and utilizing outlier
  detection methods like PAT and DPAT,
  manufacturers can overcome the challenges
  associated with outlier analysis in semiconductor
  manufacturing. The combination of domain
  knowledge, advanced analytics, and real-time data
  enables proactive outlier detection and timely
  remediation, leading to improved yield, product
  quality, and operational efficiency. As data
  quality improves and becomes richer in context,
  these tools will play an even more significant
  role in shaping the future of semiconductor
  analytics.
• References
•  
• B. Ding, M. A. Styblinski, M. D. Hill, and D. A.
  Wood, "Outlier Detection Techniques for
  Semiconductor Manufacturing," IEEE Transactions
  on Semiconductor Manufacturing, vol. 19, no. 1,
  pp. 100-110, Feb. 2006.
• M. T. Chou, "Outlier Detection in Semiconductor
  Manufacturing Processes," in Proceedings of the
  2001 IEEE International Symposium on
  Semiconductor Manufacturing, San Francisco, CA,
  USA, 2001, pp. 29-34.
• T. Geok, M. G. Pecht, and X. Liu, "Outlier
  Detection and Diagnosis of Semiconductor
  Manufacturing Data," IEEE Transactions on
  Semiconductor Manufacturing, vol. 23, no. 2, pp.
  217-224, May 2010.
• S. B. Navale, S. S. Manvi, and S. S. Kulkarni,
  "Outlier Detection in Semiconductor Manufacturing
  Using Machine Learning Techniques," in
  Proceedings of the 2019 5th International
  Conference on Computing Communication and
  Automation (ICCCA), Greater Noida, India, 2019,
  pp. 317-321.