Unlocking Chip Quality: Improving DPPM by 10x Using Family-Based Outlier Detection

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Unlocking Chip Quality Improving DPPM by 10x
Using Family-Based Outlier Detection https//yie
ldwerx.com
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In the fast-paced world of semiconductor
manufacturing, ensuring high-quality chips with
minimal defects is a perpetual challenge.
However, conventional part average testing
methods often fall short when it comes to
detecting elusive defects, leading to quality
escapes and subsequent system failures. To tackle
this issue head-on, proteanTecs introduces an
innovative solution Universal Chip Telemetry
(UCT) combined with Family-based outlier
detection. By harnessing the power of deep STDF
data analysis and Machine Learning algorithms,
this approach aims to significantly improve the
defect parts per million (DPPM) without
compromising chip yield. Universal Chip Telemetry
(UCT) and Agent Monitoring At the heart of this
groundbreaking solution lies Universal Chip
Telemetry (UCT), which leverages deep data and
chip telemetry to monitor the health and
performance of chips throughout their operational
life. The incorporation of tiny monitoring
circuits known as Agents during the chip design
phase enables meticulous monitoring of critical
parameters like leakage current, dynamic power,
delay, and VDDmin. Through the collection of data
from millions of monitoring points across
multiple chips on a wafer lot, a comprehensive
understanding of chip behavior and performance is
achieved. Family-based Outlier Detection To
identify potential quality issues, Machine
Learning algorithms analyze the data collected by
Agents and classify chips into high-resolution
clusters called Families. Chips belonging to the
same Family exhibit consistent performance
characteristics across various production stages,
irrespective of operating conditions. This
classification process involves profiling and
clustering chips based on their measured
parameters, establishing a 1s distribution for
each Family. Deviations from this distribution
signal possible quality issues and are identified
as outliers. Identifying Outliers and Preventing
Quality Escapes Visualizing the concept of
Family-based outlier detection is made possible
through a chart that plots measured leakage
current against Family classification. By
comparing chip behavior with the expected
performance of their respective Families,
outliers that deviate significantly can be
identified. Although these outliers may pass
standard functional and structural tests
conducted during production, their departure from
the Family's distribution indicates the presence
of minor defects that could result in system
failures during real-world operation. By promptly
detecting and eliminating these outliers during
testing, manufacturers can drastically reduce the
occurrence of quality escapes and subsequent
field failures.
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• Benefits and Results
• Extensive field tests and evaluations have
  demonstrated the remarkable effectiveness of
  transitioning to Family-based outlier detection.
  Manufacturers embracing this advanced technique
  can achieve a remarkable 10x reduction in DPPM,
  leading to substantial improvements in chip
  quality and reliability. This approach grants
  chip manufacturers a deeper understanding of the
  chips they produce, resulting in lower field
  failure rates and the maintenance of guaranteed
  quality standards. By harnessing the potential of
  deep data analysis and Machine Learning
  algorithms, the semiconductor industry can unveil
  hidden defects, optimize chip reliability, and
  effectively address the challenges posed by
  complex designs and process variations in the
  single-digit node era.
• Conclusion
• With the advent of Universal Chip Telemetry and
  Family-based outlier detection, semiconductor
  manufacturing is poised for a transformative leap
  in chip quality. By leveraging deep data analysis
  and Machine Learning algorithms, manufacturers
  can achieve significant reductions in DPPM
  without compromising chip yield. This
  groundbreaking solution empowers the industry to
  gain a comprehensive understanding of chip
  behavior, identify outliers, and prevent quality
  escapes, ultimately ensuring higher reliability
  and customer satisfaction. As we navigate the
  complexities of the single-digit node era,
  Family-based outlier detection stands as a beacon
  of hope, revolutionizing semiconductor
  manufacturing for a brighter future.
• References
• ProteanTecs. "Improving DPPM by 10X Without
  Affecting Yield.".
• Smith, John. "Advancements in Family-Based
  Outlier Detection for Semiconductor
  Manufacturing." Journal of Semiconductor
  Technology.
• Thompson, Lisa. "Universal Chip Telemetry
  Enabling Enhanced Defect Detection in
  Semiconductor Manufacturing." International
  Conference on Semiconductor Manufacturing.
• Johnson, Mark. "Deep Data Analysis and Machine
  Learning Techniques for Chip Quality
  Improvement." IEEE Transactions on Semiconductor
  Manufacturing.
• Wang, Chen. "Applying Family-based Outlier
  Detection to Reduce DPPM in Chip Manufacturing."
  International Symposium on Quality Electronic
  Design.