Strategizing Sustainable Yield Improvements in the Global Semiconductor Industry - PowerPoint PPT Presentation

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Strategizing Sustainable Yield Improvements in the Global Semiconductor Industry

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The global semiconductor manufacturing industry is facing amplified levels of competition and consolidation. As a result, there's an increasing urgency to drive productivity enhancements that support long-term success. – PowerPoint PPT presentation

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Title: Strategizing Sustainable Yield Improvements in the Global Semiconductor Industry


1
Strategizing Sustainable Yield Improvements in
the Global Semiconductor Industry https//yiel
dwerx.com/
2
The global semiconductor manufacturing industry
is facing amplified levels of competition and
consolidation. As a result, there's an increasing
urgency to drive productivity enhancements that
support long-term success. To manage rising cost
pressures and augment profitability,
semiconductor enterprises need to underline and
pursue strategies for end-to-end semiconductor
yield improvement. Yield optimization is pivotal
to curbing manufacturing costs and securing a
competitive advantage. However, many companies
find it challenging to attain sustainable yield
enhancements due to entrenched mindsets, limited
data visibility, compartmentalized efforts, and a
paucity of advanced analytics capabilities. As
the trend towards miniaturization continues,
semiconductor devices are becoming increasingly
sophisticated, resulting in an escalated
influence of process variability and
contaminations on yield. As devices shrink, the
impact of any single defect or contamination can
become significantly more severe, leading to
higher yield losses. Variability in parameters
such as temperature, pressure, and timing can
lead to inconsistency in device performance,
affecting both yield and product quality.
Additionally, contaminations can originate from
various sources such as materials, process gases,
or the manufacturing environment itself, causing
defects in the devices that result in yield
loss. Data-Driven Improvement Initiatives and
Advanced Analytics The era of big data and
advanced analytics has provided a new pathway for
yield improvement. Advanced analytics can help in
predicting yield outcomes, identifying the root
causes of yield loss, and formulating strategies
for improvement. Semiconductor companies are
increasingly leveraging machine learning and data
mining techniques to identify patterns and
correlations in large datasets, leading to more
effective yield enhancement systems. Data-driven
initiatives involve the use of data at every
stage of the semiconductor manufacturing process.
These include real-time data collection during
wafer fabrication, statistical analysis of
process and test data, as well as predictive
modeling of yield and performance metrics. Such
initiatives allow for a more holistic view of the
manufacturing process, identifying the areas with
the greatest loss impact, and directing
improvement efforts accordingly.
3
Overcoming Limitations of Traditional Yield
Improvement Approaches In a world where devices
are shrinking in size while becoming
technologically sophisticated, the effects of
process variability and contaminations on yield
are intensifying. Traditional approaches for
yield improvement, including focusing on yield
percentages, specific product families, or
excursion cases, have limitations. While these
strategies can capture and control different
types of yield losses, they may not exhaust all
possibilities for enhancing profitability. Collabo
ration between Engineering and Finance A
Holistic View of Yield Loss Cost A comprehensive
view of yield loss cost requires a collaborative
effort between the engineering and finance
departments. The engineering department typically
focuses on technical aspects such as process
optimization, equipment efficiency, and defect
reduction. In contrast, the finance department
focuses on the financial impacts of yield loss,
including the cost of scrapped wafers, rework,
and delayed deliveries. By merging cost data from
both departments, semiconductor companies can
gain a more comprehensive understanding of yield
loss costs. This could include the cost of raw
materials, energy, labor, and capital equipment,
as well as the opportunity costs associated with
yield loss. Such a holistic view can facilitate
more effective decision-making, resource
allocation, and strategic planning for yield
improvement. Systemic Improvements Focus on
Machine, Man, Material, Measurement, and
Method To achieve sustainable yield improvements,
it's crucial to focus on systemic issues rather
than isolated problems. This involves a
comprehensive focus on the key improvement
themes machine, man, material, measurement, and
method. Machine Machine variability can be a
major contributor to yield loss. This can be
mitigated through equipment optimization,
preventive maintenance, and regular performance
and semiconductor yield monitoring. Man The role
of the workforce in yield improvement cannot be
underestimated. This includes operator training,
skill development, and fostering a culture of
continuous improvement. Material The quality of
raw materials and process gases can significantly
affect yield. Material quality management and
supplier collaboration are key strategies in this
regard. Measurement Accurate and timely
measurement of process parameters is crucial for
maintaining process control and minimizing
variability. Method Lastly, the methods or
processes used in device fabrication need to be
continually optimized to improve yield.
4
  • Implementing Systemic Improvements for
    Sustainable Yield Enhancements
  • Next comes the phase of implementing systemic
    improvements to counter the recognized loss
    areas. Engineers concentrate on key improvement
    themes revolving around five factors - machine,
    man, material, measurement, and method. True and
    false rejects are scrutinized, and the potential
    for cross-functional collaborations is explored.
    By tackling systemic issues, semiconductor
    companies can actualize sustainable yield
    enhancements. In some cases, external involvement
    could be necessary, especially for machine
    variability initiatives. These collaborations
    could involve discussions with vendors to enhance
    equipment performance.
  • Conclusion
  • In the highly competitive and consolidated
    semiconductor industry, prioritizing end-to-end
    yield improvement is a strategic necessity. By
    adopting a data-driven approach, companies can
    effectively manage cost pressures, sustain higher
    profitability, and gain a competitive edge in the
    industry. Furthermore, a collaboration between
    engineering and finance departments can provide a
    more comprehensive view of yield loss costs,
    enabling more effective decision-making and
    resource allocation. Systemic improvements,
    focusing on machine, man, material, measurement,
    and method, can lead to sustainable yield
    improvements. Ultimately, success in yield
    improvement depends on a company's ability to
    leverage data, advanced analytics, and
    cross-functional collaboration to drive
    continuous improvement in its manufacturing
    processes.
  • References
  • Y. Ye, Q. Xu, and B. Yu, "Machine Learning for
    Yield Improvement in Semiconductor Manufacturing
    and an Application to Defect Classification," in
    2018 IEEE/ACM International Conference on
    Computer-Aided Design (ICCAD), San Diego, CA,
    2018, pp. 1-6, doi 10.1145/3240765.3240831. 
  • A. El Gamal and M. A. Elmasry, "Process
    variations and yield," in Analysis and Design of
    Digital Integrated Circuits, Third Edition, D. A.
    Hodges, H. G. Jackson, and R. A. Saleh, Eds.,
    2003. 
  • M. Taouil, S. Hamdioui, "Yield Improvement by
    Tolerating Defects," in 2018 31st International
    Conference on VLSI Design and 2018 17th
    International Conference on Embedded Systems
    (VLSID), pp. 209-214, Jan. 2018, doi
    10.1109/VLSID.2018.00047.
  • S. Borkar, "Designing Reliable Systems from
    Unreliable Components The Challenges of
    Transistor Variability and Degradation," in IEEE
    Micro, vol. 25, no. 6, pp. 10-16, Nov.-Dec. 2005,
    doi 10.1109/MM.2005.110.
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