A Holistic Approach to Yield Improvement in the Semiconductor Manufacturing Industry

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A Holistic Approach to Yield Improvement in the
Semiconductor Manufacturing Industry https
//yieldwerx.com/
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Semiconductor manufacturing is one of the most
complex and competitive industries, heavily
driven by innovation and cost-efficiency. It is
continuously grappling with increasing cost
pressures while concurrently working to meet the
demands of rapidly advancing technology. Yield
optimization, a multifaceted process aimed at
improving the number of usable chips produced
from raw materials, is an integral part of
reducing manufacturing costs. This process
involves taking into account several elements,
such as equipment performance, operator
capability, and the complexity of the design.
Achieving higher yield and profitability has
become critical in the semiconductor industry,
mandating a shift in perspectives. With the
industry's rapid evolution, the key to
sustainable yield improvement in manufacturing
lies in the incorporation of advancements in
analytics and a comprehensive yield improvement
approach. The Importance of Data-driven Insights
and Systemic Improvements As the industry
continues to adhere to Moore's law, bringing
about miniaturization and sophistication of
devices, the risk of yield loss due to process
variability and contamination increases.
Consequently, enhancing the design and machine
capabilities becomes of utmost importance,
necessitating a novel approach to yield
improvement that centralizes on data-driven
insights and systemic improvements. Historically,
yield improvement efforts have focused on
excursions, percentages, or products. However,
for a significant impact on profitability, it's
essential to translate yield loss into its actual
monetary value, thereby providing a
comprehensive, end-to-end view of the entire
manufacturing process. By understanding the cost
implications of each yield loss, semiconductor
companies can create focused strategies that
address the most impactful areas, ultimately
improving their bottom line. Aligning Engineering
and Finance Perspectives For successful
implementation, this approach requires companies
to harmonize the language and data used by the
engineering and finance departments. It is
pivotal to develop a comprehensive understanding
of the end-to-end yield, facilitating more
effective collaboration and decision-making
across the two functions. A useful tool to aid
this alignment is the loss matrix, which can help
identify the significant sources of loss. The
loss matrix provides a clear, data-driven picture
of where yield losses are happening and their
relative impact on the overall production
process. With this knowledge, companies can
formulate more targeted initiatives to boost
yield and profitability. To align the
perspectives of engineering and finance on yield
and cost, companies need to establish a
cost-of-non-quality (CONQ) baseline. This
baseline merges cost data from finance with
defect data from engineering. The CONQ baseline
enables teams to understand how defects directly
impact the cost, thereby encouraging joint
decision-making to improve yield and
profitability.
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Embracing a Holistic View of Manufacturing Moreove
r, the industry should adopt a holistic view of
the manufacturing process rather than focusing on
individual processes, products, or equipment.
Only when the process is viewed as a complete
system, can the interdependencies between
different parts of the process be fully
understood and addressed. The identification of
significant loss areas using the loss matrix
enables companies to develop insights into key
themes that drive these losses. This data-driven
approach supports proactive problem-solving and
more efficient collaborations with
cross-functional teams. In turn, this shifts the
paradigm from a reactive to a proactive approach,
empowering yield engineers to address potential
problems before they impact the yield and yield
enhancement systems. Enhanced Fabrication
Technology and Automation  Fabrication technology
plays a pivotal role in semiconductor
manufacturing. It is one of the key elements that
contribute to yield. Companies have to innovate
continuously to improve the production of
semiconductors and increase yield. An example of
such innovation is photolithography, which allows
transistors to be printed on a silicon wafer with
nanometer precision. By increasing the precision
and quality of photolithography, companies can
improve the yield significantly. Automation in
manufacturing is another important factor in
improving yield. The introduction of advanced
robotics and AI has reduced human contact and,
therefore, the likelihood of contamination, a
common issue that can lower the yield. By
integrating automation, semiconductor
manufacturers can achieve greater accuracy and
consistency, thus enhancing yield and overall
efficiency. Utilizing Advanced Analytics in Yield
Improvement  The massive amount of data generated
in semiconductor manufacturing can be a gold mine
for semiconductor yield improvement. Advanced
analytics, coupled with machine learning and AI,
can help to identify patterns and trends that may
not be apparent to human analysts. This approach
allows companies to predict defects before they
occur, enhancing the overall yield and reducing
costs. Predictive analytics can be particularly
useful in identifying the root causes of defects,
thereby preventing their recurrence. It can also
be employed to monitor and control process
parameters in real time, helping to prevent
deviations that could negatively affect yield.
Thus, the application of advanced analytics can
drive proactive, rather than reactive, actions,
leading to sustainable yield improvement.
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Streamlining Cross-Functional Collaboration   In
many semiconductor companies, different teams and
functions work in isolation, leading to
fragmented efforts that are less efficient in
improving yield. Cross-functional collaboration,
where different teams work together towards a
common goal, can drive significant improvements
in yield. For example, engineering,
manufacturing, and quality teams can work
together to identify potential issues, devise
solutions, and implement changes more rapidly and
effectively. A well-coordinated, cross-functional
collaboration can not only drive yield
improvement but also enhance problem-solving
capabilities, improve knowledge sharing, and
drive innovation. Such collaboration requires
transparent and effective communication channels,
coupled with a shared understanding of the
objectives and how each team contributes to
achieving them.     Implementing a
Cost-of-Nonquality (CONQ) Baseline   The
implementation of a cost-of-non-quality (CONQ)
baseline is a powerful strategy to bridge the
perspectives of finance and engineering. By
merging cost data from finance and defect data
from engineering, teams can create a CONQ
baseline that helps understand how defects
directly impact the cost. This approach provides
an objective measure of the economic impact of
quality issues, thereby focusing improvement
efforts where they can bring the most significant
financial return. Having a CONQ baseline also
allows for better decision-making and
prioritization of quality improvement
initiatives. With an understanding of the
financial implications, organizations can direct
their resources to areas that will yield the
highest return on investment, thus enhancing
profitability.
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• Conclusion
•  An end-to-end approach to yield improvement in
  the semiconductor industry that incorporates
  advanced analytics aligns engineering and finance
  functions and adopts a holistic view of the
  manufacturing process has the potential to
  address the industry's current challenges
  significantly. By implementing this approach,
  semiconductor manufacturers can ensure continuous
  innovation in their fabrication technology,
  reduce common problems such as particle
  contamination, and achieve sustainable yield
  improvement.
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