Next-Gen Final Test Yield Prediction in Semiconductor Manufacturing

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Next-Gen Final Test Yield Prediction in
Semiconductor Manufacturing https//yieldwerx.
com/
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The semiconductor manufacturing industry is known
for its complex processes that span several weeks
and involve hundreds of operations. This article
proposes a scalable, machine learning-based
framework that uses this wealth of data generated
during these processes to predict the Final Test
(FT) yield at the wafer fabrication stage. The
objective of this new framework is to improve
operational efficiency and reduce production
costs. The Importance of Yield in the
Semiconductor Industry  Semiconductors are the
lifeblood of modern digital technologies,
powering everything from mobile devices to
autonomous vehicles. The precision required
during their manufacturing process is paramount.
The manufacturing yield, which refers to the
proportion of chips that meet the necessary
specifications to be sold, plays a critical role
in the industry. Every increment in manufacturing
yield results in significant savings, making
yield enhancement a key focus for semiconductor
manufacturers. The Challenges of Data Utilization
in the Semiconductor Industry  Semiconductor
manufacturing processes generate a wealth of
data. However, this data is often not effectively
used due to its sheer volume and complexity. It
comprises a variety of numerical and categorical
information, each piece related to different
processes, materials, and operational parameters.
Manual filtering of this data is time-consuming,
and it can be easy to overlook important insights
that could inform strategies for enhancing
yield. Yield Prediction A Machine Learning
Approach  In the semiconductor manufacturing
process, predicting yield is traditionally
complex due to the vast, diverse data generated.
However, with machine learning advancements, we
propose a scalable framework to efficiently
handle this data. It uses Gaussian Mixture
Models, One Hot Encoder, and Label Encoder to
process numerical and categorical data, enabling
robust yield predictions. The framework allows
for automatic data processing without prior
knowledge of low yield causes, reducing the need
for manual filtering. Our model's versatility in
handling different data types leads to an
efficient, accurate yield prediction process,
revolutionizing the traditional approach.
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The Data Pre-processing Techniques  We propose a
scalable, machine learning-based framework that
can process, interpret, and model this diverse
data.  Our framework applies data pre-processing
techniques like Gaussian Mixture Models (GMMs)
for approximation of distributions, One Hot
Encoder for categorical features, and Label
Encoder for feature labeling. These techniques
enable the model to handle continuous data that
may not fit a standard distribution and transform
categorical data into a machine learning
algorithm-friendly format. Yield Management
Software (YMS) Solutions  Our framework includes
yield management software (YMS) solutions that
automatically process and interpret the
voluminous semiconductor data, negating the need
for manual filtering. These yms solutions are
crucial in identifying causes of yield loss and
developing strategies for yield enhancement
systems. The Power of Ensemble Learning The model
utilizes ensemble learning, which combines
multiple learning algorithms to achieve superior
predictive performance. The model is trained on
several product lines and can handle binary and
multi-class problems. It also provides automated
feature importance and sensitivity
analysis. Leveraging Data for Advanced Yield
Analysis  In semiconductor manufacturing,
effectively leveraging data can lead to
significant advancements in yield analysis. Our
machine learning framework capitalizes on the
massive amounts of data generated during
production to perform predictive modeling of
Final Test (FT) yield at early stages. This
proactive approach allows for timely detection of
yield-related issues and implementation of
corrective actions, reducing yield loss. The
framework can predict output yield across
multiple product lines and handle diverse types
of manufacturing data. This not only enhances its
versatility but also reduces the need for manual
data filtering, making yield analysis more
efficient and accurate.
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Early Stage Predictive Modeling   Our machine
learning-based framework represents a significant
advancement in semiconductor yield analysis. By
utilizing data analytics, the framework allows
for predictive modeling of FT yield in the early
stages of production. This means that issues
affecting yield can be detected earlier,
corrective actions can be implemented faster, and
yield loss can be significantly
reduced.     Versatility Across Product
Lines   The framework can predict output yield
across multiple product lines. This makes it a
versatile tool for yield analysis across a wide
range of products, increasing its value in the
semiconductor testing industry. Moreover, it can
handle different types of manufacturing data,
making it an adaptable tool for different
manufacturing parameters.     Broadening the
Scope Utilizing Wafer Acceptance Test Data   A
novel feature of our model is the inclusion of
Wafer Acceptance Test (WAT) data for predicting
Final Test yield. WAT data, typically used for
process monitoring and control, has been
underutilized in yield prediction. By
incorporating WAT data into yield prediction, our
model provides a broader understanding of the
process parameters that affect FT yield, leading
to more accurate and robust predictions.