Machine Learning and Data Analytics in Semiconductor Yield Management - PowerPoint PPT Presentation

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Machine Learning and Data Analytics in Semiconductor Yield Management

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In the semiconductor manufacturing industry, the need for continuous quality improvement has never been more pronounced. This demand is driven by an unprecedented influx of manufacturing data, with more than 1000 process parameters recorded for a single wafer, and tens of thousands of wafers being produced daily. – PowerPoint PPT presentation

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Title: Machine Learning and Data Analytics in Semiconductor Yield Management


1
Machine Learning and Data Analytics in
Semiconductor Yield Management https//yieldwe
rx.com/
2
In the semiconductor manufacturing industry, the
need for continuous quality improvement has never
been more pronounced. This demand is driven by an
unprecedented influx of manufacturing data, with
more than 1000 process parameters recorded for a
single wafer, and tens of thousands of wafers
being produced daily. Traditional statistical
methods have proven insufficient to fully exploit
these massive volumes of data. As such, this
paper explores the application of hybrid machine
learning techniques, specifically Memory Based
Reasoning (MBR) and Neural Network (NN) learning,
as more powerful tools for managing this
complexity and improving yield in semiconductor
manufacturing. Understanding Memory-Based
Reasoning (MBR) in Semiconductor
Manufacturing Memory Based Reasoning (MBR) is an
instance-based learning method, inspired by the
way humans learn from past experiences. In the
context of semiconductor manufacturing yield, MBR
retrieves previously learned instances from a
database (case-base) that most closely matches
the current situation or problem. This allows for
quick, adaptable solutions based on historical
data. The Implementation of MBR in Yield
Management In practice, MBR uses feature weights
to establish the importance of different
attributes. These feature weights are calculated
from the trained neural network, aiding in the
prediction of outcomes by presenting the most
similar examples from the case base. This
provides a rapid response system to identify
potential process deviations and propose
appropriate corrective measures. The Neural
Network and Memory-Based Reasoning Framework An
integrated framework is proposed for
semiconductor yield management, leveraging the
combined power of Neural Network (NN) and Memory
Based Reasoning (MBR). This hybrid system is
capable of managing large datasets, and high
dimensions, and dynamically adapting to different
situations. NNs are a form of machine learning
modeled after the human brain, capable of
learning complex patterns and relationships in
data. The NN part of the system is responsible
for learning the non-linear relationships between
manufacturing process parameters and yield. MBR,
on the other hand, is a type of instance-based
learning which utilizes experience to make
predictions about new instances. The MBR
component uses the previously learned examples to
find the most similar case in the case base when
a new instance comes up. The system operates by
calculating a feature weight set from the trained
neural network. This feature weight set connects
both learning strategies and aids in predicting
outcomes by presenting the most similar examples
from the case base.
3
Information Theory-based Model for Strategy
Selection To maximize the efficacy of the
proposed hybrid learning system, an information
theory-based model for strategy selection is
introduced. This model determines optimal
strategies for yield management by using metrics
such as the knowledge extraction rate per
experimentation cycle and per unit of time as
benchmarks. Four key yield analysis tools are
examined within this model, namely electrical
testing, automatic defect classification, spatial
signature analysis, and wafer position analysis.
These tools have distinct roles in both RD and
volume production environments, providing
critical information that informs strategy
selection and execution. Applying Information
Theory to Yield Management The proposed model
examines the utility of four yield analysis
toolselectrical testing, automatic defect
classification, spatial signature analysis, and
wafer position analysis. Each tool generates
unique insights critical for both research and
development (RD) and volume production
environments. This enables an adaptive strategy
that optimizes yield over time. The Importance of
Data Visualization Tools Data visualization
tools, such as yield analysis software, are
essential for storing, tracking, and analyzing
all data collected during chip manufacturing and
testing. These tools enable the conversion of raw
data into actionable insights, enhancing the
understanding of the manufacturing process,
increasing productivity, and improving
yield. Detailed Examination of Visualization
Tools A range of visualization tools such as
wafer mapping software, trend charts, correlation
charts, histograms, Pareto analysis, fail flip
maps, fail trends, fail category maps, and
gallery views are discussed in detail. Each of
these tools plays a specific role, enabling
detailed data analysis, anomaly detection, trend
identification, and overall process
understanding. For instance, trend charts analyze
parameter behavior over time, whereas correlation
charts observe how two different test parameters
behave similarly. Fail trends summarize recent
yield and failing parameter trends, while
histograms visualize data distribution and detect
outliers. Pareto analysis identifies the most
significant failures and core problems within a
production workflow.
4
  • Conclusion
  • The application of machine learning, specifically
    hybrid systems leveraging Neural Networks and
    Memory Based Reasoning, presents a powerful
    approach to handling the immense volume and
    complexity of data in semiconductor
    manufacturing. Coupled with an information
    theory-based model for strategy selection, this
    approach maximizes knowledge extraction and
    optimizes yield management.
  • Furthermore, the use of various data
    visualization tools facilitates the conversion of
    raw data into actionable insights, fostering a
    deeper understanding of the manufacturing process
    and enabling continuous quality improvement.
    These innovative approaches provide a more
    comprehensive understanding of the manufacturing
    process, paving the way for continuous quality
    improvement in the semiconductor industry.
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