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node, observation of the output of
Figure 7: Sample shmoo plot.
Figure 8: Output of the first CNN layer.
Figure 9: Output of the third CNN layer.
the learning of the middle layer can be
each layer of the model can help us a great help to artificial intelligence
understand how the neural network (AI) research. Exploring the black box
learns, which can also help us compose problem of neural networks is bound to
the neural network structure. be a future research direction.
From Figures 7-9 it can be found
that the feature maps of the output of Future work
the convolutional layer, which is close The new method has been verified on
to the input, can still distinguish the some test projects where shmoo tests
relationship with the input shmoo. The currently help engineers save time on
output of the higher layer has higher- data analysis. One of the future works is
level features that humans cannot to optimize the scheme so that the tool
analyze manually, but it is believed that can adapt to different types of X/Y axes
in shmoo plots. The second is to support
shmoo center point (test base point)
recognition, and the third is to simplify
the neural network structure to reduce
the number of trainable parameters
and overfitting. Lastly, our goal is to
expand the training set. Additionally,
this method can be used offline or in
near real time in conjunction with an
ATE system to enable adaptive testing
and preemptive troubleshooting based
on wafer classification results while the
next wafer is being tested.
Summary
T h i s a r t i c l e f o c u s e s o n t h e
development and application of a deep
learning method for automating the
analysis of shmoo plots in ATE testing.
The article demonstrates that the
proposed method can classify the results
of shmoo plots accurately, shortening
the process from days to minutes, which
significantly improves time to market
while ensuring high quality levels. The
findings suggest that deep learning is a
valuable tool for automating the analysis
of ATE test data.
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