Semiconductor Defect Pattern Classification by
Self-Proliferation-and-Attention Neural Network
- URL: http://arxiv.org/abs/2212.00345v1
- Date: Thu, 1 Dec 2022 08:17:21 GMT
- Title: Semiconductor Defect Pattern Classification by
Self-Proliferation-and-Attention Neural Network
- Authors: YuanFu Yang, Min Sun
- Abstract summary: We present a novel architecture that can perform defect classification in a more efficient way.
The first function is self-proliferation, using a series of linear transformations to generate more feature maps at a cheaper cost.
The second function is self-attention, capturing the long-range dependencies of feature map by the channel-wise and spatial-wise attention mechanism.
Compared with other latest methods, SP&A-Net has higher accuracy and lower cost in many defect inspection tasks.
- Score: 30.329065698451902
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Semiconductor manufacturing is on the cusp of a revolution: the Internet of
Things (IoT). With IoT we can connect all the equipment and feed information
back to the factory so that quality issues can be detected. In this situation,
more and more edge devices are used in wafer inspection equipment. This edge
device must have the ability to quickly detect defects. Therefore, how to
develop a high-efficiency architecture for automatic defect classification to
be suitable for edge devices is the primary task. In this paper, we present a
novel architecture that can perform defect classification in a more efficient
way. The first function is self-proliferation, using a series of linear
transformations to generate more feature maps at a cheaper cost. The second
function is self-attention, capturing the long-range dependencies of feature
map by the channel-wise and spatial-wise attention mechanism. We named this
method as self-proliferation-and-attention neural network. This method has been
successfully applied to various defect pattern classification tasks. Compared
with other latest methods, SP&A-Net has higher accuracy and lower computation
cost in many defect inspection tasks.
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