Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Keeping Deep Lithography Simulators Updated: Global-Local Shape-Based
  Novelty Detection and Active Learning   Learning-based pre-simulation (i.e., layout-to-fabrication) models have been
proposed to predict the fabrication-induced shape deformation from an IC layout
to its fabricated circuit. Such models are usually driven by pairwise learning,
involving a training set of layout patterns and their reference shape images
after fabrication. However, it is expensive and time-consuming to collect the
reference shape images of all layout clips for model training and updating. To
address the problem, we propose a deep learning-based layout novelty detection
scheme to identify novel (unseen) layout patterns, which cannot be well
predicted by a pre-trained pre-simulation model. We devise a global-local
novelty scoring mechanism to assess the potential novelty of a layout by
exploiting two subnetworks: an autoencoder and a pretrained pre-simulation
model. The former characterizes the global structural dissimilarity between a
given layout and training samples, whereas the latter extracts a latent code
representing the fabrication-induced local deformation. By integrating the
global dissimilarity with the local deformation boosted by a self-attention
mechanism, our model can accurately detect novelties without the ground-truth
circuit shapes of test samples. Based on the detected novelties, we further
propose two active-learning strategies to sample a reduced amount of
representative layouts most worthy to be fabricated for acquiring their
ground-truth circuit shapes. Experimental results demonstrate i) our method's
effectiveness in layout novelty detection, and ii) our active-learning
strategies' ability in selecting representative novel layouts for keeping a
learning-based pre-simulation model updated.

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The categories of this paper are: physics
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The categories of this paper are: hep-lat
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The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
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The categories of this paper are: supr-con
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ph
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The categories of this paper are: econ
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The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-bio
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The categories of this paper are: adap-org
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The categories of this paper are: q-alg
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The categories of this paper are: nlin
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The categories of this paper are: quant-ph
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The categories of this paper are: acc-phys