Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Attacking Split Manufacturing from a Deep Learning Perspective   The notion of integrated circuit split manufacturing which delegates the
front-end-of-line (FEOL) and back-end-of-line (BEOL) parts to different
foundries, is to prevent overproduction, piracy of the intellectual property
(IP), or targeted insertion of hardware Trojans by adversaries in the FEOL
facility. In this work, we challenge the security promise of split
manufacturing by formulating various layout-level placement and routing hints
as vector- and image-based features. We construct a sophisticated deep neural
network which can infer the missing BEOL connections with high accuracy.
Compared with the publicly available network-flow attack [1], for the same set
of ISCAS-85 benchmarks, we achieve 1.21X accuracy when splitting on M1 and
1.12X accuracy when splitting on M3 with less than 1% running time.

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The categories of this paper are: hep-th
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The categories of this paper are: patt-sol
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: nlin
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int