论文标题
从深度学习的角度攻击分裂制造
Attacking Split Manufacturing from a Deep Learning Perspective
论文作者
论文摘要
集成电路分开制造的概念，该概念将不同铸造厂的前端（FEOL）和后端（Beol）零件委派给了不同的铸造厂，以防止过度生产，知识产权（IP）的盗版或针对性设施中的对手对对手的硬件Trojans的目标插入。在这项工作中，我们通过将各种布局级别的放置和路由提示作为向量和基于图像的功能来挑战拆分制造的安全承诺。我们构建了一个复杂的深神经网络，可以高精度地推断丢失的Beol连接。与公开可用的网络流攻击[1]相比，对于相同的ISCAS-85基准测试，当在M1上分配时，我们达到了1.21倍的精度，并且在运行时间小于1％的M3上分开时，我们可以达到1.21倍的精度。
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.
