REFICS: Assimilating Data-Driven Paradigms Into Reverse Engineering and Hardware Assurance on Integrated Circuits
REFICS: Assimilating Data-Driven Paradigms Into Reverse Engineering and Hardware Assurance on Integrated Circuits
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DOI：
10.1109/access.2021.3114360
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发表时间：
2021
期刊：
影响因子：
3.9
通讯作者：
Ronald Wilson;Hangwei Lu;Mengdi Zhu;Domenic Forte;D. Woodard
中科院分区：
文献类型：
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作者：
Ronald Wilson;Hangwei Lu;Mengdi Zhu;Domenic Forte;D. Woodard
保证集成电路（IC）上的信任的综合硬件保证方法通常需要通过破坏性逆向工程（RE）来验证IC设计布局和功能。这是一个资源密集型的过程，将大大受益于数据驱动模式的广泛集成，特别是在成像和图像分析阶段。虽然显而易见，但由于缺乏大量高质量的标记数据，将数据驱动方法纳入RE辅助硬件保证的做法正在滞后。在本文中，一个大规模的合成扫描电子显微镜（SEM）数据集，REFICS，介绍了解决这个问题。该数据集是RE社区中的第一个开源数据集，由两种节点技术（32 nm和90 nm）以及IC的四个基本层（即掺杂层、多晶硅层、接触层和金属层）的80万张SEM图像组成。此外，一个框架，不确定性和风险的基础上，介绍了现有的RE工作流程，利用特设步骤在其执行的效率和效益进行比较。这些发展对于将RE辅助硬件保证发展为可扩展、自动化和容错方法至关重要。最后，通过对现有机器学习和深度学习方法在RE和硬件保证中进行图像分析的性能分析，总结了这项工作。
Comprehensive hardware assurance approaches guaranteeing trust on Integrated Circuits (ICs) typically require the verification of the IC design layout and functionality through destructive Reverse Engineering (RE). It is a resource intensive process that will benefit greatly from the extensive integration of data-driven paradigms, especially in the imaging and image analysis phase. Although obvious, this uptake of data-driven approaches into RE-assisted hardware assurance is lagging due to the lack of massive amounts of high-quality labelled data. In this paper, a large-scale synthetic Scanning Electron Microscopy (SEM) dataset, REFICS, is introduced to address this issue. The dataset, the first open-source dataset in the RE community, consists of 800,000 SEM images over two node technologies, 32nm and 90nm, and four cardinal layers of the IC, namely, doping, polysilicon, contact and metal layers. Furthermore, a framework, based on uncertainty and risk, is introduced to compare the efficacy and benefits of existing RE workflows utilizing ad-hoc steps in its execution. These developments are critical in developing RE-assisted hardware assurance into a scalable, automated and fault-tolerant approach. Finally, the work is concluded with the performance analysis of existing machine learning and deep learning approaches for image analysis in RE and hardware assurance.