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首页> 外文期刊>IEEE transactions on information forensics and security >Non-Invasive Recognition of Poorly Resolved Integrated Circuit Elements
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Non-Invasive Recognition of Poorly Resolved Integrated Circuit Elements

机译：分辨力差的集成电路元件的非侵入式识别

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摘要

We present a non-invasive method for recognition of components in a digital CMOS integrated circuit (IC). We use a confocal infrared laser scanning optical microscope to collect multimodal images through the backside of the IC. Individual modes correspond to passive reflectivity measurements or active measurements, such as light-induced voltage alteration. The modes are registered and stored in a multidimensional data cube. We apply a machine learning algorithm using a binary representation to identify a variety of data structures from transistors to entire logic cells. Because of the compact representation, objects can be detected rapidly. We show that by increasing the number of imaging modes used to develop the descriptor, we can significantly increase recognition accuracy. The approach allows recognition of poorly resolved components, whose primary distinguishing features are below traditional optical resolution limits, and is general enough to be applied to multiple design processes. We believe this represents a significant step toward a fully non-invasive IC reverse engineering system.
机译：我们提出了一种非侵入性的方法来识别数字CMOS集成电路（IC）中的组件。我们使用共焦红外激光扫描光学显微镜通过IC的背面收集多峰图像。各个模式对应于被动反射率测量或主动测量，例如光感应电压变化。这些模式已注册并存储在多维数据立方体中。我们使用机器学习算法，使用二进制表示来识别从晶体管到整个逻辑单元的各种数据结构。由于结构紧凑，可以快速检测物体。我们表明，通过增加用于开发描述符的成像模式数量，我们可以显着提高识别精度。该方法可以识别分辨率较差的组件，这些组件的主要区别特征是低于传统的光学分辨率极限，并且通用到足以应用于多种设计过程。我们相信，这是朝着完全非侵入式IC逆向工程系统迈出的重要一步。

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