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Real-time automated counterfeit integrated circuit detection using x-ray microscopy

机译：使用X射线显微镜进行实时自动伪造集成电路检测

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摘要

Determining the authenticity of integrated circuits is paramount to preventing counterfeit and malicious hardware from being used in critical military, healthcare, aerospace, consumer, and industry applications. Existing techniques to distinguish between authentic and counterfeit integrated circuits (ICs) often include destructive testing requiring subject matter experts. We present a nondestructive technique to detect ICs using x- ray microscopy and advanced imaging analysis with different pattern recognition approaches. Our proposed method is completely automated, and runs in real time. In our approach, images of an integrated circuit are obtained from an x- ray microscope. Local binary pattern features are then extracted from the x- ray image, followed by dimensionality reduction through principal component analysis, and alternatively through a nonlinear principal component methodology using a stacked autoencoder embedded in a deep neural network. From the reduced dimension features, we train two types of learning machines, a support vector machine with a nonlinear kernel and a deep neural network. We present experiments using authentic and ICs to demonstrate that the proposed approach achieves an accuracy of 100% in distinguishing between the counterfeit and authentic samples. (C) 2015 Optical Society of America
机译：确定集成电路的真实性对于防止伪造和恶意硬件被用于关键的军事，医疗保健，航空航天，消费和工业应用至关重要。区分真实和假冒集成电路（IC）的现有技术通常包括需要主题专家的破坏性测试。我们提出了一种使用X射线显微镜和具有不同模式识别方法的先进成像分析来检测IC的无损技术。我们提出的方法是完全自动化的，并且可以实时运行。在我们的方法中，集成电路的图像是从X射线显微镜获得的。然后从X射线图像中提取局部二进制图案特征，然后通过主成分分析或通过使用嵌入在深层神经网络中的堆叠式自动编码器的非线性主成分方法，降低维数。从降维特征开始，我们训练了两种类型的学习机，即具有非线性核和深度神经网络的支持向量机。我们目前使用真实和IC进行的实验表明，所提出的方法在区分假冒和真实样本时可达到100％的准确性。 （C）2015年美国眼镜学会

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