Research On Hardware Defect Classification Algorithm Of Integrated Circuit Chip

The rapid development of Integrated Circuit(IC)technology makes the application of Integrated chip more and more important.At the same time,the threats and losses caused by the IC chip defects are also increasingly serious.However,there are no effective means to detect the hardware defects of IC chip.In this context,the characteristics of various hardware defects of IC chips were studied and analyzed,and an algorithm to classify different hardware defects of IC chips was proposed.The main contents of this dissertation are as follows:(1)The development status of hardware defect analysis and detection technology for integrated circuit chips is investigated in this dissertation,and the application of neural networks on hardware defects' analysis,as well as its future development trend is also studied.(2)An intelligent classification algorithm for IC chip faults is studied:We first study the typical faults of IC chips.Then,we apply the wavelet analysis technique on the circuit response signals to extract fault feature,and improve the primal fault feature extraction technology.Finally,we use neural networks as classifier to classify and detect the IC chip faults.Once our algorithm is trained,the faults classification algorithm can detect intelligently.And we no longer need the “golden chip” model as a reference.(3)Two hardware Trojans classification strategies based on neural network are proposed:To detect the hardware Trojans injected into the integrated circuit chip,we use the neural network,which can identify the differences between the IC chips with hardware Trojans and the IC chips without hardware Trojans.The processed hardware Trojans detection algorithm is based on the thermal images of IC chips.It can detect hardware Trojans with different distributions and locations,whether the Trojans are activated or not.We also analyze the influences of different drive signals and chip process variation on the Trojan detection rate.At the same time,for reducing the dimension of feature information and simplifying the neural network structure,we use 2-Dimensional Principal Component Analysis(2DPCA)technology to extract features from the thermal images.It's proved that 2DPCA technology can improve the classification efficiency and detection rate of the algorithm.In addition,we apply supervised and unsupervised neural networks in the proposed algorithm to adapt to different application environments.We also compare the hardware Trojans classification methods based on these two different neural networks and analyze their advantages and disadvantages.