Verification is one of the core steps in integrated circuits (ICs) manufacturing due to the multifarious defects and malicious hardware Trojans (HTs). In most cases, the effectiveness of the detection relies on the quality of the sample images of ICs. However, the high-precision and noiseless images are hard to capture due to the mechanical precision, manual error and environmental interference. In this paper, an effective approach for processing the low-quality image data of ICs is proposed. Our approach can successfully categorize the partial pictures of multiple objected ICs with low resolution and various noise. The proposed approach extracts the high-frequency texture components (HFTC) of the images and constructs a graph with the correlationship among features. Subsequently, the spectral clustering is conducted for obtaining the final cluster indicators. The low-quality images of ICs can be successfully categorized by the proposed approach, which will provide a data foundation for the following verification tasks. In order to evaluate the effectiveness of the proposed approach, several experiments are conducted in the simulated datasets, which are generated by corrupting the real-world data in different conditions. The clustering results reveal that our approach can achieve the best performance with good stability compared to the baselines.

With the vigorous development of integrated circuit (IC) manufacturing, the harmfulness of defects and hardware Trojans is also rising. Therefore, chip verification becomes more and more important. At present, the accuracy of most existing chip verification methods depends on high-precision sample data of ICs. Paradoxically, it is more challenging to invent an efficient algorithm for high-precision noiseless data. Thus, we recently proposed a fusion clustering framework based on low-quality chip images named High-Frequency Low-Rank Subspace Clustering (HFLRSC), which can provide the data foundation for the verification task by effectively clustering those noisy and low-resolution partial images of multiple target ICs into the correct categories. The first step of the framework is to extract high-frequency texture components. Subsequently, the extracted texture components will be integrated into subspace learning so that the algorithm can not only learn the low-rank space but also retain high-frequency information with texture characteristics. In comparison with the benchmark and state-of-the-art method, the presented approach can more effectively process simulation low-quality IC images and achieve better performance.