This paper proposes a hybrid K-means clustering and support vector machine (HKCSVM) method to detect the positions of vias and metal lines from delayered IC images for subsequent netlist extraction. The main contributions of the proposed method include: 1) fully automated detection of via and metal line positions without any need of human interventions; and 2) novel hybrid methodology to embody K-means clustering and support vector machine for retrieving precise positions of vias and metal lines in contrast to the individual techniques which can only provide a region for the detected elements. From experiments on 50 SEM images of 1536 × 2048 pixels taken from fabricated IC chips (@TSMC 130nm CMOS process), our proposed HKCSVM method achieves an F-score of 99.82% for via detection, and mean intersection over union (IoU) of 94.44% and mean pixel accuracy of 95.83% for metal line detection, which are both superior to the reported approaches.

We propose a systematic training and validation approach for obtaining a deep learning model for automatic IC image analysis, i.e. IC image semantic segmentation. Our approach divides IC images into different regions of interest and provides for noise rejection training. We discuss steps for obtaining such a model. By experiment and by comparison with competing image processing techniques, deep learning models obtained by our approach demonstrate good generalization capability, high prediction accuracy and low circuit annotation error.

Automated Optical Inspection (AOI) is widely used by manufacturers for the detection of defects in Printed Circuit boards (PCBs). Recent works have proposed to apply deep learning for defect detection, which is much faster and cheaper than manual inspection. However, AOI can only capture defects on the outmost layers of PCBs using cameras, while modern high-speed circuit PCBs usually have multiple internal layers that need to be inspected. Compared to optical sensors, X-ray tomography provides non-invasive imaging results of all PCB layers. Though one can directly apply an off-the-shelf deep detection model trained on optical domains for X-ray imagery, we show that it usually leads to much lower accuracies in practice. The degraded performance is mainly due to the relatively low quality of X-ray imaging results and the gaps between optical and X-ray modalities. Furthermore, no X-ray PCB image dataset is publicly available for training deep defect detectors. To this end, we propose a novel dataset for X-ray PCB defect detection, dubbed XD-PCB. In XD-PCB, we provide a benchmark for training X-ray automated defect detection models containing synthesized X-ray images and real X-ray images with real defects. However, in a practical environment, retraining the deep model for every unseen X-ray domain is inefficient due to the domain gaps created by different X-ray machine settings and the scarcity of defects. Thus, we propose a domain adaptation framework, dubbed FDX, to improve the efficiency of X-ray PCB defect detection methods. By minimizing the differences between the deep features extracted from abundant training images and the scarce unseen images, we improve the model’s performance in a practical situation, thus enhancing the generalization ability and efficiency of deep detection algorithms when exposed to unseen domains. Our results demonstrate that XD-PCB provides a valuable training baseline for X-ray PCB defect detection, and our proposed FDX framework can effectively increase the popular deep learning model by achieving an increment of 10% in terms of APs compared to other adaptation methods.

Pattern recognition algorithms have recently been pursued for automatic analysis of delayered IC images, i.e. the detection of circuit components. Wide experimentation on the existing training-based approaches are hampered by heavy data labeling, expensive model training, or long processing time. In this paper, we propose a global template projection and matching (GTPM) method that requires no training and a minimal amount of data labeling for circuit component detection. Our proposed GTPM method achieves a higher or comparable accuracy as the reported approaches while being more computationally efficient.

In recent years, deep learning-based segmentation techniques have been applied to circuit annotation for the hardware assurance of integrated circuits (ICs). However, imperfections in circuit images often cause incorrectly segmented pixels, which result in critical circuit connection errors that are detrimental to subsequent circuit analysis. To mitigate such circuit connection errors, this paper proposes a targeted data augmentation framework for deep learning-based circuit annotation, termed Integrated Circuit Mask-GAN (ICMG), which generates circuit images containing the aforementioned imperfections through GAN-based image translation from synthetic circuit masks. The circuit masks are synthesized by a novel mask generation process that incorporates our domain knowledge of IC layouts and is configurable to emulate various image imperfections such as material residuals in the sample preparation or distortion in the imaging process. In our experiments on a microcontroller IC, our proposed ICMG greatly reduced the circuit connection errors and the required manual effort for data labeling compared to the reported techniques.