High-speed and accurate methods for chip-surface-defect detection remain a challenge in the semiconductor industry. Therefore, we propose a feature fusion and data generation-based cascade (FFDG-Cascade) detection approach. This method cascades a classification module with an object detection module. The classifier screens nondefective samples with high confidence, significantly mitigating the number of samples forwarded to the object detector, and substantially enhancing the efficiency due to classifiers’ higher operational speed than detectors. We enhance the model’s detection capability to detect small target defects by incorporating a shallow-to-deep attentional feature fusion (SDAFF) mechanism into the object detection module. In addition, we alleviate network overfitting issues by constructing a large dataset for advanced packaging chips. This dataset comprises 2270 real nondefective samples, 1241 real defective samples, and 7250 synthetic defective samples. For synthetic samples, we propose two defect generation algorithms, each satisfying the requirements of chip production in the early and subsequent stages. Evaluation results demonstrate that integrating synthetic data significantly enhances the detector’s performance by 7.49 mean Average Precision (mAP) on average. Upon incorporating these improvements into the FFDG-Cascade approach, the detection speed increases by 61.77%, while the false acceptance rate (FAR) and false rejection rate (FRR) are reduced by 80.67% and 59.93% on average, respectively. The dataset will be available at https://github.com/HiHiAllen/Chip-surface-defect-dataset .

The main challenges in reconstruction-based anomaly detection include the breakdown of the generalization gap due to improved fitting capabilities and the overfitting problem arising from simulated defects. To overcome this, we propose a new method called PRFF-AD, which utilizes progressive reconstruction and hierarchical feature fusion. It consists of a reconstructive sub-network and a discriminative sub-network. The former achieves anomaly-free reconstruction while maintaining nominal patterns, and the latter locates defects based on pre- and post-reconstruction information. Given defective samples, we find that adopting a progressive reconstruction approach leads to higher-quality reconstructions without compromising the assumption of a generalization gap. Meanwhile, to alleviate the network’s overfitting of synthetic defects and address the issue of reconstruction errors, we fuse hierarchical features as guidance for discriminating defects. Moreover, with the help of an attention mechanism, the network achieves higher classification and localization accuracy. In addition, we construct a large dataset for packaging chips, named GTanoIC, with 1750 real non-defective samples and 470 real defective samples, and we provide their pixel-level annotations. Evaluation results demonstrate that our method outperforms other reconstruction-based methods on two challenging datasets: MVTec AD and GTanoIC.

Generating artistic and coherent 3D scene layouts is crucial in digital content creation. Traditional optimization-based methods are often constrained by cumbersome manual rules, while deep generative models face challenges in producing content with richness and diversity. Furthermore, approaches that utilize large language models frequently lack robustness and fail to accurately capture complex spatial relationships. To address these challenges, this paper presents a novel vision-guided 3D layout generation system. We first construct a high-quality asset library containing 2,037 scene assets and 147 3D scene layouts. Subsequently, we employ an image generation model to expand prompt representations into images, fine-tuning it to align with our asset library. We then develop a robust image parsing module to recover the 3D layout of scenes based on visual semantics and geometric information. Finally, we optimize the scene layout using scene graphs and overall visual semantics to ensure logical coherence and alignment with the images. Extensive user testing demonstrates that our algorithm significantly outperforms existing methods in terms of layout richness and quality. The code and dataset will be available at https://github.com/HiHiAllen/Imaginarium.

Grinding chatter is a hazardous phenomenon that frequently occurred during grinding process. The topic takes radial ultrasonic vibration honing as an example to explore the dynamic changes of grinding force in ultrasonic honing process. By analyzing obtains the following conclusions: (1) The friction coefficient between work-piece and whetstone is a nonlinear time variable; (2) Adding radial ultrasonic is mainly to change the positive pressure between whetstone and work-piece, thereby affects the mutual friction between work-piece and oilstone in honing; (3) Whether ordinary honing or ultrasonic honing, friction dynamic changes along with time, performance of whetstone relative to work-piece is discontinuous sliding —jerking motion.