We report an alternative hydrothermal process for the preparation of pure phase LiCoO(2) cathode material for potential application under high rates. By adjusting the hydrothermal conditions, nanoplate-like LiCoO(2) crystals were obtained, with grain size about 200 nm. It was found in the experiment that the H(2)O(2) concentration and hydrothermal temperature were the two key factors that influence the phase purity and crystal shape, while LiOH concentration has only a slight effect on the crystal size of the product. The electrochemical test revealed a good rate behavior of the synthesized pure phase LiCoO(2) nanoplates, demonstrating a potential of the hydrothermal process for mass production.

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 .

Drawing freehand sketches of mechanical components on multimedia devices for AI-based engineering modeling has become a new trend. However, its development is being impeded because existing works cannot produce suitable sketches for data-driven research. These works either generate sketches lacking a freehand style or utilize generative models not originally designed for this task resulting in poor effectiveness. To address this issue, we design a two-stage generative framework mimicking the human sketching behavior pattern, called MSFormer, which is the first time to produce humanoid freehand sketches tailored for mechanical components. The first stage employs Open CASCADE technology to obtain multi-view contour sketches from mechanical components, filtering perturbing signals for the ensuing generation process. Meanwhile, we design a view selector to simulate viewpoint selection tasks during human sketching for picking out information-rich sketches. The second stage translates contour sketches into freehand sketches by a transformer-based generator. To retain essential modeling features as much as possible and rationalize stroke distribution, we introduce a novel edge-constraint stroke initialization. Furthermore, we utilize a CLIP vision encoder and a new loss function incorporating the Hausdorff distance to enhance the generalizability and robustness of the model. Extensive experiments demonstrate that our approach achieves state-of-the-art performance for generating freehand sketches in the mechanical domain. Project page: https://mcfreeskegen.github.io/.