Time-of-flight magnetic resonance angiography (TOF-MRA) is a prevalent vascular imaging technique for assessing cerebrovascular diseases. Compared to routine 3T TOF-MRA, 7T TOF-MRA provides vascular structures with a higher signal-to-noise ratio (SNR) and better vessel contrast, revealing greater vascular details. However, the inaccessibility of 7T scanners and specific physiological and technical concerns limit its clinical application. Therefore, we aimed to generate high-quality 7T-like TOF-MRA from 3T TOF-MRA. Considering the spatial sparsity of vessel signals, the visibility discrepancy of distal and small vessels between 3T and 7T images, and the subtle spatial misalignment between paired data, we proposed a novel aleatoric-uncertainty-aware maximum intensity projection-based generative adversarial network (AU-MIPGAN). In our method, we employed a knowledge distillation (KD) framework to incorporate multi-directional MIP information into the 3T-to-7T learning process to strengthen the learning of vessels and provide three-dimensional (3D) vascular morphological knowledge for the student model, facilitating accurate generation of vascular structures. Furthermore, we exploited AU modeling to compensate for the spatial misalignment between paired 3T and 7T images during the training procedure, which helped the model concentrate more on learning the intrinsic gap between 3T and 7T images. Qualitative and quantitative results demonstrated that the proposed AU-MIPGAN can achieve promising performance for 7T-like TOF-MRA generation.

Good-Die-in-Bad-Neighborhood (GDBN) is a technique employed to identify chips that pass initial tests but may have defects. Previous research used neural networks and expanded observation windows but ignored the impact of isolated dice. This paper improves wafer pattern information through denoising and creates a lightweight model. It also reduces training time by annotating multiple dice simultaneously. Experiments on real-world datasets show the model effectively captures more Test Escapes, reducing Defective Parts Per Million (DPPM) and improving return merchandise authorization gains.

Good-die-in-bad-neighborhood (GDBN) is a widely adopted method utilizing the fact that manufacturing defects tend to exhibit spatial dependency and form a cluster or specific pattern of bad dice on a wafer. Existing research studies on GDBN mainly focus on learning such spatial relationships within a limited observation window through simple mechanisms such as linear regression or multilayer perceptron model. In this paper, we propose MetaFormer-GDBN, a transformer-based deep learning model with the observation window extending to the entire wafer to include broader pattern information. The enhanced neighboring information and model capacity allow our method to capture more complex patterns of bad dice. Experiments show that compared to previous work, our method can achieve up to 50 % performance improvement, reducing the DPPM (defective parts per million) with minimal yield loss.

With the increasing demand for high reliability of products, how to prevent potential defective devices from shipping to customers is a serious issue about which more and more companies are concerned. Toward this end, many test methods have been developed to screen out outliers. However, basic statistical paradigm may not be enough to handle the shrinking transistor size and increasingly complex circuit design. In this paper, we propose to use the concept of Z-score derived from our proposed neural network, called single density network (SDN), to define level of abnormality. We also define new metrics called self-excluded fail rate (SE fail rate) and normalized area under curve (AUC) to be our criteria to quantify and further visualize the outcome. To filter out spatially-correlated outliers, we make use of specific information of neighboring dice and encode them into our input features for the proposed SDN. A series of experimental results on industrial data reveal the effectiveness of our methodology and the better ability to identify defective outliers than existing conventional statistical approaches for a variety of analog tests.