DDR4 Data Channel Failure Due to DC Offset Caused by Intermittent Solder Ball Fracture in FBGA Package

This paper shows that an intermittent AC coupling defect occurring in a DDR4 data channel will cause more intermittent errors in DDR4, compared to such defect in DDR3. The intermittent AC coupling defect occurs due to intermittent fracture in DDR4 package solder ball. The defect causes DC offset in DDR4, which shifts the data signal or data eye and results in DDR4 data channel failure. The DC offset occurs due to the asymmetric nature of pseudo open drain termination scheme. DDR4 data channel response is compared with DDR3 channel. It is shown that pseudo random binary sequence (PRBS) pattern will always cause failure for DDR4, but PRBS will only cause failure in DDR3 if the sequence of consecutive 0's or 1's in PRBS pattern is long enough to cause threshold violation. As a result there will be more intermittent errors in DDR4 compared to DDR3. The defect due to fracture in solder ball is modelled by an AC coupling capacitor. A 1nF AC coupling capacitor corresponding to a solder ball fracture of height about 1nm is used to show the difference between DDR4 and DDR3 response

Small Object Detection in Infrared Images: Learning from Imbalanced Cross-Domain Data via Domain Adaptation

Deep learning-based object detection is one of the most popular research topics. However, in cases where large-scale datasets are unavailable, the training of detection models remains challenging due to the data-driven characteristics of deep learning. Small object detection in infrared images is such a case. To solve this problem, we propose a YOLOv5-based framework with a novel training strategy based on the domain adaptation method. First, an auxiliary domain classifier is combined with the YOLOv5 architecture to compose a detection framework that is trainable using datasets from multiple domains while maintaining calculation costs in the inference stage. Secondly, a new loss function based on Wasserstein distance is proposed to deal with small-sized objects by overcoming the problem of the intersection over union sensitivity problem in small-scale cases. Then, a model training strategy inspired from domain adaptation and knowledge distillation is presented. Using the domain confidence output of the domain classifier as a soft label, domain confusion loss is backpropagated to force the model to extract domain-invariant features while training the model with datasets with imbalanced distributions. Additionally, we generate a synthetic dataset in both the visible light and infrared spectrum to overcome the data shortage. The proposed framework is trained on the MS COCO, VEDAI, DOTA, ADAS Thermal datasets along with a constructed synthetic dataset for human detection and vehicle detection tasks. The experimental results show that the proposed framework achieved the best mean average precision (mAP) of 64.7 and 57.5 in human and vehicle detection tasks. Additionally, the ablation experiment shows that the proposed training strategy can improve the performance by training the model to extract domain-invariant features