Defect Detection of MEMS Based on Data Augmentation, WGAN-DIV-DC, and a YOLOv5 Model.

Surface defect detection of micro-electromechanical system (MEMS) acoustic thin film plays a crucial role in MEMS device inspection and quality control. The performances of deep learning object detection models are significantly affected by the number of samples in the training dataset. However, it is difficult to collect enough defect samples during production. In this paper, an improved YOLOv5 model was used to detect MEMS defects in real time. Mosaic and one more prediction head were added into the YOLOv5 baseline model to improve the feature extraction capability. Moreover, Wasserstein divergence for generative adversarial networks with deep convolutional structure (WGAN-DIV-DC) was proposed to expand the number of defect samples and to make the training samples more diverse, which improved the detection accuracy of the YOLOv5 model. The optimal detection model achieved 0.901 mAP, 0.856 F1 score, and a real-time speed of 75.1 FPS. As compared with the baseline model trained using a non-augmented dataset, the mAP and F1 score of the optimal detection model increased by 8.16% and 6.73%, respectively. This defect detection model would provide significant convenience during MEMS production.

[An assistant artificial hip joint].

The assistant artificial hip joint (AAHJ) is a new impermanent hip support implanted in the body. It is used for treatment of ischemic necrosis of the femoral head at the early stage. It reserves the natural femoral head, increases its containment and decreases its load, thus makes the recovery of the necrosed femoral head. The AAHJ's moving axis center is the same as that of the femoral head. Therefore, the moving range of the hip joint is very close to the normal postoperatively. The patient can walk with loading in 3 weeks after the surgical operation, and can regain his (or her) daily work and life in 2 to 3 months of the operation. The AAHJ's structure is simple and the price is cheap.