Laser-induced graphene (LIG) has the advantages of one-step fabrication, prominent mechanical performance, as well as high conductivity; it acts as the ideal material to fabricate flexible strain sensors. In this study, a wearable flexible strain sensor consisting of three-dimensional (3D) wavy LIG and silicone rubber was reported. With a laser to scan on a polyimide film, 3D wavy LIG could be synthesized on the wavy surface of a mold. The wavy-LIG strain sensor was developed by transferring LIG to silicone rubber substrate and then packaging. For stress concentration, the ultimate strain primarily took place in the troughs of wavy LIG, resulting in higher sensitivity and less damage to LIG during stretching. As a result, the wavy-LIG strain sensor achieved high sensitivity (gauge factor was 37.8 in a range from 0% to 31.8%, better than the planar-LIG sensor), low hysteresis (1.39%) and wide working range (from 0% to 47.7%). The wavy-LIG strain sensor had a stable and rapid dynamic response; its reversibility and repeatability were demonstrated. After 5000 cycles, the signal peak varied by only 2.32%, demonstrating the long-term durability. Besides, its applications in detecting facial skin expansion, muscle movement, and joint movement, were discussed. It is considered a simple, efficient, and low-cost method to fabricate a flexible strain sensor with high sensitivity and structural robustness. Furthermore, the wavy-LIG strain senor can be developed into wearable sensing devices for virtual/augmented reality or electronic skin.

Image denoising is still a challenging problem in image processing. The authors propose a novel image denoising method based on a deep convolution neural network (DCNN). Different from other learning‐based methods, the authors design a DCNN to achieve the noise image. Thus, the latent clear image can be achieved by separating the noise image from the contaminated image. At the training stage, the gradient clipping scheme is employed to prevent gradient explosions and enables the network to converge quickly. Experimental results demonstrate that the proposed denoising method can achieve a better performance compared with the state‐of‐the‐art denoising methods. Also, the results indicate that the denoising method has the ability of suppressing different noises with different noise levels by means of one single denoising model.

Due to the excellent self-learning ability of deep learning, we propose a novel deep-learning-based method to inspect surface-mount technology (SMT) solder joints in this paper. In contrast to the state-of-the-art learning-based methods in which low-level features are extracted before learning, our method directly implements the inspection task without low-level feature extraction, which is based on a novel cascaded convolutional neural network (CNN). Three kinds of CNNs with different network parameters compose the proposed cascaded CNN. First, one kind of CNN is employed to adaptively learn the regions of interest (ROIs) of SMT solder joint images. Then, both the learned ROIs and the entire solder joint images are fed into the other two kinds of CNNs, respectively. Finally, inspection results are achieved by the learned cascaded CNN. Comparison experiments indicate that our proposed method can achieve more excellent inspection performance for SMT solder joints than that of the state-of-the-art methods.

With the development of science and technology, consumers’ requirements for various electronic devices present a trend of more diverse functions and thinner bodies. This makes integrated circuits mounted in electronic products and their packaging more vital to satisfying the above requirements. Ball grid array (BGA) packaging is widely used in the field of microelectronic manufacturing industries due to its multiple I/O volumes and excellent electric characteristics. However, due to environmental loads such as vibration and impact during its production and application, defects inevitably emerge in BGA solder joint defects, which will lead to the failure of electronic products. This article summarizes the state-of-the-art research on the factors, analysis methods, and models for the fatigue failure of BGA chips. After rigorous discussions concerning this research, some theoretical suggestions are provided for BGA packaging in reliability analysis and the establishment of evaluation standards.