With the rapid progress of artificial intelligence, humans are moving toward the era of the intelligent connection of all things. Therefore, the demand for sensors is drastically increasing with developing intelligent social applications. Traditional sensors must be triggered by an external power source and the energy consumption is high for equipment that is widely distributed and working intermittently, which is not conducive to developing sustainable green and healthy applications. However, self-powered sensors based on triboelectric nanogenerators (TENG) can autonomously harvest energy from the surrounding environment and convert this energy into electrical energy for storage. Sensors can also be self-powered without an external power supply, which is vital for smart cities, smart homes, smart transportation, environmental monitoring, wearable devices, and bio-medicine. This review mainly summarizes the working mechanism of TENG and the research progress of self-powered sensors based on TENG about the Internet of Things (IoT), robotics, human–computer interaction, and intelligent medical fields in recent years.

Currently, the integration method of silicon PIN radiation detectors faces challenges such as complex processes, poor reliability and thick dead layers. Novel integration methods based on metal bonding technology for realizing the integration of thick and thin PIN detectors are needed with the requirement of reducing signal crosstalk, package volume and weight. Combined with the current research on metal bonding technology, this paper presents an extensive review of metal bonding technology, especially metal Al bonding technology, to provide a certain reference to future research on the bonding processing of high-performance silicon PIN detector devices.

Al-Sn-Al wafer bonding is a new semiconductor manufacturing technology that plays an important role in device manufacturing. Optimization of the bonding process and testing of the bonding strength remain key issues. However, using only physical experiments to study the above problems presents difficulties such as repeating many experiments, high costs, and low efficiency. Deep learning algorithms can quickly simulate complex physical correlations by training large amounts of data, which is a good solution to the difficulties in studying wafer bonding. Therefore, this paper proposes the use of deep learning models (2-layer CNN and 50-layer ResNet) to achieve autonomous recognition of bonding strengths corresponding to different bonding conditions, and the results from a comparative test set show that the ResNet model has an accuracy of 99.17%, outperforming the CNN model with an accuracy of 91.67%. Then, the identified images are analyzed using the Canny edge detector, which showed that the fracture surface morphology of the wafer is a hole-shaped structure, with the smaller the area of hole movement on the wafer surface, the higher the bonding strength. In addition, the effects of bonding time and bonding temperature on bonding strength are verified, showing that relatively short bonding times and relatively low bonding temperatures resulted in better wafer bonding strength. This research demonstrates the potential of using deep learning to accelerate wafer bonding strength identification and process condition optimization.