Toward smart building and smart home, floor as one of our most frequently interactive interfaces can be implemented with embedded sensors to extract abundant sensory information without the video-taken concerns. Yet the previously developed floor sensors are normally of small scale, high implementation cost, large power consumption, and complicated device configuration. Here we show a smart floor monitoring system through the integration of self-powered triboelectric floor mats and deep learning-based data analytics. The floor mats are fabricated with unique “identity” electrode patterns using a low-cost and highly scalable screen printing technique, enabling a parallel connection to reduce the system complexity and the deep-learning computational cost. The stepping position, activity status, and identity information can be determined according to the instant sensory data analytics. This developed smart floor technology can establish the foundation using floor as the functional interface for diverse applications in smart building/home, e.g., intelligent automation, healthcare, and security.

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To enable smart homes and relative applications, the floor monitoring system with embedded triboelectric sensors has been proven as an effective paradigm to capture the ample sensory information from our daily activities, without the camera-associated privacy concerns. Yet the inherent limitations of triboelectric sensors such as high susceptibility to humidity and long-term stability remain a great challenge to develop a reliable floor monitoring system. Here we develop a robust and smart floor monitoring system through the synergistic integration of highly reliable triboelectric coding mats and deep-learning-assisted data analytics. Two quaternary coding electrodes are configured, and their outputs are normalized with respect to a reference electrode, leading to highly stable detection that is not affected by the ambient parameters and operation manners. Besides, due to the universal electrode pattern design, all the floor mats can be screenprinted with only one mask, rendering higher facileness and cost-effectiveness. Then a distinctive coding can be implemented to each floor mat through external wiring, which permits the parallel-array connection to minimize the output terminals and system complexity. Further integrating with deep-learning-assisted data analytics, a smart floor monitoring system is realized for various smart home monitoring and interactions, including position/trajectory tracking, identity recognition, and automatic controls. Hence, the developed low-cost, large-area, reliable, and smart floor monitoring system shows a promising advancement of floor sensing technology in smart home applications.

In metaverse, a digital‐twin smart home is a vital platform for immersive communication between the physical and virtual world. Triboelectric nanogenerators (TENGs) sensors contribute substantially to providing smart‐home monitoring. However, TENG deployment is hindered by its unstable output under environment changes. Herein, we develop a digital‐twin smart home using a robust all‐TENG based information mat (InfoMat), which consists of an in‐home mat array and an entry mat. The interdigital electrodes design allows environment‐insensitive ratiometric readout from the mat array to cancel the commonly experienced environmental variations. Arbitrary position sensing is also achieved because of the interval arrangement of the mat pixels. Concurrently, the two‐channel entry mat generates multi‐modality information to aid the 10‐user identification accuracy to increase from 93% to 99% compared to the one‐channel case. Furthermore, a digital‐twin smart home is visualized by real‐time projecting the information in smart home to virtual reality, including access authorization, position, walking trajectory, dynamic activities/sports, and so on.

A study of the Sn‐Ag‐Cu lead‐free solder reflow profile has been conducted. The purpose of the work was to determine the Sn‐Ag‐Cu reflow profile that produced solder bumps with a thin intermetallic compound (IMC) layer and fine microstructure. Two types of reflow profiles were studied. The results of the experiment indicated that the most significant factor in achieving a joint with a thin IMC layer and fine microstructure was the peak temperature. The results suggest that the peak temperature for the Sn‐Ag‐Cu lead‐free solder should be 230°C. The recommended time above liquidus is 40 s for the RSS reflow profile and 50‐70 s for the RTS reflow profile.

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