Integration of Silicon PIN Detectors and TENGs for Self-Powered Wireless AI Intelligent Recognition

In this study, we explore the integration of a cost-effective triboelectric nanogenerator (TENG) with an large silicon PIN detector (diameter: 12 mm) for intelligent wireless recognition applications. Wireless communication eliminates the need for physical connections, enabling greater flexibility and scalability in deployment. It allows for seamless integration of AI systems into a wide range of environments without the constraints of wiring, reducing installation complexity and enhancing mobility. Additionally, we demonstrate the TENG’s functionality as an autonomous communication unit. The TENG is employed to convert various environmentally triggered signals into digital formats and to autonomously power optoelectronic devices, thus eliminating the need for an external power supply. By integrating optoelectronic components within the self-powered sensing system, the TENG can identify specific trigger information and reduce extraneous noise, thereby improving the accuracy of information transmission. Moreover wireless technology facilitates real-time data transmission and processing. This setup not only enhances the overall efficiency and adaptability of the system but also supports continuous operation in diverse and dynamic settings. This paper introduces a novel convolutional neural network-long short-term memory (CNN-LSTM) fusion neural network model. Utilizing the sensing system in combination with the CNN-LSTM neural network enables the collection and identification of variations in the flicker frequency and luminosity of optoelectronic devices. This capability allows for the recognition of environmental trigger signals generated by the TENG. The classification and recognition results of human body trigger signals indicate a recognition accuracy of 92.94%.