FABRICATION OF SELF-POWERED DEVICES AND THE APPLICATION IN HUMAN-COMPUTER INTERFACE

Abstract

The development of computation resources, sensing technologies\nand artificial intelligence has been the critical driving force for the development\nof next-generation smart devices in this Internet of Things (IoT) era. The\nrequirement of sustainable and reliable sensing operation is becoming more and\nmore important due to the exponential increment in the number of sensors with\ndifferent functions deployed everywhere. Such a sensing network provides a more\nconvenient way for connection and communication, making the boundary among\nhumans, machines, and the external environment gradually blurred. Self-powered\nsensors which can directly utilize the external input energy to drive the\noperation of their own are highly desirable compared with the current sensing\ntechnology with a limited life cycle caused by power shortage. The invention of\nTriboelectric Nanogenerators (TENG) and Piezoelectric Nanogenerators (PENG) provides\na novel direction for self-powered sensing technology. Both TENG and PENG can\ndirectly convert mechanical energy to an electrical signal, which could be\nfurther processed and understood by computers and humans. \n\nIn this dissertation, the research efforts have led to the design\nand fabrication of self-powered devices as well as system integration in the Human-Computer\nInterface (HCI). Materials modification was carried out to boost the performance\nof TENG output. A Piezotronic device using new semiconductor material,\nTellurium, was fabricated and its fundamental charge transport characteristics\nwere carefully studied for a new understanding in piezotronics. Beyond materials\nscience, a system-level demonstration of using TENG for HCI application was\nalso carried out. With the help of artificial intelligence technology such as\nmachine learning and deep learning, more in-depth information was successfully\nextracted from the general TENG signal. The combination between Finite Element\nAnalysis (FEA) and deep learning provided a more powerful platform for the\ndevelopment and verification of TENG-based sensing devices with improved working\nreliability. The presented concepts and results in this dissertation show the\npotential for the implementation of novel self-powered sensing technology in\nthe future development for smart sensors, virtual/augmented reality (VR/AR) and\nother HCI-related areas.