Experimental Investigation and Numerical Optimization of Key Factors Affecting the Programming Efficacy of Passive UHF RFID Tags

Abstract

Radio Frequency Identification (RFID) is a disruptive technology that uses radio waves to uniquely identify objects. As such, it has the potential to bring significant benefits to numerous government and private sector initiatives. However, significant technical challenges remain. A key area of study is in system performance: while the major hardware components in an RFID system (i.e., tags, readers and middleware) have been and continue to be studied extensively, there has been little research, comparatively, in characterizing RFID system performance. The research presented in this paper was inspired, in part, by a laser printer RFID solution; i.e., one in which the printer simultaneously prints and programs ultra-high frequency (UHF) tags embedded in print media. In this paper, we have conducted a detailed experimental investigation of the primary factors influencing the performance of RFID systems similar to the print solution. This study aims to provide a systematic experimental process for investigating key factors — e.g., the air gap between reader antenna and tag, in-plane orientation of the tag with respect to the reader antenna, and power level output of the reader — which affect the programmability of UHF RFID tags. Results provide a baseline evaluation of the functionality of RFID systems of similar designs and provide a basis for a detailed exploration of the primary factors which affect RFID UHF passive tag dynamic programming capabilities. By understanding which factors significantly affect the readability and programming of RFID tags, this research suggests optimal designs for system functionality and provides data needed in order to advance such designs. Additionally, a key obstacle for RFID implementation is tag selection. Effectively matching tags to applications requires numerous economic and technical considerations; these considerations generate different implementation constraints. This paper lays the foundation for a multi-objective optimization algorithm to help determine optimal tag selection for a given application, based upon tag performance and cost.