This paper describes the Passive Active RFID Tag (PART). The first innovation is an automated method to generate RFID tag controllers based on high-level descriptions of a customised set of RFID primitives. We are capable of targeting microprocessor-based or custom hardware-based controllers. The second innovation is a passive burst switch front-end to the active tag. This switch reduces power consumption by allowing the active transceiver and controller to sleep when no reader is querying the tag. When RF energy is supplied by the reader, the burst switch 'wakes-up' the tag to process the primitive. A prototype burst switch is demonstrated using a Real-Time Spectrum Analyser (RTSA) from our RFID Center for Excellence. We demonstrate the customised RFID tag controller with 40 primitives using a Xilinx Coolrunner-II requiring 1.29 mW and 50 µW of power when active and asleep, respectively. We also present a PIC-microcontroller and hardwarebased Nano Tag at 2.7µW.
While RFID is starting to become a ubiquitious technology, the variation between different RFID systems still remains high. This paper presents several prototyping environments for different components of radio frequency identification (RFID) tags to demonstrate how many of these components can be standardized for many different purposes. We include two active tag prototypes, one based on a microprocessor and the second based on custom hardware. To program these devices we present a design automation flow that allows RFID transactions to be described in terms of primitives with behavior written in ANSI C code. To save power with active RFID devices we describe a passive transceiver switch called the "burst switch" and demonstrate how this can be used in a system with a microprocessor or custom hardware controller. Finally, we present a full RFID system prototyping environment based on real-time spectrum analysis technology currently deployed at the University of Pittsburgh RFID Center of Excellence. Using our prototyping techniques we show how transactions from multiple standards can be combined and targeted to several microprocessors include the Microchip PIC, Intel StrongARM and XScale, and AD Chips EISC as well as several hardware targets including the Altera Apex, Actel Fusion, Xilinx Coolrunner II, Spartan 3 and Virtex 2, and cell-based ASICs.
This paper describes an ultra low power active RFID tag and its automated design flow. RFID primitives to be supported by the tag are enumerated with RFID macros and the behavior of each primitive is specified using ANSI-C within the template to automatically generate the tag controller. Two power saving components, a passive transceiver/burst switch and a smart buffer, are presented to save power and increase tag lifetime. Based on a test program, the processors required 183, 43, and 19 µJ per transaction for StrongARM, XScale, and EISC processors, respectively. Three hardware controllers using a Fusion FPGA, Coolrunner II CPLD, and ASIC required 13 nJ, 1.3 nJ, and 0.07 nJ per transaction.
While RFID has become a ubiquitous technology, there is still a need for RFID systems with different capabilities, protocols, and features depending on the application. This article describes a design automation flow and power estimation technique for fast implementation and design feedback of new RFID systems. Physical layer features are described using waveform features, which are used to automatically generate physical layer encoding and decoding hardware blocks. RFID primitives to be supported by the tag are enumerated with RFID macros and the behavior of each primitive is specified using ANSI-C within the template to automatically generate the tag controller. Case studies implementing widely used standards such as ISO 18000 Part 7 and ISO 18000 Part 6C using this automation technique are presented. The power macromodeling flow demonstrated here is shown to be within 5% to 10% accuracy, while providing results 100 times faster than traditional methods. When eliminating the need for certain features of ISO 18000 Part 6C, the design flow shows that the power required by the implementation is reduced by nearly 50%. ACM Reference Format: Dontharaju, S., Tung, S., Cain, J. T., Mats, L., Mickle, M. H., and Jones, A. K. 2009. A design automation and power estimation flow for RFID systems.
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