Memristor-Based Circuits and Architectures Enabling Next-Generation Neuromorphic RFID Systems

Abstract

Current RFID circuits, designed primarily for basic low-power communication and data storage, are not suitable to meet the computational needs of future AI-based IoT applications. While effective for simple identification tasks, these systems fall short in supporting advanced data processing and on-chip intelligence. Next-generation neuromorphic RFID circuits are expected to dynamically adapt based on external inputs and emulate biological neuron activity, paving the way for intelligent, low-power, and autonomous devices. This paper explores the potential of neuromorphic RFID systems driven by memristor-based architectures, leveraging ReRAM technology and crossbar arrays. ReRAM offers key advantages, including reduced energy consumption, essential for enabling local processing and real-time decision-making in intelligent RFID nodes. To demonstrate this potential, a $2\times 2$ crossbar circuit was designed and simulated in LTspice using Biolek’s memristor model. The analysis examined the circuit’s response to read and EPC-like inputs, state variable dynamics, and digital output behavior. Operating at microwatt-level power consumption and capable of processing sensor signals, the proposed architecture shows promise as a foundational building block for future low-power, intelligent, and autonomous RFID systems.