{"title":"基于忆阻器的电路和架构实现下一代神经形态RFID系统","authors":"Riccardo Colella;Alberto Arciello;Giuseppe Grassi;Massimo Merenda","doi":"10.1109/JRFID.2025.3579260","DOIUrl":null,"url":null,"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 <inline-formula> <tex-math>$2\\times 2$ </tex-math></inline-formula> 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.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"9 ","pages":"384-394"},"PeriodicalIF":2.3000,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Memristor-Based Circuits and Architectures Enabling Next-Generation Neuromorphic RFID Systems\",\"authors\":\"Riccardo Colella;Alberto Arciello;Giuseppe Grassi;Massimo Merenda\",\"doi\":\"10.1109/JRFID.2025.3579260\",\"DOIUrl\":null,\"url\":null,\"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 <inline-formula> <tex-math>$2\\\\times 2$ </tex-math></inline-formula> 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.\",\"PeriodicalId\":73291,\"journal\":{\"name\":\"IEEE journal of radio frequency identification\",\"volume\":\"9 \",\"pages\":\"384-394\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-06-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE journal of radio frequency identification\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11032125/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE journal of radio frequency identification","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/11032125/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Memristor-Based Circuits and Architectures Enabling Next-Generation Neuromorphic RFID Systems
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.