Lili Li , Yiyuan Xie , Xiao Jiang , Ye Su , Yichen Ye , Zelin Li , Yuhan Tang
{"title":"基于互耦加降微环谐振器的延时储层计算","authors":"Lili Li , Yiyuan Xie , Xiao Jiang , Ye Su , Yichen Ye , Zelin Li , Yuhan Tang","doi":"10.1016/j.chaos.2025.116627","DOIUrl":null,"url":null,"abstract":"<div><div>Add-drop silicon microring resonator (MRR) has notable advantages in low power consumption, light weight and scalability, making it one of the research hotspots in optical reservoir computing (ORC) and optical chip. In this paper, for the first time, we propose a novel nonlinear dynamic system using mutually coupled (MC) add-drop MRRs with clockwise and counter-clockwise optical injection. Utilizing the proposed dynamical model, which is grounded in modified nonlinear dynamic equations incorporating coupled mode theory (CMT), we further construct an ORC system. Different dynamic behaviors and internal physical mechanisms, affected by key parameters, are analyzed in detail through bifurcation diagrams. Based on this, the effects of key parameters including injection strength, pump power, and injection delay time on the performance of ORC are detailedly analyzed in results. Through comprehensive analysis and optimization, the proposed ORC can achieve the normalized mean square error (NMSE) of 0.4% for the prediction task and the symbol error rate (SER) of 0.2% with SNR of 24 dB for nonlinear channel equalization. By analyzing the effects of the system output state, the number of virtual node, and scaling factor on the above tasks, we achieve remarkable recognition accuracies, attaining 99% on the MNIST dataset and 86.8% on the Fashion-MNIST dataset. The results and analysis underscore the importance of mastering the dynamic mechanism of the proposed model to achieve optimal application performance for constructed ORC systems. An in-depth understanding of the proposed model offers valuable insights and inspiration for the subsequent development of integrated topologies.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":"199 ","pages":"Article 116627"},"PeriodicalIF":5.6000,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Time delay reservoir computing based on mutually coupled add-drop microring resonators\",\"authors\":\"Lili Li , Yiyuan Xie , Xiao Jiang , Ye Su , Yichen Ye , Zelin Li , Yuhan Tang\",\"doi\":\"10.1016/j.chaos.2025.116627\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Add-drop silicon microring resonator (MRR) has notable advantages in low power consumption, light weight and scalability, making it one of the research hotspots in optical reservoir computing (ORC) and optical chip. In this paper, for the first time, we propose a novel nonlinear dynamic system using mutually coupled (MC) add-drop MRRs with clockwise and counter-clockwise optical injection. Utilizing the proposed dynamical model, which is grounded in modified nonlinear dynamic equations incorporating coupled mode theory (CMT), we further construct an ORC system. Different dynamic behaviors and internal physical mechanisms, affected by key parameters, are analyzed in detail through bifurcation diagrams. Based on this, the effects of key parameters including injection strength, pump power, and injection delay time on the performance of ORC are detailedly analyzed in results. Through comprehensive analysis and optimization, the proposed ORC can achieve the normalized mean square error (NMSE) of 0.4% for the prediction task and the symbol error rate (SER) of 0.2% with SNR of 24 dB for nonlinear channel equalization. By analyzing the effects of the system output state, the number of virtual node, and scaling factor on the above tasks, we achieve remarkable recognition accuracies, attaining 99% on the MNIST dataset and 86.8% on the Fashion-MNIST dataset. The results and analysis underscore the importance of mastering the dynamic mechanism of the proposed model to achieve optimal application performance for constructed ORC systems. An in-depth understanding of the proposed model offers valuable insights and inspiration for the subsequent development of integrated topologies.</div></div>\",\"PeriodicalId\":9764,\"journal\":{\"name\":\"Chaos Solitons & Fractals\",\"volume\":\"199 \",\"pages\":\"Article 116627\"},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2025-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chaos Solitons & Fractals\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S096007792500640X\",\"RegionNum\":1,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chaos Solitons & Fractals","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S096007792500640X","RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Time delay reservoir computing based on mutually coupled add-drop microring resonators
Add-drop silicon microring resonator (MRR) has notable advantages in low power consumption, light weight and scalability, making it one of the research hotspots in optical reservoir computing (ORC) and optical chip. In this paper, for the first time, we propose a novel nonlinear dynamic system using mutually coupled (MC) add-drop MRRs with clockwise and counter-clockwise optical injection. Utilizing the proposed dynamical model, which is grounded in modified nonlinear dynamic equations incorporating coupled mode theory (CMT), we further construct an ORC system. Different dynamic behaviors and internal physical mechanisms, affected by key parameters, are analyzed in detail through bifurcation diagrams. Based on this, the effects of key parameters including injection strength, pump power, and injection delay time on the performance of ORC are detailedly analyzed in results. Through comprehensive analysis and optimization, the proposed ORC can achieve the normalized mean square error (NMSE) of 0.4% for the prediction task and the symbol error rate (SER) of 0.2% with SNR of 24 dB for nonlinear channel equalization. By analyzing the effects of the system output state, the number of virtual node, and scaling factor on the above tasks, we achieve remarkable recognition accuracies, attaining 99% on the MNIST dataset and 86.8% on the Fashion-MNIST dataset. The results and analysis underscore the importance of mastering the dynamic mechanism of the proposed model to achieve optimal application performance for constructed ORC systems. An in-depth understanding of the proposed model offers valuable insights and inspiration for the subsequent development of integrated topologies.
期刊介绍:
Chaos, Solitons & Fractals strives to establish itself as a premier journal in the interdisciplinary realm of Nonlinear Science, Non-equilibrium, and Complex Phenomena. It welcomes submissions covering a broad spectrum of topics within this field, including dynamics, non-equilibrium processes in physics, chemistry, and geophysics, complex matter and networks, mathematical models, computational biology, applications to quantum and mesoscopic phenomena, fluctuations and random processes, self-organization, and social phenomena.