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In-Sensor Reservoir Computing Employing Reconfigurable Optoelectronic Transistors for Multi-Task Learning 认领 引用
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作者 Shanshan Jiang Hainan Zhang +4 位作者 Kesheng Wang Shuo Cheng Can Fu Huanhuan Wei Gang He 《Rare Metals》 SCIE EI CAS CSCD 2026年第4期629-639,共11页
The edge deployment of artificial intelligence has driven the exploitation of compact,energy-efficient information processing systems that integrate sensing,memory,and multi-task processing functions.However,conventio... The edge deployment of artificial intelligence has driven the exploitation of compact,energy-efficient information processing systems that integrate sensing,memory,and multi-task processing functions.However,conventional vision systems suffer from significant energyime overhead,extra hardware costs,and an unaffordable algorithm.Herein,we demonstrate an in-sensor computing system employing reconfigurable optoelectronic transistors(ROETs)for multi-task learning.These transistors exhibit reconfigurable volatile and nonvolatile characteristics under both optical and electrical stimuli.Capitalizing on this reconfigurability,we establish an in-sensor reservoir computing(RC)system operating in multi-signal modes:volatile dynamics function as the reservoir,whereas nonvolatile properties configure the readout layer.The abundant optoelectronic reservoir states display exceptional feature separability and prolonged stability in the ambient atmosphere.Such a reliable RC system successfully achieves multi-task processing of images.Notably,under the optoelectronic coordination mode,it effectively alleviates feature degradation while sustaining consistently high recognition accuracy.Furthermore,the system exhibits remarkable dynamic information processing capabilities,achieving recognition accuracies of 89.02%for dynamic gestures and 96.04%for moving vehicles recognition,respectively.Supplemental functionalities,including light adaptation and image sharpening,are also implemented.This work presents a configurable multimodal platform featuring a flexible in-sensor reservoir computing architecture,providing a potential solution for efficient multi-task processing. 展开更多
关键词 in-sensor reservoir computing multi-task learning neuromorphic applications optoelectronic transistors reconfigurable devices
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Back-gate-tuned organic electrochemical transistor with temporal dynamic modulation for reservoir computing 认领 引用
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作者 Qian Xu Jie Qiu +6 位作者 Mengyang Liu Dongzi Yang Tingpan Lan Jie Cao Yingfen Wei Hao Jiang Ming Wang 《Journal of Semiconductors》 EI CAS CSCD 2026年第1期118-123,共6页
Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal sca... Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal scale tasks.In this study,we report an OECT-based neuromorphic device with tunable relaxation time(τ)by introducing an additional vertical back-gate electrode into a planar structure.The dual-gate design enablesτreconfiguration from 93 to 541 ms.The tunable relaxation behaviors can be attributed to the combined effects of planar-gate induced electrochemical doping and back-gateinduced electrostatic coupling,as verified by electrochemical impedance spectroscopy analysis.Furthermore,we used theτ-tunable OECT devices as physical reservoirs in the RC system for intelligent driving trajectory prediction,achieving a significant improvement in prediction accuracy from below 69%to 99%.The results demonstrate that theτ-tunable OECT shows a promising candidate for multi-temporal scale neuromorphic computing applications. 展开更多
关键词 neuromorphic computing reservoir computing OECT tunable dynamics trajectory prediction
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A low-thermal-budget MOSFET-based reservoir computing for temporal data classification 认领 引用
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作者 Yanqing Li Feixiong Wang +5 位作者 Heyi Huang Yadong Zhang Xiangpeng Liang Shuang Liu Jianshi Tang Huaxiang Yin 《Journal of Semiconductors》 EI CAS CSCD 2026年第1期42-48,共7页
Neuromorphic devices have garnered significant attention as potential building blocks for energy-efficient hardware systems owing to their capacity to emulate the computational efficiency of the brain.In this regard,r... Neuromorphic devices have garnered significant attention as potential building blocks for energy-efficient hardware systems owing to their capacity to emulate the computational efficiency of the brain.In this regard,reservoir computing(RC)framework,which leverages straightforward training methods and efficient temporal signal processing,has emerged as a promising scheme.While various physical reservoir devices,including ferroelectric,optoelectronic,and memristor-based systems,have been demonstrated,many still face challenges related to compatibility with mainstream complementary metal oxide semiconductor(CMOS)integration processes.This study introduced a silicon-based schottky barrier metal-oxide-semiconductor field effect transistor(SB-MOSFET),which was fabricated under low thermal budget and compatible with back-end-of-line(BEOL).The device demonstrated short-term memory characteristics,facilitated by the modulation of schottky barriers and charge trapping.Utilizing these characteristics,a RC system for temporal data processing was constructed,and its performance was validated in a 5×4 digital classification task,achieving an accuracy exceeding 98%after 50 training epochs.Furthermore,the system successfully processed temporal signal in waveform classification and prediction tasks using time-division multiplexing.Overall,the SB-MOSFET's high compatibility with CMOS technology provides substantial advantages for large-scale integration,enabling the development of energy-efficient reservoir computing hardware. 展开更多
关键词 schottky barrier MOSFET back-end-of-line integration reservoir computing
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Coplanar Floating-Gate Antiferroelectric Transistor with Multifunctionality for All-in-One Analog Reservoir Computing 认领 引用 被引量：1
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作者 Yufei Shi Zijie Zheng +5 位作者 Jiali Huo Yu‑Chieh Chien Sifan Li Haofei Zheng Xiao Gong Kah‑Wee Ang 《Nano-Micro Letters》 SCIE EI CAS CSCD 2026年第6期640-655,共16页
Analog reservoir computing(ARC)systems offer an energy-efficient platform for temporal information processing.However,their physical implementation typically requires disparate materials and device architectures for d... Analog reservoir computing(ARC)systems offer an energy-efficient platform for temporal information processing.However,their physical implementation typically requires disparate materials and device architectures for different system components,leading to complicated fabrication processes and increased system complexity.In this work,we present a coplanar floating-gate antiferroelectric field-effect transistor(FG AFeFET)that unifies multiple neural functionalities within a single device,enabling the physical implementation of a complete ARC system.By combining a coplanar layout design with an area ratio engineering strategy,we achieve tunable device behaviors,including volatile responses for artificial neuron emulation,nonvolatile states for synaptic functions,and fading memory dynamics for reservoir operations.The mechanisms underlying these functionalities and their operating mechanism are systematically elucidated using load line analysis and energy band diagrams.Leveraging these insights,we demonstrate an all-in-one ARC system based on the unified coplanar FG AFeFET architecture,which achieves recognition accuracies of 95.6%and 83.4%on the MNIST and Fashion-MNIST datasets,respectively.These findings highlight the potential of coplanar FG AFeFETs to deliver area-efficient,design-flexible neuromorphic hardware for next-generation computing systems. 展开更多
关键词 Hafnium zirconium oxide Coplanar structure Antiferroelectric field-effect transistor Reservoir computing Multifunctionality
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Optoelectronic memristor based on a-C:Te film for muti-mode reservoir computing 认领 引用 被引量：3
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作者 Qiaoling Tian Kuo Xun +7 位作者 Zhuangzhuang Li Xiaoning Zhao Ya Lin Ye Tao Zhongqiang Wang Daniele Ielmini Haiyang Xu Yichun Liu 《Journal of Semiconductors》 EI CAS CSCD 2025年第2期144-149,共6页
Optoelectronic memristor is generating growing research interest for high efficient computing and sensing-memory applications.In this work,an optoelectronic memristor with Au/a-C:Te/Pt structure is developed.Synaptic ... Optoelectronic memristor is generating growing research interest for high efficient computing and sensing-memory applications.In this work,an optoelectronic memristor with Au/a-C:Te/Pt structure is developed.Synaptic functions,i.e.,excita-tory post-synaptic current and pair-pulse facilitation are successfully mimicked with the memristor under electrical and optical stimulations.More importantly,the device exhibited distinguishable response currents by adjusting 4-bit input electrical/opti-cal signals.A multi-mode reservoir computing(RC)system is constructed with the optoelectronic memristors to emulate human tactile-visual fusion recognition and an accuracy of 98.7%is achieved.The optoelectronic memristor provides potential for developing multi-mode RC system. 展开更多
关键词 optoelectronic memristor volatile switching muti-mode reservoir computing
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Exploitation of temporal dynamics and synaptic plasticity in multilayered ITO/ZnO/IGZO/ZnO/ITO memristor for energy-efficient reservoir computing 认领 引用 被引量：2
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作者 Muhammad Ismail Seungjun Lee +2 位作者 Maria Rasheed Chandreswar Mahata Sungjun Kim 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2025年第32期37-52,共16页
As the demand for advanced computational systems capable of handling large data volumes rises,nano-electronic devices,such as memristors,are being developed for efficient data processing,especially in reservoir comput... As the demand for advanced computational systems capable of handling large data volumes rises,nano-electronic devices,such as memristors,are being developed for efficient data processing,especially in reservoir computing(RC).RC enables the processing of temporal information with minimal training costs,making it a promising approach for neuromorphic computing.However,current memristor devices of-ten suffer from limitations in dynamic conductance and temporal behavior,which affects their perfor-mance in these applications.In this study,we present a multilayered indium-tin-oxide(ITO)/ZnO/indium-gallium-zinc oxide(IGZO)/ZnO/ITO memristor fabricated via radiofrequency sputtering to explore its fil-amentary and nonfilamentary resistive switching(RS)characteristics.High-resolution transmission elec-tron microscopy confirmed the polycrystalline structure of the ZnO/IGZO/ZnO active layer.Dual-switching modes were demonstrated by controlling the current compliance(ICC).In the filamentary mode,the memristor exhibited a large memory window(103),low-operating voltages(±2 V),excellent cycle-to-cycle stability,and multilevel switching with controlled reset-stop voltages,making it suitable for high-density memory applications.Nonfilamentary switching demonstrated stable on/off ratios above 10,en-durance up to 102 cycles,and retention suited for short-term memory.Key synaptic behaviors,such as paired-pulse facilitation(PPF),post-tetanic potentiation(PTP),and spike-rate dependent plasticity(SRDP)were successfully emulated by modulating pulse amplitude,width,and interval.Experience-dependent plasticity(EDP)was also demonstrated,further replicating biological synaptic functions.These tempo-ral properties were utilized to develop a 4-bit reservoir computing system with 16 distinct conductance states,enabling efficient information encoding.For image recognition tasks,convolutional neural net-work(CNN)simulations achieved a high accuracy of 98.45%after 25 training epochs,outperforming the accuracy achieved following artificial neural network(ANN)simulations(87.79%).These findings demon-strate that the multilayered memristor exhibits high performance in neuromorphic systems,particularly for complex pattern recognition tasks,such as digit and letter classification. 展开更多
关键词 Memristors Temporal dynamics Synaptic plasticity Reservoir computing Neuromorphic systems Image recognition
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Nano device fabrication for in-memory and in-sensor reservoir computing 认领 引用 被引量：3
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作者 Yinan Lin Xi Chen +4 位作者 Qianyu Zhang Junqi You Renjing Xu Zhongrui Wang Linfeng Sun 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2025年第1期46-71,共26页
Recurrent neural networks(RNNs)have proven to be indispensable for processing sequential and temporal data,with extensive applications in language modeling,text generation,machine translation,and time-series forecasti... Recurrent neural networks(RNNs)have proven to be indispensable for processing sequential and temporal data,with extensive applications in language modeling,text generation,machine translation,and time-series forecasting.Despite their versatility,RNNs are frequently beset by significant training expenses and slow convergence times,which impinge upon their deployment in edge AI applications.Reservoir computing(RC),a specialized RNN variant,is attracting increased attention as a cost-effective alternative for processing temporal and sequential data at the edge.RC’s distinctive advantage stems from its compatibility with emerging memristive hardware,which leverages the energy efficiency and reduced footprint of analog in-memory and in-sensor computing,offering a streamlined and energy-efficient solution.This review offers a comprehensive explanation of RC’s underlying principles,fabrication processes,and surveys recent progress in nano-memristive device based RC systems from the viewpoints of in-memory and in-sensor RC function.It covers a spectrum of memristive device,from established oxide-based memristive device to cutting-edge material science developments,providing readers with a lucid understanding of RC’s hardware implementation and fostering innovative designs for in-sensor RC systems.Lastly,we identify prevailing challenges and suggest viable solutions,paving the way for future advancements in in-sensor RC technology. 展开更多
关键词 reservoir computing memristive device fabrication compute-in-memory in-sensor computing
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Spatiotemporal multiplexed photonic reservoir computing:parallel prediction for the high-dimensional dynamics of complex semiconductor laser network 认领 引用 被引量：2
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作者 Tong Yang Li-Yue Zhang +3 位作者 Song-Sui Li Wei Pan Xi-Hua Zou Lian-Shan Yan 《Opto-Electronic Advances》 SCIE EI CAS CSCD 2025年第12期42-58,共17页
Accurately forecasting the high-dimensional chaotic dynamics of semiconductor laser(SL)networks is essential in photonics research.In this study,we propose a spatiotemporal multiplexed photonic reservoir computing(STM... Accurately forecasting the high-dimensional chaotic dynamics of semiconductor laser(SL)networks is essential in photonics research.In this study,we propose a spatiotemporal multiplexed photonic reservoir computing(STM-PRC)architecture,specifically designed for parallel prediction of the high-dimensional chaotic dynamics in complex SL networks.This is accomplished by decomposing the prediction task into multiple simplified reservoirs,leveraging the intrinsic topological characteristics of the network.Additionally,we introduce a dimensionality reduction technique for high-dimensional chaotic datasets,which exploits the symmetrical properties of the network topology and cluster synchronization patterns derived from complex network theory.This approach further simplifies the prediction process and enhances the computational efficiency of the parallel STM-PRC system.The feasibility and effectiveness of the proposed framework are demonstrated through numerical simulations and corroborated by experimental validation.Our results expand the application potential of SL networks in all-optical communication systems and suggest new directions for optical information processing. 展开更多
关键词 photonic reservoir computing complex network semiconductor lasers
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Model-free prediction of chaotic dynamics with parameter-aware reservoir computing 认领 引用 被引量：1
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作者 Jianmin Guo Yao Du +3 位作者 Haibo Luo Xuan Wang Yizhen Yu Xingang Wang 《Chinese Physics B》 SCIE EI CAS CSCD 2025年第4期143-152,共10页
Model-free,data-driven prediction of chaotic motions is a long-standing challenge in nonlinear science.Stimulated by the recent progress in machine learning,considerable attention has been given to the inference of ch... Model-free,data-driven prediction of chaotic motions is a long-standing challenge in nonlinear science.Stimulated by the recent progress in machine learning,considerable attention has been given to the inference of chaos by the technique of reservoir computing(RC).In particular,by incorporating a parameter-control channel into the standard RC,it is demonstrated that the machine is able to not only replicate the dynamics of the training states,but also infer new dynamics not included in the training set.The new machine-learning scheme,termed parameter-aware RC,opens up new avenues for data-based analysis of chaotic systems,and holds promise for predicting and controlling many real-world complex systems.Here,using typical chaotic systems as examples,we give a comprehensive introduction to this powerful machine-learning technique,including the algorithm,the implementation,the performance,and the open questions calling for further studies. 展开更多
关键词 chaos prediction time-series analysis bifurcation diagram parameter-aware reservoir computing
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Flexible switching devices with dynamic anchoring of Ag/Ag+ coupling for reservoir computing 认领 引用
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作者 Xuefang Liu Jianyong Pan +5 位作者 Wentong Li Xiaoyu Zhang Zhe Li Beining Zheng Yang Li Jiaqi Zhang 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2025年第10期914-922,共9页
Memristive devices based on in-memory computing architectures offer a promising strategy for overcoming the energy bottlenecks inherent in big data systems.However,uncontrolled ion migration at the material level rema... Memristive devices based on in-memory computing architectures offer a promising strategy for overcoming the energy bottlenecks inherent in big data systems.However,uncontrolled ion migration at the material level remains a key challenge,compromising device stability and hindering practical applications.Here,we employ a chemical optimization strategy that dynamically induces the precipitation of Ag atoms under applied voltage,creating fixed atomic sites to achieve precise control over ion migration,synergistically enhancing the memory and computing capabilities of the device.Compared to unoptimized samples,the proposed device exhibits an approximately 8-fold improvement in robustness,a 3-fold enhancement in stability,high mechanical endurance,and reliable multilevel data storage capability.We further construct a device array and incorporate an efficient reservoir computing model,achieving handwritten digit recognition with an accuracy of up to 90.81%.In summary,this work proposes a dynamic Ag/Ag+anchoring strategy and demonstrates a memristor-based approach that integrates storage and computation to enable energy-efficient artificial intelligence processing,offering a scalable solution for sustainable intelligence in the big data era. 展开更多
关键词 Memristors Conductive filaments Dynamic anchoring Silver-cyano coordination compounds Reservoir computing
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Optoelectronic reservoir computing based on complex-value encoding 认领 引用 被引量：2
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作者 Chunxu Ding Rongjun Shao +5 位作者 Jingwei Li Yuan Qu Linxian Liu Qiaozhi He Xunbin Wei Jiamiao Yang 《Advanced Photonics Nexus》 2024年第6期47-54,共8页
Optical reservoir computing(ORC)offers advantages,such as high computational speed,low power consumption,and high training speed,so it has become a competitive candidate for time series analysis in recent years.The cu... Optical reservoir computing(ORC)offers advantages,such as high computational speed,low power consumption,and high training speed,so it has become a competitive candidate for time series analysis in recent years.The current ORC employs single-dimensional encoding for computation,which limits input resolution and introduces extraneous information due to interactions between optical dimensions during propagation,thus constraining performance.Here,we propose complex-value encoding-based optoelectronic reservoir computing(CE-ORC),in which the amplitude and phase of the input optical field are both modulated to improve the input resolution and prevent the influence of extraneous information on computation.In addition,scale factors in the amplitude encoding can fine-tune the optical reservoir dynamics for better performance.We built a CE-ORC processing unit with an iteration rate of up to∼1.2 kHz using high-speed communication interfaces and field programmable gate arrays(FPGAs)and demonstrated the excellent performance of CE-ORC in two time series prediction tasks.In comparison with the conventional ORC for the Mackey–Glass task,CE-ORC showed a decrease in normalized mean square error by∼75%.Furthermore,we applied this method in a weather time series analysis and effectively predicted the temperature and humidity within a range of 24 h. 展开更多
关键词 optical reservoir computing complex-value encoding time series analysis weather forecast
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Multi-target ranging using an optical reservoir computing approach in the laterally coupled semiconductor lasers with self-feedback 认领 引用
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作者 Dong-Zhou Zhong Zhe Xu +5 位作者 Ya-Lan Hu Ke-Ke Zhao Jin-Bo Zhang Peng Hou Wan-An Deng Jiang-Tao Xi 《Chinese Physics B》 SCIE EI CAS CSCD 2022年第7期309-320,共12页
We utilize three parallel reservoir computers using semiconductor lasers with optical feedback and light injection to model radar probe signals with delays.Three radar probe signals are generated by driving lasers con... We utilize three parallel reservoir computers using semiconductor lasers with optical feedback and light injection to model radar probe signals with delays.Three radar probe signals are generated by driving lasers constructed by a threeelement laser array with self-feedback.The response lasers are implemented also by a three-element lase array with both delay-time feedback and optical injection,which are utilized as nonlinear nodes to realize the reservoirs.We show that each delayed radar probe signal can be predicted well and to synchronize with its corresponding trained reservoir,even when parameter mismatches exist between the response laser array and the driving laser array.Based on this,the three synchronous probe signals are utilized for ranging to three targets,respectively,using Hilbert transform.It is demonstrated that the relative errors for ranging can be very small and less than 0.6%.Our findings show that optical reservoir computing provides an effective way for applications of target ranging. 展开更多
关键词 coupled semiconductor lasers lidar ranging optical reservoir computing chaos synchronization
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Lightweight Intrusion Detection Using Reservoir Computing 认领 引用 被引量：1
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作者 Jiarui Deng Wuqiang Shen +4 位作者 Yihua Feng Guosheng Lu Guiquan Shen Lei Cui Shanxiang Lyu 《Computers, Materials & Continua》 SCIE EI 2024年第1期1345-1361,共17页
The blockchain-empowered Internet of Vehicles(IoV)enables various services and achieves data security and privacy,significantly advancing modern vehicle systems.However,the increased frequency of data transmission and... The blockchain-empowered Internet of Vehicles(IoV)enables various services and achieves data security and privacy,significantly advancing modern vehicle systems.However,the increased frequency of data transmission and complex network connections among nodes also make them more susceptible to adversarial attacks.As a result,an efficient intrusion detection system(IDS)becomes crucial for securing the IoV environment.Existing IDSs based on convolutional neural networks(CNN)often suffer from high training time and storage requirements.In this paper,we propose a lightweight IDS solution to protect IoV against both intra-vehicle and external threats.Our approach achieves superior performance,as demonstrated by key metrics such as accuracy and precision.Specifically,our method achieves accuracy rates ranging from 99.08% to 100% on the Car-Hacking dataset,with a remarkably short training time. 展开更多
关键词 Echo state network intrusion detection system Internet of Vehicles reservoir computing
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Exploring reservoir computing:Implementation via double stochastic nanowire networks 认领 引用
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作者 唐健峰 夏磊 +3 位作者 李广隶 付军 段书凯 王丽丹 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第3期572-582,共11页
Neuromorphic computing,inspired by the human brain,uses memristor devices for complex tasks.Recent studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data ana... Neuromorphic computing,inspired by the human brain,uses memristor devices for complex tasks.Recent studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data analysis.This paper presents a model based on these nanowire networks,with an improved conductance variation profile.We suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage pulses.The nanowire network layer generates dynamic behaviors for pulse voltages,allowing time series prediction analysis.Our experiment uses a double stochastic nanowire network architecture for processing multiple input signals,outperforming traditional reservoir computing in terms of fewer nodes,enriched dynamics and improved prediction accuracy.Experimental results confirm the high accuracy of this architecture on multiple real-time series datasets,making neuromorphic nanowire networks promising for physical implementation of reservoir computing. 展开更多
关键词 double-layer stochastic(DS)nanowire network architecture neuromorphic computation nanowire network reservoir computing time series prediction
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Laser processing induced nonvolatile memory in chaotic graphene oxide films for flexible reservoir computing applications 认领 引用
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作者 Bo Chen Baojie Zhu +4 位作者 Yifan Wu Pengpeng Sang Jixuan Wu Xuepeng Zhan Jiezhi Chen 《Journal of Semiconductors》 EI CAS CSCD 2024年第12期130-134,共5页
Graphene oxide,as a 2D material with nanometer thickness,offers ultra-high mobility,chaotic properties,and low cost.These make graphene oxide memristors beneficial for reservoir computing(RC)networks.In this study,con... Graphene oxide,as a 2D material with nanometer thickness,offers ultra-high mobility,chaotic properties,and low cost.These make graphene oxide memristors beneficial for reservoir computing(RC)networks.In this study,continuous-wave(CW)laser processing is used to reduce chaotic graphene oxide(CGO)films,resulting in the non-volatile storage capability based on the reduced chaotic graphene oxide(rCGO)films.Laser power significantly impacts the characteristics of the rCGO memristor.Material characterization indicates that laser radiation can effectively reduce the oxygen content in CGO films.With optimized laser power,the rCGO memristor achieves a large ratio at 18 mW laser power.Benefiting from the short-term mem-ory characteristics,distinct conductive states are achieved,which are further utilized to construct RC networks.With a third con-trol probe,the rCGO memristor can express rich reservoir states,demonstrating accuracy in predicting the Hénon map with an NRMSE below 0.3.These findings provide the potential for developing flexible RC networks based on graphene oxide memris-tors via laser processing. 展开更多
关键词 chaotic graphene oxide laser processing reservoir computing
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Optical next generation reservoir computing 认领 引用 被引量：1
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作者 Hao Wang Jianqi Hu +4 位作者 YoonSeok Baek Kohei Tsuchiyama Malo Joly Qiang Liu Sylvain Gigan 《Light: Science & Applications》 SCIE EI CAS CSCD 2025年第9期2605-2615,共11页
Artificial neural networks with internal dynamics exhibit remarkable capability in processing information.Reservoir computing(RC)is a canonical example that features rich computing expressivity and compatibility with ... Artificial neural networks with internal dynamics exhibit remarkable capability in processing information.Reservoir computing(RC)is a canonical example that features rich computing expressivity and compatibility with physical implementations for enhanced efficiency.Recently,a new RC paradigm known as next generation reservoir computing(NGRC)further improves expressivity but compromises its physical openness,posing challenges for realizations in physical systems.Here we demonstrate optical NGRC with computations performed by light scattering through disordered media.In contrast to conventional optical RC implementations,we directly and solely drive our optical reservoir with time-delayed inputs.Much like digital NGRC that relies on polynomial features of delayed inputs,our optical reservoir also implicitly generates these polynomial features for desired functionalities.By leveraging the domain knowledge of the reservoir inputs,we show that the optical NGRC not only predicts the short-term dynamics of the low-dimensional Lorenz63 and large-scale Kuramoto-Sivashinsky chaotic time series,but also replicates their long-term ergodic properties.Optical NGRC shows superiority in shorter training length and fewer hyperparameters compared to conventional optical RC based on scattering media,while achieving better forecasting performance.Our optical NGRC framework may inspire the realization of NGRC in other physical RC systems,new applications beyond time-series processing,and the development of deep and parallel architectures broadly. 展开更多
关键词 artificial neural networks compatibility physical implementations Time Delayed Inputs next generation reservoir computing ngrc further optical ngrc Next Generation Reservoir Computing processing informationreservoir computing rc Optical Next Generation Reservoir Computing
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In-sensor reservoir computing for biometric identification based on MoTe2/BaTiO3optical synapses 认领 引用 被引量：3
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作者 Zhenqiang Guo Gongjie Liu +5 位作者 Weifeng Zhang Xinhao Li Zhen Zhao Qiuhong Li Haoqi Liu Xiaobing Yan 《InfoMat》 SCIE CSCD 2025年第8期81-96,共16页
The artificial intelligence era has witnessed a surge of demand in detection and recognition of biometric information,with applications from financial services to information security.However,the physical separation o... The artificial intelligence era has witnessed a surge of demand in detection and recognition of biometric information,with applications from financial services to information security.However,the physical separation of sensing,memory,and computational units in traditional biometric systems introduces severe decision latency and operational power consumption.Herein,an in-sensor reservoir computing(RC)system based on MoTe2/BaTiO3optical synapses is proposed to detect and recognize the faces and fingerprints information.In optical operation mode,the device exhibits low energy consumption of 41.2 pJ,long retention time of 3×104s,high endurance of 104switching cycles,and multifunctional sensing-memory-computing visual simulations.The light intensity-dependent optical sensing and multilevel optical storage properties are exploited to achieve sunburned eye simulation and image memory functions.These nonlinear,multi-state,short-term storage,and long-term memory characteristics make MoTe2/BaTiO3optical synapses a suitable reservoir layer and readout layer,with short-term properties to project complicated input features into high-dimensional output features,and long-term properties to be used as a readout layer,thus further building an in-sensor RC system for face and fingerprint recognition.Under the 40%Gaussian noise environment,the system achieves 91.73%recognition accuracy for face and 97.50%for fingerprint images,and experimental verification is carried out,which shows potential in practical applications.These results provide a strategy for constructing a high-performance in-sensor RC system for high-accuracy biometric identification. 展开更多
关键词 2D/ferroelectric heterostructure artificial vision system in-sensor reservoir computing optical synapse sensing-memory-computing
All-optical spiking processing and reservoir computing with a passive silicon microring and wavelength-time division multiplexing 认领 引用 被引量：3
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作者 GIOVANNI DONATI STEFANO BIASI +1 位作者 LORENZO PAVESI ANTONIO HURTADO 《Photonics Research》 SCIE EI CAS CSCD 2025年第9期2641-2653,共13页
Neuromorphic photonic systems offer significant advantages for parallel,high-speed,and low-power computing,among which spiking neural networks emerge as a powerful bio-inspired alternative.This study demonstrates,to o... Neuromorphic photonic systems offer significant advantages for parallel,high-speed,and low-power computing,among which spiking neural networks emerge as a powerful bio-inspired alternative.This study demonstrates,to our knowledge,a novel approach to all-optical spiking processing and reservoir computing using passive silicon microring resonators(MRRs).A key innovation is the demonstration of deterministic optical spiking and spectro-temporal coincidence detection without the need for pump-and-probe methods,simplifying the architecture and improving efficiency.By leveraging injection of excitatory optical signals at negative wavelength detuning relative to the MRR’s cold resonances,the system delivers prompt and high-contrast optical spiking events,essential for effective chip-integrated photonic spiking neural networks.Building on this,a photonic spiking reservoir computer is implemented using a single silicon MRR.The system encodes input information through a novel spectro-temporal scheme and classifies the Iris-Flower dataset with 92%accuracy.This performance is achieved with just 48 reservoir virtual nodes,averaging only three spikes per flower sample,hence highlighting the system’s efficiency and sparsity.These findings unlock novel neuromorphic photonic frameworks with MRRs,enabling sparse all-optical spiking processing and reservoir computing,particularly promising to be adapted in future coupled MRR structures and with binary output weights for light-enabled edge computing and sensing applications. 展开更多
关键词 neuromorphic photonic systems deterministic optical spiking passive silicon microring reservoir computing passive silicon microring resonators mrrs spiking neural networks wavelength time division multiplexing all optical spiking processing
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Crystallinity-controlled volatility tuning of ZrO2memristor for physical reservoir computing 认领 引用 被引量：1
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作者 Dae Kyu Lee Gichang Noh +11 位作者 Seungmin Oh Yooyeon Jo Eunpyo Park Min Jee Kim Dong Yeon Woo Heerak Wi YeonJoo Jeong Hyun Jae Jang Sangbum Kim Suyoun Lee Kibum Kang Joon Young Kwak 《InfoMat》 SCIE CSCD 2025年第2期83-96,共14页
Memristors have been emerging as promising candidates for computing systems in post-Moore applications,particularly electrochemical metallizationbased memristors,which are poised to play a crucial role in neuromorphic... Memristors have been emerging as promising candidates for computing systems in post-Moore applications,particularly electrochemical metallizationbased memristors,which are poised to play a crucial role in neuromorphic computing and machine learning.These devices are favored for their high integration density,low power consumption,rapid switching speed,and significant on/off ratio.Despite advancements in various materials,achieving adequate electrical performance—characterized by threshold switching(TS)behavior,spontaneous reset,and low off-state resistance—remains challenging due to the limitations in conductance filament control within the nanoscale resistive switching layer.In this study,we introduce an efficient method to control the ZrO2 crystallinity for tunable volatility memristor by establishing the filament paths through a simple thermal treatment process in a single oxide layer.The effect of ZrO2 crystallinity to create localized filament paths for enhancing Ag migration and improving TS behavior is also investigated.In contrast to its amorphous counterpart,crystallized ZrO2 volatile memristor,treated by rapid thermal annealing,demonstrates a steep switching slope(0.21 mV dec–1),a high resistance state(25 GΩ),and forming-free characteristics.The superior volatile performance is attributed to localized conductive filaments along lowenergy pathways,such as dislocations and grain boundaries.By coupling with enhanced volatile switching behavior,we believe that the volatility is finely tuned to function as short-term memory for reservoir computing,making it particularly well-suited for tasks such as audio and image recognition. 展开更多
关键词 crystallization electrochemical metallization memristor reservoir computing threshold switching zirconium oxide
All physical reservoir computing system with tunable temporal dynamics for multi-timescale information processing 认领 引用 被引量：1
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作者 Wanxin Huang Yiru Wang +9 位作者 Jianyu Ming Shanshuo Liu Jing Liu Jingwei Fu Haotian Wang Wen Li Yannan Xie Linghai Xie Haifeng Ling Wei Huang 《InfoMat》 SCIE CSCD 2025年第6期126-137,共12页
Physical reservoir computing(PRC)offers an effective computing paradigm for spatiotemporal information processing with low training costs.Achieving controllable regulation over the temporal dynamics of devices to meet... Physical reservoir computing(PRC)offers an effective computing paradigm for spatiotemporal information processing with low training costs.Achieving controllable regulation over the temporal dynamics of devices to meet the computational demands of each physical layer is a key challenge for realizing highperformance PRC chips.Here,we proposed a homogeneously integrated all-PRC with tunable temporal dynamics.Utilizing the modulation effect of oxygen vacancies on the energy barrier of the pentacene/ZnO interface,shortterm memory,and long-term memory switching characteristics have been achieved within the same device structure.Furthermore,by altering the gate voltage,the reservoir exhibited a broad range ratio of temporal characteristics(>102),which provides the potential to map information with different temporal characteristics.Inspired by the process of encoding and reconstructing spatiotemporal information in the human visual system,a biomimetic obstacle recognition system has been constructed to assist visually impaired individuals in walking,demonstrating excellent accuracy in obstacle types(100%)and distances(97.2%)recognition.This work offers a promising avenue for the development of an integrated PRC system with multi-timescale information processing capability. 展开更多
关键词 homogeneous integration OFET oxygen vacancies physical reservoir computing temporal dynamics
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