Dynamic Memristors for Temporal Signal Processing DOI

Fuming Song,

He Shao,

Jianyu Ming

et al.

Advanced Materials Technologies, Journal Year: 2024, Volume and Issue: 9(16)

Published: July 20, 2024

Abstract The rapid advancement of neuromorphic computing demands innovative hardware solutions capable efficiently mimicking the functionality biological neural systems. In this context, dynamic memristors have emerged as promising candidates for realizing reservoir (RC) architectures. characterized by their ability to exhibit nonlinear conductance variations and transient memory behaviors offer unique advantages constructing RC Unlike recurrent networks (RNNs) that face challenges such vanishing or exploding gradients during training, leverages a fixed‐size layer acts memory. Researchers can capitalize on adaptable efficient characteristics integrating into systems enable information processing with low learning costs. This perspective provides an overview recent developments in applications RC. It highlights potential revolutionize artificial intelligence offering faster speeds enhanced energy efficiency. Furthermore, it discusses opportunities associated architectures, paving way developing next‐generation cognitive

Language: Английский

Memristive arrangements of nanofluidic pores DOI
Patricio Ramı́rez, Sergio Portillo, Javier Cervera

et al.

Physical review. E, Journal Year: 2024, Volume and Issue: 109(4)

Published: April 24, 2024

We demonstrate that nanofluidic diodes in multipore membranes show a memristive behavior can be controlled not only by the amplitude and frequency of external signal but also series parallel arrangements membranes. Each memristor consists polymeric membrane with conical nanopores allow current rectification due to electrical interaction between ionic solution pore surface charges. This charge-regulated transport shows rich nonlinear physics, including memory inductive effects, which are characterized here current-voltage curves impedance spectroscopy. Also, neuromorphiclike potentiation conductance following voltage pulses (spikes) is observed. The physical concepts should have application for information processing conversion iontronics hybrid devices.

Language: Английский

Citations

15
Versatile NbOx‐Based Volatile Memristor for Artificial Intelligent Applications DOI Open Access

Dongyeol Ju,

Sungjun Kim

Advanced Functional Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 2, 2024

Abstract To achieve cost‐effectiveness, researchers are exploring various memristors for their adaptation in neuromorphic computing. Recent studies have focused on developing versatile functioning singular memristors, such as those involved on‐receptor computing, which integrates sensory functions into current computing paradigms. Additionally, adaptations like reservoir being investigated systems. In this study, a memristor composed of stack Ti/NbO x /Pt layers is fabricated to explore multifunctional behaviors within single memristor. By applying bias toward the top Ti electrode, gradual changes with volatile features demonstrated, revealing an ion‐migration‐based nonfilamentary switching Leveraging functionality, artificial nociceptor first implemented, demonstrating key biological nociceptors including thresholding, relaxation, no‐adaptation, and sensitization. Subsequently, synapse emulation akin brain achieved through easy conductance potentiation depression diverse functions, enabling mimic learning activities spike firing. Lastly, computational applications explored by adapting edge multi‐bit expanding memristor's across fields behaviors.

Language: Английский

Citations

12
Robust hybrid perovskite self-rectifying memristor for brain-inspired computing and data storage DOI Creative Commons

Manish Khemnani,

Muskan Jain,

Denish Hirpara

et al.

Journal of Applied Physics, Journal Year: 2025, Volume and Issue: 137(4)

Published: Jan. 23, 2025

Conventional computing architectures are not suited to meet the unique workload requirements of artificial intelligence and deep learning, which has sparked a growing interest in memory-centric computing. One primary challenge this field is sneak path current memory devices, degrades data storage reliability. Another critical issue ensuring device performance stability over time under varying environmental conditions. To overcome these challenges, work, we introduce Dion–Jacobson perovskite-based self-rectifying cell that only reduces but also demonstrates remarkable electrical parameters. The fabricated maintains consistent performance, including rectification ratio (∼103), on/off set voltage (∼0.52 V), for 200+ days within temperature range 25–70 °C relative humidity conditions up 70%RH. Importantly, our work represents an innovative step forward observation self-rectification stable showing way their widespread application architectures. Furthermore, understand behavior across its different states, i.e., high resistance state low state, electrochemical impedance spectroscopy performed, gives insight into individual contribution resistance, capacitance, inductance.

Language: Английский

Citations

2
Hardware‐Software Codesign of 2D Neuromorphic Optoelectronic Device for Dynamic Gesture Recognition DOI Creative Commons
Jiarui Wang,

Yinan Lin,

Jieyu You

et al.

Advanced Intelligent Systems, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

In‐sensor reservoir computing has recently gained considerable attention for its efficient training process and advanced integration of sensing, storage, processing functionalities. However, developing a highly in‐sensor system remains challenging, mainly due to the lack suitable devices with appropriate architectures. In this study, graphene/MoSe 2 ‐based ohmic contact optoelectronic synaptic memory device optimized (RC) is introduced, designed emulate biological functions enable neuromorphic computing. Based on dynamic characteristics fading device, gesture recognition, including six types gestures, stimulated, achieving recognition rate 95%. This work provides potential solution hardware‐software co‐design in recognition.

Language: Английский

Citations

1
Dynamic memristor for physical reservoir computing DOI
Qirui Zhang,

Wei-Lun Ouyang,

Xuemei Wang

et al.

Nanoscale, Journal Year: 2024, Volume and Issue: 16(29), P. 13847 - 13860

Published: Jan. 1, 2024

This minireview explores the tunable dynamic properties and potential applications of memristor-based physical reservoir computing.

Language: Английский

Citations

3
Neuron‐Inspired Biomolecular Memcapacitors Formed Using Droplet Interface Bilayer Networks DOI Creative Commons

Braydon G. Segars,

Kenny Rosenberg,

Som Shrestha

et al.

Advanced Electronic Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 13, 2025

Abstract Brain‐inspired (or neuromorphic) computing circumvents costly bottlenecks in conventional Von Neumann architectures by collocating memory and processing. This is accomplished through dynamic material architectures, strengthening or weakening internal conduction pathways similar to synaptic connections within the brain. A new class of neuromorphic materials approximates interfaces using lipid membranes assembled via droplet interface bilayer (DIB) technique. These DIB have been studied as novel memristors memcapacitors owing soft, reconfigurable nature both membrane geometry embedded ion‐conducting channels. In this work, a biomolecular approach expanded from model synapses charge‐integrating neuron . these serial networks, it possible create distributions voltage‐sensitive gates capable trapping ionic charge. trapped charge creates transmembrane potential differences that drive changes system's net capacitance electrowetting, providing weight response history timing input signals. fundamental change interfacial (dimensions membrane) (charge droplets) provides functional plasticity multiple weights, longer‐term retention roughly an order magnitude greater than stored alone, programming‐erasure.

Language: Английский

Citations

0
Self‐Rectifying Volatile Memristor for Highly Dynamic Functions DOI Open Access

Dongyeol Ju,

Minseo Noh,

Seung Jun Lee

et al.

Advanced Functional Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 19, 2025

Abstract In this study, a highly rectifying memristor composed of Pt/TaO x /TiN stack, incorporating complementary metal‐oxide semiconductor‐friendly metal oxide switching layer, is fabricated to assess its performance in diverse range applications. The exhibits characteristics due the Schottky barrier formed by work function difference between Pt and TiN electrodes. For compliance current 1 mA, displays volatile memory properties, attributed migration oxygen ions within TaO layer. Leveraging behavior, synaptic functions—where changes plasticity occur response incoming spikes—are emulated. Additionally, complete functions biological nociceptor are demonstrated, including threshold, relaxation, no‐adaptation, sensitization, recovery. These dynamic then utilized mimic neuronal firing with array, Morse code implementation enabling data generation, computing through cost‐effective reservoir computing. simplicity fabrication process broad implemented single make device promising candidate for future

Language: Английский

Citations

0
Nanoelectronics-enabled reservoir computing hardware for real-time robotic controls DOI
Mingze Chen,

Xiaoqiu An,

Seung Jun Ki

et al.

Science Advances, Journal Year: 2025, Volume and Issue: 11(13)

Published: March 26, 2025

Traditional robotic vehicle control algorithms, implemented on digital devices with firmware, result in high power consumption and system complexity. Advanced systems based different device physics are essential for the advancement of sophisticated vehicles miniature mobile robots. Here, we present a nanoelectronics-enabled analog mimicking conventional controllers’ dynamic responses real-time controls, substantially reducing training cost, consumption, footprint. This uses reservoir computing network interconnected memristive channels made from layered semiconductors. The network’s nonlinear switching short-term memory characteristics effectively map input sensory signals to high-dimensional data spaces, enabling generation motor simply trained readout layer. approach minimizes software analog-to-digital conversions, enhancing energy resource efficiency. We demonstrate this two tasks: rover target tracking drone lever balancing, achieving similar performance traditional controllers ~10-microwatt consumption. work paves way ultralow-power edge systems.

Language: Английский

Citations

0
Physical reservoir computing for Edge AI applications DOI
Jianquan Liu,

Guangdi Feng,

Wei Li

et al.

The Innovation Materials, Journal Year: 2025, Volume and Issue: unknown, P. 100127 - 100127

Published: Jan. 1, 2025

<p>Reservoir computing has emerged as an efficient computational paradigm for processing temporal and dynamic data, driving advancements in neuromorphic electronics physical implementation. This review covers the devices implementing reservoir computing, emphasizing device-level innovations that address challenges of low-latency, energy-efficient, multimodal implementations. The advantages, disadvantages, core various spatial architectures building systems are discussed. Realistic paths on algorithmic implementations input output layers system investigated, issues such heterogeneous device integration, consistent readout, stability analyzed. topical emphasizes reconfigurability scalability fully analogized adaptive nodes. We discuss future directions across algorithmic, device, architectural, application domains. establishes a foundational framework provides strategic guidance edge artificial intelligent systems.</p>

Language: Английский

Citations

0
An efficient reservoir computing system based on 2D mask processing and dynamic memristor DOI
Zhuosheng Lin,

Jingliang Deng,

Z W Chen

et al.

Nonlinear Dynamics, Journal Year: 2025, Volume and Issue: unknown

Published: April 19, 2025

Language: Английский

Citations

0