Mimicking Classical Conditioning of Fear Using a Dynamic Synaptic Memristor DOI Creative Commons

Dongyeol Ju,

Sungjun Kim

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

Published: July 17, 2024

Abstract The growing demand for energy‐efficient computing has prompted investigations into the diverse functionalities of resistive switching memristors, which show promise neuromorphic computing. These memristors can emulate artificial synapses, nociceptors, and computational capabilities like reservoir However, integration emotions, a critical aspect brain function, remains unexplored in memristors. This study explores emulation fear, crucial emotion that enables individuals to avoid potential danger through learned behavior, using two‐terminal Al/NbO x /Pt memristor structure. Leveraging volatile behavior non‐filamentary mechanism memristor, synaptic functions plasticity changes based on incoming spikes are mimicked. Furthermore, classical fear conditioning is employed demonstrate simulation within including aspects extinction, generalization, avoidance. results showcase efficient synapse applications, as well its ability provide enhanced insights function emulation, enabling versatile future applications memristive device.

Language: Английский

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

1
Dynamic resistive switching of WOx-based memristor for associative learning activities, on-receptor, and reservoir computing DOI

Minseo Noh,

Hyogeun Park,

Sungjun Kim

et al.

Chaos Solitons & Fractals, Journal Year: 2025, Volume and Issue: 196, P. 116381 - 116381

Published: March 31, 2025

Language: Английский

Citations

0
Quantum Dots for Resistive Switching Memory and Artificial Synapse DOI Creative Commons
Gyeongpyo Kim,

Seoyoung Park,

Sungjun Kim

et al.

Nanomaterials, Journal Year: 2024, Volume and Issue: 14(19), P. 1575 - 1575

Published: Sept. 29, 2024

Memristor devices for resistive-switching memory and artificial synapses have emerged as promising solutions overcoming the technological challenges associated with von Neumann bottleneck. Recently, due to their unique optoelectronic properties, solution processability, fast switching speeds, low operating voltages, quantum dots (QDs) drawn substantial research attention candidate materials memristors synapses. This review covers recent advancements in QD-based resistive random-access (RRAM) Following a brief introduction QDs, fundamental principles of mechanism RRAM are introduced. Then, materials, synthesis techniques, device performance summarized relative comparison materials. Finally, we introduce discuss its implementation

Language: Английский

Citations

3
Coexistence of volatile and non-volatile characteristics in SiO2/CoOx memristor for in-materia reservoir computing DOI
Inho Oh, James Jungho Pak

Journal of Alloys and Compounds, Journal Year: 2025, Volume and Issue: unknown, P. 179383 - 179383

Published: Feb. 1, 2025

Language: Английский

Citations

0
Cognitive Learning and Neuromorphic Systems Using Resistive Switching Random-Access Memory DOI

Minseo Noh,

Hyogeun Park,

Sungjun Kim

et al.

ACS Applied Electronic Materials, Journal Year: 2025, Volume and Issue: unknown

Published: March 8, 2025

Language: Английский

Citations

0
TiN/TiOx/WOx/Pt heterojunction memristor for sensory and neuromorphic computing DOI

Dongyeol Ju,

Jungwoo Lee,

Hyojin So

et al.

Journal of Alloys and Compounds, Journal Year: 2024, Volume and Issue: 1004, P. 175830 - 175830

Published: Aug. 5, 2024

Language: Английский

Citations

2
Recent Developments on Novel 2D Materials for Emerging Neuromorphic Computing Devices DOI Creative Commons

Muhammad Hamza Pervez,

Ehsan Elahi,

Muhammad Asghar Khan

et al.

Small Structures, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 8, 2024

The rapid advancement of artificial intelligent and information technology has led to a critical need for extremely low power consumption excellent efficiency. capacity neuromorphic computing handle large amounts data with garnered lot interest during the last few decades. For applications, 2D layered semiconductor materials have shown pivotal role due their distinctive properties. This comprehensive review provides an extensive study recent advancements in materials‐based devices especially multiterminal synaptic devices, two‐terminal neuronal integration devices. Herein, wide range potential applications memory, computation, adaptation, intelligence is incorporated. Finally, limitations challenges based on novel are discussed. Thus, this aims illuminate design fabrication van der Waals (vdW) heterostructure materials, leveraging promising engineering techniques excel hardware implementations.

Language: Английский

Citations

2
Two-Terminal Neuromorphic Devices for Spiking Neural Networks: Neurons, Synapses, and Array Integration DOI

Y. S. Kim,

Ji Hyun Baek, In Hyuk Im

et al.

ACS Nano, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 12, 2024

The ever-increasing volume of complex data poses significant challenges to conventional sequential global processing methods, highlighting their inherent limitations. This computational burden has catalyzed interest in neuromorphic computing, particularly within artificial neural networks (ANNs). In pursuit advancing hardware, researchers are focusing on developing computation strategies and constructing high-density crossbar arrays utilizing history-dependent, multistate nonvolatile memories tailored for multiply-accumulate (MAC) operations. However, the real-time collection massive, dynamic sets require an innovative paradigm akin that human brain. Spiking (SNNs), representing third generation ANNs, emerging as a promising solution spatiotemporal information due event-based capabilities. ideal hardware supporting SNN operations comprises neurons, synapses, integrated arrays. Currently, structural complexity SNNs spike-based methodologies requires components with biomimetic behaviors distinct from those memristors used deep networks. These distinctive characteristics required neuron synapses devices pose challenges. Developing effective building blocks SNNs, therefore, necessitates leveraging intrinsic properties materials constituting each unit overcoming integration barriers. review focuses progress toward memristor-based spiking network emphasizing role individual such array along relevant biological insights. We aim provide valuable perspectives working next brain-like computing systems based these foundational elements.

Language: Английский

Citations

1
Mimicking Classical Conditioning of Fear Using a Dynamic Synaptic Memristor DOI Creative Commons

Dongyeol Ju,

Sungjun Kim

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

Published: July 17, 2024

Abstract The growing demand for energy‐efficient computing has prompted investigations into the diverse functionalities of resistive switching memristors, which show promise neuromorphic computing. These memristors can emulate artificial synapses, nociceptors, and computational capabilities like reservoir However, integration emotions, a critical aspect brain function, remains unexplored in memristors. This study explores emulation fear, crucial emotion that enables individuals to avoid potential danger through learned behavior, using two‐terminal Al/NbO x /Pt memristor structure. Leveraging volatile behavior non‐filamentary mechanism memristor, synaptic functions plasticity changes based on incoming spikes are mimicked. Furthermore, classical fear conditioning is employed demonstrate simulation within including aspects extinction, generalization, avoidance. results showcase efficient synapse applications, as well its ability provide enhanced insights function emulation, enabling versatile future applications memristive device.

Language: Английский

Citations

1