Results 51 to 60 of about 16,903,176 (225)

Multimodal Optoelectronic Synapses for Neuromorphic Intelligence: Coupling Light, Electrical, Thermal, and Beyond

Advanced Functional Materials, EarlyView.
This review revisits optoelectronic neuromorphic devices that extend from unimodal sensing toward multimodal perception. Five types of stimuli‐to‐output information flow that are classified based on the device roles within the system and applications are summarized and discussed.
Jihyun Shin, Junhyung Cho, Wangmyung Choi, Jaehyun Hur, Jae Kyeong Jeong, Ulrike Kraft, Hocheon Yoo   +6 more
wiley   +1 more source

Emerging Post‐CMOS Hardware Neurons for Brain‐Inspired Computing: Devices, Circuits, and System Integration

Advanced Functional Materials, EarlyView.
The physical realization of artificial neurons is a critical challenge for energy‐efficient neuromorphic computing. This review presents a comprehensive analysis of the evolution of artificial neuron implementations from conventional CMOS to emerging post‐CMOS technologies.
Kannan Udaya Mohanan, Hocheon Yoo, Benoît H. Lessard, Ioannis Kymissis, Chang‐Hyun Kim   +4 more
wiley   +1 more source

Resistive random access memory: introduction to device mechanism, materials and application to neuromorphic computing

Nanoscale Research Letters, 2023
The modern-day computing technologies are continuously undergoing a rapid changing landscape; thus, the demands of new memory types are growing that will be fast, energy efficient and durable.
Furqan Zahoor, Fawnizu Azmadi Hussin, Usman Bature Isyaku, Shagun Gupta, Farooq Ahmad Khanday, Anupam Chattopadhyay, Haider Abbas   +6 more
doaj   +1 more source

Electrically Induced Phase Transition and Synaptic Functionality in MoTe2/Graphene Memristors

Advanced Functional Materials, EarlyView.
An electrically induced reversible 2H ↔ 1T′ phase transition is demonstrated in a vertical Au/Ti/MoTe2/graphene memristor with a laterally contacted graphene electrode. Resistive switching proceeds through a compositionally invariant amorphous/2H ↔ amorphous/1T′ transformation, in which a self‐formed amorphous MoTe2 interfacial region is proposed to ...
Chien‐Hua Wang, Mu‐Pai Lee, Jhen‐Dong Tsai, Liang‐Ying Hsieh, Jui‐Yuan Chen, Chun‐Wei Huang, Yen‐Fu Lin, Wen‐Wei Wu   +7 more
wiley   +1 more source

Atomic‐Scale Visualization of Topotactic Resistive Switching in Epitaxial La0.5Sr0.5CoOx Neuromorphic Memristors

Advanced Functional Materials, EarlyView.
Forming‐free epitaxial Pt/La0.5Sr0.5CoOx (LSCO)/La0.5Sr0.5MnO3 (LSMO) memristors exhibit heterogeneous and partially reversible topotactic switching involving brownmillerite (BM)‐, Grenier (GN)‐like‐, and perovskite (PV)‐like configurations. Atomic‐resolution scanning transmission electron microscopy and in situ transmission electron microscopy resolve
Zi‐Qin Hong, Yen‐Jung Chen, Shih‐Chao Chang, Jui‐yuan Chen, Jan‐Chi Yang, Wen‐Wei Wu   +5 more
wiley   +1 more source

Oxide-Based RRAM: A Novel Defect-Engineering-Based Implementation for Multilevel Data Storage

, 2012
A novel strategy based on defect engineering is proposed for high-performance multilevel data storage in oxide-based resistive random access memory (RRAM).
Liu, L. F., Gao, B., H. Y. Yu, L. F. Liu, B. Yu, Kang, J. F., Gao, Bin, X. Y. Liu, J. F. Kang, Liu, X. Y., Yu, H. Y., Wang, Z. R., B. Gao, Z. R. Wang, Yu, B., Yu, Hongyu, Chen, B., B. Chen   +17 more
core   +1 more source

Accurate Inference With Inaccurate RRAM Devices: A Joint Algorithm-Design Solution

IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, 2020
Resistive random access memory (RRAM) is a promising technology for energy-efficient neuromorphic accelerators. However, when a pretrained deep neural network (DNN) model is programmed to an RRAM array for inference, the model suffers from accuracy ...
Gouranga Charan, Abinash Mohanty, Xiaocong Du, Gokul Krishnan, Rajiv V. Joshi, Yu Cao   +5 more
doaj   +1 more source

A 0.9 V, 8T2R nvSRAM Memory Cell with High Density and Improved Storage/Restoration Time in 28 nm Technology Node

Active and Passive Electronic Components, 2023
Combining with a static random-access memory (SRAM) and resistive memory (RRAM), an improved 8T2R nonvolatile SRAM (nvSRAM) memory cell is proposed in this study. With differential mode, a pair of 1T1R RRAM is added to 6T SRAM storage node. By optimizing
Jiayu Yin, Wenli Liao, Chengying Chen
doaj   +1 more source

A Fast Weight Transfer Method for Real-Time Online Learning in RRAM-Based Neuromorphic System

IEEE Access, 2022
In this work, a synaptic weight transfer method for a neuromorphic system based on resistive-switching random-access memory (RRAM) is proposed and validated.
Min-Hwi Kim, Sin-Hyung Lee, Sungjun Kim, Byung-Gook Park   +3 more
doaj   +1 more source

Neuromorphic Devices for Extreme Environments: State‐Variable Reliability and Stabilization Strategies

Advanced Functional Materials, EarlyView.
Neuromorphic devices for extreme environments are interpreted through the physical states that store and process information. Radiation, temperature extremes, and vacuum can perturb vacancies, filaments, trapped charges, polarization, interfaces, and readout signals.
Taehyun Park, Geon Park, Junhyung Cho, Jaechan Song, Wangmyung Choi, Jaehyun Hur, Nicola Gasparini, Hocheon Yoo   +7 more
wiley   +1 more source