Results 11 to 20 of about 2,451 (213)

Exploring resistive switching in flexible, forming-free Ti/NiO/AZO/PET memory device for future wearable electronics [PDF]

Scientific Reports
Resistive Random Access Memory (ReRAM) is an emerging class of non-volatile memory that stores data by altering the resistance of a material within a memory cell.
Adiba Adiba, Ph Nonglen Meitei, Tufail Ahmad   +2 more
doaj   +2 more sources

Modeling and Design Optimization of ReRAM

The 20th Asia and South Pacific Design Automation Conference, 2015
Resistive switching memories (ReRAM) have been widely studied for applications in next-generation data storage and neurormorphic computing systems. To enable device-circuit-system co-design and optimization, a SPICE model of ReRAM that can reproduce the ...
H.-S. P. Wong, Jiang, Z. Z., Chen, Z., Gao, B., Huang, P., Li, H. T., Z. Chen, H. T. Li, Kang, J. F., X. Y. Liu, J. F. Kang, P. Huang, Liu, X. Y., Z. Z. Jiang, Wong, H.S. P., B. Gao   +15 more
core   +2 more sources

Medium-Temperature-Oxidized GeO x Resistive-Switching Random-Access Memory and Its Applicability in Processing-in-Memory Computing [PDF]

Nanoscale Research Letters, 2022
Processing-in-memory (PIM) is emerging as a new computing paradigm to replace the existing von Neumann computer architecture for data-intensive processing. For the higher end-user mobility, low-power operation capability is more increasingly required and
Kannan Udaya Mohanan, Seongjae Cho, Byung-Gook Park   +2 more
doaj   +2 more sources

Automatic ReRAM SPICE Model Generation From Empirical Data for Fast ReRAM-Circuit Coevaluation

IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2019
This paper presents an automatic resistive random access memory (ReRAM) SPICE model generator, which enables fast ReRAM circuit evaluation with standard SPICE.
Seo, Jaehyun, Lee, Sangheon, Sangheon Lee, HWANG, HYUNSANG, Kwangmin Kim, Jaehyun Seo, Byungsub Kim, Lee, Sooeun, Kim, Kwangmin, Kim, Byungsub, Sooeun Lee   +10 more
core   +3 more sources

NeoHebbian synapses to accelerate online training of neuromorphic hardware [PDF]

Scientific Reports
Neuromorphic systems that employ advanced synaptic learning rules, such as the three-factor learning rule, require synaptic devices of increased complexity.
S. Pande, S. S. Bezugam, T. Bhattacharya, E. Wlazlak, A. Chakravorty, B. Chakrabarti, D. Strukov   +6 more
doaj   +2 more sources

A keyword-based approach to analyzing scientific research trends: ReRAM present and future [PDF]

Scientific Reports
Research trend analysis is a primary step in defining research structures and predicting research directions from scientific papers. Recently, due to millions of annual scientific publications, researchers demand analytical methods to interpret the ...
Hyeon Kim, Seong Hun Kim, Jaeseon Kim, Eun Ho Kim, Jun Hyeong Gu, Donghwa Lee   +5 more
doaj   +2 more sources

Leveraging Capacitance Modulation of ReRAM for CMOS-ReRAM Image Sensor

2025 Device Research Conference (DRC)
The rapid expansion of applications in the Internet of Things, biomedical imaging, unmanned aerial vehicle imaging, and smart devices drives the increasing demand for advanced image sensors.
Chourasia, S., Padovani, A., Pande, S., Chakrabarti, B.   +3 more
core   +2 more sources

Honey-ReRAM Enabled Sustainable Edge AI System for IoT Applications

IEEE Access
This paper is toward a promising solution to address the environmental sustainability challenge in computing by building brain-inspired and green non-Von Neumann systems with Resistive Random-Access Memory (ReRAM) made from natural organic materials ...
Jinhui Wang, Feng Zhao, Mohammad Rafeeq Khan, Md. Mehedi Hasan Tanim, Zoe Templin, Harshvardhan Uppaluru   +5 more
doaj   +2 more sources

Implementation of binarized neural networks immune to device variation and voltage drop employing resistive random access memory bridges and capacitive neurons [PDF]

Communications Engineering
Resistive Random Access Memories (ReRAM) arrays provides a promising basement to deploy neural network accelerators based on near or in memory computing.
Mona Ezzadeen, Atreya Majumdar, Olivier Valorge, Niccolo Castellani, Valentin Gherman, Guillaume Regis, Bastien Giraud, Jean-Philippe Noel, Valentina Meli, Marc Bocquet, Francois Andrieu, Damien Querlioz, Jean-Michel Portal   +12 more
doaj   +2 more sources

ReRAM-based neuromorphic computing

, 2016
Artificial neural networks are intended to solve complex machine-learning tasks by using massive parallel data processing in a similar way as in biological neural systems.
Stefano Ambrogio, Eike Linn, Giacomo Indiveri, Linn, Eike, Indiveri, Giacomo, Ambrogio, Stefano   +5 more
core   +3 more sources