Competing memristors for brain-inspired computing

Kim, Seung Ju; Kim, Sang Bum; Jang, Ho Won

doi:10.1016/j.isci.2020.101889

Cited by 65 publications

(48 citation statements)

References 135 publications

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“…The larger the pore size, the longer the possible diffusion length denoted by the red arrow inside the pore, which exerts a reduced confinement effect on CF. For the porous device, it might be possible to grant tunability in the retention property depending on the type of applications by adjusting the pore size ( 54 , 55 ). Furthermore, it is meaningful because it has been experimentally shown that resistive switching is due to the pores rather than by the bulk.…”

Section: Resultsmentioning

confidence: 99%

Reliable multilevel memristive neuromorphic devices based on amorphous matrix via quasi-1D filament confinement and buffer layer

et al. 2022

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Conductive-bridging random access memory (CBRAM) has garnered attention as a building block of non–von Neumann architectures because of scalability and parallel processing on the crossbar array. To integrate CBRAM into the back-end-of-line (BEOL) process, amorphous switching materials have been investigated for practical usage. However, both the inherent randomness of filaments and disorders of amorphous material lead to poor reliability. In this study, a highly reliable nanoporous–defective bottom layer (NP–DBL) structure based on amorphous TiO 2 is demonstrated (Ag/a-TiO 2 /a-TiO x /p-Si). The stoichiometries of DBL and the pore size can be manipulated to achieve the analog conductance updates and multilevel conductance by 300 states with 1.3% variation, and 10 levels, respectively. Compared with nonporous TiO 2 CBRAM, endurance, retention, and uniformity can be improved by 10 6 pulses, 28 days at 85°C, and 6.7 times, respectively. These results suggest even amorphous-based systems, elaborately tuned structural variables, can help design more reliable CBRAMs.

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Section: Resultsmentioning

confidence: 99%

Reliable multilevel memristive neuromorphic devices based on amorphous matrix via quasi-1D filament confinement and buffer layer

et al. 2022

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“…The most common of these are the transistor and the diode. Memory could also be realized through nonlinear historydependent resistors known as memristors [159][160][161][162][163][164], but to the authors' knowledge polymer or gel ionic memristors do not yet exist. Almost any analog circuit can be produced by a combination of these five elements.…”

Section: Transmuting the Signalsmentioning

confidence: 99%

Soft Ionics: Governing Physics and State of Technologies

Tepermeister

Bosnjak²,

Dai³

et al. 2022

Front. Phys.

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Soft ionic materials combine charged mobile species and tailored polymer structures in a manner that enables a wide array of functional devices. Traditional metal and silicon electronics are limited to two charge carriers: electrons and holes. Ionic devices hold the promise of using the wide range of chemical and molecular properties of mobile ions and polymer functional groups to enable flexible conductors, chemically specific sensors, bio-compatible interfaces, and deformable digital or analog signal processors. Stand alone ionic devices would need to have five key capabilities: signal transmission, energy conversion/harvesting, sensing, actuation, and signal processing. With the great promise of ionically-conducting materials and ionic devices, there are several fields working independently on pieces of the puzzle. These fields range from waste-water treatment research to soft robotics and bio-interface research. In this review, we first present the underlying physical principles that govern the behavior of soft ionic materials and devices. We then discuss the progress that has been made on each of the potential device components, bringing together findings from a range of research fields, and conclude with discussion of opportunities for future research.

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“…To solve the von Neumann bottleneck phenomenon, non-von-Neumann architectures, such as near memory computing, in which a memory unit is located near a processing unit, are proposed. Among them, the most efficient method is neuromorphic computing system, − which is a computer architecture that imitates the human brain. , Unlike the existing von Neumann architecture, the bottleneck can be avoided because the calculation unit and the memory unit are integrated into one chip, resulting in a high speed in processing many data (Figure a, b). , Moreover, neuromorphic computing enables parallel computing and shows superior performance compared to existing hardware in terms of speed and energy efficiency . This advantage can be maximized if a nonvolatile analog memory device is used as a synaptic device since voltage can be calculated using Ohm’s law or Kirchhoff’s law without peripheral circuits as adders or multiplier.…”

Section: Challenges and Strategiesmentioning

confidence: 99%

Catalyze Materials Science with Machine Learning

Kim

Kang

Kim

et al. 2021

ACS Materials Lett.

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Discovering and understanding new materials with desired properties are at the heart of materials science research, and machine learning (ML) has recently offered special shortcuts to the ultimate goal. Thanks to the nourishment of computer hardware and computational chemistry, the development of calculated scientific data repositories could fuel the ML models to investigate the vast materials space. At this moment, understanding this revolutionary paradigm is urgent, and this Review aims to deliver comprehensive information about the collaboration of ML with materials science. This Review summarizes recent achievements in catalysts design, which can be benefitted from ML because of the complex nature of catalytic reactions and vast candidate materials space. ML models for catalyst design could be transferred to applications in other domains and vice versa. The basic concepts of ML algorithms and practical guides to materials scientists are also demonstrated. Moreover, challenges and strategies of applying ML are discussed, which should be addressed collaboratively between materials scientists and ML communities. Ultimate integrations of ML in materials science are expected to accelerate the design, discovery, optimization, and interpretation of materials in both industry and academia, and this Review hopes to be the informative base camp for that journey.

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Competing memristors for brain-inspired computing

Cited by 65 publications

References 135 publications

Reliable multilevel memristive neuromorphic devices based on amorphous matrix via quasi-1D filament confinement and buffer layer

Reliable multilevel memristive neuromorphic devices based on amorphous matrix via quasi-1D filament confinement and buffer layer

Soft Ionics: Governing Physics and State of Technologies

Catalyze Materials Science with Machine Learning

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