Alloying conducting channels for reliable neuromorphic computing

Yeon, Han-Wool; Lin, Peng; Choi, Chanyeol; Tan, Scott H.; Park, Yongmo; Lee, Doyoon; Lee, Jae Yong; Xu, Feng; Gao, Bin; Wu, Huaqiang; Qian, He; Nie, Yifan; Kim, Seyoung; Kim, Jeehwan

doi:10.1038/s41565-020-0694-5

Cited by 180 publications

(195 citation statements)

References 30 publications

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“…Highly insulating Y7C film in the pristine state becomes electrically conducting with the help of protons. When a positive voltage is applied, metal ions at the top electrode are generated and migrate to the bottom electrode, forming conducting filaments in the Y7C film via a mechanism similar to that of other resistive switching devices [25][26][27][28][29][30][31][32][33] . (Supplementary Note 2) However, a key difference is that under voltage bias, a phenolic hydroxyl group of tyrosine donates an electron to reduce Ag ions by generating a tyrosyl radical with the loss of one proton 34 .…”

Section: Resultsmentioning

confidence: 99%

Proton-enabled activation of peptide materials for biological bimodal memory

et al. 2020

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The process of memory and learning in biological systems is multimodal, as several kinds of input signals cooperatively determine the weight of information transfer and storage. This study describes a peptide-based platform of materials and devices that can control the coupled conduction of protons and electrons and thus create distinct regions of synapse-like performance depending on the proton activity. We utilized tyrosine-rich peptide-based films and generalized our principles by demonstrating both memristor and synaptic devices. Interestingly, even memristive behavior can be controlled by both voltage and humidity inputs, learning and forgetting process in the device can be initiated and terminated by protons alone in peptide films. We believe that this work can help to understand the mechanism of biological memory and lay a foundation to realize a brain-like device based on ions and electrons.

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Section: Resultsmentioning

confidence: 99%

Proton-enabled activation of peptide materials for biological bimodal memory

et al. 2020

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“…[ 4 ] Neuromorphic computing architectures for fully connected [ 5–7 ] and convolutional [ 8,9 ] neural networks have been developed. Despite significant research into memory technologies such as conductive‐bridge random access memory, [ 10–12 ] ferroelectric memory, [ 13 ] phase‐change memory, [ 14–16 ] among others, the search for a CMOS compatible analogue non‐volatile memory element, or artificial synapse, with accurate and efficient switching has been elusive.…”

Section: Figurementioning

confidence: 99%

Filament‐Free Bulk Resistive Memory Enables Deterministic Analogue Switching

et al. 2020

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Digital computing is nearing its physical limits as computing needs and energy consumption rapidly increase. Analogue‐memory‐based neuromorphic computing can be orders of magnitude more energy efficient at data‐intensive tasks like deep neural networks, but has been limited by the inaccurate and unpredictable switching of analogue resistive memory. Filamentary resistive random access memory (RRAM) suffers from stochastic switching due to the random kinetic motion of discrete defects in the nanometer‐sized filament. In this work, this stochasticity is overcome by incorporating a solid electrolyte interlayer, in this case, yttria‐stabilized zirconia (YSZ), toward eliminating filaments. Filament‐free, bulk‐RRAM cells instead store analogue states using the bulk point defect concentration, yielding predictable switching because the statistical ensemble behavior of oxygen vacancy defects is deterministic even when individual defects are stochastic. Both experiments and modeling show bulk‐RRAM devices using TiO2‐X switching layers and YSZ electrolytes yield deterministic and linear analogue switching for efficient inference and training. Bulk‐RRAM solves many outstanding issues with memristor unpredictability that have inhibited commercialization, and can, therefore, enable unprecedented new applications for energy‐efficient neuromorphic computing. Beyond RRAM, this work shows how harnessing bulk point defects in ionic materials can be used to engineer deterministic nanoelectronic materials and devices.

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“…The multilevel states mimic synaptic behavior. Although this linearity is necessary for the programmability of neuromorphic devices for storing and training neural networks for large‐scale array implementation, [ 23 ] it was not achieved in previous reports using conducting channels. Demonstration of this linearity using a hydrogen‐driven transition in LSMO films is important for neuromorphic devices.…”

Section: Reversible Metal–insulator Transition Via Annealing Ferromagmentioning

confidence: 99%

Hydrogen Control of Double Exchange Interaction in La_0.67Sr_0.33MnO₃ for Ionic–Electric–Magnetic Coupled Applications

Lee

2021

Advanced Materials

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The dynamic tuning of ion concentrations has attracted significant attention for creating versatile functionalities of materials, which are impossible to reach using classical control knobs. Despite these merits, the following fundamental questions remain: how do ions affect the electronic bandstructure, and how do ions simultaneously change the electrical and magnetic properties? Here, by annealing platinum‐dotted La0.67Sr0.33MnO3 films in hydrogen and argon at a lower temperature of 200 °C for several minutes, a reversible change in resistivity is achieved by three orders of magnitude with tailored ferromagnetic magnetization. The transition occurs through the tuning of the double exchange interaction, ascribed to an electron‐doping‐induced and/or a lattice‐expansion‐induced modulation, along with an increase in the hydrogen concentration. High reproducibility, long‐term stability, and multilevel linearity are appealing for ionic–electric–magnetic coupled applications.

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Alloying conducting channels for reliable neuromorphic computing

Cited by 180 publications

References 30 publications

Proton-enabled activation of peptide materials for biological bimodal memory

Proton-enabled activation of peptide materials for biological bimodal memory

Filament‐Free Bulk Resistive Memory Enables Deterministic Analogue Switching

Hydrogen Control of Double Exchange Interaction in La_0.67Sr_0.33MnO₃ for Ionic–Electric–Magnetic Coupled Applications

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Alloying conducting channels for reliable neuromorphic computing

Cited by 180 publications

References 30 publications

Proton-enabled activation of peptide materials for biological bimodal memory

Proton-enabled activation of peptide materials for biological bimodal memory

Filament‐Free Bulk Resistive Memory Enables Deterministic Analogue Switching

Hydrogen Control of Double Exchange Interaction in La0.67Sr0.33MnO3 for Ionic–Electric–Magnetic Coupled Applications

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Hydrogen Control of Double Exchange Interaction in La_0.67Sr_0.33MnO₃ for Ionic–Electric–Magnetic Coupled Applications