NEUROTEC I: Neuro-inspired Artificial Intelligence Technologies for the Electronics of the Future

Galicia, Melvin; Menzel, Stephan; Merchant, Farhad; Müller, Maximilian J.; Chen, Hsin-Yu; Zhao, Qing‐Tai; Cüppers, Felix; Jalil, Abdur Rehman; Shu, Qi; Schuffelgen, Peter; Mußler, Gregor; Funck, Carsten; Lanius, Christian; Wiefels, Stefan; Witzleben, Moritz von; Bengel, Christopher; Kopperberg, Nils; Ziegler, T.; Ahmad, R. Walied; Kruger, Alexander Y.; Pohls, Leticia; Dittmann, Ralf W.; Hoffmann‐Eifert, Susanne; Rana, Vikas; Grützmacher, Detlev; Wuttig, Matthias; Wouters, Dirk J.; Vescan, A.; Gemmeke, Tobias; Knoch, J.; Lemme, Max C.; Leupers, Rainer; Waser, Rainer

doi:10.23919/date54114.2022.9774755

Cited by 1 publication

(1 citation statement)

References 31 publications

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“…[1][2][3] One promising innovation is the redox-based resistive switch called ReRAM. [4][5][6][7][8][9] These devices are known for their excellent performance in important figures of merits such as high endurance, fast switching speed, and, long data retention. [10][11][12][13][14][15][16][17] They can be toggled between different resistance states by well-chosen voltage stimuli.…”

Section: Introductionmentioning

confidence: 99%

A Physical Description of the Variability in Single‐ReRAM Devices and Hardware‐Based Neuronal Networks

Funck,

Wiefels,

Bengel

et al. 2023

Advanced Intelligent Systems

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Resistive switching devices are candidates to be used as weight for the future hardware realization of neuronal networks. Herein, these resistive switches base on oxygen vacancy migration and can be toggled between different resistance states. Internal processes lead to a variability over time in these states. To understand this, a dynamical model is developed, which links the internal physical mechanism to the current response. Thereby, the electronic current is calculated by tunneling processes over oxygen vacancies. The model allows to understand the physical transport properties and the current–voltage dependence. Integrating ionic hopping processes clarifies the variability in the electronic conduction. The derived model does not require empirical input parameters and only depends on the properties of the materials. This allows to use literature values and independently compare them to electrical measurements. The model predicts the experiments correctly. Thereupon, the physical properties of the conduction are investigated with respect to the transport over discrete energy levels, which change under the hopping of oxygen vacancies. Afterwards, the current level and variability are simulated for increasing oxygen vacancy concentrations to investigate analog switching, which is required for neuronal network layers. Finally, the variability is tested with multiple interacting cells in a neuronal layer.

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