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Record W4386854053 · doi:10.1149/ma2023-01331854mtgabs

(Invited) Potential of Silicon Oxide Films for Low-Cost and High-Performance Resistive Switching Devices

2023· article· en· W4386854053 on OpenAlex
Yasuhisa Ōmura

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueECS Meeting Abstracts · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsAssociation of Canadian Archivists
Fundersnot available
KeywordsOxideMaterials scienceSiliconSilicon oxideOptoelectronicsSputteringSubstrate (aquarium)NanotechnologyElectric fieldThin filmEngineering physicsMetallurgyPhysics

Abstract

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Although resistance switching in transition-metal oxide (TMO) films has been widely studied [1], the physics and chemistry of resistance switching in non-transition-metal oxide (nTMO) films remains under-studied [2]. This must be corrected because inexpensive and chemically stable device configurations are required for the future Internet-of-Things society. It is easily anticipated that the switching electric field of silicon oxide films is apt to be higher than that of TMO films because the dielectric constant of silicon oxide films is smaller than those of the TMO films. However, the use of two-layer stacks like SiOx/hi- k oxide reduces the switching electric field [3]. Therefore, the study of silicon oxide films is still meaningful. Many scientists have recently investigated resistance switching in sputter-deposited silicon oxide films in detail [4,5] because this structure dispenses with the TMO (Fig. 1). Many papers addressed the role of silicon sub-oxide (SiOx) [6] because it is anticipated that the unstable bonds of non-stoichiometric silicon oxide can create degraded, but reversible, conductive paths inside the film. However, it is not yet clear how the SiOx region can trigger resistance switching, how important the SiOx region is, and whether the SiOx region is the only determiner of resistance switching [7] (Figs. 2, 3). The author demonstrated that hot-electron injection from the Si substrate had great potential in triggering resistance switching and lowering the switching voltage of sputter-deposited Si oxide films [5,8]; it was also mentioned that Si precipitates played an important role in realizing repeatable bipolar switching [9] (Fig. 2). Relating to this study, the author also proposed the physical and chemical structure of conductive filaments and their switching behavior based on an analysis of a possibly equivalent circuit model [10] (Fig. 4). However, it was not definitely elucidated why unipolar switching is not easily observed in sputter-deposited silicon oxide films, even though it is not a TMO. Recently, the author performed various Monte Carlo simulations to elucidate the physical and chemical parameters that rule the unipolar switching process in sputter-deposited silicon oxide films. Generations of simple bond breaking, oxygen vacancies, metallic Si sites, and E’’ centers were implemented in the simulation algorithm [8]. All-positive voltage stress mode for both the electroforming process and the reset process will not yield devices with stable, repeatable switching [11]. On the other hand, the all-negative stress mode results in stable, repeatable switching because the recovery of the internal degradation of the Si oxide film is not completed [11] (Fig. 5). This difference stems from the physical asymmetry of the electrode materials (Fig. 1). Though some may consider that silicon oxide films are not preferable to ReRAM devices from the chemical points of view, the theoretical analysis provided by the author in this paper suggests that silicon oxide films can be applied to the ReRAM device. [1] Y. Tokura, Physics Today , vol. 56, pp. 50-55, 2003. [2] T. Yanagida, et al ., Sci. Rep. , vol. 3, No.1657, pp.1-6, 2013. [3] P. Broqvist and A. Pasquarello, Appl. Phys. Lett., vol. 91, 192905, 2007. [4] J. Yao, et al ., Appl. Phys. Lett , vol. 93, pp. 253101-1-253101-3, 2008. [5] R. Yamaguchi, S. Sato, and Y. Omura, Jpn. J. Appl. Phys. , vol. 56, pp. 041301-1-041301-6, 2017. [6] A. Mehonic, et al. , J. Appl. Phys. , vol. 111, pp. 074507-1-074507-9, 2012. [7] Y. Wang, et al ., Appl. Phys. Lett ., vol.102, pp. 042103-1-042103-5, 2013. [8] Y. Omura, Ind. J. Electrical Eng. & Comput. Sci ., vol. 24, pp. 1367-1378, 2021. [9] Y. Omura, R. Yamaguchi, and S. Sato, IEEE Trans. Device Reliab. and Mat . Vol. 17, pp. 561-567 (2017). [10] Y. Omura, ECS J. Solid State Sci. and Technol ., vol. 10, pp. 124006-1-124006-10, 2021. [11] Y. Omura, Materials Today Proc ., vol. 20, pp. 273-282, 2020; the advanced study will be published in the ECS J. Solid State Sci. and Technol . Figure 1

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Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.234
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it