MétaCan
Menu
Back to cohort
Record W3159146981 · doi:10.1021/acsaelm.1c00271

A True Random Number Generator Based on Ionic Liquid Modulated Memristors

2021· article· en· W3159146981 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Applied Electronic Materials · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsUniversity of Waterloo
FundersDepartment of Science and Technology of Sichuan ProvinceNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMemristorIonic liquidNeuromorphic engineeringMaterials scienceNanotechnologyComputer scienceUltrashort pulseTopology (electrical circuits)Artificial neural networkElectronic engineeringElectrical engineeringArtificial intelligenceChemistryPhysicsEngineering

Abstract

fetched live from OpenAlex

The memristor-based neuromorphic computing application, which is highly flexible and capable of handling large amounts of parallel information, is one of the major breakthroughs in the past decade. It sheds light on future high-density storage, ultrafast logic computing systems, advanced artificial intelligence, etc. To explore the applications based on memristive devices, a memristive device using an indium–gallium–zinc oxide (IGZO)–TiO2 bilayer film as the functional layer is demonstrated. Its memristive behavior can be modulated by diethylmethyl(2-methoxyethyl)ammonium bis(trifluoromethylsulfonyl)imide (DEME-TFSI) ionic liquid (IL). Further, a true random number generator was designed on the basis of eight memristive units. This work demonstrates that ionic liquid regulated memristors can not only be used for data storage but also open up potential applications for cryptography.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score1.000

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)0.0010.000

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 head0.006
GPT teacher head0.208
Teacher spread0.202 · 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