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Record W2527982577 · doi:10.1109/jsac.2016.2603663

Mitigation of Inter-Cell Interference in Flash Memory With Capacity-Approaching Variable-Length Constrained Sequence Codes

2016· article· en· W2527982577 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

VenueIEEE Journal on Selected Areas in Communications · 2016
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Storage Technologies
Canadian institutionsUniversity of Alberta
FundersDivision of Electrical, Communications and Cyber SystemsNatural Sciences and Engineering Research Council of CanadaAlberta Innovates - Technology Futures
KeywordsComputer scienceFlash memorySequence (biology)AlgorithmInterference (communication)Constraint (computer-aided design)Computer hardwareTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

We propose using variable-length constrained sequence codes to mitigate inter-cell interference (ICI) in all-bit-line flash memory with multi-page programming for single-level cell, multi-level cell, and triple-level cell flash memory structures. We outline constraints that mitigate ICI in these systems based on an observation of the Gray mapping of data symbols, and we derive the capacity of each constraint. Based on a finite state machine representation of each constraint, we construct variable-length constrained sequence codes with code rates very close to capacity to mitigate ICI in these flash memories. We then exploit the inherent error control capability of the proposed constrained sequence codes to alleviate error propagation. Finally, we integrate these codes with error control codes and present simulation results that demonstrate the enhanced bit error rate performance that can be achieved.

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)0.001
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.552
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0030.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.273
Teacher spread0.230 · 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