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Implications of Helium and Neon Ion Beam Chemistry for Advanced Circuit Editing

2013· article· en· W2471460436 on OpenAlex
Huimeng Wu, David C. Ferranti, Lewis Stern, Deying Xia, M. W. Phaneuf

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

VenueProceedings - International Symposium for Testing and Failure Analysis · 2013
Typearticle
Languageen
FieldEngineering
TopicIntegrated Circuits and Semiconductor Failure Analysis
Canadian institutionsFibics (Canada)
Fundersnot available
KeywordsNeonSputteringEtching (microfabrication)Ion beamHeliumFocused ion beamOptoelectronicsDeposition (geology)Materials scienceIon sourceIonDielectricNanotechnologyEngineering physicsChemistryAtomic physicsThin filmArgonPhysics

Abstract

fetched live from OpenAlex

Abstract Gallium focused ion beams (Ga-FIB) have been used historically in the semiconductor industry for failure analysis, as well as circuit edit. However, in spite of the best of these efforts, as integrated circuit dimensions continue to shrink, Ga-FIB induced processes are being driven to their physical limits. The main purpose of this paper is to report the helium and neon ion beams' induced chemistry, including metal deposition, dielectric deposition, and chemically enhanced etching. Two simple examples are shown as proofs of concept demonstrating gas field ion source (GFIS) development for circuit edit applications. The paper summarizes the general utility of helium and neon ion beams for metal deposition, dielectric deposition, and sputtering and etching processes, and discusses some of the technical challenges associated with current GFIS technology. Using GFIS ion beams, it has been observed that the top and buried metal lines can be cut precisely and then reconnected.

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 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 score0.640
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.212
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