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Record W2133615958 · doi:10.1039/c3tc00874f

Single component photoacid/photobase generators: potential applications in double patterning photolithography

2013· article· en· W2133615958 on OpenAlexaff
Geniece L. Hallett-Tapley, Tse-Luen Wee, Hoang Tran, Shankar B. Rananavare, James M. Blackwell, J. C. Scaiano

Bibliographic record

VenueJournal of Materials Chemistry C · 2013
Typearticle
Languageen
FieldEngineering
TopicNanofabrication and Lithography Techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMaterials sciencePhotolithographyLithographyResistDissolutionLaserOptoelectronicsPhotochemistryNanotechnologyOpticsChemistry

Abstract

fetched live from OpenAlex

193 nm light as an excitation source for resist patterning is limited due to the inability to achieve pitch division much below limits of λ/2. Current techniques are examining the use of a variety of photochemical manipulations as a means to extend lithographic patterning to small, more defined images. Double patterning, or dual tone lithography, has recently garnered considerable interest due to the potential of patterning two features within one excitation exposure. In this contribution, single component carbamate photoacid/photobase (PAG/PBG) generators are studied as potential substrates for implementing dual tone lithography. At lower exposure powers, only the acid is generated and complete film dissolution was observed, while at higher 193 nm laser powers, photobase activation resulted in little to no film dissolution. Ideally, at intermediate laser doses, both the photoacid and photobase are activated giving rise to the desired double patterning. The energy required to initiate dual tone characteristics was found to be easily adjusted using the additional of amine quenchers or via manipulation of the PAG/PBG concentration. Film thickness measurements were used to determine the energies required for both photoacid and photobase activation, while laser flash photolysis and NMR spectroscopy studies were used in an attempt to understand the PAG/PBG activation mechanism.

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.

How this classification was reachedexpand

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.803

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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2013
Admission routes1
Has abstractyes

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