Single component photoacid/photobase generators: potential applications in double patterning photolithography
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".