Automated Defect and Correlation Length Analysis of Block Copolymer Thin Film Nanopatterns
Bibliographic record
Abstract
Line patterns produced by lamellae- and cylinder-forming block copolymer (BCP) thin films are of widespread interest for their potential to enable nanoscale patterning over large areas. In order for such patterning methods to effectively integrate with current technologies, the resulting patterns need to have low defect densities, and be produced in a short timescale. To understand whether a given polymer or annealing method might potentially meet such challenges, it is necessary to examine the evolution of defects. Unfortunately, few tools are readily available to researchers, particularly those engaged in the synthesis and design of new polymeric systems with the potential for patterning, to measure defects in such line patterns. To this end, we present an image analysis tool, which we have developed and made available, to measure the characteristics of such patterns in an automated fashion. Additionally we apply the tool to six cylinder-forming polystyrene-block-poly(2-vinylpyridine) polymers thermally annealed to explore the relationship between the size of each polymer and measured characteristics including line period, line-width, defect density, line-edge roughness (LER), line-width roughness (LWR), and correlation length. Finally, we explore the line-edge roughness, line-width roughness, defect density, and correlation length as a function of the image area sampled to determine each in a more rigorous fashion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".