Bibliographic record
Abstract
Imagine if the only way to determine whether bridges were designed with sufficient strength to support the weight of fully loaded trucks was to build each bridge in its desired location according to an engineer's best guess, drive trucks across to see if the bridge collapsed, and redesign the bridge if it failed. This approach to bridge design is clearly impractical. What has made it possible to build large bridges are the mathematical theories of beams and plates enabling civil engineers to predict whether their designs will work or not, prior to construction. Similarly, advancements in lithography technology have been facilitated by the availability of predictive theoretical models and tools. Today, imaging performance can be simulated on personal computers and engineering workstations in order to determine what the design parameters should be for lenses that cost millions of dollars to fabricate. Lithography simulations involve several key steps: (1) The calculation of optical images. These are intensities I(x,y) in the plane of the wafer, that are applicable for low-numerical-aperture optics, or they can be fully three dimensional intensities I(x,y,z), that may be needed for accurate simulation of exposures using high-numerical-aperture lenses. (2) Prediction of the photochemical reactions resulting from the exposure of photoresist to the previously calculated light distributions. This provides a calculated state-of-exposure at every point (x,y,z) of interest in the resist film. (3) Computation of changes in chemical distributions within the resist as a consequence of diffusion that occurs during post-exposure bakes. (4) Calculation of resist profiles following resist development. Models for all of these key steps are discussed in this chapter.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".