Bibliographic record
Abstract
As k 1 factors [Eq. (2.8)] fall below 0.8, a number of effects become observable that are not seen when k 1 is larger. Processes with small values of k 1 began to appear in the mid-1990s, and are quite common today, as seen from Table 8.1. Some of the effects seen in patterns generated with low-k 1 processes are discussed in this chapter. Several techniques also described—such as off-axis illumination and phase-shifting masks—have been developed to address the shortcomings of optical imaging as feature sizes become smaller than the wavelength of light. Methods to improve image contrast that involve modification of the mask or illumination are referred to collectively as resolution enhancement techniques (RETs) and are discussed in this chapter. 8.1 Off-axis illumination As discussed earlier, light (coherent) that illuminates a grating is diffracted in very specific directions [Eq. (2.1)]. For normally incident light, sufficiently small dimensions result in situations where all beams except the zero-order are diffracted outside the entrance pupil of the imaging optics (Fig. 8.1). In this case, no pattern is formed, because a single beam is a plane wave, containing no spatial information, as explained in Chapter 2. For normally incident (“on-axis”) illumination, the grating is not imaged when the pitch is too small, because only a single beam, the zero-order beam, is transmitted through the lens. This illustrates the limitation to resolution imposed by diffraction.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".