Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of statistical process control is the identification of abnormal variations in the materials, equipment, parameters or procedures used as inputs for particular processes (see Table 3.1). This is usually accomplished by measuring physical objects on the output, such as linewidths or overlay structures. When processes require test wafers, which often occurs during process development or the initiation of a new manufacturing process, the output of the process is decoupled from the input, and statistical process control cannot fulfill its primary purpose. This situation is shown schematically in Fig. 3.1. The inputs collectively comprise the process. A single wafer is taken from a lot of wafers and processed through the lithography operation. After the processing is complete, this test wafer is measured for parameters of interest, such as linewidths or overlay. From the values of these measurements, in comparison to the process targets, the remaining wafers in the lot are processed through the lithography operation with adjusted process parameters. For example, the exposure dose might be adjusted to bring linewidths to the process target. Changes in the dose might be required to compensate for drift in the stepper's dose control system or the changes in the resist process. Measurements of the linewidths of the lot, except for the test wafer, will not reveal a drift in the stepper's dose control system or a change in the resist process, because the exposure dose has been adjusted to compensate for these instabilities. In order for statistical process control to reveal variations and instabilities in the inputs, it must be applied to a simple process, where the input variables are directly coupled to the measurable output. In this chapter a process control methodology applicable to situations in which test wafers are used is presented
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it