Bibliographic record
Abstract
11.1 Cost of ownership The high price tags of exposure tools have made the cost of lithography a concern since the advent of projection lithography, and lithography costs may ultimately limit patterning capability, more so than technical challenges. while there will always be a market for electronics where price is secondary to performance, the large personal computer and portable phone markets have proven to be extremely elastic. To meet the demands of the consumer, lithography will need to be cost-effective, in addition to providing technical capability. Lithography costs have several components. Among them are: (1) Capital equipment costs, throughput, and utilization (2) Consumables, such as photochemicals (3) Masks (4) Rework and yield (5) Metrology (6) Maintenance (7) Labor (8) Facilities These factors can be considered in various degrees of sophistication. A detailed cost of ownership model was generated by Sematech, and an enhanced version of this model is commercially available. In this chapter, the basic components of such cost-of-ownership models are introduced and discussed. Lithography tools are often the most expensive in the wafer fab. Even when they are not, the fact that lithography is required for patterning many layers in IC manufacturing processes, while most other tools are used for only a few steps, means that a large number of lithography tools are needed for each wafer fab, resulting in high total costs for lithography equipment. Wafer steppers are the most expensive pieces of equipment in the lithography tool set. Their prices have increased by an average of 17% per year since they were introduced in the late 1970s, to the point where leading-edge step-and-scan systems now cost close to $20M, and their prices are projected to increase in the future (Fig. 11.1 and Fig. 11.2). Because equipment costs are so central to economic considerations, the cost of lithography is usually referred to as “cost of ownership.”
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.167 | 0.052 |
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".