Bibliographic record
Abstract
As the limits of electrical performance come within sight, photons are poised to take over for the electron. But the search continues for the materials, topologies, and fabrication technologies capable of producing photonic devices at a reasonable speed and cost. Taking a fundamentallook at the development of photonic technology from the macro- to the microscale, Applied Microphotonics introduces the major principles and technologies underlying the field. Following an overview of historical and commercial driving forces, the authors briefly review the underlying physics, emphasizing the practical and design implications for photonic systems. This general discussion lays the foundation for the remainder of the book, where the authors first introduce the photonic node and then discuss each subsystem in detail, including transmitters, couplers and switches, multiplexers and demultiplexers, receivers, amplifiers, and compensators. The following chapters explore new technologies such as photonic band gap structures, materials and fabrication processes, integration methodologies, and advanced devices such as photonic computers. The book concludes with a brief introduction to quantum photonics and a forward look at potential directions of photonics. Applied Microphotonics encapsulates the recent push toward all-optical networks and devices with an applications-oriented perspective. It is ideal for newcomers to the field as well as anyone curious to know how photonic technology can benefit their own field.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.086 | 0.032 |
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".