Bibliographic record
Abstract
Energy-saving technology that reduces power consumption is of increasing importance due to the ever-increasing demand for Internet services. To prevent the traffic growth from being strangled by energy bottlenecks, novel architectural and technological solutions are indispensable. The most obvious way to cope with the issue is to reduce the energy consumed by the network elements. Fast optical switching is an important enabler of advanced optical networks, in particular such functions as routing burst and packet optical signals, optical path provisioning and fault restoration. Semiconductor Digital Optical Switches (DOSs) can fulfill such high speed applications due to their nanosecond switching times, step-like switching responses, and immunity to variations in temperature, wavelength, polarization, refractive index and device fabrication tolerances. Moreover, semiconductor DOSs offer the potential for integration with other semiconductor optoelectronic components and thus promise considerable reductions in the size, complexity and cost of an overall optical system. For optical waveguide switches, fast optical switching may be achieved by a refractive index change, induced either by carrier injection (Zegaoui et al., 2009; Bennett et al., 1990) or by the electro-optic effect (Cao et al., 2009; Agrawal et al., 1995), within III-V semiconductors, such as GaAs-based and InP-based. Compared to carrier-injection switches, electro-optic switches have faster switching speeds but larger switching voltages since the refractive index change induced by an electro-optic effect is about two orders of magnitude smaller than that by carrier injection. Therefore, until now, most of the commercially available semiconductor DOS products have been based on carrier-injection (Ikezawa et al., 2008). These devices typically utilize carrierinduced Total Internal Reflection (TIR) at a waveguide branching or crossing point to switch the light path from one waveguide to another. Such TIR-based semiconductor switches typically require a large index modulation, e.g. in the order of 0.01, with the region of changed index having a well-defined boundary. Accordingly, efforts have been made to restrict current spreading and to confine the injected carriers to the desired region. Typically, achieving such carrier confinement involves using relatively complex semiconductor device technologies, such as ion implantation (Abdalla et al., 2004; Zhuang et al., 1996), electron-beam lithography (Shimomura et al., 1992), Zn diffusion (Yanagawa et al., 1990), and epitaxial regrowth (Thomson et al., 2008).
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".