(Invited) Carbon Nanotube Based Photonics
Bibliographic record
Abstract
The use of optics in microelectronic circuits to overcome the limitation of metallic interconnects is more and more considered as a viable solution. Numerous photonic building blocks, compatible with CMOS technology, have been developed. However, integration of all these building blocks on the same chip is a bottleneck, due to the various materials used (Ge, Si, III-V). This drawback could be significantly overcome by considering carbon nanotubes, which have the ability to emit, modulate and detect light in the wavelength range of silicon transparency. That makes them a promising candidate and in consequence an alternative material for active device in silicon photonics technology. Few years ago, we have developed an efficient method to extract semiconducting nanotube (s-SWNT), using a polyfluorene agent in toluene followed by ultracentrifugation steps [1]. We demonstrated that this method allows obtaining metallic-free s-SWNT samples, as confirmed by photoluminescence, absorption and Raman spectroscopy, and the realisation of high Ion/Ioff FET devices. This achievements leads to the first experimental demonstration of a strong optical gain of 160 cm-1 at a wavelength of 1.3 µm in (8,7) s-SWNT at room temperature [2]. A special emphasis will be put on the s-SWNT extraction, as optical gain could not be achieved in a raw or lowly extracted sample. Carbon nanotube properties were then relied on the existing silicon photonic platform, and we envision the use of carbon nanotubes as active optoelectronic devices in silicon. A complete study of the coupling between carbon nanotubes and silicon waveguides was performed [3]. In particular, temperature independent emission up to 100°C from carbon nanotubes in silicon was demonstrated, which opens bright perspectives for future high performance integrated circuits (Figure : Integration scheme of carbon nanotube with silicon waveguide, showing carbon nanotube emission throught the waveguide) [1] N. Izard, S. Kazaoui, K. Hata, T. Okazaki, T. Saito, S. Iijima and N. Minami, Appl. Phys. Lett., 92, 243112 (2008) [2] E. Gaufrès, N. Izard, X. Le Roux, D. Marris-Morini, S. Kazaoui, E. Cassan and L. Vivien, Appl. Phys. Lett., 96, 231105 (2010) [3] E. Gaufrès, N. Izard, A. Noury, X. Le Roux, G. Rasigade, A. Beck and L. Vivien, ACS Nano, 6, 3813 (2012)
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.106 | 0.077 |
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".