InGaAs amplifier for loss-compensation in nanoplasmonic circuits
Bibliographic record
Abstract
Surface plasmon polaritons have become a contender for next-generation optical computing with their superior subwavelength modal confinement and nonlinearity over conventional photonics. Gap plasmon waveguides, also known as metal-insulator-metal (MIM) waveguides, have been shown to have one of the best balances in the inherent trade-off between confinement (<200nm) and propagation length (several microns) among plasmonic waveguide geometries. There is great interest in introducing gain into these plasmonic systems to compensate for their innate short propagation lengths. To this end, we present an electrically pumped Ag/HfO<sub>2</sub>/In<sub>0.485</sub>Ga<sub>0.515</sub>As/HfO<sub>2</sub>/Ag metal-insulatorsemiconductor-insulator-metal (MISIM) amplifier design for loss-compensation in nanoplasmonic interconnects at thetelecommunication wavelength of 1.55 μm. Finite difference time domain simulations utilizing the full rate equations were used to study the signal gain experienced upon transmission through the device. The direct bandgap semiconductorgain medium In<sub>0.485</sub>Ga<sub>0.515</sub>As was modeled as a four level laser system with homogeneous broadening. The effect of varying critical amplifier dimensions, namely the HfO<sub>2</sub> spacer layer thickness and the width of the In<sub>0.485</sub>Ga<sub>0.515</sub>As core, on the amplifier's performance was studied. A 3 μm long linear amplifier is shown to be capable of restoring a 500 GHz, 500 fs FWHM pulse train after 150 μm of propagation through a nanoplasmonic interconnect network without significant pulse distortion at a pump current density of 36.6 kA/cm<sup>2,</sup>or 1 mA total current. This pump current is shown to cause acceptable levels of device heating. A periodic arrangement of such devices could therefore be used to indefinitely increase the effective propagation length of a signal.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".