Bibliographic record
Abstract
The past decade has seen intense activity in the field of microwave photonics in which optical components are used to generate, distribute, control and process microwave and millimeter-wave signals.In parallel there have been significant advances in the area of silicon photonics enabling the inclusion of microwave photonic components into photonic integrated circuits (PICs) ushering in the active area of integrated microwave photonics (IMWP).This has led to the co-existence of electrical and optical components at the level of the integrated circuit.The existence of electrical and optical devices at the same design level requires design tools that can handle components belonging to both physical domains simultaneously for performing system simulation.The technique presented in this thesis addresses this need by presenting a method to perform steady-state simulation of optical-electrical systems using Harmonic Balance (HB).One of the unique features of this method is that it includes phase of the optical signal in finding the steady-state solution of the system.The inclusion of phase in a framework using HB poses several challenges since all system variables in HB are assumed to be periodic and the phase of optical signals is in general nonperiodic owing to the non-zero chirp present in laser diodes that are used as drivers for optical systems.Several examples are presented that demonstrate the feasibility of the proposed method and where possible the results are compared with existing techniques. CMemory matrix of MNA formulation.Non-linear vector of MNA formulation.F ( X) Non-linear vector of HB formulation.g 0 Gain slope constant.G Conductance matrix of MNA formulation.xv h Step size of integration technique .h p Plancks constant.I D (t) Electrical current . JJacobian matrix in NR iterations. KNew tone introduced in the proposed method due to the effect of optical phase. MNumber of trunctated frequencies. N (t) Carrier density of laser diode.N t Carrier density at transparency.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".