Development of Microwave Kinetic Inductance Detectors for a THz On-Chip Spectrometer
Bibliographic record
Abstract
Abstract Far-infrared (FIR) wavelength light provides a wealth of information about astrophysical processes. Large field-of-view FIR spectrometers face a variety of challenges that can be bypassed by utilizing on-chip filter bank technology. To this end, we have been developing microwave kinetic inductance detectors (MKIDs) to couple to on-chip spectrometric technologies. We utilize a unique two-silicon wafer design to create a waveguide for an on-chip spectrometer between 1.4 and 2 THz. The spectrometer's principle of operation is electromagnetic radiation traveling down a center waveguide before being picked off by a resonance cavity and absorbed by a detector. The MKIDs are designed to couple directly to the waveguide using an annular antenna shape for the inductor. Alignment of the two-wafer design is achieved with two dowel pins and a hole and slot pattern in our custom designed box. We present the design and early test data from a 18-channel prototype device. The prototype yielded the full 18 resonances with quality factors Qc of about 5 ∗ 104 and Qi of about 1 ∗ 104. We plan to use the technology we developed to create an n × n pixel device targeting R = 100.
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 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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".