Hardware timebase calibration in the multi-GSa/s LABRADOR-4 ASIC
Bibliographic record
Abstract
In recent years inexpensive, multi-Giga sample per second CMOS waveform samplers have become available, enabling a new generation of low-power, high channel count experiments in particle and astroparticle physics. Power savings over other architectures is realized by having nothing operating at the direct sampling rate of interest. Instead, the Switched Capacitor Array sampling is driven by timing generators based upon voltage-controlled delay lines. Stabilization of the timebase and the significant calibration effort required, due to the non-uniform time-steps introduced by CMOS process variations in these delay lines, have limited their more wide-spread adoption in the community. In most of the CMOS processes used, the sample-to-sample time step difference is of order 10-20%, and cannot be neglected in many applications. Especially for applications involving real-time processing of the waveform samples from these devices, splining and resampling the smoothed waveforms on a uniform time grid is computationally very expensive. To address this issue, in the 4th generation LABRADOR ASIC, individual time sample trim DACs have been implemented to tune out this time step variance. Available results of this hardware-level calibration are reported.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".