LAB4D: A Low Power, Multi-GSa/s, Transient Digitizer with Sampling Timebase Trimming Capabilities
Bibliographic record
Abstract
The LAB4D is a new application-specific integrated circuit (ASIC) of the Large Analog Bandwidth Recorder and Digitizer with Ordered Readout (LABRADOR) family, for use in direct wideband radio frequency digitization such as is used in ultrahigh energy neutrino and cosmic ray astrophysics. The LAB4D is a single channel switched-capacitor array (SCA) 12-bit sampler with integrated analog-to-digital converters (ADC), developed in the TSMC 0.25um process. The LAB4D, operating at 3.2GSa/s, contains 4096 total samples arranged in 32 windows, for a total record length of 1280ns. The 3dB bandwidth is approximately 1.3GHz, with a directly-coupled 50ohm input. This represents a factor of 16 increase in the sample depth and an increase in analog bandwidth and sampling depth in comparison to the previous generation LAB3 digitizer. Individually addressable windows allow for sampling and digitization to occur simultaneously, leading to nearly deadtime-free readout for kHz readout rates. All biases and current references are generated via internal digital-to-analog converters (DACs), resulting in a stand-alone digitizer with no additional support circuitry. In addition, the LAB4D contains sample cell timebase trimming capabilities, reducing the intrinsic sample-to-sample time variance to less than 5ps; an improvement of about 80%. This allows the LAB4D to be used in precision timing applications with minimal post-hoc calibration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".