eLine10k: A high dynamic range front-end ASIC for LCLS detectors
Why this work is in the frame
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Bibliographic record
Abstract
The eLine10k is a fast-frame, 64-channel readout ASIC for SLAC Linac Coherent Light Source (LCLS) detectors. The circuit has been designed to integrate the charge from high-capacitance 2D sensors with rolling shutter and ID strip sensors. It is suitable for applications requiring large input signal range, on the order of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> photons/pixel/pulse at 8keV (22Me <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-</sup> ), and a resolution of half a photon FWHM (500e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-</sup> r.m.s). 2D sensors with a rolling shutter like the X-ray Active Matrix Pixel Sensor (XAMPS), for which the ASIC has been optimized, present several pixels which are bussed on the same readout line. Large input capacitance to each channel is expected leading to stringent noise optimization requirements. The large required number of pixels per channel, and the fixed LCLS beam period impose limitations on the time available for the readout of each single pixel. Giving the periodic nature of the LCLS beam, the ASIC developed for this application is a time-variant system, providing low-noise charge integration, filtering and correlated double-sampling, and a processing speed up to 500k pixel/s on each channel. To cope with the large input dynamic range, a charge pump scheme has been implemented using a synchronous zero-balance measurement method. It provides on-chip 4-bit coarse digital conversion of the integrated charge. The residual charge is sampled using correlated double sampling into an analog memory, multiplexed and measured with the required resolution using an external ADC. In this paper, the ASIC architecture and performance of the final release are presented.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it