A Bipolar Analog Front-end Integrated Circuit For The SDC Silicon Tracker
Bibliographic record
Abstract
A low-noise, low-power, high-bandwidth, radiation hard, silicon bipolar-transistor full-custom integrated circuit (IC) containing 64 channels of analog signal processing has been developed for the SDC silicon tracker. The IC was designed and tested at LBL and was fabricated using AT&T`s CBIC-U2, 4 GHz f{sub T} complementary bipolar technology. Each channel contains the following functions: low-noise preamplification, pulse shaping and threshold discrimination. This is the first iteration of the production analog IC for the SDC silicon tracker. The IC is laid out to directly match the 50 {mu}m pitch double-sided silicon strip detector. The chip measures 6.8 mm {times} 3.1 mm and contains 3,600 transistors. Three stages of amplification provide 180 mV/fC of gain with a 35 nsec peaking time at the comparator input. For a 14 pF detector capacitance, the equivalent noise charge is 1300 el. rms at a power consumption of 1 mW/channel from a single 3.5 V supply. With the discriminator threshold set to 4 times the noise level, a 16 nsec time-walk for 1.25 to 10fC signals is achieved using a time-walk compensation network. Irradiation tests at TRIUMF to a {Phi}=10{sup 14} protons/cm{sup 2} have been performed on the IC, demonstrating the radiation hardness of the complementary bipolar process.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".