Fine resolution delay tuning method to improve the linearity of an unbalanced time‐to‐digital converter on a Xilinx FPGA
Bibliographic record
Abstract
In this study, a method for fine adjustment of Xilinx field programmable gate array (FPGA) routing delays is proposed and applied to improve the linearity of an unbalanced multi‐measurement time‐to‐digital converter (TDC). The delay control method increases load capacitances of interconnect points of switch matrices by small amounts using additional connections to unused interconnects in the FPGA fabric. The novel delay control method uses the tool command language (TCL) scripting feature available in the Xilinx Vivado tool to automatically add wires into a fully placed and routed design. A total of 61 additional wires were successfully and automatically added to reduce the differential and integral non‐linearities of the target TDC from 0.51 and −0.54 LSB to 0.05 and 0.06 LSB, respectively (reduction factors of 10.2 and 9) for an LSB equal to 333 ps.
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 | 0.000 | 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 | 0.000 | 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 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".