A Configurable Multi-Rail Power and I/O Pad Applied to Wafer-Scale Systems
Bibliographic record
Abstract
We propose in this paper a novel configurable multi-power-rail pad that combines power supply support circuits and a digital input/output (I/O) buffers designed for a wafer-scale system. This wafer-scale platform includes a reconfigurable wafer-scale circuit, the WaferIC, comprising an alignment-insensitive surface that can be configured to interconnect any digital components manually deposited on its surface. The proposed multi-power-rail pad minimizes power losses and heat dissipation within the circuit. The pad that is fed from two distinct voltage sources providing power at 1.8 and 3.3 V has been implemented and tested. This pad has two merged configurable control loops that can select the power source. Merging takes place through shared transistors. This dual supply pad embeds a voltage regulator that achieves a fast response time of 21.1 ns and that can operate over a wide range of configurable regulated output voltage, from 500 mV up to 2.955 V. This regulator is capable of providing a maximum output current of 40 mA while needing only a very small quiescent current of 126 μA. The regulator's power supply noise rejection ranges from -25 down to -40 dB for frequencies ranging from 1 kHz up to 1 MHz. The embedded digital I/O pad shares a common output with the power distribution and can be configured from 0.5 up to 3.3 V for a maximum speed of 250 MHz.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".