Bibliographic record
Abstract
This chapter presents a possible design flow, along with biasing, isolation and layout strategies suitable for mm-wave SoCs. Competing transceiver architectures, self-test, and packaging approaches are reviewed next, followed by examples of mm-wave SoCs for a wide range of new applications. What is a high-frequency SoC? Although a precise definition is difficult to formulate, we define a high-frequency SoC as a single-chip radar, sensor, radio or wireline communication transmitter, receiver or transceiver that includes all high-frequency blocks, sometimes even the antennas, along with digital control and signal-processing circuitry. Examples include: 2GHz cell-phone or 5GHz wireless LAN transceivers 40Gb/s or 100Gb/s SERDES 60GHz radio transceiver 77GHz automotive radar transceiver W-, D-, and G-Band active and passive imagers. DESIGN METHODOLOGY FOR HIGH-FREQUENCY SoCs Most foundries have recently decided that the MOSFET and SiGe HBT compact models should capture the parasitic capacitance and resistance of only the first 1–2 metal layers, the minimum required for contacting all the device terminals. The rationale invoked is that this approach allows circuit designers more flexibility in the physical layout of transistors and of commonly encountered transistor groupings such as interdigitated differential pairs, interdigitated Gilbert cell quads, and latches. One (major) impediment is that, since most of the backend parasitics are not accounted for in schematic-level simulations, the burden is passed on to the designer to first lay out and then extract the parasitic impedance of the full wiring stack above the transistor in order to get an accurate simulation of circuit performance. This, in turn, creates a problem since, in most cases, the design starts with schematic-level simulations to find the optimal transistor size.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.176 | 0.059 |
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".